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https://ch.mathworks.com/matlabcentral/cody/problems/43977-converting-binary-to-decimals/solutions/1692291
[ "Cody\n\n# Problem 43977. Converting binary to decimals\n\nSolution 1692291\n\nSubmitted on 14 Dec 2018 by HH\n• Size: 9\n• This is the leading solution.\nThis solution is locked. To view this solution, you need to provide a solution of the same size or smaller.\n\n### Test Suite\n\nTest Status Code Input and Output\n1   Pass\nd = ('010111'); y_correct = 23; assert(isequal(bin_to_dec(d),y_correct))\n\n[Warning: Function assert has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict.] [> In unix (line 32) In bin_to_dec (line 2) In ScoringEngineTestPoint1 (line 3) In solutionTest (line 3)] ans = 1\n\n2   Pass\nd = ('110000'); y_correct = 48; assert(isequal(bin_to_dec(d),y_correct))\n\n[Warning: Function assert has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict.] [> In unix (line 32) In bin_to_dec (line 2) In ScoringEngineTestPoint2 (line 3) In solutionTest (line 5)] ans = 1\n\n3   Pass\nd = ('1000110'); y_correct = 70; assert(isequal(bin_to_dec(d),y_correct))\n\n[Warning: Function assert has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict.] [> In unix (line 32) In bin_to_dec (line 2) In ScoringEngineTestPoint3 (line 3) In solutionTest (line 7)] ans = 1" ]
[ null ]
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https://rpgmaker.net/forums/topics/24439/?post=875370
[ "", null, "# [RM2K3] IS THERE A WAY TO CHECK REQUIRED EXP FOR NEXT LEVEL?\n\n## Posts\n\nPages: 1\nSo Rm2k3 lets you store the EXP of a character in a variable, but only the current EXP, not the EXP needed for the next level. So if I want to manually track that, and my max lvl is 99, do I have to make 99 conditional branches for every character that check their levels and enters a value inside a variable?\n\nThe only way to do that is to manually set variables to the EXP required I entered in the Database. If I change my mind later on and change EXP values required for my characters, I'm looking at updating hundreds of conditional branches.\n\nSo yeah. Any neat little trick to do this?\nI'd suggest looking up the RM2k3 EXP algorithm and implement that in event code. Take a character's level (and if each character has different EXP curve parameters the ID too), calculate the EXP needed to reach character level+1, then subtract their current EXP. It'd be better than the hell of 99 conditional branches. idk the algorithm off the top of my head and my google-fu can't find it but it's hopefully in the help documentation.\nIDK if my solution is the roughly the same as GRS's. But when setting a character's level it'll set their EXP to what's required. You could duplicate a hero with the same exp table and have their level always = the real characters level but +1 and grab their exp from there.\nIt's definitely more convenient than mine, that's a clever trick!\nI don't really understand what both of you just said, but I'll look through the help file\nTo expand on Darken's solution: We have Hero Higgins who at some point we want to find the EXP to next level on. To do so we make a clone of HH in the database with the exact same EXP curve, we'll call it Clone Higgins. Then when you want to find HH's EXP to their next level you take Clone Higgins and set their level to HH's level+1. RM2k3 will give Clone Higgins the amount of EXP needed to reach that level which you can then assign to a variable. You take Clone Higgin's EXP and subtract Hero Higgin's current EXP and that will give you the EXP HH needs to reach the next level.\n\nExample with numbers!\n\nHero Higgins is lv10. Our level curve is 10,000 EXP to reach level 10, 14,350 EXP to reach lv11 (not real RM2k3 exp numbers). Hero Higgins has 11,840 EXP right now. We want to find out the EXP remaining:\n\n1) Assign Hero Higgin's current EXP to variable 1. V1 is now 11,840.\n2) Set Clone Higgin's level to 11. The safest way is to reduce Clone Higgin's level by 99 then increase it by Hero Higgin's current level. So something like:\n`VARIABLE: V3 = Hero Higgin's LevelCHANGE LEVEL: Clone Higgins -99 (this will set it to level 1, 0 exp)CHANGE LEVEL: Clone Higgins + VARIABLE: V3(this will add Hero Higgin's level 10, to Clone Higgin's lv1 giving him level 11)`\n3) Assign Clone Higgin's current EXP to variable 2. Since Clone Higgins level was set to lv11 in step 2 his current EXP would be 14,350, the EXP required to reach lv11 with our curve.\n4) Subtract V2 by V1. 14,350 - 11,840: 2,510. This is Hero Higgin's EXP needed to reach level 11, available to do what you want with it.\n\nI can throw together a demo project tonight too if it'll help.\nAh. I see what you're saying.\n\nI have to say, Rm2k3's way of handling EXP and level ups is pretty peculiar. Most RPGs of the SNES era reset your EXP to zero when you reach a new level, and the EXP required for the next level is higher than the previous one, but not a number added on top of the amount needed for the previous one. Unlike Rm2k3, which doesn't reset your EXP to zero, but lets you keep the EXP you needed for the level up as your current EXP, and just adds more on top.\n\nSo what I kinda wanna do is make a common event that checks if a character's level increased, and reset their EXP to zero. But it only needs to do it once, at the moment the level is increased. Then I'd use my own EXP values instead of Rm2k3's. Do you think that's feasible, or is this fighting the engine too much? And how could I pull this off without once again checking every level?\nPersonally trying to make a custom EXP curve in 2k3 sounds like hell but all those dynRPG(?) plugins might've made it more convenient. I haven't used 2k/3 for ages though so I can't offer any real support on this though. Hopefully some 2k/3 power users can help, or maybe a new thread about trying to create a custom EXP curve might get them to pop in?\nTo me, resetting the experience every level seems like it'd be pretty tedious. To your point about SNES RPGs doing this, I don't know if that's even true. A quick glance at all the major final fantasy games of the SNES era keeps a cumulative total of EXP, and then tells you how much is needed for the next level. BUT! Regardless, here is how you calculate experience needed.\n\nEvery experience curve in RM2k3 has three properties: Base Level, Acceleration, and Extra Value. (WARNING: in the official translation, the values are mislabeled and Extra should be labeled Acceleration! These formulas below are written assuming the 2nd value is Acceleration and the 3rd value is Extra.)\n\nSo, given your experience curve has:\nB = Base Value\nA = Acceleration\nX = Extra value\n\nExperience needed to get from level L to L+1:\nB + X + A(L)\n\nTotal experience needed to reach level L:\n(L - 1)(B + X) + A( L(L-1) / 2 )\n\nExamples..\nGiven B=25, X=15, A=8\nExperience needed to get from 5 to 6:\nB + E + A(L) = 25 + 15 + 8(5) = 40 + 40 = 80\n\nTotal experience needed to reach level 6:\n(L-1)(B+X) + A( L(L-1) / 2)\n= (6-1)(25+15) + 8( 6(5) / 2 )\n= 5(40) + 8(30/2)\n= 200 + 8(15)\n= 200 + 120\n= 320\n\nIf you want to know exactly how much EXP is needed to get to the next level:\nGiven the hero's level is 6, we want to target level 7.\nTake the hero's EXP (assign it to a variable) and subtract it from the Total Experience needed to reach level 7.\n\nSo say the hero has 350 EXP.\nGiven the same base, acceleration, and extra value parameters..\nTotal exp needed to reach level 7:\n(L-1)(B+X) + A(L(L-1) / 2)\n= 6(40) + 8(7(6) / 2)\n= 240 + 8(42 / 2)\n= 240 + 8(21)\n= 240 + 168\n= 408\nNeeded for next level: 408 - 350 = 58\n\nIt's a nontrivial amount of calculation to do in RM2k3, but simple enough to code up in a common event and reference whenever you need it. Hope this helps you out!\nIn my project I use this method to get the experience.\nTalk to the boy to see the experience and with the girl to get a quantity.\nhttp://www.mediafire.com/file/31w9wv2cil8h9vx/hero+exp.rar\nauthor=narcodis\nTo me, resetting the experience every level seems like it'd be pretty tedious. To your point about SNES RPGs doing this, I don't know if that's even true. A quick glance at all the major final fantasy games of the SNES era keeps a cumulative total of EXP, and then tells you how much is needed for the next level. BUT! Regardless, here is how you calculate experience needed.\n\nEvery experience curve in RM2k3 has three properties: Base Level, Acceleration, and Extra Value. (WARNING: in the official translation, the values are mislabeled and Extra should be labeled Acceleration! These formulas below are written assuming the 2nd value is Acceleration and the 3rd value is Extra.)\n\nSo, given your experience curve has:\nB = Base Value\nA = Acceleration\nX = Extra value\n\nExperience needed to get from level L to L+1:\nB + X + A(L)\n\nTotal experience needed to reach level L:\n(L - 1)(B + X) + A( L(L-1) / 2 )\n\nExamples..\nGiven B=25, X=15, A=8\nExperience needed to get from 5 to 6:\nB + E + A(L) = 25 + 15 + 8(5) = 40 + 40 = 80\n\nTotal experience needed to reach level 6:\n(L-1)(B+X) + A( L(L-1) / 2)\n= (6-1)(25+15) + 8( 6(5) / 2 )\n= 5(40) + 8(30/2)\n= 200 + 8(15)\n= 200 + 120\n= 320\n\nIf you want to know exactly how much EXP is needed to get to the next level:\nGiven the hero's level is 6, we want to target level 7.\nTake the hero's EXP (assign it to a variable) and subtract it from the Total Experience needed to reach level 7.\n\nSo say the hero has 350 EXP.\nGiven the same base, acceleration, and extra value parameters..\nTotal exp needed to reach level 7:\n(L-1)(B+X) + A(L(L-1) / 2)\n= 6(40) + 8(7(6) / 2)\n= 240 + 8(42 / 2)\n= 240 + 8(21)\n= 240 + 168\n= 408\nNeeded for next level: 408 - 350 = 58\n\nIt's a nontrivial amount of calculation to do in RM2k3, but simple enough to code up in a common event and reference whenever you need it. Hope this helps you out!\n\nI think you're right actually, for some reason I assumed EXP was reset to 0 with every level.\n\nI'm really bad at the math stuff, but why not just swap those labels around to suit the official translation? Seems less confusing to go off of that. The rest of your post looks like Einstein's calculations to me, but I'll try to make it work.\nPages: 1" ]
[ null, "https://www.facebook.com/tr", null ]
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https://dotnettutorials.net/lesson/while-loop-in-c/
[ "# While Loop in C\n\n## While Loop in C Language with Examples\n\nIn this article, I am going to discuss While Loop in C Language with Definitions, Syntax, Flow Charts, and Examples. Please read our previous articles, where we discussed Switch Statements in C Language with Examples. Loop Control Statements are very, very important for logical programming. The Looping Statements are also called Iteration Statements. So, we can use the word Looping and Iteration and the meanings are the same. Before understanding the while loop in C, let us first understand what is a loop, why we need the loop, and the different types of loops available in the C program.\n\n##### What is looping?\n\nThe process of repeatedly executing a statement or group of statements until the condition is satisfied is called looping. In this case, when the condition becomes false the execution of the loops terminates. The way it repeats the execution of the statements will form a circle that’s why iteration statements are called loops.\n\n##### Why do we need looping?\n\nThe basic purpose of the loop is code repetition. In implementation whenever the repetitions are required, then in place of writing the statements, again and again, we need to go for looping.\n\n##### Iteration or looping Statements:\n\nIteration statements create loops in the program. It repeats the same code fragment several times until a specified condition is satisfied is called iteration. Iteration statements execute the same set of instructions until a termination condition is met. C provides the following loop for iteration statements:\n\n1. while loop\n2. for loop\n3. do-while loop\n##### What is While Loop in C Language:\n\nA loop is nothing but executes a block of instructions repeatedly as long as the condition is true. How many times it will repeat means as long as the given condition is true. When the condition fails, it will terminate the loop execution.\n\nA while loop is used for executing a statement repeatedly until a given condition returns false. Here, statements may be a single statement or a block of statements. The condition may be any expression, and true is any nonzero value. The loop iterates while the condition is true. If you see the syntax and flow chart parallelly, then you will get more clarity of the while loop.\n\n###### While Loop Syntax in C Language:\n\nFollowing is the syntax to use the while loop in C Programming Language.", null, "While we are working with a while loop first we need to check the condition, if the condition is true then the control will pass within the body if it is false the control pass outside the body.\n\nWhen we are working with an iteration statement after execution of the body control will be passed back to the condition, and until the condition become false. If the condition is not false then we will get an infinite loop.\n\nIt is something similar to the if condition, just condition, and statements, but the flow is different from the if condition. How it is different lets us understand through the flow-chart.\n\n##### Flow Chart of While Loop in C Language:\n\nThe following diagram shows the flow chart of the while loop.", null, "The flow chart will start. The start is represented by the oval symbol. Then it will check the condition. As discussed earlier, every condition is having two outputs i.e. true and false. If it is true what will happen and it is false what will happen, we need to check.\n\nSuppose, the condition is true, then what all statements defined inside the block (within the while loop block) will execute. After execution of statements, will it end? NO, it will not end. After execution of statements, once again it will go and check the condition. It will repeat the same process as long as the given condition is true. Suppose, the condition is false, then it will come to end. This is the execution flow of a while loop.\n\n##### Program to understand While loop in C Language:\n```#include<stdio.h>\nint main()\n{\nint a=1;\nwhile(a<=4)\n{\nprintf(\"%d\", a);\na++;\n}\nreturn 0;\n}\n```\n\nOutput: 1234\n\nThe variable a is initialized with value 1 and then it has been tested for the condition. If the condition returns true then the statements inside the body of the while loop are executed else control comes out of the loop. The value of a is incremented using the ++ operator then it has been tested again for the loop condition.\n\n##### Example to Print no 1 to 5 using a while loop in C Language\n```#include <stdio.h>\nint main()\n{\nint i = 1;\nwhile(i <= 5)\n{\nprintf(\"%d \", i);\ni = i + 1;\n}\nreturn 0;\n}```\n\nOutput: 1 2 3 4 5\n\n##### Example: Print the nos 10, 9, 8…. 1 using while loop\n```#include <stdio.h>\nint main()\n{\nint i;\ni = 10;\nwhile(i >= 1)\n{\nprintf(\"%d \", i);\ni = i - 1;\n}\nreturn 0;\n}```\n\nOutput: 10 9 8 7 6 5 4 3 2 1\n\nExample: Print the numbers in the following format up to a given number and that number is entered from the keyboard.\n\n2 4 6 8 …………………….. up to that given number\n\n```#include <stdio.h>\nint main()\n{\nint i, n;\nprintf(\"Enter a Number : \");\nscanf(\"%d\", &n);\ni = 2;\nwhile(i <= n)\n{\nprintf(\"%d \", i);\ni = i + 2;\n}\nreturn 0;\n}```\n###### Output:", null, "###### Program to Enter a number and print the Fibonacci series up to that number using a while loop in C Language.\n```#include <stdio.h>\nint main ()\n{\nint i, n, j, k;\nprintf (\"Enter a Number : \");\nscanf (\"%d\", &n);\n\ni = 0;\nj = 1;\nprintf (\"%d %d \", i, j);\nk = i + j;\nwhile (k <= n)\n{\nprintf (\" %d\", k);\ni = j;\nj = k;\nk = i + j;\n}\n\nreturn 0;\n}```\n###### Output:", null, "Example: Enter a number from the keyboard and then calculate the no of digits and sum of digits of that number using a while loop.\n\n```#include <stdio.h>\nint main()\n{\nint no, nd, sd, rem;\nprintf(\"Enter a Number : \");\nscanf(\"%d\", &no);\nnd = 0;\nsd = 0;\nwhile (no > 0)\n{\nrem = no % 10;\nnd = nd + 1;\nsd = sd + rem;\nno = no / 10;\n}\nprintf(\"The number of digit is %d\", nd);\nprintf(\"\\nThe sum of digit is %d\", sd);\nreturn 0;\n}```\n###### Output:", null, "##### What is the pre-checking process or entry controlled loop?\n\nThe pre-checking process means before the evaluation of the statement block conditional part will be executed. When we are working with a while loop always pre-checking process will occur. The loop in which before executing the body of the loop if the condition is tested first then it is called an entry controlled loop.\n\nWhile loop is an example of entry controlled loop because in the while loop before executing the body first condition is evaluated if the condition is true then the body will be executed otherwise the body will be skipped." ]
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https://www.hindawi.com/journals/aaa/2013/838302/
[ "#### Abstract\n\nA nonlinear generalization of the famous Camassa-Holm model is investigated. Provided that initial value and satisfies an associated sign condition, it is shown that there exists a unique global weak solution to the equation in space in the sense of distribution, and .\n\n#### 1. Introduction\n\nIn recent years, a lot of works have been carried out to investigate the Camassa-Holm equation , which is a completely integrable equation. In fact, the Camassa-Holm equation arises as a model describing the unidirectional propagation of shallow water waves over a flat bottom . The equation was originally derived much earlier as a bi-Hamiltonian generalization of the Korteweg-de Vries equation (see ). Johnson , Constantin and Lannes derived models which include the Camassa-Holm equation (1). It has been found that (1) conforms with many conservation laws (see [6, 7]) and possesses smooth solitary wave solutions if [3, 8] or peakons if [3, 9]. Equation (1) is also regarded as a model of the geodesic flow for the right invariant metric on the Bott-Virasoro group if and on the diffeomorphism group if (see ). The well-posedness of local strong solutions for generalized forms of (1) has been given in . The sharpest results for the global existence and blow-up solutions are found in Bressan and Constantin [18, 19].\n\nRecently, Li et al. studied the following generalized Camassa-Holm equation: where is a natural number. Obviously, (2) reduces to (1) if . The authors applied the pseudoparabolic regularization technique to build the local well-posedness for (2) in Sobolev space with via a limiting procedure. Provided that the initial value satisfies a sign condition and , it is shown that there exists a unique global strong solution for (2) in space . However, the existence and uniqueness of the global weak solution for (2) is not investigated in .\n\nThe objective of this paper is to establish the well-posedness of global weak solutions for (2). Using the estimates in with , which are derived from the equation itself, we prove that there exists a unique global weak solution to (2) in space with if , and satisfies an associated sign condition.\n\nThe structure of this paper is as follows. The main result is given in Section 2. Several lemmas are given in Section 3. Section 4 establishes the proof of the main result.\n\n#### 2. Main Results\n\nFirstly, we give some notations.\n\nThe space of all infinitely differentiable functions with compact support in is denoted by . is the space of all measurable functions such that . We define with the standard norm . For any real number , we let denote the Sobolev space with the norm defined by where .\n\nFor and nonnegative number , let denote the Frechet space of all continuous -valued functions on . We set .\n\nDefining and letting with and (convolution of and ), we know that for any with . Notation (or equivalently ) means that (or equivalently ) for an arbitrary sufficiently small .\n\nFor the equivalent form of (2), we consider its Cauchy problem\n\nDefinition 1. A function is called a global weak solution to problem (5) if for every , , and all , it holds that with .\n\nNow, we give the main result of this work.\n\nTheorem 2. Let , , , and (or equivalently , ). Then, problem (5) has a unique global weak solution in the sense of distribution, and .\n\n#### 3. Several Lemmas\n\nLemma 3 (see ). Let with . Then, the Cauchy problem (5) has a unique solution where depends on .\n\nLemma 4 (see ). Let , , and (or equivalently , . Then, problem (5) has a unique solution satisfying\n\nUsing the first equation of system (5) derives from which one has the conservation law\n\nLemma 5 (see ). Let , and the function is a solution of problem (5) and the initial data . Then, the following inequality holds:\nFor , there is a constant such that\nFor , there is a constant such that\n\nFor (2), consider the problem\n\nLemma 6 (see ). Let , , and let be the maximal existence time of the solution to problem (5). Then, problem (14) has a unique solution . Moreover, the map is an increasing diffeomorphism of with for .\n\nDifferentiating (14) with respect to yields which leads to\n\nThe next lemma is reminiscent of a strong invariance property of the Camassa-Holm equation (the conservation of momentum ).\n\nLemma 7 (see ). Let with , and let be the maximal existence time of the problem (5). It holds that where and .\n\nLemma 8. If , , such that , (or equivalently, ), then the solution of problem (5) satisfies\n\nProof. Using , it follows from Lemma 7 that . Letting , we have from which we obtain On the other hand, we have The inequalities (19), (20), and (21) derive that inequality (18) is valid. Similarly, if , , we still know that (18) is valid.\n\nLemma 9. For , , it holds that where is a constant independent of .\n\nThe proof of this lemma can be found in Lai and Wu .\n\nFrom Lemma 3, it derives that the Cauchy problem has a unique solution depending on the parameter . We write to represent the solution of problem (23). Using Lemma 3 derives that since .\n\nLemma 10. Provided that , , , and (or equivalently , ), then there exists a constant independent of such that the solution of problem (23) satisfies\n\nProof. Using identity (10) and Lemma 9, if with , we have where is independent of .\nFrom Lemma 8, we have which completes the proof.\n\nLemma 11. For any , with , it holds that\n\nThe proof of this lemma can be found in .\n\n#### 4. Existence and Uniqueness of Global Weak Solution\n\nProvided that , for problem (23), applying Lemmas 5, 9, and 10, and the Gronwall’s inequality, we obtain the inequalities where , and is a constant independent of . It follows from the Aubin’s compactness theorem that there is a subsequence of , denoted by , such that and their temporal derivatives are weakly convergent to a function and its derivative in and , respectively, where is an arbitrary fixed positive number. Moreover, for any real number , is convergent to the function strongly in the space for and converges to strongly in the space for .\n\n##### 4.1. The Proof of Existence for Global Weak Solution\n\nFor an arbitrary fixed , from Lemma 10, we know that is bounded in the space . Thus, the sequences , , , and are weakly convergent to , , , and in for any , separately. Using , we know that satisfies the equation with and . Since is a separable Banach space and is a bounded sequence in the dual space of , there exists a subsequence of , still denoted by , weakly star convergent to a function in . As weakly converges to in , it results that almost everywhere. Thus, we obtain . Since is an arbitrary number, we complete the global existence of weak solutions to problem (5).\n\nProof of Uniqueness. Suppose that there exist two global weak solutions and to problem (5) with the same initial value , , we consider its associated regularized problem (23). Letting , from Lemma 10, we get and which is independent of . Still denoting , and , it holds that\nMultiplying both sides of (30) by , we get Using , , , , we have Applying Lemma 11 repeatedly, we have For , using Lemma 11 derives Using (32)–(34), we get Applying results in . Consequently, we know that the global weak solution is unique.\n\n#### Acknowledgment\n\nThis work is supported by the Fundamental Research Funds for the Central Universities (JBK120504)." ]
[ null ]
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http://grr.blahnet.com/home/software/microsoft/excel
[ "Home‎ > ‎Software‎ > ‎Microsoft‎ > ‎\n\n## Excel calculated value in a text string\n\n`=\"Buy In \\$50 Pot - Total Pot Winning pot \" &TEXT(COUNTA(A3:A21)*50,\"\\$0.00\")`\n\n## Calculate for non blank values\n\n`=IF(ISBLANK(D3),\"\",(\\$C3/D3)-1)`\n\n## Convert minutes to Hours and minutes\n\nFormat the cell as time format\n=H13/1440\n\n## Macro to hide columns\n\n`Sub Button2_Click()`\n`    Dim cl As Range, rTest As Range`\n`     `\n`    Set rTest = Range(\"d2\", Range(\"d2\").End(xlToRight))`\n`    For Each cl In rTest`\n`        If Not cl.Value = \"blah\" Then`\n`            cl.EntireColumn.Hidden = True`\n`        End If`\n`    Next cl`\n`End Sub`\n\n## Substring\n\nServername\n`Datacenter      =MID(A2,17,3)`\n`Product Code    =MID(A2,8,3)`\n`Swimlane        =MID(A2,8,3) `\n`Type            =MID(A2,11,2)`\n`Environment     =MID(A2,3,4)`\n`Tier            =MID(A2,2,1)`" ]
[ null ]
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https://studyforce.com/verify-trigonometric-identities/
[ "# How to Verify Trigonometric Identities\n\nProving identities is a major part of any trigonometry course. This involves getting one of the equation to be identical to the other side. Try using one of the following strategies to begin, and then use others if necessary to continue the verification process.\n\n• First, start with what appears to be the more difficult side of the given identity.\n• Try to transform this side so that it eventually matches identically to the other side.\n• Don’t hesitate to start over and work with the other side of the identity if you are having trouble.\n\n1. Look for ways to use known identities such as the reciprocal identities, quotient identities, and even/odd properties. If the identity includes a squared trigonometric expression. try using a variation of a Pythagorean identity.\n\n2. Try rewriting each trigonometric expression in terms of sines and cosines.\n\n3. Factor out a greatest common factor and use algebraic factoring techniques such as factoring the difference of two squares or the sum/difference of two cubes.\n\n4. If a single term appears in the denominator of a quotient, try separating the quotient in two or more quotients:\n\n$$\\frac{A+B}{C} = \\frac{A}{C}+\\frac{B}{C}$$\n\n5. If there are two or more fractional expressions, try combining the expressions using a common denominator:\n\n$$\\frac{A}{C}+\\frac{B}{D} = \\frac{AD+BC}{CD}$$\n\n6. If the numerator or denominator of one or more quotients contains an expression of the form $B + \\sqrt{C}$, try multiplying the numerator and denominator by its conjugate $B – \\sqrt{C}$:\n\n$$\\frac{A}{B+\\sqrt{C}}$$ $$= \\frac{A}{B+ \\sqrt{C}} \\cdot \\color{red} \\frac{B- \\sqrt{C}}{B-\\sqrt{C}}$$ $$=\\frac{A(B-\\sqrt{C})}{B^2-C}$$\n\nThe idea here is that multiplying by the denominator’s conjugate, you force the denominator to become radical-less. This can lead to further manipulation of the terms." ]
[ null ]
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https://oeis.org/A156921
[ "The OEIS Foundation is supported by donations from users of the OEIS and by a grant from the Simons Foundation.", null, "Hints (Greetings from The On-Line Encyclopedia of Integer Sequences!)\n A156921 FP1 polynomials related to the generating functions of the right hand columns of the A156920 triangle. 14\n 1, 1, 1, 1, -6, 1, 7, -79, 119, 126, -270, 1, 28, -515, 1654, 8689, -65864, 142371, -82242, -99090, 113400, 1, 86, -2255, 5784, 300930, -3904584, 20663714, -41517272, -80232259, 657717054 (list; graph; refs; listen; history; text; internal format)\n OFFSET 0,5 COMMENTS The FP1 polynomials appear in the numerators of the GF1 o.g.f.s. of the right hand columns of A156920. The FP1 can be calculated with the formula for the RHC sequence, see A156920, and the formula for the general structure of the generating function GF1, see below. An appropriate name for the FP1 polynomials seems to be the flower polynomials of the first kind because the zero patterns of these polynomials look like flowers. The zero patterns of the FP2, see A156925, and the FP1 resemble each other closely. A Maple program that generates for a right hand column with a certain RHCnr its GF1 and FP1 can be found below. RHCnr stands for right hand column number and starts from 1. LINKS FORMULA G.f.: GF1(z;RHCnr) := FP1(z;RHCnr)/product((1-(2*m-1)*z)^(RHCnr+1-m),m=1..RHCnr) Row sums (n) = (-1)^(1+(n+1)*(n+2)/2)*A098695(n). EXAMPLE The first few rows of the \"triangle\" of the coefficients of the FP1 polynomials. In the columns the coefficients of the powers of z^m, m=0,1,2,... , appear.         [1, 1, -6]   [1, 7, -79, 119, 126, -270]   [1, 28, -515, 1654, 8689, -65864, 142371, -82242, -99090, 113400] Matrix of the coefficients of the FP1 polynomials. The coefficients in the columns of this matrix are the powers of z^m, m=0,1,2,.. .   [1, 0 ,0, 0, 0, 0, 0, 0, 0, 0]   [1, 0 ,0, 0, 0, 0, 0, 0, 0, 0]   [1, 1, -6, 0 ,0, 0, 0, 0, 0, 0]   [1, 7, -79, 119, 126, -270, 0, 0, 0, 0]   [1, 28, -515, 1654, 8689, -65864, 142371, -82242, -99090, 113400] The first few FP1 polynomials are:   FP1(z; RHCnr=1) = 1   FP1(z; RHCnr=2) = 1   FP1(z; RHCnr =3) = 1+z-6*z^2 Some GF1(z;RHCnr) are:   GF1(z;RHCnr= 3) = (1+z-6*z^2)/((1-5*z)*(1-3*z)^2*(1-z)^3)   GF1(z;RHCnr= 4) = (1+7*z-79*z^2+119*z^3+126*z^4-270*z^5)/((1-7*z)*(1-5*z)^2*(1-3*z)^3*(1-z)^4) MAPLE RHCnr:=4: if RHCnr=1 then RHCmax :=1; else RHCmax:=(RHCnr-1)*(RHCnr)/2 end if: RHCend:=RHCnr+RHCmax: for k from RHCnr to RHCend do for n from 0 to k do S2[k, n]:=sum((-1)^(n+i)*binomial(n, i)*i^k/n!, i=0..n) end do: G(k, x):= sum(S2[k, p]*((2*p)!/p!) *x^p/(1-4*x)^(p+1), p=0..k)/(((-1)^(k+1)*2*x)/(-1+4*x)^(k+1)): fx:=simplify(G(k, x)): nmax:=degree(fx); RHC[k-RHCnr+1]:= coeff(fx, x, k-RHCnr)/2^(k-RHCnr) end do: a:=n-> RHC[n]: seq(a(n), n=1..RHCend-RHCnr+1); for nx from 0 to RHCmax do num:=sort(sum(A[t]*z^t, t=0..RHCmax)); nom:=Product((1-(2*u-1)*z)^(RHCnr-u+1), u=1..RHCnr): RHCa:= series(num/nom, z, nx+1); y:=coeff(RHCa, z, nx)-A[nx]; x:=RHC[nx+1]; A[nx]:=x-y; end do: FP1[RHCnr]:=sort(num, z, ascending); GenFun[RHCnr] :=FP1[RHCnr]/product((1-(2*m-1)*z)^(RHCnr-m+1), m=1..RHCnr); CROSSREFS Cf. A156920, A156925, A156927, A156933. For the first few GF1's see A000340, A156922, A156923, A156924. The number of FP1 terms follow the triangular numbers A000217, with quite surprisingly one exception here a(0)=1. Abs(Row sums (n)) = A098695(n). For the polynomials in the denominators of the GF1(z;RHCnr) see A157702. Sequence in context: A204205 A143019 A337369 * A094214 A001622 A186099 Adjacent sequences:  A156918 A156919 A156920 * A156922 A156923 A156924 KEYWORD easy,sign,tabf,uned,changed AUTHOR Johannes W. Meijer, Feb 20 2009 STATUS approved\n\nLookup | Welcome | Wiki | Register | Music | Plot 2 | Demos | Index | Browse | More | WebCam\nContribute new seq. or comment | Format | Style Sheet | Transforms | Superseeker | Recent\nThe OEIS Community | Maintained by The OEIS Foundation Inc.\n\nLast modified April 20 23:46 EDT 2021. Contains 343143 sequences. (Running on oeis4.)" ]
[ null, "https://oeis.org/banner2021.jpg", null ]
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https://space.stackexchange.com/questions/2880/are-there-any-natural-circular-orbits
[ "# Are there any natural circular orbits?\n\nI just saw How does orbital eccentricity affect positions of Lagrange points $L_4$ and $L_5$? and it questions the difference between circular and elliptical orbits.\n\nWe know the Moon does not have a circular orbit. Do we know of any natural bodies that are actually in a circular orbit? Is there any possible way for a circular orbit to occur naturally?\n\n• How do you define circular? Perfectly 100% circle according to x squared plus y squared equals value? Then I think that the answer will be \"No\". How much variation will you accept? – Hennes Nov 23 '13 at 15:22\n• – Everyone Nov 23 '13 at 17:27\n• Having conveyed that, I wonder whether A*.SE is possibly a better fit for the question – Everyone Nov 23 '13 at 17:28\n\nIf my understanding in correct, the orbit is perfectly circular if the dimensionless orbital parameter of eccentricity (e) is zero. However, I'm not sure how the inclination works with this, and that comes down to the question \"circular in what plane?\" Anyway, I present to you:\n\nAsteroid 113474 (2002 ST57).\n\nhttp://ssd.jpl.nasa.gov/sbdb.cgi?sstr=113474\n\nElement Value Uncertainty (1-sigma) Units\ne 9.338379075815981E-5 1.3966e-07", null, "Sadly, however, the uncertainty is sufficiently lower than the value itself to show that it does, in fact, have some eccentricity. If you are satisfied with any object that could theoretically be circular with our current understanding then that's easy. There are lots of objects with an uncertainty enough such that a perfectly circular orbit is possible. Actually, this turns up a huge number of candidates due to the simple fact that we don't know the the orbital parameters in the first place!\n\nI argue that this above orbit is pretty darn circular. If you want a theoretically perfect circle, then the answer is probably \"no\". If you want further navel-gazing, I will question whether the concept of a circle is valid in non-Euclidean geometry. Since we have the spacetime distortion of Jupiter thrown in there, perhaps circles don't exist?\n\nIn case it wasn't obvious, my example was possible because I used a large sample set and just searched for a candidate that fit my criteria\n\nNonetheless, there is a very good reason that things are circular-ish. Our solar system's plane exists for very good physical reasons, which is that a collapsing gas cloud has an angular momentum value that it can't get rid of, and can't be held in a single body by gravity. Then, the planets are relatively circular, because if they were not, the solar system would not be as stable. Orbital resonances (which is what the long term dynamics depend upon) apply to relatively circular orbits, and like to keep things relatively circular. There are certain to be some exoplanet systems that more strongly favored circularizing the orbits. Because of that, I'm sure that one day we'll discover some exquisitely circular orbit. But that still doesn't make it a literal circle by the theoretical standard.\n\n• A little late, but I just read Deimos also has a close to circular orbit with an eccentricity of 0.00033 – Everyone Oct 10 '14 at 16:24" ]
[ null, "https://i.stack.imgur.com/aEScE.gif", null ]
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https://yetanothermathblog.com/tag/quartic-graph/
[ "# Quartic graphs with 12 vertices\n\nThis is a continuation of the post A table of small quartic graphs. As with that post, it’s modeled on the handy wikipedia page Table of simple cubic graphs.\n\nAccording to SageMath computations, there are 1544 connected, 4-regular graphs. Exactly 2 of these are symmetric (ie, arc transitive), also vertex-transitive and edge-transitive. Exactly 8 of these are vertex-transitive but not edge-transitive. None are distance regular.\n\nExample 1: The first example of such a symmetric graph is the circulant graph with parameters (12, [1,5]), depicted below. It is bipartite, has girth 4, and its automorphism group has order 768, being generated by", null, "$(9,11), (5,6), (4,8), (2,10), (1,2)(5,9)(6,11)(7,10), (1,7), (0,1)(2,5)(3,7)(4,9)(6,10)(8,11)$.\n\nExample 2: The second example of such a symmetric graph is the cuboctahedral graph, depicted below. It has girth 3, chromatic number 3, and its automorphism group has order 48, being generated by", null, "$(1,10)(2,7)(3,6)(4,8)(9,11), (1,11)(3,4)(6,8)(9,10), (0,1,9)(2,8,10)(3,7,11)(4,5,6)$." ]
[ null, "https://s0.wp.com/latex.php", null, "https://s0.wp.com/latex.php", null ]
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https://www.faceprep.in/print-the-armstrong-numbers-between-the-two-intervals/
[ "# Print the Armstrong numbers between the two intervals | faceprep\n\nProgram to print all the Armstrong numbers between the two intervals is discussed here. A number is said to be an Armstrong number when the sum of nth power of digit of the number is equal to the number itself.", null, "## Algorithm to print Armstrong numbers between two intervals\n\n1. Input the start and end values.\n2. Repeat from i = start_value to end_value.\n3. Repeat until (temp != 0)\n4. remainder = temp % 10\n5. result = resut + pow(remainder,n)\n6. temp = temp/10\n7. if (result == number)\n8. Print the number\n9. Repeat steps from 2 to 8 until the end_value is encountered.\n\n## Program to print the Armstrong numbers between the two intervals\n\n// C program to print the Armstrong numbers between the two intervals\n\n#include <stdio.h>\n#include <math.h>\n\nint main()\n{\nint start, end, i, temp1, temp2, remainder, n = 0, result = 0;\n\nprintf(“Enter start value and end value : “);\nscanf(“%d %d”, &start, &end);\nprintf(“\\nArmstrong numbers between %d an %d are: “, start, end);\n\nfor(i = start + 1; i < end; ++i)\n{\ntemp2 = i;\ntemp1 = i;\n\nwhile (temp1 != 0)\n{\ntemp1 /= 10;\n++n;\n}\n\nwhile (temp2 != 0)\n{\nremainder = temp2 % 10;\nresult += pow(remainder, n);\ntemp2 /= 10;\n}\n\nif (result == i) {\nprintf(“%d “, i);\n}\n\nn = 0;\nresult = 0;\n\n}\nprintf(“\\n”);\nreturn 0;\n}\n\n// C++ program to print the Armstrong numbers between the two intervals\n\n#include <iostream>\n#include <math.h>\nusing namespace std;\n\nint main()\n{\nint start, end, i, temp1, temp2, remainder, n = 0, result = 0;\n\ncout << “Enter start value and end value : “;\ncin >> start >> end;\ncout << “\\nArmstrong numbers between ” << start << ” and ” << end << ” are : “;\n\nfor(i = start + 1; i < end; ++i)\n{\ntemp2 = i;\ntemp1 = i;\n\nwhile (temp1 != 0)\n{\ntemp1 /= 10;\n++n;\n}\n\nwhile (temp2 != 0)\n{\nremainder = temp2 % 10;\nresult += pow(remainder, n);\ntemp2 /= 10;\n}\n\nif (result == i) {\ncout << i << ” “;\n}\n\nn = 0;\nresult = 0;\n\n}\ncout << endl;\nreturn 0;\n}\n\n//Java program to print the Armstrong numbers between the two intervals\n\nimport java.util.*;\npublic class sum_of_primes {\n\npublic static void main(String[] args)\n{\nScanner sc = new Scanner(System.in);\nSystem.out.print(“Enter start and end values: “);\nint start = sc.nextInt();\nint end = sc.nextInt();\nint i, temp1, temp2, remainder, n = 0, result = 0;\n\nfor(i = start + 1; i < end; ++i)\n{\ntemp2 = i;\ntemp1 = i;\n\nwhile (temp1 != 0)\n{\ntemp1 /= 10;\n++n;\n}\n\nwhile (temp2 != 0)\n{\nremainder = temp2 % 10;\nresult += Math.pow(remainder, n);\ntemp2 /= 10;\n}\n\nif (result == i) {\nSystem.out.print(i + ” “);\n}\n\nn = 0;\nresult = 0;\n\n}\n\n}\n\n}\n\n# Python program to print the Armstrong numbers between the two intervals\n\nimport math\nstart = int(input(“Enter start value : “))\nend = int(input(“Enter end value : “))\nresult = 0\nn = 0\nfor i in range(start+1 ,end,1):\ntemp2 = i\ntemp1 = i\n\nwhile (temp1 != 0):\ntemp1 = int(temp1/10)\nn = n + 1\n\nwhile (temp2 != 0):\nremainder = temp2 % 10\nresult = result + math.pow(remainder, n)\ntemp2 = int(temp2/10)\n\nif (result == i):\nprint(i,” “)\n\nn = 0\nresult = 0\n\nOutput:", null, "Time complexity: O(n)" ]
[ null, "https://i1.faceprep.in/Companies-1/given-number-is-armstrong-or-not.png", null, "https://i1.faceprep.in/Companies-1/armstrong numbers between the two intervals.PNG", null ]
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https://lc101-advent.herokuapp.com/challenges/3
[ "# Advent of Code, Day 3: wishlist snooping\n\nSome kids are very secretive, and encrypt their wishlists. Luckily, they don't know much about cryptography and only ever use caesar ciphers.\n\nCaesar ciphers are susceptible to frequency analysis attacks. In English, the most common letters are `e`, then `t`, then `a`. If a letter to Santa is encrypted using the `m` as the key, it's pretty likely that the most common letters will be `q`, then `f`, then `m`.\n\nWe have a plaintext frequency table. (You can get this off of wikipedia, or just use your `frequency_table` on a book from Project Gutenberg. It looks something like this:\n\n```Letter: A B C D E F ... Z\nFrequency: .08167 .01492 .02782 .04253 .12702 .02228 .00074\n```\n\nTo figure out the encryption key, we will guess each one in turn, and compute the frequency table of the message under this decryption key. That looks something like this:\n\n```Letter: A B C D E F ... Z\nFrequency: .02228 .02015 .06094 .06966 .00153 .00772 .12702\n```\n\nWe need to compare each of these 26 tables to the normal English-language table to figure out which key gives us output that looks most like English. There are a number of good ways to do that, but here's one:\n\n```def distance(freq_table1, freq_table2):\n\"\"\"\nCompute the sum-of-squares distance between\ntwo letter-frequency distributions\n\"\"\"\n# dist= 0\n# For every letter a..z:\n# dist += ((freq of letter in freq_table1) - (freq of letter in freq_table2))**2\n# return dist\n```\n\nhere are some example wishlists to try out:\n\n• ```Jkgx Ygtzg,\nGrr O xkgrre xkgrre cgtz lux inxoyzsgy oy g zaxzrk. O'bk grcgey cgtzkj g zaxzrk hkigayk zaxzrky gxk znk iuurkyz.\n\nZngtq eua,\nRubk,\nKsore\n```\n(This one is pretty easy to do by hand because I left in the punctuation, capitalization and spaces)\n• `xyulmuhnucfceysioxisiofceygycqiofxfceyuxmgulcijfyumyhyrnsyulcqcffacpysiogihysbiqxiymnbunmiohxjlynnswiifcnxiymnigynbunmnbyxyuf`\n• `uvrijrekrpfligifsrscpaljkjfdvgvijfeyzivukfivrukyzjzwefkkyvezjkreutfiivtkvuznzccjkzcckvccpflznrekrgcrpjkrkzfezdknvcmvpvrijslkzdnizkzexkyzjsvtrljvzsvczvmvkyvivzjtyizjkdrjdrxztjfdvnyviv`\n\nWhen you've got a solution, submit the plaintext of the following letter: `ghduvdqwdlzrxogolnhdwrbsxsdwrbnlwwhqdwrbexqqbdqgdwrbkruvhdqgdovrdvpdoosodqhwkdwlfrxogulghlqlzrxogolnhdfrorulqjerrnwrr`" ]
[ null ]
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https://listserv.uni-heidelberg.de/cgi-bin/wa?A2=9710&L=LATEX-L&D=0&H=N&P=1698098
[ "", null, "## LATEX-L Archives\n\n##### Mailing list for the LaTeX3 project\n Options: Use Forum View Use Monospaced Font Show Text Part by Default Show All Mail Headers Message: [<< First] [< Prev] [Next >] [Last >>] Topic: [<< First] [< Prev] [Next >] [Last >>] Author: [<< First] [< Prev] [Next >] [Last >>]\n\n Subject: Re: LPPL under review at savannah.gnu.org From: \"C.M. Connelly\" <[log in to unmask]> Reply To: Mailing list for the LaTeX3 project <[log in to unmask]> Date: Tue, 9 Jul 2002 13:40:25 -0700 Content-Type: text/plain Parts/Attachments: text/plain (48 lines)\n```\"MS\" == Martin Schroeder <[log in to unmask]>\n\nLD> I think the LPPL is trying to define and enforce a\nLD> distribution policy within the license. This is a strange\nLD> idea. Imagine what mess it would be if the Linux kernel\nLD> imposed the same restrictions on system calls ?-) Instead\nLD> a specification was issued\nLD> (http://www.opengroup.org/onlinepubs/007908799/) to\nLD> encourage the necessary standardization and\nLD> uniformity. Defining a standard interface and behaviour is\nLD> a complex matter that can hardly be implemented by a\n\nMS> \"The Single UNIX® Specification, Version 2\" -- which I\nMS> find irrelevant here.\n\nYes, the specification is irrelevant, but Loic's point was that a\nlicense cannot force standardization; that job has to be left to a\ngroup of interested parties who draft a standards document that\ndefine what bits make up a complete system, how they interact,\ntheir interface, what sort of output they produce, and so on.\n\nIn other words, he wasn't suggesting you look at the document\nbecause it would tell you anything about LaTeX, he was suggesting\nthat the process that produced the document is worth looking at.\n(And perhaps that the document itself might serve as a model for a\nLaTeX standard.)\n\nThe basic idea here is that if you want to keep LaTeX pure, you\ncan take one of two approaches:\n\n1. Impose a restrictive, non-free license that prevents\nmodification of key components\n\n2. Develop an open standard that defines the behavior of the\nsystem in a testable way\n\nClaire\n\n+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\nMan cannot be civilised, or be kept civilised by what he does in his\nspare time; only by what he does as his work.\nW.R. Lethaby\n+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+" ]
[ null, "https://listserv.uni-heidelberg.de/archives/images/default_archive_home_64x64.png", null ]
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http://www.dailymathguide.com/2020/05/unlike-terms.html
[ "UNLIKE TERMS - Daily Math Guide\n\n## DMG\n\nHome » , » UNLIKE TERMS\n\n## THE UNLIKE TERMS\n\nUnlike term is the opposite of like terms. The unlike term(s) is an expression composed of numerical or literal coefficients. It is said that when we identify Algebraic terms, it should be a variable or a constant separated by the \"+\" or \"-\" sign. Either of them. In this article, we are looking for literal coefficients, or variables, which are not the same nor similar. By definition, it is a term or term in a series of Algebraic expressions that have no literal coefficients the same, or they are the dissimilar literal coefficients in Algebraic expression. It is also a term that has not the same literal coefficients including exponents. It is said to be that the unlike terms cannot be added nor subtracted because the said operation only happened in the Algebraic expressions with like terms. Like and unlike terms are totally opposite.\n\n### Sample Step Guide on How to Do It\n\nIllustration:\n\n, look at the terms of the given expression. They are obviously dissimilar. The first term is composed of 2xy and only y for the second. In this series of expressions, we cannot perform its indicated operation, hence there's no term similar. It will remain as is to its position.\n- this is obviously an, unlike terms. This expression is three terms with no term similar The literal coefficients are xy and -3y thus, xy and -3y  are unlike terms or dissimilar terms. The \"4\" is also part of the series of expression but it's a constant.\n-this is another example of expression which of, unlike terms. Notice that \"x\" is present to the first two terms but they don't have similar powers or exponents. So this means that the first term has \"three- x's\" and the second term has \"two-x's\". Hence, they are not the same.\n\n-this is an expression with \"like terms\"  or similar terms. The literal coefficients of the first and second terms are x3 and x3. This is so because its powers are the same which is 3. Therefore this is \"not\" an, unlike terms as an example.\n, this expression is also an example of \"like terms\". Their literal coefficient is the same in terms of variables and powers. There are three terms in this expression with one constant which is -7.\n\n### Example Problems with Solutions\n\nIdentify if the given is similar (like) terms or dissimilar (unlike) terms.\n\n-like terms\n\n-unlike terms\n\n-unlike terms\n\n-like terms\n\n-unlike terms\n\n-like terms\n\n-unlike terms\n\n-like terms\n\n-like terms\n\n-like terms\n\n### Problems with Partial Solutions\n\nWrite \"like\" if the given expression has like terms and can be simplified, otherwise  \"unlike\". Write your answer by clicking the blank after each series of expressions.\n\n= _________\n\n= _________\n\n= _________\n\n= _________\n\n= _________\n\n= _________\n\n= _________\n\n= _________\n\n### Simple Quizzes\n\nWrite \"like\" if the given expression has like terms, otherwise  \"unlike\".\n\n## Click for a PDF worksheet!\n\nRelated References: like terms, unlike and like terms\n\nCheck our Select button to Share at the bottom for more services!\n\nSelect button to Share :" ]
[ null ]
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http://cybergis.cigi.uiuc.edu/cyberGISwiki/doku.php/ct?do=diff&rev2%5B0%5D=1442431771&rev2%5B1%5D=&difftype=sidebyside
[ "", null, "", null, "# Differences\n\nThis shows you the differences between two versions of the page.\n\nLink to this comparison view\n\nct [2015/09/16 14:29]\nyanliu\nct [2015/09/16 14:36] (current)\nyanliu\nLine 7: Line 7:\n* [[ct/pgap|PGAP]]   * [[ct/pgap|PGAP]]\n* [[ct/pabm|Parallel Agent-Based Modeling]]   * [[ct/pabm|Parallel Agent-Based Modeling]]\n-  * [[ct/sptw|Simple Parallel Tiff Writer]]\n* [[ct/gkde|Parallel Kernel Density Estimation]]   * [[ct/gkde|Parallel Kernel Density Estimation]]\n* [[ct/mapalgebra|Parallel Map Algebra]]   * [[ct/mapalgebra|Parallel Map Algebra]]\n+  * [[ct/sptw|Simple Parallel Tiff Writer]]\n\n====Introduciton==== ====Introduciton====\nLine 24: Line 24:\n- [[ct/ppysal|Parallel PySAL]]. The Parallel PySAL library provides a set of scalable [[http://pysal.org|PySAL]] functions. Currently, parallel PySAL components implemented using the multiprocessing python library. The Fisher-Jenks classification algorithm is integrated in the toolkit;   - [[ct/ppysal|Parallel PySAL]]. The Parallel PySAL library provides a set of scalable [[http://pysal.org|PySAL]] functions. Currently, parallel PySAL components implemented using the multiprocessing python library. The Fisher-Jenks classification algorithm is integrated in the toolkit;\n- [[ct/prasterblaster|pRasterBlaster]]. pRasterBlaster is a high-performance map reprojection software contributed by the Center of Excellence for Geospatial Information Science ([[http://cegis.usgs.gov|CEGIS]]) within the [[http://usgs.gov|U.S. Geological Survey]];   - [[ct/prasterblaster|pRasterBlaster]]. pRasterBlaster is a high-performance map reprojection software contributed by the Center of Excellence for Geospatial Information Science ([[http://cegis.usgs.gov|CEGIS]]) within the [[http://usgs.gov|U.S. Geological Survey]];\n-  - [[ct/pgap|PGAP]]. PGAP is a scalable Parallel Genetical Algorithm (PGA) solver for the Generalized Assignment Problem (GAP). This code provides an efficient PGA implementation for combinatorial optimization problem-solving and scaled up to 262K processor cores on BlueWaters with marginal communcation cost.+  - [[ct/pgap|PGAP]]. PGAP is a scalable Parallel Genetical Algorithm (PGA) solver for the Generalized Assignment Problem (GAP). This code provides an efficient PGA implementation for combinatorial optimization problem-solving and scaled up to 262K processor cores on BlueWaters with marginal communication cost;\n- [[ct/pabm|Parallel Agent-Based Modeling (PABM)]]. PABM is an illustrative software for scalable spatially explicit agent-based modeling (ABM);   - [[ct/pabm|Parallel Agent-Based Modeling (PABM)]]. PABM is an illustrative software for scalable spatially explicit agent-based modeling (ABM);\n+  - [[ct/gkde|Parallel Kernel Density Estimation]] A multi-GPU code for data-intensive kernel density estimation (KDE). Spatial computational domain is first estimated for KDE. Then the study area is decompose into sub-regions through an adaptive partitioning approach. Each sub-region is processed by a GPU node. These GPU nodes are communicated through massage passing interface (MPI);\n+  - [[ct/mapalgebra|Parallel Map Algebra]] This package contains a parallel map algebra code using CUDA, MPI, and Parallel I/O. It is extracted from a parallel geospatial programming models training package;\n+  - [[ct/sptw|Simple Parallel Tiff Writer]] This is a parallel IO code for writing GeoTIFF file on a parallel file system.\n\nLine 46: Line 49:", null, "" ]
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https://www.hindawi.com/journals/isrn/2011/312789/
[ "Research Article | Open Access\n\nVolume 2011 |Article ID 312789 | 11 pages | https://doi.org/10.5402/2011/312789\n\n# Bivariate Poincaré Series for the Algebra of Covariants of a Binary Form\n\nAccepted17 May 2011\nPublished29 Jun 2011\n\n#### Abstract\n\nA formula for computation of the bivariate Poincaré series for the algebra of covariants of binary -form is found.\n\n#### 1. Introduction\n\nLet be the complex vector space of binary forms of degree endowed with the natural action of the special linear group . Consider the corresponding action of the group on the coordinate rings and . Denote by and by the subalgebras of -invariant polynomial functions. In the language of classical invariant theory, the algebras and are called the algebra of invariants and the algebra of covariants for the binary form of degree , respectively. The algebra is a finitely generated bigraded algebra: where each subspace of covariants of degree and order is finite dimensional. The formal power series , is called the bivariate Poincaré series of the algebra of covariants . It is clear that the series is the Poincaré series of the algebra and the series is the Poincaré series of the algebra with respect to the usual grading of the algebras under degree. The finitely generation of the algebra of covariants implies that its bivariate Poincaré series is the power series expansion of a rational function of two variables . We consider here the problem of computing efficiently this rational function.\n\nCalculating the Poincaré series of the algebras of invariants and covariants was an important object of research in invariant theory in the 19th century. For the cases , the series were calculated by Sylvester, see in [1, 2] the big tables of , named them as generating functions for covariants, reduced form. All those calculations are correct up to .\n\nRelatively recently, Springer found an explicit formula for computing the Poincaré series of the algebra of invariants . In the paper we have proved a Cayley-Sylvester-type formula for calculating of and a Springer-type formula for calculation of . By using the formula, the bivariate Poincaré series is calculated for .\n\n#### 2. Cayley-Sylvester-Type Formula for dim(𝒞𝑑)𝑖,𝑗\n\nTo begin, we give a proof of the Cayley-Sylvester-type formula for the dimension of the graded component .\n\nLet and be standard irreducible representation of the Lie algebra . The basis elements ,, of the algebra act on by the derivations : The action of is extended to an action on the symmetrical algebra in the natural way.\n\nLet be the maximal unipotent subalgebra of . The algebra , defined by is called the algebra of semi-invariants of the binary form of degree . For any element , a natural number is called the order of the element if the number is the smallest natural number such that It is clear that any semi-invariant of order is the highest weight vector for an irreducible -module of the dimension in .\n\nThe classical theorem of Roberts implies an isomorphism of the algebra of covariants and the algebra of semi-invariants. Furthermore, the order is preserved through the isomorphism. Thus, it is enough to compute the Poincaré series of the algebra .\n\nThe algebra is -graded and each is a completely reducible representation of the Lie algebra . Thus, the following decomposition holds here is the multiplicity of the representation in the decomposition of . On the other hand, the multiplicity of the representation is equal to the number of linearly independent homogeneous semi-invariants of degree and order for the binary -form. This argument proves the following.\n\nLemma 2.1.\n\nThe set of weights (eigenvalues of the operator ) of a representation denote by , in particular, .\n\nA formal sum is called the character of a representation , here denotes the multiplicity of the weight . Since, the multiplicity of any weight of the irreducible representation is equal to 1, we have\n\nThe character of the representation equals (see ), where is the complete symmetrical function\n\nBy replacing with , , we obtain the specialized expression for : here is the number nonnegative integer solutions of the equation on the assumption that . In particular, the coefficient of (the multiplicity of zero weight ) is equal to , and the coefficient of is equal to .\n\nOn the other hand, the decomposition (*) implies the equality for the characters: We can summarize what we have shown so far in the following.\n\nTheorem 2.2.\n\nProof. The weight appears once in any representation , for . Therefore Similarly, Thus, By using Lemma 2.1 we obtain\n\nFor another proof of the formula see .\n\nNote that the original Cayley-Sylvester formula is Also, in we proved that Here are the components of standard grading of the algebras , under degree.\n\n#### 3. Calculation of dim(𝐶𝑑)𝑖,𝑗\n\nIt is well known that the number of nonnegative integer solutions of the following system is given by the coefficient of of the generating function We will use the notation to denote the coefficient of in the series expansion of . Thus It is clear that\n\nSimilarly, the number of nonnegative integer solutions of the following system\n\nequals\n\nTherefore, Thus, the following statement holds.\n\nTheorem 3.1. The number of linearly independent covariants of degree and order for the binary d- form is given by the formula\nIt is clear that By using the decomposition where is the -binomial coefficient one obtains the well-known formula for instance, see .\n\n#### 4. Explicit Formula for 𝒫𝑑(𝑧,𝑡)\n\nLet us prove Springer-type formula for the bivariate Poincaré series of the algebra covariants of the binary -form. Consider the -algebra of formal power series. For an integer define the -linear function in the following way\n\nThe main idea of the ensuing calculations is that the Poincaré series can be expressed in terms of function . The following simple but important statement holds.\n\nLemma 4.1.\n\nProof. Theorem 2.2 implies that . Then\n\nLet be a -linear function defined by\n\nfor . Note that and , . Also, put for . It is clear that if , .\n\nIn important special cases, calculating the functions can be reduced to calculating the functions . The following statements hold.\n\nLemma 4.2. (i) For holds .\n(ii) For and for holds\n\nProof. (i) The statement follows from the linearity of the function and from the following simple observation: for and .\n(ii) Let . Then for we have\n\nNow we can present Springer-type formula for calculating of the bivariate Poincaré series .\n\nTheorem 4.3. here is -shifted factorial.\n\nProof. Consider the partial fraction decomposition of the rational function : It is easy to see, that Using the above Lemmas we obtain\n\nCorollary 4.4. A denominator of the bivariate Poincaré series , can be written in the form where are the degrees of elements of homogeneous system of parameters for the algebra of invariants .\n\nProof. The formula of Theorem 4.3 implies that the bivariate Poincaré series has the form for some polynomials . Thus, the Poincaré series for the algebra of invariants has the form The algebra of invariants is Cohen-Macaulay, and its transcendence degree for equals , see . Therefore it has a homogeneous system of parameters and the denominator of its Poincaré series can be written in the following way , where are the degrees of elements of this homogeneous system of parameters.\n\n#### 5. Examples\n\nFor direct computations we use the following technical lemma.\n\nLemma 5.1. For one has here , and are natural numbers.\n\nProof. Taking into account Lemma 4.2 we get In a similar fashion we prove the general case.\n\nBy using Lemma 5.1 the bivariate Poincaré series for are found. All these results agree with Sylvester's calculations up to , see [1, 2].\n\nBelow is the list of several series:\n\n1. J. J. Sylvester and F. Franklin, “Tables of the generating functions and groundforms for the binary quantics of the first ten orders,” American Journal of Mathematics, vol. 2, no. 3, pp. 223–251, 1879. View at: Publisher Site | Google Scholar\n2. J. J. Sylvester, “Tables of the generating functions and groundforms of the binary duodecimic, with some general remarks, and tables of the irreducible syzygies of certain quantics,” American Journal of Mathematics, vol. 4, no. 1–4, pp. 41–61, 1881. View at: Publisher Site | Google Scholar\n3. T. A. Springer, “On the invariant theory of SU(2),” Indagationes Mathematicae, vol. 42, no. 3, pp. 339–345, 1980. View at: Google Scholar | Zentralblatt MATH\n4. M. Roberts, “The covariants of a binary quantic of the n-th degree,” The Quarterly Journal of Mathematics, vol. 4, pp. 168–178, 1861. View at: Google Scholar\n5. W. Fulton and J. Harris, Representation Theory, vol. 129 of Graduate Texts in Mathematics, Springer, New York, NY, USA, 1991.\n6. L. Bedratyuk, “The Poincaré series for the algebra of covariants of a binary form,” International Journal of Algebra, vol. 4, no. 25, pp. 1201–1207, 2010. View at: Google Scholar" ]
[ null ]
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https://www.arxiv-vanity.com/papers/hep-th/9808014/
[ "Adv. Theor. Math. Phys. 2 (1998) 1249-1286\n\nBranes at conical singularities\n\nand holography\n\nB.S. Acharya, J. M. Figueroa-O’Farrill, C.M. Hull, B. Spence\n\nDepartment of Physics\n\nQueen Mary and Wesfield College\n\nLondon E1 4NS, UK\n\nfootnotetext: e-print archive: http://xxx.lanl.gov/abs/hep-th/9705006\n\nAbstract For supergravity solutions which are the product of an anti-de Sitter space with an Einstein space , we study the relation between the amount of supersymmetry preserved and the geometry of . Depending on the dimension and the amount of supersymmetry, the following geometries for are possible, in addition to the maximally supersymmetric spherical geometry: Einstein-Sasaki in dimension , -Sasaki in dimension , -dimensional manifolds of weak holonomy and -dimensional nearly Kähler manifolds. Many new examples of such manifolds are presented which are not homogeneous and hence have escaped earlier classification efforts. String or theory in these vacua are conjectured to be dual to superconformal field theories. The brane solutions interpolating between these anti-de Sitter near-horizon geometries and the product of Minkowski space with a cone over lead to an interpretation of the dual superconformal field theory as the world-volume theory for branes at a conical singularity (cone branes). We propose a description of those field theories whose associated cones are obtained by (hyper-)Kähler quotients.\n\n## 1 Introduction and Motivation\n\nMaldacena has conjectured that the ’t Hooft large limit of supersymmetric Yang–Mills with gauge group is dual to type IIB superstring theory on , where the subscript indicates that the sizes of the spaces grow with . A more precise version of the conjecture was formulated in [46, 71] where a simple recipe was given for relating gauge theory correlators to string theory S-matrix elements, and these are given in terms of classical supergravity in the large limit.\n\nThis conjecture was motivated by considering parallel -branes for large and taking a limit in which the gauge theory on the brane decouples from the physics of the bulk . When is small (with the string coupling), the system is well-described by -dimensional super Yang-Mills theory with gauge group, while if is large, the system is described by IIB string theory in the near-horizon geometry, which is . These are then dual descriptions of the same system, leading to the conjectured equivalence. String loop corrections correspond to corrections in the gauge theory so that in the large limit, one can use classical supergravity theory in the background.\n\nThis can be generalised to any -brane of superstring theory or theory and this leads to a relation between the worldvolume theory with gauge symmetry and the string or theory in the space-time which arises in the near-horizon limit of the -brane spacetime . Of particular interest are those cases—the -brane and the and -branes—in which the near-horizon geometry is a supersymmetric anti-de Sitter space solution of the form () and the worldvolume theory is a superconformal field theory. In these cases, there is a holographic duality between the string or theory in anti-de Sitter space and the superconformal field theory (which may be thought of as being at the boundary of the anti-de Sitter space [59, 71]). The superconformal symmetry in dimensions is identified with the anti-de Sitter supersymmetry in dimensions.\n\nAn interesting extension of these ideas is to explore -branes which exhibit near-horizon geometries of the form , where is an Einstein manifold. If the geometry is supersymmetric, then the string or theory in that background is expected to be holographically dual to a superconformal field theory. These vacua are not maximally supersymmetric unless is a round sphere or the real projective space, which is the near-horizon geometry of branes on an orientifold , and as a result the dual field theories have less than maximal supersymmetry. Many such cases have been studied already, particularly in the context of type IIB superstring theory, but also in theory . A simple modification is to let be a finite quotient of the sphere [54, 58, 63, 6, 2, 36, 26]. Alternatively, if we recall that is a circle bundle over , one can replace with other Kähler–Einstein -folds. This was done in the type IIB context (i.e., ) in , generalising . Another generalisation is to replace with another homogeneous space . Homogeneous vacua of supergravity theories were studied intensively in the early days of Kaluza–Klein supergravity (see, e.g., ). There is a complete classification in dimension seven and a partial list in dimension five . One such five-dimensional example is , whose dual conformal field theory was recently discussed by Klebanov and Witten (see also ). They interpreted the vacuum in terms of the near-horizon geometry of parallel -branes at a conical singularity of a Calabi–Yau threefold. Similar ideas have been considered in .\n\nThe purpose of the present paper, which subsumes , is to study those theory or superstring vacua of the form that preserve some supersymmetry. Our aim will be to understand the constraints supersymmetry imposes on the geometry of the Einstein manifolds , and then to use the geometry of these manifolds in order to identify the superconformal symmetries which are required under the adS/CFT correspondence. This is certainly likely to be useful in understanding generic features of this correspondence in supersymmetric cases.\n\nAny solution of the form is the near-horizon geometry of a -brane solution which will be supersymmetric if the near-horizon geometry is, and which interpolates between and a vacuum where is a cone over (defined below) and is ()-dimensional Minkowski space. The case in which (with ) is a coset space was considered in , but we will consider general Einstein spaces which in addition preserve some supersymmetries. The cone is Ricci-flat and the number of supersymmetries of the vacuum depends on the number of covariantly constant spinors. This is determined by the holonomy group of , and such holonomies have been classified (see, e.g., ). The number of supersymmetries on depends on the number of Killing spinors on , but these all arise from covariantly constant spinors on , leading to a characterisation of supersymmetric anti-de Sitter solutions.\n\nWe will show that for those vacua which are supersymmetric, the geometry of is highly constrained. Depending on , the possible geometries of are as follows: nearly Kähler for , weak holonomy for , Einstein–Sasaki for , and -Sasaki for ; this gives many supersymmetric compactifications of string and theory which have not been considered previously.\n\nGiven a -brane solution of the above type, the interpolating solution argument then leads to a conjectured duality between the string or theory in a supersymmetric background of the form and a -dimensional superconformal field theory on the worldvolume of coincident -branes located at the conical singularity of the vacuum. The detailed description of the dual theories will be left to future investigations, and in this paper we will focus instead on generic features of the field theories which follow from the common properties of each of the geometries listed above.\n\nThis paper is organised as follows. In Section 2 we set the notation concerning the supersymmetric branes which we will study in this paper and we review the simple solutions with spherical near-horizon geometries. In Section 3 we discuss the solutions which can be interpreted as branes sitting at a conical singularity in a Ricci-flat manifold , and we characterise the supersymmetric solutions in terms of the holonomy of . In Section 4 we discuss their near-horizon geometries in detail. Section 5 contains many examples including those in the early Kaluza–Klein literature as well as some more recent ones which have hereto not been considered in relation with supergravity. As an application of our results, in Section 6 we describe how the near-horizon geometry induces the superconformal symmetry of the dual theory. Finally in Section 7 we offer some conclusions.\n\n## 2 Supersymmetric Branes and their Near-Horizon Geometries\n\nIn this section we review the near-horizon geometries of the elementary brane solutions of string and theory. This section serves mostly to establish the notation.\n\n### 2.1 M-branes\n\nEleven-dimensional supergravity consists of the following fields [62, 19]: a Lorentzian metric , a closed -form with a quantised flux and a gravitino . By a supersymmetric vacuum we will mean any solution of the equations of motion with for which the supersymmetry variation , regarded as a linear equation on the spinor parameter , has nontrivial solutions. The eleven-dimensional spinorial representation is -dimensional and real, so there at most linearly independent solutions. An important physical invariant of a supersymmetric vacuum is the fraction of the supersymmetry that the solution preserves. For example, and the flat metric on eleven-dimensional Minkowski spacetime is a supersymmetric vacuum with ; that is, it is maximally supersymmetric. Other maximally supersymmetric vacua are and with and having quantised flux on the and , respectively.\n\nEleven-dimensional supergravity has four types of elementary solutions with : the -wave and the Kaluza–Klein monopole [43, 68, 52], and the elementary brane solutions: the -brane and the -brane . There should also be an -brane solution [52, 5]. In what follows we will focus on the - and -branes.\n\n#### 2.1.1 The M2-brane\n\nThe following background describes a number of parallel -branes :\n\n g =H−23g2+1+H13g8 (1) F =±dvol2+1∧dH−1 ,\n\nwhere and are the metric and volume form on the three-dimen-sional Minkowskian worldvolume of the branes; is the metric on the eight-dimensional euclidean space transverse to the branes; and is a harmonic function on . For example, if we demand that depend only on the transverse radial coordinate on , we then find that\n\n H(r)=1+a6r6 ;a6≡25π2Nℓ6p (2)\n\nis the only solution with . This corresponds to coincident membranes at . Here is the eleven-dimensional Planck length.\n\nOther choices of are possible. For example one can consider multicentre generalisations, where is an arbitrary harmonic function on with pointlike singularities and suitable asymptotic behaviour , say, as . This corresponds to parallel branes localised at the singularities of . More generally, we can take to be invariant under some subgroup of isometries of . These solutions correspond to branes which are ‘delocalised’ or smeared on the -orbits.\n\nWe can easily determine the fraction of the supersymmetry which the above solution preserves . The supersymmetry variation of the gravitino in a bosonic background is given by\n\n δεΨM=∇Mε−1288(ΓMPQRS−8δMPΓQRS)FPQRSε , (3)\n\nwhere is the spin connection. In the -brane Ansatz given by (1) and (2), equation (3) is solved by\n\n ε=H−16ε∞ ,\n\nwhere, choosing to be the directions tangent to the worldvolume of the brane, obeys\n\n Γ012ε∞=ε∞ .\n\nBecause squares to the identity and is traceless, we see that . Nevertheless, the -brane interpolates between two maximally supersymmetric solutions: flat Minkowski space infinitely far away from the brane, and near the brane horizon [35, 21].\n\nIndeed, notice that the metric on the transverse space is given by\n\n g8=dr2+r2gS , (4)\n\nwhere is the metric on the unit sphere . In the near-horizon region ,\n\n H(r)∼a6r6 ,\n\nso that the membrane metric becomes\n\n g=a−4r4g2+1+a2r−2dr2+a2gS .\n\nThe last term is the metric on a round of radius ; whereas the first two combine to produce the metric on -dimensional anti-de Sitter spacetime with “radius” :\n\nwith .\n\n#### 2.1.2 The M5-brane\n\nThe -brane is the magnetic dual to the -brane. The background describing a number of parallel -branes is given by :\n\n g =H−13g5+1+H23g5 (6) F =±3⋆5dH ,\n\nwhere and are the metric and volume form on the six-dimensional Minkowskian worldvolume of the branes; and are the metric and Hodge operator on the five-dimensional euclidean space transverse to the branes; and is a harmonic function on . For example, if we demand that depend only on the transverse radial coordinate on , we then find that\n\n H(r)=1+a3r3 ;a3≡πNℓ3p (7)\n\nis the only solution with . This corresponds to coincident fivebranes at .\n\nAs for the membrane solution, multicentre and ‘delocalised’ fivebrane solutions also exist.\n\nThe fraction of the supersymmetry which is preserved can be computed as before . The gravitino shift equation in the bosonic background given by above is solved by\n\n ε=H−112ε∞ ,\n\nwhere, choosing to be the directions tangent to the worldvolume of the fivebrane, obeys\n\n Γ012345ε∞=ε∞ .\n\nsquares to the identity and is traceless, so that again . Nevertheless, as for the -brane, the -brane also interpolates between two maximally supersymmetric solutions: flat Minkowski space infinitely far away from the brane, and near the brane horizon [35, 21].\n\nIndeed,\n\n g5=dr2+r2gS , (8)\n\nwhere is now the metric on the unit sphere . In the near-horizon region ,\n\n H(r)∼a3r3 ,\n\nso that the fivebrane metric becomes\n\n g∼a−1rg5+1+a2r−2dr2+a2gS .\n\nThe last term is the metric on a round of radius , whereas the first two combine to produce the metric on -dimensional anti-de Sitter spacetime with “radius” , analogous to (5),\n\nwith now .\n\n### 2.2 String Branes\n\nThe near-horizon geometries of -branes in type II string theory are not of the form\n\nwith the exception of the -brane of type IIB string theory, for which the near-horizon geometry is . For other -branes () the near-horizon geometry is conformal to (9), the conformal factor being nontrivial, and either singular for or zero for as .\n\nGeometries of the form\n\nfor some space also arise, and compactifying on leads to a geometry. For example, arises from a D1-brane lying inside a D5-brane , while is the near-horizon geometry for the extreme Reissner-Nordström black hole.\n\n#### 2.2.1 The D3-brane in Type IIB\n\nThe metric for the -brane of type IIB is given by ,\n\n g=H−12g3+1+H12g6 , (11)\n\nwhere is the metric on the Minkowski worldvolume of the brane and is the euclidean metric on the transverse . The self-dual -form has (quantised) flux on the unit transverse five-sphere and the dilaton is constant. is again harmonic in and the unique spherically symmetric solution with is\n\n H(r)=1+a4r4 ;a4≡4πgNℓ4s\n\nwhere is the string coupling constant, given by the exponential of the constant dilaton, and is the string length. The solution corresponds to parallel -branes at . The ten-dimensional Planck length is , so that can be rewritten as\n\n a4=4πNℓ4p . (12)\n\nThe near-horizon analysis is similar to that for the and branes above, and the near-horizon geometry is\n\nwhere the anti-de Sitter and sphere radii are now equal, .\n\n## 3 Branes at Conical Singularities\n\nAll the branes considered in the previous section interpolate between flat Minkowski spacetime asymptotically far away from the brane and near the brane horizon. Both of these vacua are maximally supersymmetric and the branes themselves preserve of the supersymmetry. In this section we discuss the brane solutions of that interpolate between near-horizon geometries of the form and an asymptotic geometry of the form , where is a -dimensional Einstein manifold and is the cone over . This can be interpreted as a set of coincident branes at a conical singularity in a Ricci-flat manifold. These brane solutions interpolate between vacua which are not maximally supersymmetric and the brane solutions themselves will preserve a smaller fraction of the supersymmetry. In this section we will characterise those branes with in terms of the holonomy group of the transverse space.\n\n### 3.1 Cone Branes\n\nThe -brane solutions above all have metrics of the form\n\n g=H−αgp+1+H2βgE , (13)\n\nwhere\n\n H(r)=1+(ar)β , (14)\n\nis the Minkowski metric on the worldvolume of the -brane, and where is the flat Euclidean metric on . For the -brane, -brane, and -brane, respectively, and . The Euclidean metric can be written as\n\n gE=dr2+r2gS (15)\n\nwhere is the round metric on the unit sphere and is a radial coordinate. In the near-horizon limit in which the constant term in can be dropped, the metric (13) becomes\n\n g∼(ra)αβgp+1+a2r−2dr2+a2gS ,\n\nwhich is the metric on , as can be seen by changing variables to , where the anti-de Sitter radius is .\n\nReplacing the sphere by any other -dimensional Einstein manifold with the same cosmological constant gives another solution of the field equations on , and we will be interested in those choices of that give spontaneous compactifications to anti-de Sitter space that preserve some nonzero fraction of the supersymmetry. Note that as is a complete Einstein space with positive cosmological constant, it follows by Myer’s Theorem that it is compact (see, e.g., ). There is a brane solution of the form \n\n g=H−αgp+1+H2βgC , (16)\n\nwhere is again given by (14) and the metric is given by replacing the spherical metric with the metric of in (15) to give\n\n gC=dr2+r2gX .\n\nThe transverse space to the brane is now the metric cone over with topology , with the open half-line and metric . Since is Einstein with , it follows that is Ricci-flat; however unlike the case of the sphere, where is actually flat, is now metrically singular at the apex of the cone .\n\nIn the near-horizon region , the constant term in can be dropped and the metric becomes approximately\n\n g∼(ar)αβgp+1+a2r−2dr2+a2gC ,\n\nwhich is the metric on . For large , and the metric becomes\n\n g∼gp+1+gC ,\n\non the product of ()-dimensional Minkowski space with the cone . We interpret these solutions as describing coincident branes located at the conical singularity of , or cone branes for short. Note that whereas the solution has a conical singularity at , the brane metric (16) approaches the metric of as , which is non-singular at . However is a horizon for the brane and the solution can be continued through the horizon. In general there will be a singularity inside the horizon.\n\nThese solutions can in principle be generalised by replacing with more general harmonic functions on the cone .\n\n### 3.2 Supersymmetry and Holonomy\n\nIn this subsection we wish to describe the amount of supersymmetry preserved by the solutions and , and the brane solution (16) that interpolates between them. In each case, the number of supersymmetries is the number of linearly independent spinors such that the supersymmetry variation of the gravitini vanish in this background: . On , such spinors are of the form where is a Killing spinor on and is a Killing spinor on , satisfying\n\n ∇(X)Mψ=ϵ12ΓMψ (17)\n\nwhere is either or , depending on the field . For simplicity, and also for ease of comparison with the mathematical literature on Killing spinors, we have rescaled the metric on the Einstein manifold in such a way that the coefficient on the right-hand side of equation (17) is . The number of supersymmetries of the solution is given by where is the number of Killing spinors on with , is the number of Killing spinors on and for theory and for Type II strings.\n\nIf the dimension of is even, then there are as many solutions with as with , whereas if is odd, then changing the sign of corresponds to reversing the orientation of . Thus for odd-dimensional , the number of solutions depends on the orientation. For example, for , if there are Killing spinors with one choice of orientation of , there will be no Killing spinors with the opposite choice of orientation, unless is the round -sphere, in which case with either choice of orientation . We will see below that this is very easy to understand in terms of the holonomy of the cone .\n\nOn , the supersymmetries are of the form where and are covariantly constant spinors on Minkowski space and the cone , respectively. In particular, satisfies\n\n ∇(C)ψ=0 . (18)\n\nThe number of supersymmetries of the solution is then given by where is the number of covariantly constant spinors on the cone and is the number of parallel spinors on .\n\nAs shown in , there is a one to one correspondence between Killing spinors on satisfying (17) and covariantly constant spinors on satisfying (18). Each covariantly constant spinor on restricts to a Killing spinor on satisfying (17) for a particular orientation of . If is odd, so that is even, then the sign of is also correlated to the chirality of the parallel spinor on . If is even, so that is odd, then the sign of depends on which one of the two irreducible representations of the Clifford algebra we use to embed the unique spinor representation of .\n\nIn the round -sphere compactification of theory, there are solutions of (17) with and solutions with , of which only have the right sign to be Killing spinors, while has covariantly constant spinors. In this case, , , and , so there are supersymmetries on both and . For other , there are only solutions of (17) with one particular orientation , and parallel spinors on restrict to spinors satisfying (17) with that choice of orientation. This is because the spinors left invariant by any of the possible holonomy groups which act irreducibly in eight dimensions all have the same chirality. On the other hand, as will be seen shortly, when is an Einstein manifold admitting Killing spinors and has dimension , both orientations give the same numbers of supersymmetries. For Type IIB compactifications on -manifolds, this conflicts with a claim made in . At any rate, we are interested in the supersymmetric cases, so we choose .\n\nFor -, - and -branes, = respectively, whereas = . Using our result for the numbers of asymptotic and near-horizon supersymmetries we find that for - and -branes the number of supersymmetries of the near-horizon geometry is twice the number of supersymmetries for the asymptotic conical geometry , unless is a round sphere, in which case the number of supersymmetries is the same in both cases. Applying this to the -brane gives the same number of supersymmetries asymptotically and near the horizon, but in this case, as we shall see, the only smooth spaces that admit Killing spinors are and . In each of these cases, the number of supersymmetries can be further reduced by orbifolding. In particular, for the -brane with non-spherical near-horizon geometry, the asymptotic space is actually an orbifold and in this case the near-horizon limit has twice the number of supersymmetries as the asymptotic region.\n\nAs an example, consider the squashed -sphere of , which has , and , so that or for the squashed -sphere compactification, depending on the orientation. The cone has one parallel spinor, so the near-horizon geometry has or , while the asymptotic conical geometry has .\n\nIn summary, the amount of supersymmetry on the brane and in the near-horizon geometry is determined by the number of parallel spinors on the cone , and this can now be analysed group-theoretically.\n\nAssume that the base of the cone, , is simply-connected. Its cone will be simply-connected also, since and are homotopy equivalent. Simply-connected manifolds admitting parallel spinors are classified by their holonomy group . Because is Ricci-flat, we know that it cannot be a locally symmetric space. Moreover, by a theorem of Gallot , the holonomy group acts on irreducibly unless is flat, in which case is the round sphere. Therefore, we need only consider irreducible holonomy groups of manifolds which are not locally symmetric. In other words, those in Berger’s table (see, e.g., [7, 66]).\n\nOf those, the ones which admit parallel spinors are given in the following table, which also lists the number (or in even dimension) of linearly independent parallel spinors.\n\nIf , and hence , were not simply-connected, then we could still use part of the above analysis. Gallot’s theorem still applies and the existence of parallel spinors constrains the restricted holonomy group of the manifold to be contained in the above Table. However a spinor which is invariant under the restricted holonomy group need not be parallel, because it may still transform nontrivially under parallel transport along noncontractible loops. Therefore the number of parallel spinors in will be at most the number shown in Table 1.\n\n## 4 Near-horizon Geometries of Cone Branes\n\nIn this section we will discuss the relation between the geometry of and the holonomy of its cone . We will do this in some generality, and give the relation between the geometry of and the number of supersymmetries preserved by a solution, when one exists, and this characterises the near-horizon geometries of the supersymmetric cone branes discussed in the previous section.\n\nOn every cone there is a privileged vector field , called the Euler vector, which generates the rescaling diffeomorphisms. Moreover on any manifold of reduced holonomy, the holonomy principle guarantees the existence of certain parallel tensors, corresponding to the singlets under the holonomy group in the tensor products of the holonomy representation. Therefore on a cone of reduced holonomy we will be able to build interesting geometric structures out of these parallel tensors and the Euler vector. These structures will in turn induce interesting geometric structures on , which we identify with . These geometric structures are summarised in the following table, which also lists the numbers of Killing spinors; that is, solutions of equation (17), with the number of solutions with .\n\nWe now proceed to describe these geometries in detail.\n\n### 4.1 Cones with Spin7 Holonomy\n\nAny eight-dimensional manifold with holonomy possesses a parallel self-dual -form , known as the Cayley form. Contracting the Euler vector into the Cayley form gives a -form on which restricts to a -form on :\n\n ϕ(u,v,w)≡Ω(ξ,u,v,w) ,\n\nfor any vectors tangent to . In fact, one can write the Cayley form restricted to as\n\n Ω=dr∧ϕ+⋆ϕ ,\n\nwith the Hodge operator on . Notice that is a -form on , and by its restriction to we simply mean evaluating it at on vector fields tangent to . In other words, acts both on vectors tangent and normal to . From the fact that is parallel in , it follows that, in , obeys\n\n ∇ϕ=⋆ϕ .\n\nThis condition says that has weak holonomy , and we say that defines a nearly parallel structure. In fact, as proven in a manifold has weak holonomy if and only if its metric cone has holonomy contained in . In the early Kaluza–Klein literature it would have been said that has Weyl holonomy, but we will not follow this nomenclature.\n\n### 4.2 Cones with G2 holonomy\n\nOn any seven-dimensional manifold with holonomy there is a parallel -form , known as the associative form. Contracting the Euler vector into the associative form yields a -form on which restricts to a -form on :\n\n ω(u,v)≡Φ(ξ,u,v) ,\n\nfor any vectors tangent to . We can define a -tensor on as follows:\n\n ⟨J(u),v⟩≡ω(u,v) .\n\nIt is possible to show that is an orthogonal almost complex structure:\n\n J2=−1and⟨J(u),J(v)⟩=⟨u,v⟩ ,\n\nwhence is an almost hermitian manifold. From the fact that is parallel on , it follows that on ,\n\n ∇vJ(v)=0for any v;\n\nalthough . Moreover is not integrable. This means that is a non-Kähler nearly Kähler manifold .\n\nIn fact, the converse is also true and a six-dimensional manifold is non-Kähler nearly Kähler if and only if its cone has holonomy contained in . A different proof that a six-dimensional manifold admits Killing spinors if and only if it is non-Kähler nearly Kähler appeared earlier in .\n\nNearly Kähler -manifolds share many properties with Calabi–Yau -folds. For example, there is a natural -form defined by contracting the Euler vector with the coassociative -form on the cone. In particular, nearly Kähler -manifolds have vanishing first Chern class . For example, is nearly Kähler, but cannot be Kähler because is trivial.\n\n#### 4.2.1 Scholium on Almost Hermitian Manifolds\n\nThe notion of nearly Kähler manifolds should not be confused with the notion of an almost Kähler manifold, which simply means an almost hermitian manifold whose associated -form is closed; or in other words, a symplectic manifold with a compatible almost complex structure. Unfortunately, given the many different generalisations of the notion of Kähler manifolds, there is a large possibility for confusion, so we digress momentarily to settle the notation once and for all. As shown in , there are 16 classes of almost hermitian manifolds, the class of Kähler manifolds being but one of them. The classes are defined in the following way.\n\nOne can define almost hermitian geometry in terms of -structures. Just like a Riemannian metric on an -dimensional manifold allows us to reduce the structure group of the tangent bundle from to , an almost hermitian structure allows us to reduce the group further to . This means that tensor bundles can be consistently decomposed into -irreducible sub-bundles. Let be the associated -form. Its covariant derivative is a section through a sub-bundle , with the cotangent bundle of . is not irreducible under , but rather decomposes into four irreducible components . There are therefore sixteen -invariant sub-bundles (not necessarily irreducible) in : , , (),…, . The sixteen classes of hermitian manifolds correspond to these sixteen sub-bundles: a manifold belonging to the class corresponding to the sub-bundle of to which belongs.\n\nClearly corresponds to the class of Kähler manifolds; but there are other classes of manifolds which are close to Kähler in some sense. The class of non-Kähler nearly Kähler manifolds can be shown to be the one for which . The class of almost Kähler manifolds consists of those for which . To make matters even more confusing there exist also semi-Kähler manifolds for which and quasi-Kähler manifolds which have a more complicated definition which we will not need. For details the reader is referred to .\n\n### 4.3 Calabi–Yau Cones\n\nThe class of Calabi–Yau -folds is the class of compact Ricci-flat Kähler -folds. Let us concentrate first on the Kähler condition. Any Kähler manifold has a parallel -form, the Kähler form, and a corresponding orthogonal complex structure which is also parallel. Together with the Euler vector, we can therefore build two objects: a -form obtained by contracting the Euler vector into the Kähler form, and a vector obtained by acting with the complex structure on the Euler vector. The vector is clearly tangent to and restricts there to a vector , whereas the -form restricts to a -form on which is naturally dual to :\n\n χ=I(ξ)andθ=⟨χ,−⟩ .\n\nIt follows from the definition that has unit norm and is a Killing vector. Furthermore, the covariant derivative of defines a -tensor in ,\n\n T(v)=∇vχ , (19)\n\nwhich, because is parallel in , satisfies the following identity:\n\n ∇uT(v)=θ(v)u−⟨u,v⟩χ . (20)\n\nThe triple defines a Sasaki structure on . More precisely, a triple on an odd-dimensional Riemannian manifold , where is a unit norm Killing vector, its dual -form, and obeys equation (20), is called a Sasaki structure on , and is called Sasakian. Equivalently , a manifold is Sasakian if and only if its metric cone is Kähler.\n\nIf in addition is Ricci-flat (hence Calabi–Yau), then is Einstein. We say that is Sasaki–Einstein. Therefore a manifold is Sasaki–Einstein if and only if its metric cone is Calabi–Yau [29, 30, 4].\n\nThis means, in particular, that a Sasaki–Einstein manifold of dimension also possesses two distinguished -forms obtained by contracting the Euler vector into the real and imaginary parts of the complex volume ()-form on .\n\nThe Killing vector in a -dimensional Sasakian manifold defines a foliation whose leaves are the integral curves of . The manifold is called regular if these curves are closed and have the same length. If this is the case, defines a action on and can be understood as a circle bundle over the orbit space, a -dimensional manifold , which can be shown to be Kähler. Moreover if is Sasaki–Einstein then is Kähler–Einstein. Regularity is a very stringent condition which is not satisfied by most Sasaki(–Einstein) manifolds. When the integral curves of are closed but of different lengths, the orbit space is an orbifold which is everywhere smooth except at a finite number of points [29, 10].\n\n### 4.4 Hyperkähler Cones\n\nIn a hyperkähler manifold we have a parallel quaternionic structure consisting of three orthogonal complex structures satisfying the quaternion algebra, as well as their corresponding Kähler forms: . From the discussion above, on a cone each complex structure gives rise to one Sasaki structure on . Moreover the fact that the three complex structures on satisfy the quaternion algebra means that the Killing vectors in the three Sasaki structures are orthonormal and obey an Lie algebra. Three Sasaki structures satisfying these conditions define a -Sasaki structure on (see for the latest word on -Sasakian manifolds). Equivalently, one can prove the converse and show that a manifold is -Sasakian if and only if its cone is hyperkähler [30, 4, 11].\n\nThe three Killing vectors define a foliation of the -Sasakian manifold . We say that is regular if the foliation fibres. This means that is an or bundle over a quaternionic Kähler space . Equivalently, is a circle bundle over the twistor space of . If is not regular, but the Killing vectors are complete, then the orbit space defines a quaternionic Kähler orbifold .\n\n## 5 Examples\n\nIn this section we list the known near-horizon geometries for different types of cone branes. The homogeneous examples are all known from the early days of Kaluza–Klein supergravity, but we do list some non-homogeneous examples as well. For each type of cone brane we discuss the possible smooth near-horizon geometries and where applicable we discuss orbifolds.\n\n### 5.1 M2 Cone Branes\n\nThe transverse space to an cone brane is the metric cone over a -dimensional manifold . From Table 2 one can read off the following possibilities for simply-connected , which are listed in Table 3. In the non-simply-connected case the number of Killing spinors is at most the one shown in the table.\n\nBesides the sphere and its quotients, we have the following types of near-horizon geometries.\n\n#### 5.1.1 X7 is 3-Sasakian\n\nWe must distinguish between regular and non-regular -Sasakian -manifolds. The regular manifolds were classified in [30, 11] and they all happen to be homogeneous spaces. Therefore they have been known since the early days of Kaluza–Klein supergravity . In the present context they have been discussed in . Apart from and , the only other homogeneous regular example is , which is called in and in .\n\nOn the other hand there are an infinite number of different non-regular -Sasakian -manifolds. The topology of a seven-dimensional -Sasakian manifold is highly constrained. First of all, Bochner’s theorem implies that the first Betti number of a -Sasakian manifold vanishes: . In seven dimensions, by Poincaré duality. Furthermore, as shown in [32, 31], , so that the only nonzero Betti numbers are . Recently an infinite family with arbitrary has been constructed in [13, 9]. This implies that there exist -Sasakian -manifolds of every possible rational homotopy type allowed. These examples are toric; that is, they admit an action of a torus preserving the -Sasakian structure. They are constructed via a -Sasakian quotient akin to the hyperkähler quotient . Indeed, the two quotients are related via the cone construction. In other words, suppose is a -Sasakian manifold and is its hyperkähler cone. Then if admits a triholomorphic action commuting with the Euler vector, then the hyperkähler quotient of is a cone over the -Sasaki quotient of . The cones over the toric -Sasakian manifolds in [13, 9] form a subclass of the toric hyperkähler manifolds studied in and which have been considered in the context of intersecting branes in .\n\nAll -Sasakian manifolds have an infinitesimal isometry. If the Killing vectors are complete, they integrate to an action of or . In the regular case, the orbit space is smooth; otherwise not. In any case, the infinitesimal isometry is a generic feature of these manifolds and one which will play a role in Section 6, when we discuss the dual field theories.\n\n#### 5.1.2 X7 is Sasaki–Einstein\n\nThere are many known Sasaki–Einstein -manifolds. The regular examples can be understood as bundles over Kähler–Einstein -folds . They have not been classified. The known results are summarised in the following table .\n\nThe first four examples are homogeneous and have therefore appeared in the early Kaluza–Klein literature. The first three are listed using the nomenclature of . In [24, 31] these spaces are called , and , respectively. Here is the Stiefel manifold of orthonormal oriented -frames in and is the Grassmannian of oriented -planes in . is the complex flag manifold in consisting of pairs where is a complex plane in and is a complex line. The last class of examples does not consist of homogeneous manifolds: are the del Pezzo surfaces consisting of blowing up points in general position on .\n\nIn addition, there are two infinite families of homogeneous non-regular Sasaki–Einstein -manifolds: the of [73, 15] and the of . Of course they are mentioned in .\n\n#### 5.1.3 X7 has Weak G2 Holonomy\n\nThe canonical example of a weak holonomy manifold which is not Sasaki–Einstein is the squashed -sphere, which is a homogenous space\n. It turns out that this generalises, and every -Sasakian -manifold can be squashed to a manifold with weak holonomy [32, 31]. The squashing is done as follows. A -Sasakian manifold is foliated by the action of the Sasakian Killing vectors. At any point , the Killing vectors are tangent to the unique leaf of the foliation passing through . The tangent space to at has an orthogonal decomposition . The squashing of the metric is done by introducing a parameter and rescaling the metric on the leaves of the foliation by . We can then compute the Ricci tensor as a function of and notice that there are two values for which it is Einstein: one is the original -Sasakian metric, and the other gives rise to a weak holonomy structure.\n\nBy squashing the infinite toric family of [13, 9] in this way, one obtains a huge number of weak holonomy manifolds which are not Sasaki–Einstein. These spaces are not homogeneous and hence have not been considered before in the Kaluza–Klein supergravity context. There are examples with arbitrary and all other Betti numbers vanishing, since squashing does not change the topology.\n\nAnother infinite dimensional family of weak holonomy manifolds consists of the Aloff–Wallach spaces , with defined by\n\n eiθ↦⎛⎜⎝eikθeiℓθe−i(k+ℓ)θ⎞⎟⎠ .\n\nThis family is remarkable because it contains an infinite number of distinct homotopy types and even exotic pairs, i.e., homeomorphic non-diffeomorphic pairs . For or these spaces admit metrics with weak holonomy. The Aloff–Wallach spaces agree, allowing for some redundancy in the notation, with the spaces of . Finally, the other weak holonomy manifold from the early Kaluza–Klein literature is the homogeneous space of .\n\nIn summary, all known examples are homogeneous, hence previously known in the Kaluza–Klein context, except for the squashed toric -Sasakian manifolds.\n\n### 5.2 M5 Cone Branes\n\nThe transverse space to an cone brane is a five-dimensional cone over a four-dimensional manifold . As proven in , any complete connected -dimensional spin manifold, with , admitting real Killing spinors is locally isometric to the round -sphere. Therefore the only possible smooth near-horizon geometry for an cone brane is either spherical or elliptical ; these describe the near-horizon geometry of branes at a point in or on the orientifold . More generally we can consider orbifolds of which are of the form . Such an orbifold will preserve supersymmetry if is an ADE subgroup of the hyperkähler which acts on . This clearly induces a near-horizon geometry of the form which has two singular points induced from the orbifold fixed points of acting on . These orbifolds preserve half of the supersymmetries obtained in the maximally supersymmetric case.\n\n### 5.3 D3 Cone Branes\n\nThe transverse space to a cone brane is a six-dimensional cone over a five-dimensional manifold . From Table 2 we see that there are two simply-connected possibilities. This means is a sphere or a Sasaki–Einstein manifold. Non-simply connected examples, e.g., or more generally , can be obtained by taking quotients. There are an infinite number of smooth quotients of the sphere which possess Killing spinors. These can be determined as follows .\n\nIn his solution of the spherical space problem, Wolf classified all the discrete subgroups , for which is smooth . Given one such subgroup, the spin structures on are in one-to-one correspondence with the lifts of to an isomorphic subgroup ; that is, to a which is mapped to isomorphically under the covering map . Finally, for with spin structure given by , the Killing spinors in are precisely the -invariant spinors in . The results are as follows.\n\nThere are two families of subgroups of for which is smooth: and described below.\n\nLet , where be the cyclic subgroup of of order generated by the following element:\n\n ⎛⎜ ⎜ ⎜ ⎜⎝R(1n)R(an)R(bn)⎞⎟ ⎟ ⎟ ⎟⎠with(a,n)=(b,n)=11≤a,b\n\nwhere denotes the rotation matrix\n\n R(θ)=(−cos2πθsin2πθ−sin2πθcos2πθ) ,\n\nand denotes the greatest common divisor, so that means that and are coprime.\n\nSimilarly let , with and , denote the subgroup of generated by elements and subject to the relations: and . The generators can be written as\n\nwhere is a identity matrix, and where and and is the identity matrix of order 2. This subgroup has order .\n\nIt is not hard to show that has precisely one spin structure when is odd, and none for even. Similarly has precisely one spin structure when both and are odd, and none otherwise.\n\nComputing the -invariant spinors for each of the cases above is an easy matter. One sees that is of type whenever any one of the following equalities is satisfied:\n\n a+b±1=nora−b=±1 ,\n\nand has no Killing spinors otherwise. Similarly," ]
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https://mathexpressionsanswerkey.com/math-expressions-grade-4-unit-5-lesson-8-answer-key/
[ "# Math Expressions Grade 4 Unit 5 Lesson 8 Answer Key Focus on Mathematical Practices\n\n## Math Expressions Common Core Grade 4 Unit 5 Lesson 8 Answer Key Focus on Mathematical Practices\n\nMath Expressions Grade 4 Unit 5 Lesson 8 Homework\n\nMath Expressions Grade 4 Focus on Mathematical Practices Lesson 8 Pdf Answer Key Question 1.\nYonni has a 5 gallon fish tank. He needs to change the water in the fish tank. How many cups of water will Yonni need to replace all the water in the fish tank?\nYonni needs 80 cups of water to replace all the water in the fish tank.\n\nExplanation:\nYonni has a 5 gallon fish tank\nIf 1 gallon is equal to 16 cups then 5 gallons will be 5 x 16 = 80 cups.", null, "Therefore, Yonni needs 80 cups of water to replace all the water in the fish tank.\n\nMath Expressions Grade 4 Focus on Mathematical Practices Pdf Answer Key Question 2.\nBarry is building a fence around his backyard. The backyard is in the shape of a rectangle and the longest side of the yard is 20 meters. The fence will have a perimeter of 60 meters. How many meters long is the short side of the backyard?\nThe length of the shortest side of the backyard is 10 meters\n\nExplanation:\nTo find the width, multiply the length given by 2 and subtract the result from the perimeter.Now you will have the total length for the remaining 2 sides.The remaining number divided by 2 is the width.\nMultiply length of the longest side 20 by 2\n20 x 2 = 40\nSubtract 40 from the perimeter 60\n60-40= 20\nDivide the number 20 by 2\n20 didided by 2 = 10\nTherefore, the length of the shortest side of the backyard is 10 meters.\n\nMath Expressions Grade 4 Lesson 8 Pdf Answer Key Unit 5 Question 3.\nYesi’s dog weighed 5 pounds when she got him. He now weighs 45 pounds. How much weight did Yesi’s dog gain, in ounces?\nThe weight Yesi’s dog gained is 640 ounces\n\nExplanation:\nYesi’s dog weighed 5 pounds when she got him. He now weighs 45 pounds\nThe weight of now is 45 – 5 = 40 pounds\nIf 1 pound is 16 ounces then 40 pounds will be 40 x 16 = 640 ounces", null, "Therefore, the weight Yesi’s dog gained is 640 ounces.\n\nMath Expressions Grade 4 Focus on Mathematical Practices Pdf Question 4.\nFiona’s family ¡s replacing the carpet in their living room. The living room is in the shape of a square. The length of one wall is 16 feet. How many square feet of carpet does Fiona’s family need to buy to replace their old carpet?\nFiona’s family need to buy 256 square feet of carpet to replace the old carpet.\n\nExplanation:\nTo know the number of square feet of carpet the family need to buy we need to find the area of their square shaped living room\nLength of the living room is 16 feet\nArea of a square is l x l\n16 x 16 = 256", null, "Therefore, Fiona’s family need to buy 256 square feet of carpet to replace the old carpet.\n\nTrevon drew the two rectangles below. He wanted to know the difference between the areas of the two rectangles. What is the difference between the two areas?", null, "The difference between the areas of the two rectangles is 60 dm", null, "Explanation:\nTrevon drew the two rectangles\nArea of a rectangle is l x w\nThe length and width of recatngle 1 are l= 16dm and  w = 9 dm\nArea = l x w = 16 x 9 = 144\nThe length and width of recatngle 2 are l= 12dm and  w = 7 dm\nArea = l x w = 12 x 7 = 84\nThe difference of areas of 2 rectangles is 144 – 84 = 60 dm.\n\nMath Expressions Grade 4 Unit 5 Lesson 8 Remembering\n\nSolve. Then explain the meaning of the remainder.\n\nFocus on Mathematical Practices Grade 5 Answer Key Unit 5 Question 1.\nThere are 43 students at a musical performance. Each row in the auditorium has 8 seats.If the students fill seats row by row from front to back, how many people are in the last row?\n3 people are in the last row\n\nExplanation:\nThere are 43 students at a musical performance. Each row in the auditorium has 8 seats\nIf the students fill seats row by row from front to back then\nAfter 1 st row filled by 8 students their will be 43-8=35 students left\n35-8=27\n27-8=19\n19-8=11\n11-8=3\nSimilarly after 5 ows getting filled by 8 students their will be 3 students left in the last rwo.\n\nWrite whether each number is prime or composite.\n\n49\n49 is not a prime number\n\nExplanation:\n49 is not a prime number because it can be divided not only by itself and 1 but also by 7.\n\nQuestion 3.\n31\n31 is a prime number\n\nExplanation:\n31 is a prime number as it can be divided exactly only by self and 1.\n\nQuestion 4.\n17\n17 is a prime number\n\nExplanation:\n17 is a prime number as it can be divided exactly only by self and 1.\n\nQuestion 5.\nThe perimeter of a postage stamp is 90 millimeters. The longer side of the stamp is 25 millimeters. What is the length of the shorter side?\nThe length of the shortest side of the postage stamp is 20 millimeters\n\nExplanation:\nTo find the width, multiply the length given by 2 and subtract the result from the perimeter.Now you will have the total length for the remaining 2 sides.The remaining number divided by 2 is the width.\nMultiply length of the longest side 25 by 2\n25 x 2 = 50\nSubtract 300 from the perimeter 960\n90-50= 40\nDivide the number 40 by 2\n40 didided by 2 = 20", null, "Therefore, the length of the shortest side of the postage stamp is 20 millimeters.\n\nQuestion 6.\nStretch Your Thinking The directions for lemonade say to put 2 cups of the liquid concentrate into 1 gallon of water. If Olivia only wants to make 1 pint of lemonade, how many fluid ounces of concentrate should she use? Explain." ]
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https://www.cut-the-knot.org/Curriculum/Algebra/GregBrockman/GregBrockmanDucciSequences.shtml
[ "# Ducci Sequences\n\nBy Greg Brockman\n\n### Some Background\n\nThis applet demonstrates the behavior of arbitrary vector-length Ducci sequences over the real numbers. (For the case of integers, see Integer Iterations on a Circle.) So just what does that mean? Well, first of all, we start with any list of n numbers, or a vector of length n. Next, we build a new vector by replacing each number by the absolute value of the difference between it and its neighbor to the right (we think about these vectors as being cyclic). We can repeat this process, and the sequence of vectors thus obtained is called a Ducci sequence.\n\nMore formally, let f: Rn → Rn be defined by\n\nf(ν1, ..., νn) = (|ν1 - ν2|, |ν2 - ν3|, ..., |νn - ν1|).\n\nThen the Ducci sequence of an n-vector ν is defined as {f i(ν)}, where i = 1, 2, 3, ... (f i is the iterated f. For example, f 2(ν) = f(f(ν)).)\n\nNow, since when generating successive elements in a Ducci sequence, we wrap a vector as if cyclic, it can be helpful to think of a vector in a Ducci sequence as simply some numbers arranged around a circle. I have taken this approach in this applet.\n\n### Instructions\n\nSo here's how to run the applet: First of all, click \"Add Circle\" to begin. There are now two modes: Edit and Run. By default, you start out in Edit mode.\n\n#### Options for Edit Mode\n\n• Add Slot (green button): Each new circle you add corresponds to increasing the length of the corresponding vector by 1.\n\n• You can select a given circle by clicking on it. If it's already selected, then nothing happens.\n\n• To add a value to the selected circle, enter values into the text boxes at the top. Values in the selected circle will automatically update, or you can press \"Enter\" if you wish.\n\n• You can remove the selected circle by pushing the \"Del\" key.\n\n• When ready, click \"Play Ducci!\" to enter Run mode.", null, "#### Options for Run Mode\n\n• Next Step calculates the next vector in the Ducci sequence. If animation is enabled, then you get a cool movie effect.\n\n• The Automate button automatically runs Next Step multiple times.\n\n• If you want to skip to a certain generation number (for example, maybe you want to find the 507th vector in the Ducci sequence under consideration), you can use the \"See generation number\" box. Just type in the number you want and press enter.\n\n• The buttons at top are fairly self-explanatory: Reset Values takes you back to the 0th vector (the starting one); Edit Values switches you back to Edit Mode; and Create New Game completely deletes all information and starts you with a fresh vector.", null, "#### Common Options\n\n• Toggling Display Exact/Show Approx. changes whether the exact values of numbers in circles are displayed or whether a decimal approximation is shown.\n\n##### Hint\n• If you aren't sure what a button does, you can always hold your mouse over it and wait for the tooltip.\n\n##### Note\n• The Ducci sequences are calculated using 300 digit precision on π, e, and 2. Thus, after about 1000 generations, your typical Ducci sequence incorrectly starts behaving like a homogeneous one (for the meaning of this term, see my paper) rather than a heterogeneous one. The program automatically shows a warning after 1000 generations.\n\n• The color of a circle indicates its magnitude relative to the maximum element in the current vector. This color is blue for the maximum element and becomes (linearly) darker as the element's magnitude shrinks.\n\n### Some Suggestions of What To Do\n\nMy paper discusses under what conditions Ducci sequences of odd length vectors start repeating. You can use the applet to examine the behavior of various Ducci sequences. What happens if you start out with an irrational, but then replace it by a good rational approximation to it? Can you get any non-repeating sequences that don't go to the zero vector (the answer to this question is actually unknown; such sequences exist in general, but the known ones cannot be built using the given input method)?\n\nSome cool phenomena, connected with papers before mine, show up when you work with vectors of length 4. Can you find a vector that never actually hits the zero vector? If not, can you build any that take a long time to get there? (Spoiler—using the given input tools, you won't be able to build a vector that never actually hits the zero vector. But given sufficient cleverness, it's possible to built vectors that take arbitrarily long to get there.)\n\nYou should also take a look at the exact values, and see as they behave as you iterate. How quickly do the coefficients on the various numbers grow? Under what conditions must they grow without bound?\n\nOn the whole, there is an essentially unlimited spectrum of questions that one can ask. I recommend using this applet to get an intuition of how Ducci sequences over the real numbers behave.", null, "" ]
[ null, "https://www.cut-the-knot.org/gifs/tbow_sh.gif", null, "https://www.cut-the-knot.org/gifs/tbow_sh.gif", null, "https://www.cut-the-knot.org/gifs/tbow_sh.gif", null ]
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https://www.khanacademy.org/math/algebra2/rational-expressions-equations-and-functions/simplify-rational-expressions/v/simplifying-rational-expressions-w-common-monomial-factors
[ "Main content\n\n# Simplifying rational expressions: common monomial factors\n\nCCSS Math: HSA.APR.D.6\n\n## Video transcript\n\n- [Voiceover] So I have a rational expression here and my goal is to simplify it, but while I simplify it, I wanna make the simplified expression be algebraically equivalent. So, if there are certain x values that would make this thing undefined, that I have to restrict my simplified expression by those x values. So, you could pause this video and take a go at it and then we'll do it together. All right. So let's just think real quick. What x values would make this expression undefined? Well, it's undefined if we try to divide by zero. So if x is zero, 14 times zero is zero, it's going to be undefined. And so, we could say this is, we could say x does not, cannot be equal to zero. For any other x, we can evaluate this expression. Now let's actually try to simplify it. So, when you look at the numerator and the denominator, every term we see is divisible by x and every term is divisible by seven. So it looks like we can factor seven x out of the numerator and seven x out of the denominator. So the numerator, we can rewrite as seven x times, we factor seven x out of 14 x squared, we're gonna be left with two x and then you factor seven x out of seven x, you're gonna be left with a one. And one way to think about it is we did the distributive property in reverse, if we were to do it again, seven x times two x is 14 x squared, seven x times one is seven x. All right. And now let's factor seven x out of the denominator. So, 14 x could be rewritten as seven x times two, times two, and remember, I wanna keep this algebraically equivalent, so I wanna keep the constraint that x cannot be equal to zero. And so we divide the numerator and the denominator by seven x or one way to think about it, you can divide seven x by seven x and just get one and we are left with two x plus one over two. Now this was the original expression, x could take on any value, but if we want it to be algebraically equivalent to our original expression, it has to have the same constraints on. So we're going to have to say x, x does not equal zero, and it's a very subtle but really important thing. For example, if you defined a function by this right over here, the domain of the function could not include zero. And so, if you simplified how you defined that function to this, if you want that function to be the same, it needs to have the same domain. It has to be defined for the same inputs. And so that's why we're putting the exact same constraints for them to be equivalent. If you got rid of this constraint, these two would be equivalent everywhere except for x equal zero. This one would have been defined for x equal zero, this one would not have, and so they wouldn't have been algebraically equivalent. This makes them algebraically equivalent. And of course you can write this in different ways. You can divide each of these terms by two, if you like. So you could divide two x by two and get x and then divide one by two and get 1/2. Once again, we would wanna keep the x cannot be equal to zero. Let's do another one of these. So it's a slightly hairier expression, but let's do the same drill. See if we could simplify it. But as we simplify it, we really wanna be conscientious of restricting the Zs here so that we get an algebraically equivalent expression. So, let's think about where this is undefined. We can think about where it's undefined by factoring the denominator here. Actually, let me just... So, this is going to be equal to, actually, let me just... Let me just do the first step where I could say, \"Well, what's a common factor \"in the numerator and the denominator?\" Every term here is divided by z squared and every term is also divided by 17, so it looks like 17 z squared can be factored out. So, 17 z squared can be factored out of the numerator and then we would be left with, we factor out of 17 z squared out of 17 z to the third and we're gonna be left with just a z. 17 z squared, you factor 17 z squared out of that and you're gonna be left with just a one. And once again, you can distribute the 17 z squared, multiply it times z, you get 17 z to the third. 17 z squared times one is 17 z squared. All right. All of that is going to be over, we wanna factor out of 17 z squared out of the denominator, 17 z squared times, and so 34 z to the third divided by 17 z squared, 34 divided by 17 is two, and z to the third divided by z squared is z and then we have minus 51 divided 17 is three and z squared divided by z squared is just one. So we'll just leave it like that. And so over here, it becomes a little bit clear of, well one, how we're gonna simplify it. We're just gonna divided 17 z squared by 17 z squared, but let's be careful to restrict the domain here. So, we can tell if z is equal to zero, then this, 17 z squared is gonna be equal to zero and we make the denominator equal to zero and we could see that even looking here. So we could say z cannot be equal to zero. Now what else? Z cannot be equal to whatever makes this two z minus three equals zero. So let's think about what makes two z minus three equal to zero. Two z minus three is equal to zero. You can add three to both sides and you would get two z is equal to three. Divide both sides by two and you would get z is equal to 3/2. So z cannot be equal to zero and z cannot be equal to 3/2. So that's how we're gonna restrict our domain. But now let's simplify. So, if we simplify it, these two cancel out and we are going to be left with, we're gonna be left with z plus one over two z minus three and we wanna keep that constraint. z cannot be equal to zero. And actually, this second constraint is redundant because we still have the two z minus three here. If someone were to just look at this expression, like, well, the denominator can't be equal to zero and so, z cannot be equal to 3/2. So this is still, if we just left it the way it is, we don't even have to rewrite this, that would be redundant since looking at this expression is clearly not defined for z equals 3/2. So, there you go. And so I'm gonna ask you for what values does this expression not defined, well then you would also include that. Actually, let me just write it. It doesn't hurt to be redundant. z does not equal 3/2. But this constraint right here is really important because it's not obvious by looking at this expression. This expression by itself would be defined for z equals zero, but if we want it to be algebraically equivalent to this one and that one, it has to be constrained in the same way." ]
[ null ]
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https://www.tutorialathome.in/wbut-mca-sem-i-c-language/sum-digit-until-single-digits-2011
[ "Sum of its Individual Digit Until Single Digits - WBUT MCA 2011\n18-11-2019    170 times", null, "### Write a program to accept a number and find sum of its individual digits repeatedly till the result is a single digit. [WBUT MCA 2011]\n\n```#include <stdio.h>\nint main()\n{\nint no,i,sum;\nprintf(\"Enter A Number : \");\nscanf(\"%d\",&no);\ndo{\nsum=0;\nwhile(no>0)\n{\nsum += no%10;\nno = no/10;\n}\nno=sum;\n}while(sum>9);\nprintf(\"One Digit Sum is %d\",sum);\n}\n```", null, "" ]
[ null, "https://www.tutorialathome.in/postimg/2019/11/sum-of-digit-upto-single-digit.jpg", null, "https://www.tutorialathome.in/wbut-mca-sem-i-c-language/adsimg/internship.jpg", null ]
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http://www.neverendingbooks.org/noncommutative-geometry-2
[ "", null, "Again I\nspend the whole morning preparing my talks for tomorrow in the master\nclass. Here is an outline of what I will cover :\n– examples of\nnoncommutative points and curves. Grothendieck’s characterization of\ncommutative regular algebras by the lifting property and a proof that\nthis lifting property in the category alg of all l-algebras is\nequivalent to being a noncommutative curve (using the construction of a\ngeneric square-zero extension).\n– definition of the affine\nscheme rep(n,A) of all n-dimensional representations (as always,\nl is still arbitrary) and a proof that these schemes are smooth\nusing the universal property of k(rep(n,A)) (via generic\nmatrices).\n– whereas rep(n,A) is smooth it is in general\na disjoint union of its irreducible components and one can use the\nsum-map to define a semigroup structure on these components when\nl is algebraically closed. I’ll give some examples of this\nsemigroup and outline how the construction can be extended over\narbitrary basefields (via a cocommutative coalgebra).\n\ndefinition of the Euler-form on rep A, all finite dimensional\nrepresentations. Outline of the main steps involved in showing that the\nEuler-form defines a bilinear form on the connected component semigroup\nwhen l is algebraically closed (using Jordan-Holder sequences and\nupper-semicontinuity results).\n\nAfter tomorrow’s\nlectures I hope you are prepared for the mini-course by Markus Reineke on non-commutative Hilbert schemes\nnext week.\n\nPublished in featured" ]
[ null, "http://www.neverendingbooks.org/DATA/nog.jpg", null ]
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http://davidson16807.github.io/mental-slide-rule/slide.rule.html
[ "# Visualizing Math with a Mental Slide Rule\n\nSlide rules are really neat.\n\nThey give you a great intuitive understanding of mathematics. If you never used them before, they're easy enough. Here's a primer. If you haven't noticed, you can perform addition and subtraction using two ordinary rulers. There's obviously nothing magical here: you just mete out two distances, stacking them end to end, then measure their sum.\n\nDrag the bottom ruler\n\nSlide rules work the same way, only with multiplication. This is made possible because slide rules place numbers on a logarithmic scale. An ordinary ruler just uses a linear scale. Slide rules work on the principle that log(a*b) = log(a) + log(b).\n\nDrag the bottom ruler\n\nSlide rules aren't normally used for mental math. This is because the markers on a slide rule indicate linear increments. Line spacing always changes, so unless you have a photographic memory, it is very difficult to visualize the addition of two distances.\n\nIf line spacing were constant though, we could multiply just by visualizing the slide rule.\n\nIt is possible to create a slide rule where line spacing is constant, but our lines could no longer represent linear increments. They would instead represent logarithmic increments. Every increment would represent a multiplication by some constant number.\n\nLet's try to design this sort of slide rule. There are many values we could assign to its increments, but there are constraints we should consider. The scale must approximate the form a^(1//n), a^(2//n), … a^(n//n). This is necessary to keep line spacing constant. Here, a is the base of our log scale. For us humans, it makes sense to set a = 10, since anything else will reduce the usability of our slide rule. If a != 10, our slide rule would not repeat once at every power of 10 and we'd likely have to convert number bases before calculations.\n\nWith these constraints in mind, we can browse from a pool of candidate log scales. Any viable log scale is of the form 10^(1//n), 10^(2//n), … 10^(n//n), where n is the number of increments on the scale. n is also the number of increments we'll need to memorize, so n should be small enough to remember but large enough to offer precision.\n\nWhen school children memorize their multiplication tables, they generally memorize no more than 32 combinations, so we should be able to start simple, with n<32. Unlike the school children, though, every number we memorize goes further. Multiplication tables have a space complexity of O(N^2), where N is the number of digits that can be multiplied. That means if we want to add just one extra number to our multiplication table, the number of combinations we have to memorize increases exponentially. Not only that, but the multiplication table only works well for multiplication. On the other hand, our slide rule only has a space complexity of O(N). That means if we want to add one extra number to our ruler, we only have to remember one extra value. And as we will see, memorizing that value will allow us to do way more than multiplication.\n\nIt turns out n = 10 is a rather good start, and if you want to learn more you can set n to some other multiple of 10.\n\nDrag the bottom ruler\n\nQuite a few increments on this scale are conveniently close to whole numbers, those being 2, 4, 5, and 8. This probably has to do with the fact 10 is divisible by 2 and 5, but this is just a guess. Other numbers resemble rational fractions of 10, those being 1.25 and 2.5. There are 3 increments on this scale that correspond to neither whole numbers nor rational fractions, but we find these numbers are very close to pi and its multiples, ¹/₂pi and 2 pi. It's fortunate that multiplication by pi is so common that most normal slide rules already come with a special notch for pi. Using n = 10 has an added benefit: it leverages our existing knowledge of addition in base 10.\n\nBy visualizing the slide rule, we can divide and multiply just as easily as we add and subtract.\n\nBecause there are 10 numbers on our scale, our answers will be accurate to within ~10% the actual answer, assuming we round correctly. We can achieve greater accuracy by memorizing additional numbers.\n\nWe note that multiplication will be just as easy as single digit addition, but in fact it may be even easier. This is because there are additional mneumonics that help us visualize the scale. We will go through some mneumonics now.\n\nThe scale repeats at every power of 10, just as with arabic numerals, so we can visualize the scale as a sort of clock. Just like a clock, the numbers \"roll over\" if they go above or below the limits of the scale - 2 hours past 11:00 is 1:00.\n\nDrag the inner wheel\n\nThe clock is divided into two halves. The halves are marked by two values. The first value is 1, which is multiplicative unity. The second is sqrt(10), which is close to pi. There are four \"steps\" within each half. There are a total of 10 steps. The halves have a symmetry about them, so that the values on one half bear the same mantissa as the inverse of values on the other half. On the left half, we have 4, 5, 2 pi, and 8. On the right half, we have the mantissae of ¹/₄, ¹/₅ (or 2), ¹/₂pi (very close to pi/₂), and ¹/₈.\n\nIf we want to multiply two numbers, we count distance clockwise from the first number. The distance is determined by the second number - how far away that second number is from 1 (that is, the start of the scale)\n\nWhat's 158 * 2?\n\n1.58 (¹/₂pi) is the 2nd step from 1, so we find 2 on the clock and count 2 steps clockwise: 2, 2.5, pi. The mantissa is near the same as pi's, so the answer is close to 314.\n\nYou can also go the other way around: start at 1.58 and count 3 steps clockwise\nI find it's easier to visualize smaller distances, so in this case I start from 2 and count 2 steps clockwise.\nWhat's 158 * 8?\n\n1.58 (¹/₂pi) is the 2nd step from 1. 8 is the 9th step on the scale. Therefore, we find 2 on the clock and count 8 steps clockwise. The mantissa is about 1.25, so the answer is close to 1250.\n\nThere's a better way to visualize it though. Short distances are easier to visualize than large distances. 8 is much closer to 1 in the counter-clockwise direction, so it's easier to start at 1.58 and look 1 step counter-clockwise.\n\nIf we want to divide two numbers, we do the same thing, only counter-clockwise.\n\nWhat's 1.58 // 2?\n\nTake ¹/₂pi and count 3 steps counter-clockwise: ¹/₂pi, ¹/₈, 1, 8. The mantissa is 8, so the answer is close to 0.8. The answer is infact 0.79.\n\nSince these numbers are close together, an easier way is to count the steps from 1.58 to 2. The two numbers are seperated only by a single step, so the answer must be 1 step counter-clockwise from 1.\nWhat's 2 // 1.58?\n\nOur answer here is merely the inverse of the last. The two numbers are seperated only by a single step, so the answer must be 1 step clockwise from 1. This is a general rule of thumb: if the denominator is larger, count the steps going down; if the denominator is smaller, count the steps going up.\n\nWhat's 1.58 // 8?\n\nJust as with multiplying by 8, there are 2 ways to go about division. There are 9 clockwise steps between 1 and 8, and that means to divide we can count 9 steps counter-clockwise.\n\nBut if the number is greater than sqrt(10), it make sense to consider the other direction. There's only 1 counter-clockwise step between 1 and 8, and for division that means we can count 1 step clockwise.\n\nTo divide by large mantissae, it's often easier to multiply by the inverse of the number. Finding the inverse is easy - it's just the value on the opposite side of the clock. Let's divide 1.58 by 8. ¹/₈ is the first step on the clock, so the answer is one step clockwise from ¹/₂pi. The mantissa is 2. The answer is near 0.2. The actual answer is 0.19625.\n\nWhat do we do if a number is between steps?\n\nThere's a few things we can do in that case. We can of course memorize additional numbers on the scale, but memory only takes us so far. Oftentimes, the best solution is interpolation - we estimate where the number lies between steps, then tack that on to an approximation that uses one of the two nearest values. If an answer is between steps, we interpolate between the two nearest values.\n\nA more precise solution is to find the correction. Finding the correction in the log scale is just the same as finding the correction in arabic numerals - take the difference between the number and the approximation, multiply it seperately, then tack it on to the rest of your answer. You should be able to get an accurate number much faster than you would by multiplying with arabic numerals, though often you'll find the first order approximations are good enough.\n\nThis all sounds like stuff we can solve with other mental math. Oftentimes, I still find it easier to calculate in more traditional ways, particularly when dealing with simple whole numbers. However, the method does help us to make intuitive sense of the calculations. It also helps us focus on what's most important when calculating problems in the wild: first get the order of magnitude right, then get a good approximation for significant figures. Only then do we refine as needed. In lots of everyday problems, the order of magnitude will just \"feel\" right, so only the mantissa needs to be considered.\n\n## There are other things we can do with this visualization, too.\n\nFor example, what's log_10(1.58)?\n\nEasy - 0.2. ¹/₂pi is the 2nd step, and each of the 10 steps indicate a log_10 increment of 0.1. This is due to the way we designed our scale - I tricked you into learning the log_10 table.\n\nThis brings us to our next point:\n\nVisualizing the slide rule is very similar to memorizing the log_10 table\n\nBoth are equally valid strategies. The difference lies with the part of the brain your using. If I memorized the log_10 table and was asked to multiply two numbers, I'd map the two numbers to their log_10 values, add the log_10 values together, then map the sum back to get the answer. With the log_10 table, I'm using rote memorization along with existing skillsets in addition. If I memorized the slide rule, I'd find the two numbers on the slide rule, visualize the addition, and estimate the order of magnitude. There's still rote memorization to a certain extent, but now I'm also using more spatial reasoning, intuition, and visual memory.\n\nThe good news is this: if you visualize the slide rule, the log_10 values come easy and you can still fall back on using the log_10 table if you find it useful. I find using the log_10 table useful for calculating the powers and roots of large numbers.\n\nWhat's log_10(158)?\n\nEasy - 2.2. It's the same mantissa, but now it's on the order of 10^2, so it's 0.2 + 2 = 2.2.\n\nWhat's the square of 1.58?\n\nWe know that's the 2nd step, so the answer must be double the distance, on the 4th step. The answer is near 2.5. The exact answer to 4 figures is 2.465.\n\nWhat's the square root of 1.58?\n\nAgain, we know that's the 2nd step, so the answer is half that, on the 1st step. The answer is near 1.25. The exact answer to 4 figures is 1.253.\n\nWhat's the cube of 1.58?\n\nThat's on the 6th step, just a step past pi, near 4, and in fact it is 3.87.\n\nThe fourth power of 1.58?\n\nThat's on the 8th step, 2 steps shy of 10, near 2 pi, and in fact it is 6.076.\n\nThe fifth power?\n\nNear 10, and in fact it is 9.54.\n\nWe start to notice our answers become less accurate at higher powers. This is because the minor inaccuracies we started out with are amplified by repeated multiplication. This is a concern common to all slide rules.\n\nOkay, we've shown what this can do. Now we want greater resolution. There are a number of integers missing on the scale.\n\nWhat if I want to multiply by 9?\n\nWell, 9 is the squareroot of 81. Look at the slide rule and we know log_10(81) ~ 1.9, so log_10(9) ~~ 1.9/2, or 0.95. To be exact, it is 0.954. It's just over half past the last step.\n\nWhat if I want to multiply by 7?\n\nWell, 7 is the squareroot of 49. We know log_10(49) ~~ 1.7, so log_10(7) ~~ 0.85. It is in fact 0.845. There's another way to figure out: 72 = 8*9, so 7 is half step shy of 8.\n\nWhat if I want to multiply by 3?\n\nWe know 3^2 = 9, and log_10(9) ~~ 0.95, so log_10(3) ~~ 0.475. That's quarter step shy of a half circle. It is in fact 0.477.\n\n6 is merely 2*3, so log_10(6) ~~ log_10(2)+log_10(3) ~~ 0.3 + 0.475 ~~ 0.775. 6 is quarter step shy of 2pi.\n\nIf we find the answers to the questions above, we will immediately know log_10 of ¹/₉, ¹/₇, ¹/₃, and ¹/₆ - we need only look at the opposite side of the clock!\n\nIt's particularly useful knowing positions for ¹/₉. We know ¹/₉ = 1.111…, so 1/2 step from any whole number is approximately that number repeated. Let's look at the whole numbers.\n\n 10^0.0 ~~ 1, so 10^0.05 ~~ 1.111… ~~ ¹/₉ 10^0.3 ~~ 2, so 10^0.35 ~~ 2.222… 10^0.475 ~~3, so 10^0.525 ~~ 3.333… ~~ ¹/₃ 10^0.6 ~~ 4, so 10^0.65 ~~ 4.444… 10^0.7 ~~ 5, so 10^0.75 ~~ 5.555… 10^0.775 ~~6, so 10^0.825 ~~ 6.666… ~~ ²/₃ 10^0.85 ~~ 7, so 10^0.9 ~~ 7.777… ~~ 8 10^0.9 ~~ 8, so 10^0.95 ~~ 8.888… ~~ 9 10^0.95 ~~ 9, so 10^1.0 ~~ 9.999… = 10 (by definition)\n\nThe actual numbers will be given shortly, but the above explanation is a useful mneumonic for memorizing the precise values of half steps. Once you memorize the precise values you can improve your accuracy to 6%.\n\nYou know what? Knowing the position for ¹/₉ is so useful, why don't we take it further? If we memorize all the quarter steps from 1 to 1.58, the values for all the other powers of two will fall into place. Once we memorize all the quarter steps, our accuracy will improve to 3%.\n\n1 2 4 8\n1.0 2.1 4.2 8.41\n1.1 2.2 4.4 8.91\n1.1 2.3 4.7 9.44\n1.2 2.5 5 1\n1.3 2.6 5.31\n1.4 2.8 5.62 1.41 is the squareroot of 2\n1.5 3 5.96\n1.58 3.16 6.31 all these are near multiples of pi\n\nFor the rest of the numbers, I use seperate mneumonics. They are as follows:\n\nThe quarter steps past 1pi and 2pi are all at or near noteworthy whole numbers and fractions:\n\n 3.35 6.68 around multiples of ¹/₃ 3.54 7.08 around multiples of ⁷/₂ 3.76 7.5 around multiples of ³/₈\n\nThe quarter steps past 1.58 all end in 8. The 10th's place is sequential:\n\n 1.58 1.68 1.78 1.88\n\nThe scale appears as follows:\n\nWhen I first memorized the scale, I was driving alone on a long car ride. There wasn't a lot I could do that was productive without becoming distacted, so I'd recite the scale to myself, occasionally referring to the copy of the slide rule above. By the end of the trip, I could recite the thing forwards and backwards. I was also able to recite the inverse of every quarter step, the log of every quarter step, and the multiples of every quarter step from 1 to 1.25. I recommend doing something similar for anyone interested in learning the system.\n\n## With greater precision, we find we can solve more complex problems.\n\nWhat's the double log of 158?\n\nFrom our previous answer we found log_10(158) = 2.2. The answer here is log_10(2.2), which is about 3.5. The exact answer to 4 digits is 0.342.\n\nWhat's log_2(158)?\n\nWe know log_10(2) = 0.3 and log_10(158) = 2.2. Find a half circle past 2 and the answer is a quarter step past that. It's near 7.25. It is in fact 7.304.\n\nBut there's an easier way! We know 2^3 = 8 and 2^4 = 16, so 10 is somewhere between them. Every time we multiply by 2, we take 3 steps on the scale, and 8 is 1 step from 10, so 2^3.33 = 10. 158 is close to 10*16, so the log must be very to 3.33+4, or 7.33.\n\nBut that's not enough. What can we do to visualize log_2? There is in fact an easy way to do so: the distances on any log scale represent the exact same thing, but the units we use to measure distance will differ. We can think of the log base as this unit of measure. On our base 10 scale, we indicate distance relative to the number \"10\", and due to the design of our clock, this is the distance covered by one full turn. On a base 2 scale, we indicate distance relative to 2, which is the third step on our clock. This means the \"1\" appears on the third step clockwise from the top, and everything else is scaled according to that.\n\nWhat's the natural log of 158?\n\nFirst we have to find log_10(e).\n\nWe'll do the same thing we did for log_2. e is between the 4th and 5th step, so log_10(e) is 0.45. To convert the log base we know we must divide by log_10(e).\n\nWe know log_10(158) = 2.2, so ln(158) is just 2.2 // 4.5, which is 3.5 steps clockwise of 3.5 steps, 2 steps past pi, or 5. When we run it through a calculator, we find the exact answer to 4 figures is 5.056 - not too shabby.\n\nNatural log is so common it might serve us just to remember that number - if we want the natural log of a number, just take log_10 and find the number 3.5 steps clockwise from that (well, 3.6 if you must be exact).\n\nHowever, as with log₂, there is another way. If we remember ln(10) = 2.3, then we know 158 is 2*2.3 + ln(1.58). 1.58 is on the 2nd step, and 2.71 maps to ~4.5, so what's 2//4.5? 2 is 2 steps from pi, so 2//pi is 2 steps from 1, and 2//4.5 is 1.5 steps past that, around 4.44. 2*2.3 + 0.444 = 5.0.\n\nWhat's e^2.5?\n\ne is between 4.25 steps and 4.5 steps. We'll interpolate that to 4.4 steps. That means e^2.5 is near 2.5*4.4 steps. Ok, so what's 2.5*4.4? 4.4 is a 1.5 steps past pi. 2.5 is 1 step short of pi, so 2.5*pi is 1 step short of 1, and our answer is just 1.5 steps past that, on the first half step, or 1.12. So what's on the 1.12th step? That's around 1.25. Our intuition tells us it's on the order of ten, so we guess 12.5. The exact answer is 12.18\n\nWhat's 1.58 tetrated by 2, i.e. 1.58^1.58?\n\nThis is where our \"intuitive understanding\" comes in handy. What happens when you calculate an exponent like 1.58^2?\n\nWe find the distance covered by 1.58 (finding the logarithm), add two of those distances together (finding the sum), then answer the number that corresponds to that distance (finding the power of 10). In other words, we understand that all the following are equivalent:\n\n log_10(1.58^2) log_10(1.58 * 1.58) log_10(1.58) + log_10(1.58) 2log_10(1.58)\n\nAs a result, we know log_10(1.58^1.58) = 1.58*log_10(1.58). We are asked to find 1.58^1.58, which we know is 10^(1.58 * log_10(1.58)).\n\nThe exponent is close to 1.58 * 0.2, or 0.316. We know 10^0.316 is a little over halfway between 2 and 2.12, so we interpolate between the two and answer 2.06. As it turns out, that's only off by 1 out of 20000.\n\n## We also find ourselves with better math reasoning skills.\n\nWhat's 40 rounded to the nearest order of magnitude?\n\nWe normally round on a linear scale. On a linear scale, we round up if the number is 5 or greater; we round down if otherwise. This is because 5 is halfway between 0 and 10 on a linear scale. But what if we're doing a rough order of magnitude calculation? The halfway mark between 0 and 10 isn't 5 on a log scale.\n\nThe halfway mark on our scale is closer to pi (3.16 to be precise). We therefore know to round up for numbers bigger than 3.16, and round down for numbers less than 3.16. 40 is therefore closer to the order of 100 than the order of 10.\n\nI have a set of numbers from 0 to 10: 0.2, 7.8, 6.9, 9.8, 5.4, 8.7, 2.7, 7.5, 7.1, 8.5. Are these more likely to be uniformly distributed or log-uniformly distributed?\n\nWhat would happen if we place the mantissa for those numbers on our clock? Most of numbers fit on the left side of the clock. This indicates the set is more likely a log distribution.\n\nWhat about another set? 5.7, 1.9, 4.2, 3.4, 7.1, 2.0, 1.2, 3.8, 1.0, 4.9\n\nSome of the numbers appear to the left of the clock, but it's definitely not so skewed to that side. This suggests it's more likely a log-based distribution.\n\nWhat's the frequency of the musical note, \"Middle E\"?\n\nMiddle C has a frequency of 261 Hz. E is 4 notes from C. An octave represents a doubling in frequency, so an octave higher than C is 261*2 = 522 Hz. There's 12 notes in the octave including flats and sharps. The frequency of these notes are all evenly distributed across the log scale.\n\nThis means the frequency of every note corresponds to a multiplication by 2^(1//12). How many quarter steps are there between 1 and 2?\n\nThat's right, I tricked you into learning musical ratios, too. Every note represents a multiplication by one quarter step. E is 4 notes from C, so we look 4 quarter steps from 2.6: 3.3. We answer 330 Hz. Wikipedia tells me the correct answer is 329.6 Hz.\n\nThere are 8 full notes in an octave, so that means every full note has 12/8 = 1.5 quarter steps.\n\n## And we find we can solve real world problems.\n\n### Calculating arrival times\n\nLet's say you're driving on a highway at 65mph. The road sign says there's 50 miles left to your destination. At what time do you expect to arrive?\n\nMost people just round their speed to 60mph. That way, they know they travel about a mile a minute, so a 50 mile journey might take 50 minutes. That's a good start, but we know we can do better. 65 is a little less than 6.66 on the clock, which is a half step past 6. A half step shy of 5 is 4.47, so we know that's the mantissa for our answer. At this point, you can intuit that there's 45 minutes left to your journey, since that's the only order of magnitude that makes sense.\n\nOn further reflection you find 65 is almost halfway between the two nearest quarter steps: 6.30 and 6.66. You find the answer must then lie between 45 and 47. You interpolate and answer 46 minutes, which is in fact correct.\n\nUsing the mental slide rule is also much more versatile, since the method works with other speeds too. What if we were going 70mph?\n\n7.08 is 3 quarter steps past 6, so the answer is 3 quarter steps shy of 5: 4.22. There's around 42 minutes left to your journey.\n\nYour check comes to $8.47. You think 17% is reasonable for a tip. Your check is three quarters shy of 1. The tip therefore is three quarters shy of 17%, which is a quarter step past ¹/₂pi. The tip is a half step shy of ¹/₂pi, or$1.41. Actual answer is $1.44. ### Converting inches to mm You only have a imperial ruler. An object measures ³/₈ inch. What's that in mm?. A common rule of thumb is that there's 25.4 millimeters in an inch, around ¹/₄. 3/(8*4) = 3/32. The difference between 3 and 32 is around a quarter step, so the answer is a quarter step counter-clockwise of 1: 9.44. Our intuition tells us that's also the correct order of magnitude, so 9.44 millimeters it is. This is probably good enough for most people. Want the correction? The correction is ³/₈ * 0.4, which is the same mantissa as 4*³/₈ or 12/8. The mantissa is a step behind three quarters, or a quarter shy of 1 - 9.44. We intuit the correction is 0.0944, so a closer answer is 9.44+0.0944, or 9.53. The correct answer is 9.525. ### Calculating sales tax$3.50 for milk - what's the total with tax?\nLet's say sales tax is 106%.\n\nEvery quarter step on the log scale represents a multiplication by 106%, so sales tax is super easy. Just look a quarter step past 3.50. It's around \\$3.75 with tax.\n\n### Trigonometry\n\nWhat's the width of a 19\" 4:3 monitor?\n\nThink back to the pythagorean theorem. What's the width:diagonal ratio on a 4:3 monitor?\n\nThe diagonal has a square of 16+9 = 25, so without even consulting our mental slide rule we know the ratio is 4:5, or 0.8.\n\n4:5 is a step shy of 1. 1.9 is a quarter shy of 2, so 1.9*0.8 is a quarter shy of 1.58 - 1.5. 15\" inches is our answer. The exact answer is 15.2.\n\nWhat's the height?\n\nThe ratio is 3:5, now. That's a 2 and a quarter shy of 1. 2.25 shy of 19 is 2.5 shy of 3 steps, or a half step past 1 - 1.12. 11.2\" is our answer. The exact answer is 11.4\".\n\nWhat's the angle of the diagonal?\n\nIf we normalize the width we have ⁴/₅ or 0.8, so we want to find acos(0.8).\n\nAt times like this I'd like to remind you of something:\n\nyou don't have to get there, you just need to get closer.\n\nYou won't need the taylor series to solve this one, but you will have to think for yourself.\n\nWe know cosine starts at 1 and goes to 0 once it reaches 90°. It repeats after that, so we only care how it looks from -90° to +90°. We want a function that resembles this.\n\nIt kind of resembles a parabola, like y=1-x^2 where x is measured as a fraction of a right angle.\n\nWe know 0.8 = 1-x^2, so 1-0.8 = x^2 = 0.2. We need the squareroot of 0.2.\n\n0.2 is 7 steps backwards from 1, so our answer lies 3.5 steps backwards from 1. 0.44 is our answer expressed as a fraction of a right angle. We multiply by 90 to get the degrees - this is a half step shy of 4.4, so we answer 40°. The exact answer is 39.9°. Not too shabby.\n\nObviously, y=1-x^2 is not a perfect estimate. You could remember the taylor series, but that takes way more figuring. For mental math, it's much more effective to remember a better fitting exponent. After some trial and error, I've found y=1-x^1.75 provides much more accurate results.\n\nIf you need a more concise mental algorithm, it is as follows. To find cos(theta), where theta is an angle expressed in degrees, you must perform the following:\n\n• find the conterclockwise distance from 1 to theta (find 1-log_10(theta))\n• subtract 0.5 from the distance (x=theta/90)\n• double the distance and subtract ⅒ of it (x^1.8)\n• find the number that appears at that distance and subtract it from 1 (1-x^1.8)\n\nHow to find 1-cos(40°)\n\nWhat's e^(i*pi/5)?\n\nRemember euler's theorem: e^(theta i) = cos(theta) + sin(theta)i. It's OK to write down your answers.\n\nRemember: sine is just cosine rotated 90°.\n\npi/5 is 2/5 of a right angle. we need cos and sin of that.\n\n2/5 is 0.4. That's 4 steps counter-clockwise from 1, so 0.4^1.75 is 7 steps counter-clockwise from 1, or 0.2. 1-0.2=0.8, so that's cosine, AKA the real component of the complex number.\n\nFor sine, we just do the same thing for 1-0.4, or 0.6. 0.6 is 2.25 steps counter-clockwise, so 0.6^1.75 is ~4 steps counter-clockwise, or 4. 1-0.4=0.6, so that's the imaginary component.\n\nOur guess is 0.8+0.6i. We run it through the computer and find the correct answer is 0.81+0.59i. Noice.\n\nAnd now, your final exam: I have an object at coordinate (3.3, 2.8) and I rotate it pi//5 radians clockwise. Where is it located now?\n\nWe know from the previous question the rotation is (3.3+2.8i)*(0.81+0.59i). It's OK to write down your answers.\n\nThe neat thing with slide rules is you can set them to multiply by a certain number and from then on you know at a glance what all the answers are for when multiplying by that number.\n\nStart working with the numbers that are closest to 1 on the scale, since these are the easiest when visualizing multiplication and division.\n\n0.81 is 1 step counter-clockwise, so look 1 step counter-clockwise from 3.3 and 2.8: we have 2.66 and 2.2i.\n\n0.59 is 2.25 steps counter-clockwise, so look that distance from 3.3 and 2.8: we have 2.0i and 1.68ii.\n\nThe answer is 2.66 + 2.2i + 2.0i - 1.68, so the answer is 1 + 4.2i.\n\nThe exact answer is 1.02 + 4.20i.\n\n## Beyond quarter steps.\n\nMake sure you start small. Start by remembering the half steps. Recite the scale forwards and backwards. Try your hand at multiplying, dividing, and exponentiating random numbers. I personally train with a scripting language like python or R, but a couple d10 or d20 dice work, too.\n\nOnce you have the basics down, you can memorize smaller increments to achieve greater accuracy.\n\nFirst, you have to decide which increment you want to memorize. When you're memorizing a scale, it's important to start with an increment that accomplishes two things: 1.) it's easy to remember the scale, and 2.) it works well in base 10. A multiple like 1.1 is a great example. It's easy to calculate multiples of 1.1. For whole numbers, you just repeat the number twice, so for example 3 * 1.1 = 3.3. For multiple digits, the first and last digit stay the same, and every other number gets added to it's neighbor, so for example 1.25 * 1.1 is:\n\n{:(\\ \\ \\ 1250),(+ 0125),(_),(\\ \\ \\ 1375):}\n\nSo if we're memorizing the scale and we forget what an increment on our scale is, we can just multiply by some adjacent number that we do know. This won't excuse you from memorizing the thing, but it can help you while you memorize it.\n\nWe know log_10(1.1) = 0.4, so we can easily memorize a scale where every log increment is 0.2 since this would mean memorizing every fifth of a step. Since log_10(1.05) = 0.2, we know ever fifth step will represent a multiplication by 1.05.\n\nMemorizing the first few steps of any scale will be critical, because any other step in the scale can be derived by multiplying one of the first steps and some whole number. Let's take a look at the first few fifth step increments\n\n 1.047 1.096 1.148 1.202 1.259\n\nWhat sense can we make of them? Well, the trailing numbers are all very close to multiples of 0.05.\n\n 1.047 ~~ 1 + 0.05 * 1 1.096 ~~ 1 + 0.05 * 2 1.148 ~~ 1 + 0.05 * 3 1.202 ~~ 1 + 0.05 * 4 1.259 ~~ 1 + 0.05 * 5\n\nIn other words, it's almost like we're adding 0.05 to each number. But that's not completely the case, because 1.259 rounds up to 1.26, and that's 1.20 + 0.06. By the time we've reached the first full step, it's like we're adding 0.06. Let's look at the next few fifth steps.\n\n 1.318 ~~ 1.259 + 0.06 1.380 ~~ 1.318 + 0.06 1.445 ~~ 1.380 + 0.06 1.514 ~~ 1.445 + 0.06 1.585 ~~ 1.514 + 0.06\n\nBut that's not completely correct either, because by the time we reach 1.445 it's like we're adding by 0.07, and by the time we reach past 1.58, it's like we're adding by 0.08:\n\n 1.660 ~~ 1.58 + 0.08 1.738 ~~ 1.66 + 0.08 1.820 ~~ 1.74 + 0.08 1.905 ~~ 1.82 + 0.08 1.995 ~~ 1.91 + 0.08\n\nAnd of course the increment keeps increasing. The good news is this: it doesn't matter for our purposes. The error remains in line with the precision we want to accomplish. For us the mneumonic works just fine:\n\n• In the first full step, the fifths steps increment by ~0.05\n• In the second full step, the fifths steps increment by ~0.06\n• In the third full step, the fifths steps increment by ~0.08\n\nNow, let's say you do want to memorize the fifths scale. You memorize the scale, paying particular attention to the steps from 1 to 1.25. As you did with the half steps, you ought to recite the scale and try your hand at some random problems. If you memorized the quarter steps, the fifth steps should come easier - the ¹/₅ and ⁴/₅ steps are only slightly off from the ¹/₄ and ³/₄ steps.\n\nIf we wish to go further, we can even remember every tenth of a step (multiplication by ~1.025). The tenth scale includes both the fifth step scale and the half step scale, so by the time you remember the half and fifth steps, you'll already have at least 6 of the 10 substeps memorized for each full step of the tenths scale.\n\nThe Tenths Scale:\n\n1 2 3 4 5 6 8\n1.025 2.05 3.075 4.1 5.125 6.15 8.2\n1.05 2.1 3.15 4.2 5.25 6.3 8.4\n1.075 2.15 3.225 4.3 5.375 6.45 8.6\n1.1 2.2 3.3 4.4 5.5 6.6 8.8\n1.125 2.25 3.375 4.5 5.625 6.75 9.0\n1.15 2.3 3.45 4.6 5.75 6.9 9.2\n1.175 2.35 3.525 4.7 7.07 9.4\n1.2 2.4 3.6 4.8 7.2 9.6\n1.225 2.45 3.675 4.9 7.43 9.8\n1.26 2.5 3.75 7.6\n1.292 2.57\n1.32 2.63\n1.347 2.69\n1.38 2.75\n1.41 2.82\n1.45 2.88\n1.481 2.95\n1.51\n1.55\n1.58\n1.629\n1.66\n1.70\n1.73\n1.78\n1.82\n1.86\n1.90\n1.95" ]
[ null ]
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https://www.wolfram.com/wolfram-u/courses/finance/financial-time-series-processing-fin027/
[ "", null, "# Financial Time Series Processing\n\nEstimated Time: 18 min\n\nCourse Level: Beginner\n\n## Summary\n\nFinancial value over time can be represented as time series objects in the Wolfram Language. This video class demonstrates how to import financial data as time series and how to inspect and specify its properties, such as temporal regularity, resampling method and window length. It explains how time series objects can be used as input to other built-in functions and also directly used for arithmetic calculations. Examples illustrate computing asset returns using log and ratios and visualizing stock trading data along with buy and sell signals.\n\nFeatured Products & Technologies: Wolfram Language, Wolfram Notebooks\n\n### You'll Learn To\n\n• Construct financial time series with ordered time-value pairs\n• Access financial data from the Wolfram Knowledgebase\n• Alter the sampling frequency and viewing window of a time series object\n• Resample data using interpolation\n• Calculate the relative value of investments" ]
[ null, "https://www.wolfram.com/wolfram-u/wp-content/themes/wolfram-u/assets/img/WolframU.png", null ]
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https://forum.defold.com/t/count-object-rotation-turns/72884
[ "", null, "# Count object rotation turns\n\nThis would be a long post with question in the end.\n\nHi I need to rotate object via player input (mouse for now) and count rotation turns (or spins). Object could be rotated in any direction - clockwise (cw) or counterclockwise (ccw).\n\nFor base for movement I’ve taken this example - britzl/defold-orthographic. Shout out to author, his examples always helps me a lot =)\n\nIn update() function of the hitman/player script I add something like this to count rotation turns when player hold “mouse button 1”:\n\n``````-- [hitman.script](https://github.com/britzl/defold-orthographic/blob/master/example/shared/objects/hitman.script)\nlocal function process_spin(self, angle)\n-- transform angle from rad to degree and normilize to 0 - 360\nangle = angle * 180/3.14159\nif angle < 0 then\nangle = 360 + angle\nend\n\n-- set initial values on grab - when the player click mouse button 1\nif not self.grab then\nself.grab = true\nself.initial_angle = angle\nend\n\n-- get the current angle of the object\nlocal current_angle = angle\n\n-- calculate the difference between the current and initial angles\nlocal angle_diff = current_angle - self.initial_angle\nangle_diff = math.floor(angle_diff)\n\n-- normalize the angle difference to be between -180 and 180 degrees\nif angle_diff > 180 then\nangle_diff = angle_diff - 360\nelseif angle_diff < -180 then\nangle_diff = angle_diff + 360\nend\n\n-- check and set rotation direction: cw or ccw\nif self.rotation_direction == 0 then\nif angle_diff < 0 then\nself.rotation_direction = 1\nelseif angle_diff > 0 then\nself.rotation_direction = -1\nend\nend\n\n-- normilize rotation to 0 - 360 degree\nif angle_diff < 0 then\nangle_diff = 360 + angle_diff\nend\n\n-- count turns\nif (self.rotation_direction == 1 and angle_diff <= 5 and angle_diff > 0)\nor (self.rotation_direction == -1 and angle_diff >= 355) then\nself.turn_count = self.turn_count + 1\nself.initial_angle = angle\nend\nend\n\nfunction update(self, dt)\n...\n-- line 27\n\nif self.input[hash('touch')] then\nprocess_spin(self, angle)\nelseif self.grab then\nself.grab = false\nself.last_diff = 0\nself.rotation_direction = 0\nend\n...\nend\n``````\n\nAnd this code works at first. The bug is when player change rotation direction while holding “mouse button 1”. So I add check for rotation direction in process_spin function (don’t like how it looks, need to be cleared):\n\n``````-- check change in dirrection\n-- this if statement changed a lot, but always has some bugs...\nif self.last_diff ~= 0 and (self.last_diff - angle_diff) ~=0\nand ((angle_diff ~= 0 and self.rotation_direction == -1) or (self.rotation_direction == 1)) then\nlocal new_direction = 0\nif (self.last_diff - angle_diff) > 0 then\nnew_direction = 1\nelseif (self.last_diff - angle_diff) < 0 then\nnew_direction = -1\nend\n\nif new_direction ~= self.rotation_direction then\nself.grab = false\nself.rotation_direction = 0\nself.initial_angle = angle\nself.last_diff = 0\nend\n\nif self.last_diff ~= angle_diff then\nself.last_diff = angle_diff\nend\n``````\n\nAnd this part gave me a lot of issues, when player change direction on early start around 0 degree. When one start rotate in one direction from `angle_diff` = 0 to `angle_diff` = 1 degree and drastically changes the direction to, say, `angle_diff` = 358 degree. Code go crazy on this 1 - 358 degree change, because it looks like very quick full circle =)\n\nAfter some playing with if statement I thought that I’m going a wrong way and I need to deep dive into quaternion topic (never used them before). Long story short I came up with this code:\n\n``````-- [hitman.script](https://github.com/britzl/defold-orthographic/blob/master/example/shared/objects/hitman.script)\n-- line 24\nlocal rotation = vmath.quat_rotation_z(angle)\nlocal rot_diff = vmath.conj(go.get_rotation()) * rotation\nlocal direction = math.floor(rot_diff.z * 100)\n-- direction < 0 - clockwise; direction > 0 - counterclockwise.\ngo.set_rotation(rotation)\n``````\n\nThis code seems to be working, but I’m not sure that it is optimal for such a task. Quat conjugate and multiply could be heavy to calculate… Have not done performance testing yet.\n\nMaybe I could get rotation direction in more easy or efficient way? In fact when I set new rotation `go.set_rotation(rotation)` object rotate in right direction, “closest direction”. So engine already know it would be cw or ccw. Maybe I can get this info without additional calculations?\n\n3 Likes\n\nSorry, I was not clear of my goal.\nGame Object will be moved and rotated by player input. Now it is WASD + mouse, but I want to add controller (gamepad) support in future. Think of it like: you are rotating object around it’s axis using mouse. Here is GIF of how it looks for now:", null, "Well, the example logic of @britzl’s code still appplies imho.\nKeeping a separate value with the accumulated angle is the way I would go too.\nYes, you would have to modify it to handle the case of rotating it both ways.\n\n1 Like\n\nYeah, in my example I rotated on any action press but that can easily be changed:\n\n``````function on_input(self, action_id, action)\nif action_id == hash(\"right\") then\nif action.pressed then\nself.accumulated_rotation = 0\nself.rotation_direction = 1\nelseif action.released then\nself.rotation_direction = 0\nend\nelseif action_id == hash(\"left\") then\nif action.pressed then\nself.accumulated_rotation = 0\nself.rotation_direction = -1\nelseif action.released then\nself.rotation_direction = 0\nend\nend\nend\n``````\n\nI’m rotating not by click, but by the mouse move, as in https://github.com/britzl/defold-orthographic/blob/master/example/shared/objects/hitman.script example.\nI’m going to rework my script. I’ll post here the result.\n\n1 Like\n\nIf you want the rotation of a GO1 to be controlled by the location of another GO2 (which might be controlled by an input action) then you could attach a collision object to GO1, and on_message it. This will do rotation:\n\n``````local p = message.other_position\nlocal t = go.get_position()\nlocal angle = math.atan2(p.y- t.y, p.x - t.x)\nlocal rot = vmath.quat_rotation_z(angle)\ngo.set_rotation(rot)\n``````\n\nJust needs some tweaking to do the counting.\nEdit: Argg, just looked at the soln below, clearly I don’t know what I am talking about!\n\nIf you want the rotation of a GO1 to be controlled\n\nI’ve already done it, via example that I mentioned in first post. My initial question was exactly about rotation counting", null, "I haven’t managed to perform rotation+count as were advised in previous posts. And come up with something that I don’t like, and has bugs )\nI’ve prepared my demo here:\n\nUse Mouse to rotate Game Object around it’s axis. Use Left mouse button to start rotation count.\n\nBUT code breaks down to count rotations when you start to use WASD to move object and Mouse to rotate simultaneously. I need another way of finding rotation direction.\n\n1 Like\n\nI’ve reworked it", null, "Demo in github repo.\nUse Mouse to rotate Game Object around it’s axis. Use Left mouse button to start rotation count. Use WASD to move object.\nThanks everyone for tips", null, "Would appreciate if someone has advises how to improve my code.\n\n1 Like" ]
[ null, "https://forum.defold.com/uploads/default/original/3X/3/9/397d7884aa8851a1697ec548fb01325643938a16.png", null, "https://forum.defold.com/uploads/default/original/3X/d/1/d1e35d3d674d146197a1e826b92ab51a3788390a.gif", null, "https://forum.defold.com/images/emoji/twitter/slight_smile.png", null, "https://forum.defold.com/images/emoji/twitter/slight_smile.png", null, "https://forum.defold.com/images/emoji/twitter/handshake.png", null ]
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https://answers.everydaycalculation.com/multiply-fractions/2-4-times-21-8
[ "# Answers\n\nSolutions by everydaycalculation.com\n\n## Multiply 2/4 with 21/8\n\n1st number: 2/4, 2nd number: 2 5/8\n\nThis multiplication involving fractions can also be rephrased as \"What is 2/4 of 2 5/8?\"\n\n2/4 × 21/8 is 21/16.\n\n#### Steps for multiplying fractions\n\n1. Simply multiply the numerators and denominators separately:\n2. 2/4 × 21/8 = 2 × 21/4 × 8 = 42/32\n3. After reducing the fraction, the answer is 21/16\n4. In mixed form: 15/16\n\nMathStep (Works offline)", null, "Download our mobile app and learn to work with fractions in your own time:\nAndroid and iPhone/ iPad\n\n#### Multiply Fractions Calculator\n\n×\n\n© everydaycalculation.com" ]
[ null, "https://answers.everydaycalculation.com/mathstep-app-icon.png", null ]
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https://socratic.org/questions/a-triangle-has-sides-a-b-and-c-if-the-angle-between-sides-a-and-b-is-pi-8-the-an-1
[ "# A triangle has sides A,B, and C. If the angle between sides A and B is (pi)/8, the angle between sides B and C is pi/6, and the length of B is 13, what is the area of the triangle?\n\nApr 23, 2018\n\n$\\textrm{a r e a} \\approx 20.3798$\n\n#### Explanation:\n\nFirst of all, let's rewrite in standard notation. It really helps to do these if we always label the triangle consistently.\n\nThe triangle has sides $a , b , c$ (small letters) where $b = 13$ with opposing angles $C = \\frac{\\pi}{8} = {22.5}^{\\circ}$, $A = \\frac{\\pi}{6} = {30}^{\\circ}$.\n\nThe remaining angle is\n\n$B = \\pi - A - C = \\pi - \\frac{\\pi}{8} - \\frac{\\pi}{6} = \\frac{17 \\pi}{24} = {127.5}^{\\circ}$\n\nThe Law of Sines says\n\n$\\frac{b}{\\sin} B = \\frac{c}{\\sin} C$\n\n$c = \\frac{b \\sin C}{\\sin} B = \\frac{13 \\sin \\left({22.5}^{\\circ}\\right)}{\\sin} \\left({127.5}^{\\circ}\\right)$\n\nNow the area is\n\n$\\textrm{a r e a} = \\frac{1}{2} b c \\sin A = \\frac{{\\left(13\\right)}^{2} \\sin \\left({30}^{\\circ}\\right) \\sin \\left({22.5}^{\\circ}\\right)}{2 \\setminus \\sin {127.5}^{\\circ}}$\n\nI'm tempted to work out an exact answer, because those sines are all expressible using the usual operations including square root. But it's late, and the calculator says\n\n$\\textrm{a r e a} \\approx 20.3798$\n\nJun 18, 2018\n\ncolor(maroon)(A_t == 20.37 \" sq units\"\n\n#### Explanation:\n\n$b = 13 , \\hat{A} = \\frac{\\pi}{6} , \\hat{C} = \\frac{\\pi}{8} , \\hat{B} = \\pi - \\frac{\\pi}{6} - \\frac{\\pi}{8} = \\frac{17 \\pi}{24}$\n\nApplying the Law of sines,\n\n$a = \\frac{b \\sin A}{\\sin} B = \\frac{13 \\cdot \\sin \\left(\\frac{\\pi}{6}\\right)}{\\sin} \\left(\\frac{17 \\pi}{24}\\right) \\approx 8.19$\n\nFormula for area of \" Delta, A_t = (1/2) * a b * sin C\n\ncolor(maroon)(A_t = (1/2) * 8.19 * 13 * sin ( pi/8) = 20.37 \" sq units\"#" ]
[ null ]
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https://mathoverflow.net/questions/204443/fractional-sobolev-spaces-and-interpolation-in-unbounded-lipschitz-domains
[ "# Fractional Sobolev spaces and interpolation in unbounded Lipschitz domains\n\nI am not really familiar with the topic, thus I am looking for some references about the following problem.\n\nLet $s>0$ be a positive real and let $p\\in(1,+\\infty)$. We define the Bessel Potential spaces on $\\mathbb{R}^n$ via the Fourier transform $\\mathcal{F}$ as follows. $$H^{s,p}(\\mathbb{R}^n)=\\{f\\in L^p(\\mathbb{R^n}):\\mathcal{F}^{-1}(1+|\\xi|^2)^{\\frac{s}{2}}\\mathcal{F}f\\in L^p(\\mathbb{R^n})\\}.$$\n\nIn the case of $s\\in\\mathbb{Z}^+$ it holds that $H^{s,p}(\\mathbb{R}^n)=W^{s,p}(\\mathbb{R}^n)$ where the latter is the standard Sobolev space.\n\nMoreover, the Bessel potential spaces can be obtained via complex interpolation from the standard Sobolev spaces of integer order. Therefore the spaces $H^{s,p}(\\mathbb{R}^n)$ are also referred as Fractional Sobolev Space.\n\nLet now $\\Omega\\subset \\mathbb{R}^n$ an open subset (bounded or unbounded), we can define $$H^{s,p}(\\Omega)=\\{f\\in L^p(\\Omega): \\exists g\\in H^{s,p}(\\mathbb{R}^n): g\\big|_{\\Omega}=f \\}$$\n\nwith the norm $\\|f\\|_{H^{s,p}(\\Omega)}=\\inf\\{\\|g\\|_{H^{s,p}(\\mathbb{R}^n)}:g\\big|_{\\Omega}=f\\}$.\n\nTherefore, my question is. Let $k$ be in $\\mathbb{Z}^+$. Are the spaces $H^{s,p}(\\Omega)$ interpolation spaces between $W^{k,p}(\\Omega)$ and $W^{k+1.p}(\\Omega)$ if $k<s<k+1$ when $\\Omega$ is an unbounded Lipschitz domain with noncompact boundary?\n\nIn particular, if $T$ is bounded linear operator $T: W^{k,p}(\\Omega)\\to W^{k,p}(\\Omega)$ and $T:W^{k+1,p}(\\Omega)\\to W^{k+1,p}(\\Omega)$, do we have the boundedness $T:H^{s,p}(\\Omega)\\to H^{s,p}(\\Omega)$ for every $s\\in[k,k+1]?$\n\nCan you please suggest me some references?\n\nYou cannot expect this without imposing some condition at infinity. Otherwise, $\\Omega$ could have a tentacle\" which stretches to infinity and thins rapidly. In this fashion, you can have functions in $W^{k,p}(\\Omega)$ which do not have an extension in $W^{k,p}(R^n)$.\n\n• I expected that some regularity is needed. Can you suggest me some detailed references please?\n– Pit\nMay 1, 2015 at 15:23\n\nHere are two very good references:\n\nR. A. Adams, Sobolev Spaces, Pure and Applied Mathematics, A Series of Monographs and Textbooks, 65 (Academic Press, New York, 1975).\n\nH. Triebel, Interpolation Theory, Function Spaces, Di erential Operators (North-Holland, Amsterdam, 1978).\n\nBest regards." ]
[ null ]
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http://intermath.org/transformations-of-data/
[ "# Transformations of Data\n\nCompute the mean, median, and mode for any set of data.\n\nWhat happens to the mean, median, and mode if you add 0.7 to each element of the data? What happens to these values if you subtract 0.5 from each element of the data?\n\nIf the mean, median, and mode of the data set abcd are 16, 11 and 4, what are the mean, median, and mode of the data set a + xb + xc + xd + x, where x is any real number?", null, "Extension\nDescribe what happens to the mean, median, and mode of a data set when you multiply each data point by a number k." ]
[ null, "http://intermath.org/wp-content/uploads/2018/10/angle.gif", null ]
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https://techcommunity.microsoft.com/t5/excel/freeze-excel-formula-when-criteria-met/td-p/3702169
[ "", null, "SOLVED\n\n# Freeze Excel formula when criteria met\n\nHello Guys\n\nMight be easy but i have a headache here\n\nPreface\n\nI want the \"Days in transit\" Colum to stop updating the formula when I select the Yes answer in the \"Arrived at destination\" Colum currently the formula on \"Days in transit\" is \"=TODAY()-G2\"", null, "i have tried =IF(L3=\"yes\",\"\",\"=TODAY()-G2\")\n\nbut i don't think the formula is correct\n\nI'm out of ideas\n\n5 Replies\n\n# Re: Freeze Excel formula when criteria met\n\n=IF(L2=\"yes\",\"\",TODAY()-G2)\n\nMaybe like this.\n\n# Re: Freeze Excel formula when criteria met\n\nthank you that helped\n\nNow I just need it to stop counting when yes is selected in \"Arrived at destination\"\n\nthe no logic is working correctly so its something i need to put in the middle i believe\n\n=IF(L3=\"yes\",\"__not sure  what to put here__\",TODAY()-G3)\n\n# Re: Freeze Excel formula when criteria met\n\nIf possible please update date of arrival in column and rephrase the formula as below\nIF \"date of arrival\" column >0 then, date arrival - G2 else TODAY()-G2\nbest response confirmed by simon_70 (Occasional Contributor)\nSolution\n\n# Re: Freeze Excel formula when criteria met\n\nInstead of No/Yes in the Arrived at destination column, I'd leave it blank initially, and enter the date of arrival when the item has arrived. You can then use\n\n=IF(L2=\"\",TODAY(),L2)-G2\n\n# Re: Freeze Excel formula when criteria met\n\nhey this worked thank you all" ]
[ null, "https://www.facebook.com/tr", null, "https://techcommunity.microsoft.com/t5/image/serverpage/image-id/427942i65DDB24EB6ABC9AC/image-size/large", null ]
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https://www.smartvidya.co.in/2020/09/solved-question-paper-for-lecturer-in_87.html
[ "### Solved Question Paper for Lecturer in Electrical Engineering in DTTE, GNCTD conducted by UPSC\n\n11. For any medium, electric flux density D is related to electric field intensity E by the equation:", null, "12. In any linear resistive network, the voltage across or the current through any resistor or source may be calculated by adding algebraically all the individual voltages or currents caused by the separate independent sources acting alone, with all other independent voltage sources replaced by short circuits and all other independent current sources replaced by open circuits. Which one of the following theorems states the above definition?\n(a) Maximum power transfer theorem\n(b) Superposition theorem\n(c) Maxwell’s theorem\n(d) Source transformation theorem\n\n13. Consider the following circuit:", null, "14. If two sinusoidal waves whose phases are to be compared:\n1. Both be written as sine waves or both cosine waves\n2. Both be written with positive amplitudes\n3. Each has the same frequency\n4. One wave should be sine and the other cosine wave\n\nWhich of the above statements are correct?\n(a) 1, 2 and 4 only\n(b) 1, 3 and 4 only\n(c) 1, 2 and 3 only\n(d) 2, 3 and 4 only\n\n15. The drawback of 𝑚-derived filter is removed by connecting number of sections in addition to prototype and 𝑚-derived sections with terminating:\n(a) Full sections\n(b) One-fourth sections\n(c) Half sections\n(d) Square of three-fourth sections\n\n16. The hybrid parameter of the open circuit reverse voltage gain is indicated as:", null, "17. Two identical 750 turn coils 𝐴 and 𝐵 lie in parallel planes. A current changing at the rate of 1500 𝐴/𝑠 in 𝐴 induces an emf of 11.25 𝑉 in 𝐵. If the self-inductance of each coil is 15 mH, then the percentage of the flux produced in coil 𝐴 which links the turns of 𝐵 will be:\n(a) 40 %\n(b) 50 %\n(c) 60 %\n(d) 70 %\n\n18. What is the capacitor voltage that is associated with the current for the wave form shown when the value of the capacitance is 5 𝜇F?", null, "(a) 𝑣(𝑡)=1\n(b) 𝑣(𝑡)=2\n(c) 𝑣(𝑡)=4\n(d) 𝑣(𝑡)=8\n\n19. Current measured during a test is 30.4 𝐴, flowing in a resistor of 0.105 Ω . It is noted that the ammeter reading is low by 1.2 % and the marked resistance is high by 0.3 %. The true power will be nearly:\n(a) 79.5 𝑊\n(b) 89.3 𝑊\n(c) 99.1 𝑊\n(d) 109.0 𝑊\n\n20. Schering Bridge is used for the measurement of:\n(a) Capacitors\n(b) Inductors\n(c) Q-of inductors\n(d) Medium value resistors\n\nShare:\n\n## Featured Post\n\n### UPSC Civil Service Preliminary Paper-1 Previous Year Solved Question Papers\n\nCivil Service Preliminary Paper-1 Previous Year Solved Questions for the year 2019 Civil Service Preliminary Paper-1 Previous Year Solved Qu...", null, "Name\n\nEmail *\n\nMessage *" ]
[ null, 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", null, 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", null, 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", null, 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null, "https://1.bp.blogspot.com/-4qZDZT6za5w/Xye3-joS_aI/AAAAAAAABz0/nG2deXb6tBEo1HqjPvadUrARubRga06MwCLcBGAsYHQ/w640-h427/UPSC%2BCivil%2BService%2BPreliminary%2BPaper-1%2BPrevious%2BYear%2BSolved%2BQuestion%2BPapers.jpg", null ]
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https://community.qualtrics.com/XMcommunity/discussion/4445/answer-choice-randomization-problem-with-randomized-subset
[ "Have you already seen Work:New.0 ? Tell us in a few words what you've learned from the video on the Community!\n\n# Answer Choice Randomization: Problem with randomized subset\n\nHi,\nI have the following problem:\nI want to include 15 single-choice answers (3 groups / 5 answers possibilities per group) in one question and the respondent should get only 1 randomly chosen answer from each group - in total 3 answers.\nI tried with the 'Advanced Randomization' function, but I just managed to create one 'Random Subset'. So I need to create several 'Random Subsets' in one question.\nDo you have any idea how to create more than one subset or how to solve the problem in another way, so that the respondents just get one answer from each group? I struggle with the problem for hours. So I really hope you can help me.\n\nJennifer\n\n•", null, "Raleigh, NC Wizard ✭✭✭✭✭\n\nTo do it without JavaScript, you can use survey flow randomizers to randomly pick one choice from each group by setting flags for the choices to be shown. For example, let's say Group 1 is made up of Choices 1 through 5. Create a Group 1 randomizer with five embedded data blocks under it and randomly choose 1 block. The blocks would set a 'display' flag for each choice (e.g., choice1_flag = 1). Create a similar randomizer for each group. Then in your question add display logic to all your choices to only display the choice if the flag is set to 1.\n\n• You will probably have to do some javascript to achieve this.\n\nCreate a question with all 15 answers to use as a base.\n\nIn the javascript, make 3 random numbers to choose one from each group. Then hide the radio buttons for the non selected answers.\n\nNote that this will not change the order of the buttons, it will only hide the buttons. If you want a random order, you will have to randomize the order of all 15 answers and then use the random number to select a pointer to the answer location.\n\n• Unfortunately I'm not able to write a javascript code :-(\n\nSeriously though, it is very easy to do this in Qualtrics. If you search the forum, there was a recent post on hiding the radio buttons. There are also many posts on randomizing.\n\nOff the top of my head, here is a starting point:\nvar grp1 = [1, 4, 7, 10, 13]; // answer group 1 locations\nvar grp2 = [2, 5, 8, 11, 14];\nvar grp3 = [3, 6, 9, 12, 15];\n\nvar pick1 = Math.floor((Math.random() * 5)); // random number between 0 and 4\nvar pick2 = Math.floor((Math.random() * 5));\nvar pick3 = Math.floor((Math.random() * 5));\n\nfor (var i = 0; i < grp1.length; i++) {\nif (i <> pick1) {" ]
[ null, "https://us.v-cdn.net/6030293/uploads/userpics/486/nLFBSWRAZPTWT.jpg", null ]
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https://zilongshanren.com/post/2016-03-13-undertand-javascript-this-keyword/
[ "1. This 绑定的内容与函数无关,而与函数的执行环境有关。\n2. 函数的 This 绑定的内容可以通过 bind,apply 和 call 函数来动态进行修改。\n3. 巧用闭包可以消除不必要的 This 动态绑定,提高代码的可读性。\n\n## This 绑定内容与函数无关,而与执行环境有关\n\n```var name = \"zilong\";\n\nvar myfunc = function() {\nconsole.log(this.name);\n}\n\nmyfunc();\n```\n\n```[[scope chain]] = [\n{\nActive Object {\narguments: ...\nthis: [global Object],\n...\n},\nglobal Object: {\nname: 'zilong'\n...\n}\n}\n]\n```\n\n```var name = \"zilong\";\nvar sex = \"man\";\n\nvar myfunc = function(name) {\nthis.name = name;\n}\n\nmyfunc.prototype.print = function() {\nconsole.log(this.name);\nconsole.log(this.sex);\nconsole.log(sex);\n}\n\nvar obj = new myfunc(\"hello\");\nobj.print();\n```\n\nRESULTS:\n\n```hello\nundefined\nman\n```\n\n```[[scope chain]] = [\n{\nActive Object {\narguments: ...\nthis: obj,\n...\n},\nglobal Object: {\nname: 'zilong',\nsex: 'man',\n...\n}\n}\n]\n```\n\n## This 绑定的内容可以被动态修改\n\n```var name = \"zilong\";\nvar sex = \"man\";\n\nvar myfunc = function(name) {\nthis.name = name;\n}\n\nmyfunc.prototype.print = function() {\nconsole.log(this.name);\nconsole.log(this.sex);\nconsole.log(sex);\n}.bind(this);\n\nvar obj = new myfunc(\"hello\");\nobj.print();\n```\n\nRESULTS:\n\n```zilong\nman\nman\n```\n\n```var name = \"zilong\";\nvar sex = \"man\";\n\nvar myfunc = function(name) {\nthis.name = name;\n}\n\nmyfunc.prototype.print = function() {\nconsole.log(this.name);\nconsole.log(this.sex);\nconsole.log(sex);\n};\n\nvar obj = new myfunc(\"hello\");\nmyfunc.prototype.print.call(obj, \"hello\");\n```\n\nRESULTS:\n\n```hello\nundefined\nman\n```\n\n``` var obj = new myfunc(\"hello\");\nmyfunc.prototype.print.call(this, \"hello\");\n// 下面的 window 和 this 是等价的。\n// myfunc.prototype.print.call(window, \"hello\");\n```\n\nRESULTS:\n\n```zilongshanren\nman\nman\n```\n\n## 巧用闭包消除 This 动态绑定,提高代码可读性\n\n```var a = 10;\nvar obj = {\na : 1,\nb : 2,\n\nsum : function() {\nreturn this.a + a;\n}.bind(this);\n\n}\n}\n\nconsole.log(obj.sum());\n```\n\nRESULTS:\n\n```3\n```\n\n```var a = 10;\nvar obj = {\na : 1,\nb : 2,\n\nsum : function() {\nvar self = this;\n\nreturn self.a + a;\n};\n\n}\n}\n\nconsole.log(obj.sum());\n```\n\nRESULTS:\n\n```3\n```" ]
[ null ]
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https://discourse.matplotlib.org/t/debugging-process-gets-stuck-with-matpltlib-after-show/13612
[ "", null, "# debugging process gets stuck with matpltlib after show()\n\nHi all,\n\nI am new born in Python ( 1 week old)\n\nCan you pls help to understand the basic concept of\nmatpltlib interacting with Python\n\nthe mutter is:\nduring debugging the debug processes stacks when fig is created\nfor example, in code\n\nimport matplotlib.pyplot as plt\nfrom pylab import *\nx= 23;\ny = 111111;\nprint(23456)\nplt.plot(range(10))\nplot([1,2,3])\nshow()\nprint(11111111)\na=888\n\nit is impossible after show() to continue debug in any IDE for example Wingwar\nor pythonxy\nas stated in\nBeginning Python Visualization - Crafting Visual Transformation Scripts (2009)\npage 187\n\nNote If you’re not using matplotlib interactively in Python, be sure\nto call the function show() after all\ngraphs have been generated, as it enters a user interface main loop\nthat will stop execution of the rest of\nyour code. The reason behind this behavior is that matplotlib is\ndesigned to be embedded in a GUI as well.\nIn Windows, if you’re working from interactive Python, you need only\nissue show() once; close the figures\n(or figures) to return to the shell. Subsequent plots will be drawn\nautomatically without issuing show(), and\nyou’ll be able to plot graphs interactively.\n\nI tried the code\n\nas suggested in\nhttp://matplotlib.sourceforge.net/users/shell.html\n\ncode\ntaken from people\nfrom wingware\nhttp://www.wingware.com/doc/howtos/matplotlib\n\nt = Timer(0, show)\nt.start()\n\nbut still debugging process gets\nstuck…\n\nimport\nmatplotlib as mpl\n\nfrom\npylab import plot,show,close,ion\n\nx\n= range(10)\n\nplot(x)\n\n‘show()’\n\nfrom\n\nt\n= Timer(0, show)\n\nt.start()\n\n‘ion()\nthe same result with or not’\n\na\n= 1222233\n\ny\n= [2, 8, 3, 9, 4]\n\nplot(y)\n\nzz=\n12346\n\nprint(44444)\n\nBest Regards\n\nSandy\n\n···" ]
[ null, "https://discourse.matplotlib.org/uploads/default/original/1X/226e21a6b2d1e1ae3ffbb5d50c20c828692648a1.png", null ]
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http://www.expertsmind.com/questions/properties-of-t-distribution-30135289.aspx
[ "## Properties of t distribution, Mathematics\n\nAssignment Help:\n\nProperties of t distribution\n\n1. The t distribution ranges from - ∞ to ∞ first as does the general distribution\n\n2. The t distribution as the standard general distribution is bell shaped and symmetrical around mean zero\n\n3. The shapes of the t distribution changes like the number of degrees of freedom changes\n\n4. The t distribution is more platykurtic that the normal distribution\n\n5. The t distribution has a greater dispersion than the standard general distribution. Like n gets larger the t distribution approaches the general distribution while n = 30 the difference is extremely small\n\nRelation among the t distribution and standard general distribution is displayed in the given diagram", null, "Note that the t distribution has different shapes depending on the size of the sample. When the sample is rather small the height of the t distribution is shorter than the general distribution and the tails are wider.\n\n#### Derive a linear system - gauss jordan elimination, Suppose that, on a certa...\n\nSuppose that, on a certain day, 495 passengers want to fly from Honolulu (HNL) to New York (JFK); 605 passengers want to fly from HNL to Los Angeles (LAX); and 1100 passengers want\n\nCalculate 50%\n\n#### Develop a linear program, The production manager of Koulder Refrigerators m...\n\nThe production manager of Koulder Refrigerators must decide how many refrigerators to produce in each of the next four months to meet demand at the lowest overall cost. There is a\n\n#### Applications of rational numbers, Kaylee makes 56 packages in seven hours T...\n\nKaylee makes 56 packages in seven hours Taylor makes 20% more packages in nine hours who makes more packages per hour\n\nThe power\n\n#### Linear equations, Linear Equations We'll begin the solving portion of ...\n\nLinear Equations We'll begin the solving portion of this chapter by solving linear equations. Standard form of a linear equation: A linear equation is any equation whi\n\n#### Real number, if HCFof 657 and 963 is expressable in the form of 657x+963x-1...\n\nif HCFof 657 and 963 is expressable in the form of 657x+963x-15findx\n\n#### Find the number of zeros of the polynomial, Find the number of zeros of the...\n\nFind the number of zeros of the polynomial from the graph given. (Ans:1)\n\n#### Linear programming problem., Ask question #Minimum 100 words acca paper mil...\n\nAsk question #Minimum 100 words acca paper mill produces two grades of paper viz.,xand y.Bacause of raw material restrictions, it cannot produce more than 400 tones of grade x pape\n\n#### Simpson rule - approximating definite integrals, Simpson's Rule - Approxima...\n\nSimpson's Rule - Approximating Definite Integrals This is the last method we're going to take a look at and in this case we will once again divide up the interval [a, b] int", null, "", null, "" ]
[ null, "http://www.expertsmind.com/CMSImages/1649_Properties%20of%20t%20distribution.png", null, "http://www.expertsmind.com/questions/CaptchaImage.axd", null, "http://www.expertsmind.com/prostyles/images/3.png", null ]
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https://people.utm.my/alihassan/biography/
[ "# Biography\n\nProfessor Dr. Ali H. M. Murid received his B.Sc. (Mathematics) and M.Sc. (Applied Mathematics) from Iowa State University, USA in 1983 and 1986  respectively,  Diploma of Education from UTM in 1988, and Ph.D from UTM in 1997. His Ph.D thesis was on boundary integral equation formulations for numerical conformal mapping written under the supervisions of Allahyarham Prof. Dr. Mohd. Rashidi Md. Razali (UTM) and Prof. Dr. M. Zuhair Nashed (formerly at University of Delaware, USA). He joined UTM in 1988 and has taught several courses at both undergraduate and graduate levels.  The undergraduate courses that he has taught are Calculus, Calculus of Several Variables, Vector Calculus, Differential Equations, Numerical Methods, Functions of Complex Variables, and Mathematical Modelling. For graduate courses, he has taught Applied and Computational Complex Analysis, Mathematical Methods, Numerical Integral Equations, and Advanced Engineering Mathematics. He was the Chairperson for the Mathematics Program at the Ibnu Sina Institute for Fundamental Science Studies (now known as Ibnu Sina Institute for Scientific and Industrial Research) for two years from June 2009 until May 2011. He was a research fellow at UTM Centre for Industrial and Applied Mathematics (UTM-CIAM) from  2013-2018. His research interests are applied and computational complex analysis and mathematical modeling of industrial problems." ]
[ null ]
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https://dividedby.org/0-divided-by-50
[ "Home » Divided by 50 » 0 Divided by 50\n\n# 0 Divided by 50\n\nWelcome to 0 divided by 50, our post which explains the division of zero by fifty to you. 🙂\n\nThe number 0 is called the numerator or dividend, and the number 50 is called the denominator or divisor.\n\nThe quotient of 0 and 50, the ratio of 0 and 50, as well as the fraction of 0 and 50 all mean (almost) the same:\n\n0 divided by 50, often written as 0/50.\n\nRead on to find the result of 0 divided by 50 in decimal notation, along with its properties.\n\nNominator (Dividend)\nDenominator (Divisor)\nShow Steps\n0\n=\n0 Remainder 0\n\n## What is 0 Divided by 50?\n\nWe provide you with the result of the division 0 by 50 straightaway:\n\n0 divided by 50 = 0\n\nThe result of 0/50 is an integer, which is a number that can be written without decimal places.\n• 0 divided by 50 in decimal = 0\n• 0 divided by 50 in fraction = 0/50\n• 0 divided by 50 in percentage = 0%\n\nNote that you may use our state-of-the-art calculator above to obtain the quotient of any two integers or decimals, including 0 and 50, of course.\n\nRepetends, if any, are denoted in ().\n\nThe conversion is done automatically once the nominator, e.g. 0, and the denominator, e.g. 50, have been inserted.\n\nNo need to press the button, unless you want to start over.\n\nGive it a try now with a similar division by 50.\n\n## What is the Quotient and Remainder of 0 Divided by 50?\n\nHere we provide you with the result of the division with remainder, also known as Euclidean division, including the terms in a nutshell:\n\nThe quotient and remainder of 0 divided by 50 = 0 R 0\n\nThe quotient (integer division) of 0/50 equals 0; the remainder (“left over”) is 0.\n\n0 is the dividend, and 50 is the divisor.\n\nIn the next section of this post you can find the frequently asked questions in the context of zero over fifty, followed by the summary of our information.\n\n## Zero Divided by Fifty\n\nYou already know what 0 / 50 is, but you may also be interested in learning what other visitors have been searching for when coming to this page.\n\nThe FAQs include, for example:\n\n• What is 0 divided by 50?\n• How much is 0 divided by 50?\n• What does 0 divided by 50 equal?\n\nIf you have read our article up to this line, then we take it for granted that you can answer these FAQs and similar questions about the ratio of 0 and 50.\n\nObserve that you may also locate many calculations such as 0 ÷ 50 using the search form in the sidebar.\n\nThe result page lists all entries which are relevant to your query.\n\nGive the search box a go now, inserting, for instance, zero divided by fifty, or what’s 0 over 50 in decimal, just to name a few potential search terms.\n\nFurther information, such as how to solve the division of zero by fifty, can be found in our article Divided by, along with links to further readings.\n\n## Conclusion\n\nTo sum up, 0/50 = 0. It is a whole number with no fractional part.\n\nAs division with remainder the result of 0 ÷ 50 = 0 R 0.\n\nFor questions and comments about the division of 0 by 50 fill in the comment form at the bottom, or get in touch by email using a meaningful subject line.\n\nIf our content has been helpful to you, then you might also be interested in the Remainder of 2 Divided by 50.\n\nPlease push the sharing buttons to let your friends know about the quotient of 0 and 50, and make sure to place a bookmark in your browser.\n\nThanks for visiting our article explaining the division of 0 by 50." ]
[ null ]
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https://cs.stackexchange.com/questions/88898/determine-if-a-line-going-through-n-rectangles-exists
[ "Determine if a line going through n rectangles exists\n\nI guess this is kinda like asking if 3 (or more) rectangles are collinear.\n\nThe question is, given n rectangles on the 2D plane (given as the rectangle's top-left corner coordinates and it's width/height), does there exist a line that goes through all of them?\n\nNotes:\n\n• The rectangles' sides are perpendicular or parallel to the axes. So basically, none of the rectangle sides are slanted.\n• Rectangles themselves can intersect\n• Does not actually need to produce the line equation\n\nI've been thinking through this for a good portion of today, and can't figure out (or find online) an answer. I keep finding edge cases and can't figure out a guaranteed method. The line of best fit for the mid-point or the corners of all n rectangles seem like a good heuristic for a line, should one exist, but I don't think it always works.\n\nThe corners of the rectangles seem like good starting points to determine the extreme slopes of a potential line, but it's still unclear to me how i'd use them to determine if a line exists for all n rectangles. I also know how I'd do this intuitively IRL, but not sure how to turn it into an algorithm.\n\nVisual Example for 4 rectangles:", null, "• One necessary but not sufficient condition is that every rect (short for iso-oriented rectangle) must overlap the convex hull of \"opposite\" rects. For each dimension, one such pair is one rect with minimum and one with maximum coordinate value. – greybeard Mar 5 '18 at 8:47\n• If only I understood the paragraph Stabbing line segments in $ℝ^2$ in Hohmeyer, M.E.; Teller, S.J.: Stabbing isothetic boxes and rectangles in O(n log n) time in Computational Geometry: Theory and Applications 2 (1992) pp. 201-207. – greybeard Mar 6 '18 at 19:17\n\nThere is a line through the four rectangles if and only if you can pick one edge from each rectangle such that the line intersects each of those four edges -- or equivalently, if there is a point on each of those edges such that the resulting four points are colinear. So a plausible algorithm is to enumerate all $4^4$ ways to choose one edge from each rectangle; then check whether there exists a line that goes through those four edges.\n\nHow do we do the latter test? Let's suppose the endpoints of the first edge are the two points $P',P''$ (i.e., these are two adjacent corners of the first rectangle). Then any point $P$ on that edge can be expressed as $P = (1-\\alpha) P' + \\alpha P''$ for some $0 \\le \\alpha \\le 1$. Similarly, we can express any point $Q$ on the second edge as $Q = (1-\\beta) Q' + \\beta Q''$ where $Q',Q''$ are the endpoints of the second edge, and similarly for the two remaining points $R,S$ on the two remaining edges.\n\nWe can express algebraically the condition that $P,Q,R$ are colinear (on the same line). Let $P_x$ be the x-coordinate of the point $P$, and $P_y$ be its y-coordinate. Then $P,Q,R$ are colinear iff\n\n$$(Q_x-P_x)(R_y-P_y) = (Q_y-P_y)(R_x-P_x).$$\n\nWe can plug in $Q_x = (1-\\beta)Q'_x + \\beta Q''_x$, and so on, to get an equation in terms of the three $\\alpha,\\beta,\\gamma$ unknowns. Similarly, the condition that $P,Q,S$ are colinear can be expressed as an equation in terms of $\\alpha,\\beta,\\delta$. Now there exists a linear through these four edges iff there exists a common solution to these two equations such that $0 \\le \\alpha,\\beta,\\gamma,\\delta \\le 1$. This reduces the problem to solving two quadratic equations over four real unknowns. Now you can apply standard methods for solving such equations to determine whether a simultaneous solution exists.\n\n• There is a line through the four rectangles if and only if you can pick one edge from each rectangle such that the line goes through those four edges the rectangles' sides are perpendicular or parallel to the axes, I can't see a similar condition for the line. – greybeard Mar 5 '18 at 6:16\n• @greybeard, thanks for the comment. In retrospect, my writing was unclear. I didn't mean that the line has to be horizontal or vertical. Rather, I meant that the line intersects each of those four edges somewhere. I've edited the question to try to improve the wording. I hope it makes sense now. Thanks again for the feedback! – D.W. Mar 5 '18 at 7:28\n• (I read more into line goes through[…] edges than warranted. You seem to have posted in a hurry: straightforward (but not necessarily) algorithm?) – greybeard Mar 5 '18 at 8:40" ]
[ null, "https://i.stack.imgur.com/BEbcF.png", null ]
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https://docs.xilinx.com/r/1.4.1-English/ug1414-vitis-ai/vai_q_pytorch-QAT
[ "# vai_q_pytorch QAT - 1.4.1 English\n\n## Vitis AI User Guide (UG1414)\n\nDocument ID\nUG1414\nRelease Date\n2021-12-13\nVersion\n1.4.1 English\n\nAssuming that there is a pre-defined model architecture, use the following steps to do quantization aware training. Take the ResNet18 model from Torchvision as an example. The complete model definition is here.\n\n1. Check if there are non-module operations to be quantized. ResNet18 uses `‘+’` to add two tensors. Replace them with `pytorch_nndct.nn.modules.functional.Add`.\n2. Check if there are modules to be called multiple times. Usually such modules have no weights; the most common one is the `torch.nn.ReLu` module. Define multiple such modules and then call them separately in a forward pass. The revised definition that meets the requirements is as follows:\n``````class BasicBlock(nn.Module):\nexpansion = 1\n\ndef __init__(self,\ninplanes,\nplanes,\nstride=1,\ndownsample=None,\ngroups=1,\nbase_width=64,\ndilation=1,\nnorm_layer=None):\nsuper(BasicBlock, self).__init__()\nif norm_layer is None:\nnorm_layer = nn.BatchNorm2d\nif groups != 1 or base_width != 64:\nraise ValueError('BasicBlock only supports groups=1 and base_width=64')\nif dilation > 1:\nraise NotImplementedError(\"Dilation > 1 not supported in BasicBlock\")\n# Both self.conv1 and self.downsample layers downsample the input when stride != 1\nself.conv1 = conv3x3(inplanes, planes, stride)\nself.bn1 = norm_layer(planes)\nself.relu1 = nn.ReLU(inplace=True)\nself.conv2 = conv3x3(planes, planes)\nself.bn2 = norm_layer(planes)\nself.downsample = downsample\nself.stride = stride\n\n# Use a functional module to replace ‘+’\n\nself.relu2 = nn.ReLU(inplace=True)\n\ndef forward(self, x):\nidentity = x\n\nout = self.conv1(x)\nout = self.bn1(out)\nout = self.relu1(out)\n\nout = self.conv2(out)\nout = self.bn2(out)\n\nif self.downsample is not None:\nidentity = self.downsample(x)\n\n# Use function module instead of ‘+’\n# out += identity\nout = self.relu2(out)\n\nreturn out\n``````\n3. Insert `QuantStub` and `DeQuantStub`.\n\nUse `QuantStub` to quantize the inputs of the network and `DeQuantStub` to de-quantize the outputs of the network. Any sub-network from `QuantStub` to `DeQuantStub` in a forward pass will be quantized. Multiple QuantStub-DeQuantStub pairs are allowed.\n\n``````class ResNet(nn.Module):\n\ndef __init__(self,\nblock,\nlayers,\nnum_classes=1000,\nzero_init_residual=False,\ngroups=1,\nwidth_per_group=64,\nreplace_stride_with_dilation=None,\nnorm_layer=None):\nsuper(ResNet, self).__init__()\nif norm_layer is None:\nnorm_layer = nn.BatchNorm2d\nself._norm_layer = norm_layer\n\nself.inplanes = 64\nself.dilation = 1\nif replace_stride_with_dilation is None:\n# each element in the tuple indicates if we should replace\n# the 2x2 stride with a dilated convolution instead\nreplace_stride_with_dilation = [False, False, False]\nif len(replace_stride_with_dilation) != 3:\nraise ValueError(\n\"replace_stride_with_dilation should be None \"\n\"or a 3-element tuple, got {}\".format(replace_stride_with_dilation))\nself.groups = groups\nself.base_width = width_per_group\nself.conv1 = nn.Conv2d(\n3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)\nself.bn1 = norm_layer(self.inplanes)\nself.relu = nn.ReLU(inplace=True)\nself.layer1 = self._make_layer(block, 64, layers)\nself.layer2 = self._make_layer(\nblock, 128, layers, stride=2, dilate=replace_stride_with_dilation)\nself.layer3 = self._make_layer(\nblock, 256, layers, stride=2, dilate=replace_stride_with_dilation)\nself.layer4 = self._make_layer(\nblock, 512, layers, stride=2, dilate=replace_stride_with_dilation)\nself.fc = nn.Linear(512 * block.expansion, num_classes)\n\nself.quant_stub = nndct_nn.QuantStub()\nself.dequant_stub = nndct_nn.DeQuantStub()\n\nfor m in self.modules():\nif isinstance(m, nn.Conv2d):\nnn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\nelif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):\nnn.init.constant_(m.weight, 1)\nnn.init.constant_(m.bias, 0)\n\n# Zero-initialize the last BN in each residual branch,\n# so that the residual branch starts with zeros, and each residual block behaves like an identity.\n# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677\nif zero_init_residual:\nfor m in self.modules():\nif isinstance(m, Bottleneck):\nnn.init.constant_(m.bn3.weight, 0)\nelif isinstance(m, BasicBlock):\nnn.init.constant_(m.bn2.weight, 0)\n\ndef forward(self, x):\nx = self.quant_stub(x)\n\nx = self.conv1(x)\nx = self.bn1(x)\nx = self.relu(x)\nx = self.maxpool(x)\n\nx = self.layer1(x)\nx = self.layer2(x)\nx = self.layer3(x)\nx = self.layer4(x)\n\nx = self.avgpool(x)\nx = torch.flatten(x, 1)\nx = self.fc(x)\nx = self.dequant_stub(x)\nreturn x\n``````\n4. Use QAT APIs to create the quantizer and train the model.\n``````def _resnet(arch, block, layers, pretrained, progress, **kwargs):\nmodel = ResNet(block, layers, **kwargs)\nif pretrained:\nreturn model\n\ndef resnet18(pretrained=False, progress=True, **kwargs):\nr\"\"\"ResNet-18 model from\n`\"Deep Residual Learning for Image Recognition\" <https://arxiv.org/pdf/1512.03385.pdf>'_\n\nArgs:\npretrained (bool): If True, returns a model pre-trained on ImageNet\nprogress (bool): If True, displays a progress bar of the download to stderr\n\"\"\"\nreturn _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,\n**kwargs)\n\nmodel = resnet18(pretrained=True)\n\n# Generate dummy inputs.\ninput = torch.randn([batch_size, 3, 224, 224], dtype=torch.float32)\n\n# Create a quantizer\nquantizer = torch_quantizer(quant_mode = 'calib',\nmodule = model,\ninput_args = input,\nbitwidth = 8,\nqat_proc = True)\nquantized_model = quantizer.quant_model\nquantized_model.parameters(),\nlr,\nweight_decay=weight_decay)\n\n# Use the optimizer to train the model, just like a normal float model.\n…\n``````\n5. Convert the trained model to a deployable model.\n\nAfter training, dump the quantized model to xmodel. (```batch size=1``` is must for compilation of xmodel).\n\n``````# vai_q_pytorch interface function: deploy the trained model and convert xmodel\n# need at least 1 iteration of inference with batch_size=1\nquantizer.deploy(quantized_model)\ndeployable_model = quantizer.deploy_model\nval_dataset2 = torch.utils.data.Subset(val_dataset, list(range(1)))" ]
[ null ]
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https://www.climbwork.cz/raymond/2021-02-1183/
[ " ball mill grinding media calculation formula\n\n## ball mill grinding media calculation formula\n\n•", null, "### Calculate Top Ball Size of Grinding Media Equation &\n\n04/04/2018· The development of the ball mill during the twentieth century has been described as the most significant development in the machinery for performing the grinding of ores (Lynch and Rowland 2006). A key part of the implementation of ball mills\n\n•", null, "### Calculate and Select Ball Mill Ball Size for Optimum Grinding\n\n08/04/2021· Calculation of top size grinding media AZZARONI’s Formula; I attach Fred Bond’s first empirical equation for sizing grinding balls for ball mills as well as a few other links to good related articles on the topic. OPTIMAL BALL DIAMETER IN A MILL ; Grinding\n\n•", null, "### Bond formula for the grinding balls size calculation\n\n19/10/2017· The grinding balls diameter determined by the Bond formula has a recommendatory character and serves as a starting point for calculating the necessary proportion grinding media feeding a new mill. More precisely adjust the ball load in the mill can only by industrial test performing. During the industrial tests necessary to accurately monitor the grinding quality, mill\n\n•", null, "### Ball charges calculators thecementgrindingoffice\n\n- Ball charges: This calculator gives the surface and the average weight of the ball charges. It gives also a rough interpretation of the ball charge efficiency: Ball top size (bond formula): calculation of the top size grinding media (balls or cylpebs):-Modification of the Ball Charge: This calculator analyses the granulometry of the material inside the mill and proposes a modification of\n\n•", null, "### How to Size a Ball Mill -Design Calculator & Formula\n\n08/04/2018· 2) Ball milling a ball mill with a diameter of 2.44 meters, inside new liners, grinding wet in open circuit. When the grinding conditions differ from these specified conditions, efficiency factors (Rowland and Kjos, 1978) have to be used in conjunction with equation 1. In general, therefore, the required mill power is calculated using the\n\n•", null, "### Mill Steel Charge Volume Calculation\n\n19/03/2017· We can calculate the steel charge volume of a ball or rod mill and express it as the % of the volume within the liners that is filled with grinding media. While the mill is stopped, the charge volume can be gotten by measuring the diameter inside the liners and the distance from the top of the charge to the top of the mill. The % loading or change volume can then be read off the graph below or\n\n•", null, "### How To Calculate The Media In Ball Mill Ball Mill\n\nCalculate media charge in a ball mill xls dbm crus ball mill charge calculation in cement ball mill grinding media calculation the ball charge mill consists handbook for dry process plants 187 learn more technical notes 8 grinding r p king media or charge in the mill and dm is the diameter of e is a function of the ball media size distribution . Get Price; How Calculate The Vare In Ball Mill\n\n•", null, "### Page 1 Ball Milling Theory freeshell.org\n\ninvolve grinding). With Lloyd's ball milling book having sold over 2000 copies, there are probably over 1000 home built ball mills operating in just America alone. This article borrows from Lloyd's research, which was obtained from the commercial ball milling industry, and explains some of the key design criteria for making your own ball mill. This is a good starting point for anyone\n\n•", null, "### Ball Mill Critical Speed Mineral Processing & Metallurgy\n\n17/03/2017· A Ball Mill Critical Speed (actually ball, rod, AG or SAG) is the speed at which the centrifugal forces equal gravitational forces at the mill shell’s inside surface and no balls will fall from its position onto the shell. The imagery below helps explain what goes on inside a mill as speed varies. Use our online formula. The mill speed is typically defined as the percent of the Theoretical\n\n•", null, "### Ball Mill Grinding Media Calculation Ball Mill\n\nBall mill grinding media calculation formula metallurgy apr 05 2018 cement ball mill grinding media calculationrrcser cement ball mill grinding media calculation a ball mill is a type of grinder used to grind and blend materials for what is the gradient of overflow ball mills calculation for ball mill drive asinagpurcirclein. Get Details Ball Mill Calculations Grinding Media Filling Degree\n\n•", null, "### Calculation of Grinding Balls Surface Area and Volume\n\nNecessity of such calculations may arise when choosing a container for reloading grinding media into a ball mill;when selecting the optimum characteristics of grinding ballsto suit specific grinding requirements, etc. Most peoplecould say that this task\n\n•", null, "### How to Size a Ball Mill -Design Calculator & Formula\n\n08/04/2018· 2) Ball milling a ball mill with a diameter of 2.44 meters, inside new liners, grinding wet in open circuit. When the grinding conditions differ from these specified conditions, efficiency factors (Rowland and Kjos, 1978) have to be used in conjunction with equation 1. In general, therefore, the required mill\n\n•", null, "### Ball Mill Parameter Selection & Calculation Power\n\n30/08/2019· 1 Calculation of ball mill capacity. The production capacity of the ball mill is determined by the amount of material required to be ground, and it must have a certain margin when designing and selecting. There are many factors affecting the production capacity of the ball mill, in addition to the nature of the material (grain size, hardness, density, temperature and humidity), the degree of\n\n•", null, "### The grinding balls bulk weight in fully unloaded mill\n\n11/04/2017· Method with complete grinding media discharge from the mill inner drum. Method without unloading grinding balls. Calculations by the first method are most accurate, but require a lot of labor costs and time. In this article, we will consider the technique for determining the grinding balls bulk weight in fully unloaded mill. This method used in the mills repair (armor plates replacement). The\n\n•", null, "### Ball Mill Calculations Ball Mill\n\nBall Mill Motor Power Draw Sizing And Design Formula. The following equation is used to determine the power that wet grinding overflow ball mills should draw for mills larger than 33 meters 10 feet diameter inside liners the top size of the balls used affects the power drawn by the mill this is called the ball size factor s rod and ball mills by ca rowland and dm kjos allischalmers\n\n•", null, "### Ball Mill Grinding Media Calculation Ball Mill\n\nTo calculate grinding media charge for continuous type ball mill m 0000676 x d2 x l example to calculate grinding media charge for a 180 cm dia x 180 cm long batch type ball mill with duralox 50 mm thick bricks formula to be used is m 0000929 x d2 x l d 180 10 170 cms. Get Details.\n\n•", null, "### The working principle of ball mill Meetyou Carbide\n\n22/05/2019· Therefore, in the rolling ball mill, the grinding efficiency is increased as the ball diameter decreases. It has been proven that the highest grinding efficiency can be obtained with a small ball of .mm diameter. However, the ball diameter is too small to wear too fast, and it is also difficult to discharge due to the small gap of the ball. Therefore, the ball used in wet-grinding the mixture\n\n•", null, "### Ball Mill Speed Calculation Formula For Wet Grinding\n\nBall Mill Grinding Media Calculation. To calculate grinding media charge for continuous type ball mill m 0000676 x d2 x l example to calculate grinding media charge for a 180 cm dia x 180 cm long batch type ball mill with duralox 50 mm thick bricks formula to\n\n•", null, "### Ball mill Wikipedia\n\nA ball mill, a type of grinder, is a cylindrical device used in grinding (or mixing) materials like ores, chemicals, ceramic raw materials and paints.Ball mills rotate around a horizontal axis, partially filled with the material to be ground plus the grinding medium. Different materials are used as media, including ceramic balls, flint pebbles, and stainless steel balls.\n\n•", null, "### bead mill grinding media calculation\n\ncalculate ball mill grinding media in cement . May 19, 2014 calculate ball mill grinding media in cement, Links: goo.gl/DII9h4 More details : goo.gl/N1nfWU (Hot!!!) Zenith is quite Get Price. Energy distribution and particle trajectories in a grinding chamber of Sep 19, 2002 The calculations are restricted to a stationary operation of a stirred ball mill in the absence of" ]
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http://forums.wolfram.com/mathgroup/archive/2007/Oct/msg00598.html
[ "", null, "", null, "", null, "", null, "", null, "", null, "", null, "Re: Recursion limit: a good idea to ignore?\n\n• To: mathgroup at smc.vnet.net\n• Subject: [mg82228] Re: Recursion limit: a good idea to ignore?\n• From: Jon Harrop <jon at ffconsultancy.com>\n• Date: Tue, 16 Oct 2007 03:16:45 -0400 (EDT)\n• References: <feutsm\\$st\\$1@smc.vnet.net>\n\n```Will Robertson wrote:\n> I've written a potentially inefficient algorithm to combine multiple\n> joined polygons into a single one. On large plots I get the warning\n> \"\\$RecursionLimit::reclim: Recursion depth of 256 exceeded.\" and a\n> bunch of errors because of this.\n>\n> Now, should I try and re-write my program (the speed of it is not\n> entirely critical) to avoid recursion or is it \"okay\" to locally\n> remove the recursion limit for such code?\n\nDeep recursion is often a bad idea. In Mathematica code, it is an even worse\nidea that usual.\n\nThe following excerpt demonstrates unbounded recursion that eventually\nresults in a Segmentation Fault (on AMD64 Debian Linux):\n\n\\$ MathKernel\nMathematica 5.1 for Linux x86 (64 bit)\n-- Motif graphics initialized --\n\nIn:= f := 0\n\nIn:= f[n_] := 1+f[n-1]\n\nIn:= f\n\n\\$RecursionLimit::reclim: Recursion depth of 256 exceeded.\n\nOut= 255 + Hold[f[746 - 1]]\n\nIn:= \\$RecursionLimit=Infinity\n\nOut= Infinity\n\nIn:= f\n\nOut= 1000\n\nIn:= f\n\nOut= 10000\n\nIn:= f\nSegmentation fault\n\nSo recursing too deep (applying your function to a large problem) can cause\nthe whole Mathematica kernel to die when it overruns the system stack.\n\nThe term rewriter at the core of Mathematica is a very powerful and\ngeneral-purpose evaluator that requires much bigger stack frames when it\nrecurses compared to most other languages. So Mathematica code is more\nprone to this problem than most other languages.\n\nFor example, the equivalent OCaml code:\n\n\\$ ocamlnat\nObjective Caml version 3.10.0 - native toplevel\n\n# let rec f = function\n| 0 -> 0\n| n -> 1 + f(n - 1);;\nval f : int -> int = <fun>\n# f 1000;;\n- : int = 1000\n# f 10000;;\n- : int = 10000\n# f 100000;;\n- : int = 100000\n# f 1000000;;\nStack overflow during evaluation (looping recursion?).\n#\n\nNote that OCaml recursed ~10x deeper before failing and failed with a nice\ncatchable exception.\n\nIn the language zoo, there are some notable exceptions that do not suffer\nfrom this problem. The SML/NJ compiler is one of the most famous examples.\nBefore compiling code, it rearranges all function calls into continuation\npassing style (CPS) so that they consume heap space rather than the (far\nmore limited) system stack space.\n\nI have not studied Mathematica's implementation of tail recursion in detail\nso I cannot say whether or not performing such a transformation will help\n\nThe solution to your problem is to avoid recursive function calls that\nconsume stack space. This typically entails either rewriting the offending\nfunction in tail recursive form, or rewriting it in imperative form.\n\n--\nDr Jon D Harrop, Flying Frog Consultancy\nhttp://www.ffconsultancy.com/products/?u\n\n```\n\n• Prev by Date: Re: Convert to Text Display has no keyboard short cut\n• Next by Date: Re: Run notebook so graphs are displayed and not their code\n• Previous by thread: Re: Recursion limit: a good idea to ignore?\n• Next by thread: Re: Recursion limit: a good idea to ignore?" ]
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https://pdglive.lbl.gov/DataBlock.action?node=B071RS3&home=BXXX005
[ "# ${{\\boldsymbol N}{(2190)}}$ INELASTIC POLE RESIDUE\n\nThe normalized residue'' is the residue divided by $\\Gamma _{pole}$/2.\n\n# Normalized residue in ${{\\boldsymbol N}}$ ${{\\boldsymbol \\pi}}$ $\\rightarrow$ ${{\\boldsymbol N}{(2190)}}$ $\\rightarrow$ ${{\\boldsymbol N}}{{\\boldsymbol \\sigma}}$ INSPIRE search\n\nMODULUS PHASE ($^\\circ{}$) DOCUMENT ID TECN  COMMENT\n$0.13 \\pm0.05$ 50+-15\n 2015 A\nDPWA Multichannel\nReferences:\n SOKHOYAN 2015A\nEPJ A51 95 High-Statistics Study of the Reaction ${{\\mathit \\gamma}}$ ${{\\mathit p}}$ $\\rightarrow$ ${{\\mathit p}}$2 ${{\\mathit \\pi}^{0}}$" ]
[ null ]
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https://gitlab.lrz.de/IP/elsa/-/commit/81572177f488999fa8040c4ccb61428d2062bcc0
[ "### Removed aliases of Eigen::VectorX, also in tests (as it will not work with older Eigen versions).\n\nparent 0347810b\nPipeline #210946 passed with stages\nin 4 minutes and 41 seconds\nThis diff is collapsed.\n ... ... @@ -27,7 +27,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Constructing DataHandlerMapCPU\", \"\", float, double WHEN(\"constructing with a given vector\") { Eigen::VectorX randVec{size * 2}; Eigen::Matrix randVec{size * 2}; randVec.setRandom(); const DataHandlerCPU dh{randVec}; const auto dhMap = dh.getBlock(size / 3, size / 3); ... ... @@ -43,7 +43,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Constructing DataHandlerMapCPU\", \"\", float, double WHEN(\"copy constructing\") { Eigen::VectorX randVec{size * 2}; Eigen::Matrix randVec{size * 2}; randVec.setRandom(); const DataHandlerCPU dh{randVec}; const auto dhMap = dh.getBlock(size / 3, size / 3); ... ... @@ -67,7 +67,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing equality operator on DataHandlerMapCPU\", \" GIVEN(\"some DataHandlerMapCPU\") { index_t size = 314; Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const DataHandlerCPU realDh{randVec}; const auto dhPtr = realDh.getBlock(0, size); ... ... @@ -153,7 +153,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"copy assigning a DataHandlerCPU through base pointers\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const std::unique_ptr> dh2Ptr = std::make_unique>(randVec); ... ... @@ -183,7 +183,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"copy assigning a partial DataHandlerMapCPU through base pointers\") { const auto dhCopy = dh; Eigen::VectorX randVec{2 * size}; Eigen::Matrix randVec{2 * size}; randVec.setRandom(); const DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(0, size); ... ... @@ -211,7 +211,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"copy assigning a full DataHandlerMapCPU (aka a view) through base pointers\") { const auto dhCopy = dh; Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(0, size); ... ... @@ -238,7 +238,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"\\\"move\\\" assigning a DataHandlerMapCPU through base pointers\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const std::unique_ptr> dh2Ptr = std::make_unique>(randVec); ... ... @@ -268,7 +268,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"\\\"move\\\" assigning a partial DataHandlerMapCPU through base pointers\") { const auto dhCopy = dh; Eigen::VectorX randVec{2 * size}; Eigen::Matrix randVec{2 * size}; randVec.setRandom(); DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(0, size); ... ... @@ -296,7 +296,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"\\\"move\\\" assigning a full DataHandlerMapCPU (aka a view) through base pointers\") { const auto dhCopy = dh; Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(0, size); ... ... @@ -358,7 +358,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"copy assigning a DataHandlerMapCPU through base pointers\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const std::unique_ptr> dh2Ptr = std::make_unique>(randVec); ... ... @@ -386,7 +386,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"copy assigning a partial DataHandlerMapCPU through base pointers\") { const auto dhCopy = dh; Eigen::VectorX randVec{2 * size}; Eigen::Matrix randVec{2 * size}; randVec.setRandom(); const DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(0, size); ... ... @@ -413,7 +413,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"copy assigning a full DataHandlerMapCPU (aka a view) through base pointers\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(0, size); ... ... @@ -440,7 +440,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"\\\"move\\\" assigning a DataHandlerCPU through base pointers\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const std::unique_ptr> dh2Ptr = std::make_unique>(randVec); ... ... @@ -468,7 +468,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"\\\"move\\\" assigning a partial DataHandlerMapCPU through base pointers\") { const auto dhCopy = dh; Eigen::VectorX randVec{2 * size}; Eigen::Matrix randVec{2 * size}; randVec.setRandom(); DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(0, size); ... ... @@ -495,7 +495,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Assigning to DataHandlerMapCPU\", \"\", float, double WHEN(\"\\\"move\\\" assigning a full DataHandlerMapCPU (aka a view) through base pointers\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(0, size); ... ...\n ... ... @@ -44,7 +44,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Constructing DataHandler\", \"\", (DataHandle WHEN(\"constructing with a given vector\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const TestType dh{randVec}; ... ... @@ -77,7 +77,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Constructing DataHandler\", \"\", (DataHandle WHEN(\"move constructing\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); TestType dh{randVec}; const auto dhView = dh.getBlock(0, size); ... ... @@ -104,7 +104,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Testing equality operator on DataHandler\", GIVEN(\"some DataHandler\") { index_t size = 314; Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const TestType dh{randVec}; ... ... @@ -164,7 +164,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Assigning to DataHandlerCPU\", \"\", (DataHan WHEN(\"copy assigning\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(size / 2, size / 3); ... ... @@ -186,7 +186,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Assigning to DataHandlerCPU\", \"\", (DataHan WHEN(\"move assigning\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); DataHandlerCPU dh2{randVec}; const auto dh2View = dh2.getBlock(0, size); ... ... @@ -213,7 +213,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Assigning to DataHandlerCPU\", \"\", (DataHan WHEN(\"copy assigning a DataHandlerCPU through base pointers\") { DataHandler* dhPtr = &dh; Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const auto dh2Ptr = std::make_unique>(randVec); const auto dh2Map = dh2Ptr->getBlock(size / 2, size / 3); ... ... @@ -242,7 +242,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Assigning to DataHandlerCPU\", \"\", (DataHan { DataHandler* dhPtr = &dh; const auto dhCopy = dh; Eigen::VectorX randVec{2 * size}; Eigen::Matrix randVec{2 * size}; randVec.setRandom(); const DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(0, size); ... ... @@ -267,7 +267,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Assigning to DataHandlerCPU\", \"\", (DataHan WHEN(\"copy assigning a full DataHandlerMapCPU (aka a view) through base pointers\") { DataHandler* dhPtr = &dh; Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); const DataHandlerCPU dh2{randVec}; const auto dh2View = dh2.getBlock(0, size); ... ... @@ -297,7 +297,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Assigning to DataHandlerCPU\", \"\", (DataHan WHEN(\"move assigning a DataHandlerCPU through base pointers\") { DataHandler* dhPtr = &dh; Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); std::unique_ptr> dh2Ptr = std::make_unique>(randVec); ... ... @@ -327,7 +327,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Assigning to DataHandlerCPU\", \"\", (DataHan { DataHandler* dhPtr = &dh; const auto dhCopy = dh; Eigen::VectorX randVec{2 * size}; Eigen::Matrix randVec{2 * size}; randVec.setRandom(); DataHandlerCPU dh2{randVec}; const auto dh2Map = dh2.getBlock(0, size); ... ... @@ -351,7 +351,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Assigning to DataHandlerCPU\", \"\", (DataHan WHEN(\"\\\"move\\\" assigning a full DataHandlerMapCPU (aka a view) through base pointers\") { DataHandler* dhPtr = &dh; Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); DataHandlerCPU dh2{randVec}; const auto dh2View = dh2.getBlock(0, size); ... ... @@ -418,7 +418,7 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Testing the reduction operatios of DataHan WHEN(\"putting in some random data\") { Eigen::VectorX randVec{size}; Eigen::Matrix randVec{size}; randVec.setRandom(); TestType dh(randVec); ... ... @@ -429,7 +429,8 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Testing the reduction operatios of DataHan REQUIRE(dh.lInfNorm() == Approx(randVec.array().abs().maxCoeff())); REQUIRE(dh.squaredL2Norm() == Approx(randVec.squaredNorm())); Eigen::VectorX randVec2 = Eigen::VectorX::Random(size); Eigen::Matrix randVec2 = Eigen::Matrix::Random(size); TestType dh2(randVec2); REQUIRE(std::abs(dh.dot(dh2) - randVec.dot(randVec2)) == Approx(0.f)); ... ... @@ -442,7 +443,8 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Testing the reduction operatios of DataHan THEN(\"the dot product expects correctly sized arguments\") { index_t wrongSize = size - 1; Eigen::VectorX randVec2 = Eigen::VectorX::Random(wrongSize); Eigen::Matrix randVec2 = Eigen::Matrix::Random(wrongSize); TestType dh2(randVec2); REQUIRE_THROWS_AS(dh.dot(dh2), std::invalid_argument); ... ... @@ -463,14 +465,16 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Testing the element-wise operations of Dat WHEN(\"putting in some random data\") { Eigen::VectorX randVec = Eigen::VectorX::Random(size); Eigen::Matrix randVec = Eigen::Matrix::Random(size); TestType dh(randVec); THEN(\"the element-wise binary vector operations work as expected\") { TestType oldDh = dh; Eigen::VectorX randVec2 = Eigen::VectorX::Random(size); Eigen::Matrix randVec2 = Eigen::Matrix::Random(size); TestType dh2(randVec2); auto dhMap = dh2.getBlock(0, dh2.getSize()); ... ... @@ -607,7 +611,8 @@ TEMPLATE_PRODUCT_TEST_CASE(\"Scenario: Testing the copy-on-write mechanism\", \"\", GIVEN(\"A random DataContainer\") { Eigen::VectorX randVec = Eigen::VectorX::Random(42); Eigen::Matrix randVec = Eigen::Matrix::Random(42); TestType dh{randVec}; WHEN(\"const manipulating a copy constructed shallow copy\") ... ...\n ... ... @@ -396,7 +396,7 @@ TEMPLATE_TEST_CASE(\"Scenario: BlockLinearOperator applyAdjoint\", \"\", float, doub DataDescriptor rangeDesc(rangeIndex); const index_t n = rangeDesc.getNumberOfCoefficients(); Eigen::VectorX rangeVec(n); Eigen::Matrix rangeVec(n); rangeVec.topRows(n / 3).setConstant(5); rangeVec.middleRows(n / 3, 2 * n / 3).setConstant(7); rangeVec.bottomRows(n / 3).setOnes(); ... ...\n ... ... @@ -27,12 +27,12 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing TikhonovProblem with one regularization te numCoeff << 23, 47; DataDescriptor dd(numCoeff); Eigen::VectorX scaling(dd.getNumberOfCoefficients()); Eigen::Matrix scaling(dd.getNumberOfCoefficients()); scaling.setRandom(); DataContainer dcScaling(dd, scaling); Scaling scaleOp(dd, dcScaling); Eigen::VectorX dataVec(dd.getNumberOfCoefficients()); Eigen::Matrix dataVec(dd.getNumberOfCoefficients()); dataVec.setRandom(); DataContainer dcData(dd, dataVec); ... ... @@ -101,7 +101,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing TikhonovProblem with one regularization te WHEN(\"setting up the TikhonovProblem with x0\") { Eigen::VectorX x0Vec(dd.getNumberOfCoefficients()); Eigen::Matrix x0Vec(dd.getNumberOfCoefficients()); x0Vec.setRandom(); DataContainer dcX0(dd, x0Vec); ... ... @@ -154,12 +154,12 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing TikhonovProblem with several regularizatio numCoeff << 17, 33, 52; DataDescriptor dd(numCoeff); Eigen::VectorX scaling(dd.getNumberOfCoefficients()); Eigen::Matrix scaling(dd.getNumberOfCoefficients()); scaling.setRandom(); DataContainer dcScaling(dd, scaling); Scaling scaleOp(dd, dcScaling); Eigen::VectorX dataVec(dd.getNumberOfCoefficients()); Eigen::Matrix dataVec(dd.getNumberOfCoefficients()); dataVec.setRandom(); DataContainer dcData(dd, dataVec); ... ... @@ -228,7 +228,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing TikhonovProblem with several regularizatio WHEN(\"setting up the TikhonovProblem with x0\") { Eigen::VectorX x0Vec(dd.getNumberOfCoefficients()); Eigen::Matrix x0Vec(dd.getNumberOfCoefficients()); x0Vec.setRandom(); DataContainer dcX0(dd, x0Vec); ... ...\n ... ... @@ -31,7 +31,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing WLSProblem\", \"\", float, double) numCoeff << 7, 13; DataDescriptor dd(numCoeff); Eigen::VectorX bVec(dd.getNumberOfCoefficients()); Eigen::Matrix bVec(dd.getNumberOfCoefficients()); bVec.setRandom(); DataContainer dcB(dd, bVec); ... ... @@ -64,7 +64,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing WLSProblem\", \"\", float, double) WHEN(\"setting up a ls problem with x0\") { Eigen::VectorX x0Vec(dd.getNumberOfCoefficients()); Eigen::Matrix x0Vec(dd.getNumberOfCoefficients()); x0Vec.setRandom(); DataContainer dcX0(dd, x0Vec); ... ... @@ -97,13 +97,13 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing WLSProblem\", \"\", float, double) numCoeff << 7, 13, 17; DataDescriptor dd(numCoeff); Eigen::VectorX bVec(dd.getNumberOfCoefficients()); Eigen::Matrix bVec(dd.getNumberOfCoefficients()); bVec.setRandom(); DataContainer dcB(dd, bVec); Identity idOp(dd); Eigen::VectorX weightsVec(dd.getNumberOfCoefficients()); Eigen::Matrix weightsVec(dd.getNumberOfCoefficients()); weightsVec.setRandom(); DataContainer dcWeights(dd, weightsVec); Scaling scaleOp(dd, dcWeights); ... ... @@ -136,7 +136,7 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing WLSProblem\", \"\", float, double) WHEN(\"setting up a wls problem with x0\") { Eigen::VectorX x0Vec(dd.getNumberOfCoefficients()); Eigen::Matrix x0Vec(dd.getNumberOfCoefficients()); x0Vec.setRandom(); DataContainer dcX0(dd, x0Vec); ... ... @@ -228,7 +228,8 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing WLSProblem\", \"\", float, double) Scaling W1{desc, static_cast(-3.0)}; Eigen::VectorX anisotropicW = Eigen::VectorX::Constant(343, 1); Eigen::Matrix anisotropicW = Eigen::Matrix::Constant(343, 1); anisotropicW = -3.0; Scaling W2{desc, DataContainer(desc, anisotropicW)}; ... ... @@ -275,7 +276,8 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing WLSProblem\", \"\", float, double) Scaling W1{desc, static_cast(-3.0)}; Eigen::VectorX anisotropicW = Eigen::VectorX::Constant(343, 1); Eigen::Matrix anisotropicW = Eigen::Matrix::Constant(343, 1); anisotropicW = -3.0; Scaling W2{desc, DataContainer(desc, anisotropicW)}; ... ... @@ -306,13 +308,15 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing WLSProblem\", \"\", float, double) GIVEN(\"an OptimizationProblem with only (w)ls terms\") { DataDescriptor desc{IndexVector_t::Constant(1, 343)}; Eigen::VectorX vec = Eigen::VectorX::Random(343); Eigen::Matrix vec = Eigen::Matrix::Random(343); DataContainer b{desc, vec}; Scaling A{desc, static_cast(2.0)}; Scaling isoW{desc, static_cast(3.0)}; Eigen::VectorX vecW = Eigen::abs(Eigen::VectorX::Random(343).array()); Eigen::Matrix vecW = Eigen::abs(Eigen::Matrix::Random(343).array()); DataContainer dcW{desc, vecW}; Scaling nonIsoW{desc, dcW}; ... ... @@ -327,14 +331,15 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing WLSProblem\", \"\", float, double) dataTerms.push_back( std::make_unique>(LinearResidual{A, b}, nonIsoW)); Eigen::VectorX regVec = Eigen::VectorX::Random(343); Eigen::Matrix regVec = Eigen::Matrix::Random(343); DataContainer bReg{desc, regVec}; Scaling AReg{desc, static_cast(0.25)}; Scaling isoWReg{desc, static_cast(1.5)}; Eigen::VectorX vecWReg = Eigen::abs(Eigen::VectorX::Random(343).array()); Eigen::Matrix vecWReg = Eigen::abs(Eigen::Matrix::Random(343).array()); DataContainer dcWReg{desc, vecWReg}; Scaling nonIsoWReg{desc, dcWReg}; ... ... @@ -365,7 +370,8 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing WLSProblem\", \"\", float, double) WHEN(std::string(\"The data term \") + descriptions[i] + \". The regularization term \" + descriptions[j]) { Eigen::VectorX xVec = Eigen::VectorX::Random(343); Eigen::Matrix xVec = Eigen::Matrix::Random(343); DataContainer x{desc, xVec}; Problem prob{*dataTerms[i], *regTerms[j], x}; ... ... @@ -388,7 +394,8 @@ TEMPLATE_TEST_CASE(\"Scenario: Testing WLSProblem\", \"\", float, double) GIVEN(\"a TikhonovProblem with L2 regularization\") { DataDescriptor desc{IndexVector_t::Constant(1, 343)}; Eigen::VectorX vec = Eigen::VectorX::Random(343); Eigen::Matrix vec = Eigen::Matrix::Random(343); DataContainer b{desc, vec}; Scaling A{desc, static_cast(2.0)}; ... ...\nMarkdown is supported\n0% or\nYou are about to add 0 people to the discussion. Proceed with caution.\nFinish editing this message first!" ]
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https://metanumbers.com/51998
[ "## 51998\n\n51,998 (fifty-one thousand nine hundred ninety-eight) is an even five-digits composite number following 51997 and preceding 51999. In scientific notation, it is written as 5.1998 × 104. The sum of its digits is 32. It has a total of 2 prime factors and 4 positive divisors. There are 25,998 positive integers (up to 51998) that are relatively prime to 51998.\n\n## Basic properties\n\n• Is Prime? No\n• Number parity Even\n• Number length 5\n• Sum of Digits 32\n• Digital Root 5\n\n## Name\n\nShort name 51 thousand 998 fifty-one thousand nine hundred ninety-eight\n\n## Notation\n\nScientific notation 5.1998 × 104 51.998 × 103\n\n## Prime Factorization of 51998\n\nPrime Factorization 2 × 25999\n\nComposite number\nDistinct Factors Total Factors Radical ω(n) 2 Total number of distinct prime factors Ω(n) 2 Total number of prime factors rad(n) 51998 Product of the distinct prime numbers λ(n) 1 Returns the parity of Ω(n), such that λ(n) = (-1)Ω(n) μ(n) 1 Returns: 1, if n has an even number of prime factors (and is square free) −1, if n has an odd number of prime factors (and is square free) 0, if n has a squared prime factor Λ(n) 0 Returns log(p) if n is a power pk of any prime p (for any k >= 1), else returns 0\n\nThe prime factorization of 51,998 is 2 × 25999. Since it has a total of 2 prime factors, 51,998 is a composite number.\n\n## Divisors of 51998\n\n1, 2, 25999, 51998\n\n4 divisors\n\n Even divisors 2 2 1 1\nTotal Divisors Sum of Divisors Aliquot Sum τ(n) 4 Total number of the positive divisors of n σ(n) 78000 Sum of all the positive divisors of n s(n) 26002 Sum of the proper positive divisors of n A(n) 19500 Returns the sum of divisors (σ(n)) divided by the total number of divisors (τ(n)) G(n) 228.031 Returns the nth root of the product of n divisors H(n) 2.66656 Returns the total number of divisors (τ(n)) divided by the sum of the reciprocal of each divisors\n\nThe number 51,998 can be divided by 4 positive divisors (out of which 2 are even, and 2 are odd). The sum of these divisors (counting 51,998) is 78,000, the average is 19,500.\n\n## Other Arithmetic Functions (n = 51998)\n\n1 φ(n) n\nEuler Totient Carmichael Lambda Prime Pi φ(n) 25998 Total number of positive integers not greater than n that are coprime to n λ(n) 25998 Smallest positive number such that aλ(n) ≡ 1 (mod n) for all a coprime to n π(n) ≈ 5314 Total number of primes less than or equal to n r2(n) 0 The number of ways n can be represented as the sum of 2 squares\n\nThere are 25,998 positive integers (less than 51,998) that are coprime with 51,998. And there are approximately 5,314 prime numbers less than or equal to 51,998.\n\n## Divisibility of 51998\n\n m n mod m 2 3 4 5 6 7 8 9 0 2 2 3 2 2 6 5\n\nThe number 51,998 is divisible by 2.\n\n## Classification of 51998\n\n• Arithmetic\n• Semiprime\n• Deficient\n\n### Expressible via specific sums\n\n• Polite\n• Non-hypotenuse\n\n• Square Free\n\n## Base conversion (51998)\n\nBase System Value\n2 Binary 1100101100011110\n3 Ternary 2122022212\n4 Quaternary 30230132\n5 Quinary 3130443\n6 Senary 1040422\n8 Octal 145436\n10 Decimal 51998\n12 Duodecimal 26112\n20 Vigesimal 69ji\n36 Base36 144e\n\n## Basic calculations (n = 51998)\n\n### Multiplication\n\nn×i\n n×2 103996 155994 207992 259990\n\n### Division\n\nni\n n⁄2 25999 17332.7 12999.5 10399.6\n\n### Exponentiation\n\nni\n n2 2703792004 140591776623992 7310491200894336016 380130921464103684159968\n\n### Nth Root\n\ni√n\n 2√n 228.031 37.3246 15.1007 8.77399\n\n## 51998 as geometric shapes\n\n### Circle\n\n Diameter 103996 326713 8.49421e+09\n\n### Sphere\n\n Volume 5.88909e+14 3.39769e+10 326713\n\n### Square\n\nLength = n\n Perimeter 207992 2.70379e+09 73536.3\n\n### Cube\n\nLength = n\n Surface area 1.62228e+10 1.40592e+14 90063.2\n\n### Equilateral Triangle\n\nLength = n\n Perimeter 155994 1.17078e+09 45031.6\n\n### Triangular Pyramid\n\nLength = n\n Surface area 4.68311e+09 1.65689e+13 42456.2" ]
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https://mathematica.stackexchange.com/questions/219519/joinacross-two-datasets-with-keycollisionfunction/219520#219520
[ "# JoinAcross two datasets with KeyCollisionFunction\n\nI would like to JoinAcross two datasets and use the KeyCollisionFunction. Basically, I would like to keep all rows from the left dataset (hence \"Left\"), add columns from the right dataset, but if some are already present in the left dataset then I want to use updated values from the right dataset (hence KeyCollisionFunction->Right). Example:\n\nJoinAcross[\nDataset[{<|\"a\" -> 1, \"b\" -> 1, \"c\" -> Missing[\"Reason\"]|>, <|\n\"a\" -> 2, \"b\" -> 8, \"c\" -> Missing[\"Reason\"]|>, <|\"a\" -> 2,\n\"b\" -> 2, \"c\" -> 5|>}],\nDataset[{<|\"a\" -> 1, \"c\" -> 1|>, <|\"a\" -> 2, \"c\" -> 6|>}],\n\"a\",\n\"Left\",\nKeyCollisionFunction -> Right\n]\n\n\nThe code above fails with error JoinAcross::invlc: The argument Dataset [<<3>>] is not a list of Associations. It works without the KeyCollisionFunction, but returns column C with values from the left dataset.\n\nThe only solution I found so far is conversion to Associations and then back to a Dataset, but that seems to be quite cumbersome and I am not sure whether it can cause any problems:\n\nDataset@JoinAcross[\nNormal@Dataset[{<|\"a\" -> 1, \"b\" -> 1, \"c\" -> Missing[\"Reason\"]|>, <|\n\"a\" -> 2, \"b\" -> 8, \"c\" -> Missing[\"Reason\"]|>, <|\"a\" -> 2,\n\"b\" -> 2, \"c\" -> 5|>}],\nNormal@Dataset[{<|\"a\" -> 1, \"c\" -> 1|>, <|\"a\" -> 2, \"c\" -> 6|>}],\n\"a\",\n\"Left\",\nKeyCollisionFunction -> Right\n]\n\n\nJoinAcross[" ]
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https://socratic.org/questions/a-hydrogen-atom-is-84-carbon-by-mass-its-relative-molecular-mass-is-100-what-is-
[ "# A hydrogen atom is 84% carbon, by mass. Its relative molecular mass is 100. What is it's empirical formula?\n\nFeb 23, 2017\n\n$\\text{A hydrocarbon is 84% carbon by mass...........}$\n\n#### Explanation:\n\nIf that is what the question said, then the empirical formula of the hydrocarbon is ${C}_{7} {H}_{16}$. How do we know?\n\n$\\left(i\\right)$ We assume a mass of $100 \\cdot g$ of hydrocarbon.\n\n$\\left(i i\\right)$ We work out its atomic composition on the basis of the atomic percentages:\n\n$\\text{Moles of carbon}$ $\\equiv$ $\\frac{84.0 \\cdot g}{12.01 \\cdot g \\cdot m o {l}^{-} 1} = 7 \\cdot m o l$\n\n$\\text{Moles of hydrogen}$ $\\equiv$ $\\frac{16.0 \\cdot g}{1.00794 \\cdot g \\cdot m o {l}^{-} 1} = 16 \\cdot m o l$\n\nHow did I know that there were $16 \\cdot g$ of hydrogen based solely on the GIVEN $\\text{% percentage composition of carbon?}$\n\nOn this basis, the empirical formula, the simplest whole number representing constituent atoms in a species is ${C}_{7} {H}_{16}$. Clearly the molecular formula is identical.\n\nIt is a fact that the $\\text{molecular formula}$ is always a whole number multiple of the $\\text{empirical formula}$.\n\nThus $\\text{molecular formula}$ $=$ $n \\times \\text{empirical formula}$. But we have been given an estimate of molecular mass. So.............\n\nSo $100 \\cdot \\text{amu} \\equiv \\left({C}_{7} {H}_{16}\\right) \\times n$\n\nThus $100 \\cdot \\text{amu\"-=(7xx12.011+16xx1.00794)*\"amu} \\times n$\n\nWe solve for $n$ (how), and find $n \\equiv 1$.\n\n$\\text{And thus the molecular formula} \\equiv {C}_{7} {H}_{16}$." ]
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https://www.physicsforums.com/threads/chemical-reactions-and-stoichiometry.133725/
[ "Chemical reactions and stoichiometry\n\nhello, i hope someone can help me.\ni have a few questions.\n\nThe elements sulfur and chlorine react to give disulfur dichloride according to the equation:\n\nS8 + 4Cl2 --> 4S2Cl2.\nThere are 0.5987 mol S8.\n\na) How many moles of Cl2 are needed for complete reaction?\nb) What mass of S2Cl2 in grams can be produced? The molar mass of thi sis 135.0374 g/mol.\n\nCan someone walk me through it ? Lost!\n\nWell think about it. The reaction shows you the molar ratios for one complete reaction, that should give you the first bit\n\nfor the second bit you do the same thing but find the number of moles of products produced from your amount of reactants then using the molar mass find the mass of product produced\n\nfor a)\nWould it be 0.5987 mols of S8 x 4 moles Cl2/1 mole S8 = 2.3978 mole of Cl2?\n\nfor b)\n2.3948 moles of S2Cl2 x 4S2Cl2/4Cl2 = 2.3948 moles S2Cl2\n\n2.3948 moles of S2Cl2 x 135.0374 g/mol = 323.39g?\n\nI hope this is right, thanks for the reply.\n\nthe calculations look correct\n\nthank you very much!\n\nBorek\nMentor" ]
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https://mathhelpforum.com/threads/construct-a-plane-from-a-coordinate-and-a-unit-vector-and-find-its-x-y-z-angles.255483/
[ "# Construct A Plane From A Coordinate And A Unit Vector And Find Its X,Y,Z Angles\n\n#### SESruss\n\nI'm trying to learn how to figure out the X,Y,Z rotation angles of a plane so that it is normal to a unit vector. I have a coordinate point that is on the plane and a unit vector that is normal to plane to start with. Appreciate any help I can get.\n\n#### HallsofIvy\n\nMHF Helper\nLet (a, b, c) be the point and <P, Q, R> the normal vector. Then any point (x, y, z) that lies on that plane satisfies <x- a, y- b, z- c>.<P, Q, R>= P(x- a)+ Q(y- b)+ R(z- c)= 0. It is easy to get the angles from the equation of the plane.\n\n#### SESruss\n\nSo for a point(0,0,0) and a unit vector (-.0088, .0030, -.9999) the equation would be:\n\n-.0088(x-0) + .003(y-0) -.9999(z-0) = 0\n-.0088x + .003y -.9999z = 0\n\nIn order to find the angles of the plane I tried calculating the angle between two planes. My thought was the axes unit vectors would be:\n\n(1,0,0) for x\n(0,1,0) for y\n(0,0,1) for z\n\nAnd their equations of a plane with the same point (0,0,0):\n\nx=0\ny=0\nz=0\n\nUsing the equation:\n\ncos α = |A1·A2 + B1·B2 + C1·C2|\n√A1² + B1² + C1² √A2² + B2² + C2²\n\nThe results seem wrong so I believe I'm on the wrong track.\n\n#### Attachments\n\n• 3.7 KB Views: 1" ]
[ null ]
{"ft_lang_label":"__label__en","ft_lang_prob":0.88434684,"math_prob":0.99746877,"size":552,"snap":"2019-51-2020-05","text_gpt3_token_len":228,"char_repetition_ratio":0.122262776,"word_repetition_ratio":0.0,"special_character_ratio":0.4692029,"punctuation_ratio":0.18243243,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9997993,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2020-01-17T19:12:48Z\",\"WARC-Record-ID\":\"<urn:uuid:de756b2b-8ff1-4f81-b8d7-d1783f9098b2>\",\"Content-Length\":\"67324\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:71d9d76b-5661-4696-ae1c-2c6b1d249671>\",\"WARC-Concurrent-To\":\"<urn:uuid:4523f383-f293-4562-aeb7-5fdc761cd242>\",\"WARC-IP-Address\":\"138.68.240.87\",\"WARC-Target-URI\":\"https://mathhelpforum.com/threads/construct-a-plane-from-a-coordinate-and-a-unit-vector-and-find-its-x-y-z-angles.255483/\",\"WARC-Payload-Digest\":\"sha1:WIWS4NAA62J2UZLSKLUWGRZLY5JQYKY5\",\"WARC-Block-Digest\":\"sha1:A7KGGM5E5F6SFCCEQ7U5DFTC2KHWAG6B\",\"WARC-Identified-Payload-Type\":\"text/html\",\"warc_filename\":\"/cc_download/warc_2020/CC-MAIN-2020-05/CC-MAIN-2020-05_segments_1579250590107.3_warc_CC-MAIN-20200117180950-20200117204950-00365.warc.gz\"}"}
https://proceedings.neurips.cc/paper_files/paper/2011/hash/16a5cdae362b8d27a1d8f8c7b78b4330-Abstract.html
[ "#### Authors\n\nYoonho Hwang, Hee-kap Ahn\n\n#### Abstract\n\nGiven a set V of n vectors in d-dimensional space, we provide an efficient method for computing quality upper and lower bounds of the Euclidean distances between a pair of the vectors in V . For this purpose, we define a distance measure, called the MS-distance, by using the mean and the standard deviation values of vectors in V . Once we compute the mean and the standard deviation values of vectors in V in O(dn) time, the MS-distance between them provides upper and lower bounds of Euclidean distance between a pair of vectors in V in constant time. Furthermore, these bounds can be refined further such that they converge monotonically to the exact Euclidean distance within d refinement steps. We also provide an analysis on a random sequence of refinement steps which can justify why MS-distance should be refined to provide very tight bounds in a few steps of a typical sequence. The MS-distance can be used to various problems where the Euclidean distance is used to measure the proximity or similarity between objects. We provide experimental results on the nearest and the farthest neighbor searches." ]
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http://www.freepatentsonline.com/y2004/0091046.html
[ "Title:\nMethod and system for video sequence real-time motion compensated temporal upsampling\nKind Code:\nA1\n\nAbstract:\nAn improved method and system for motion-compensated temporal interpolation of video frames during video processing for computer video images, video games and other video effects where changes of an object's form or movement exist. One or more new intermediate video frames are generated and introduced in a processed video sequence in order to produce the appearance of more realistic motion of subjects. Information is obtained from existing video frames and an improved motion estimation algorithm, one that determines the true motion vectors of subject movement, is applied to re-create one or more missing frames. The addition of intermediate video frames further allows for an increase in the viewable frame rate and thereby provides improved frame-by-frame continuity of movement.\n\nInventors:\nAkimoto, Hiroshi (Kawasaki-shi, JP)\nKramer, Sergey S. (Kawasaki-shi, JP)\nApplication Number:\n10/646635\nPublication Date:\n05/13/2004\nFiling Date:\n08/21/2003\nExport Citation:\nPrimary Class:\nOther Classes:\n348/699, 348/700, 348/E7.013, 375/240.01, 375/240.24\nInternational Classes:\nH04N7/01; (IPC1-7): H04N7/12\nView Patent Images:\nRelated US Applications:\n 20070250898 Automatic extraction of secondary video streams October, 2007 Scanlon et al. 20070237260 Digital predistortion transmitter October, 2007 Hori et al. 20030051254 Information presentation device and method March, 2003 Weidenfeller 20090110088 System and Method for Providing a Versatile RF and Analog Front-End for Wireless and Wired Networks April, 2009 Di Giandomenico et al. 20070237221 Adjusting quantization to preserve non-zero AC coefficients October, 2007 Hsu et al. 20090122873 METHOD AND ARRANGEMENT FOR PERSONALIZED VIDEO ENCODING May, 2009 Van Den 20080152250 Low-Complexity Film Grain Simulation Technique June, 2008 Gomila et al. 20060227868 System and method of reduced-temporal-resolution update for video coding and quality control October, 2006 Chen et al. 20040150750 Systems and methods for monitoring visual information August, 2004 Phillips et al. 20040111756 DSL video service with storage June, 2004 Stuckman et al. 20090207912 Reducing key picture popping effects in video August, 2009 Holcomb\n\nPrimary Examiner:\nPHILIPPE, GIMS S\nAttorney, Agent or Firm:\nGARRISON ASSOCIATES (2001 SIXTH AVENUE, SEATTLE, WA, 981212522)\nClaims:\n\n# What is claimed is:\n\n1. A method for temporal interpolation of video frames within a source video sequence having a plurality of video frames sequentially arranged to form an image, each video frame containing image information data arranged within a video image border, said method comprising the steps of: reading two adjacent video frames of said source video sequence; detecting and removing video frame defects; broadening a video frame beyond its original border, whereby interpolation of a new video frame is enabled; splitting said video frame into a plurality of blocks; determining a displacement vector for each block; providing a means of automatic adjustment of system parameters for a given source video sequence; providing a means of data storage, whereby electronic video frame information and operational information is stored and retrieved; providing a means of motion estimation between said video frames; providing a means of detection of scene changes within said source video sequence; selecting among at least one type of interpolation means based on said motion estimation; interpolating and inserting at least one new video frame into said source video sequence; providing a means of evaluating the quality of a new video sequence following interpolation; and reiterating the steps of motion estimation and detection of scene changes until satisfactory quality is achieved.\n\n2. The method of claim 1 further comprising the step of determining true motion vectors that precisely correspond to motion taking place in said source video sequence.\n\n3. The method of claim 1 further comprising the step of forming intermediate video frames, whereby breaks caused by overlapping of said video frame blocks is eliminated.\n\n4. The method of claim 1 further comprising the step of extrapolating and searching beyond said video frame borders.\n\n5. The method of claim 1, whereby insertion of a plurality of video frames between two adjacent video frames is enabled.\n\n6. The method of claim 1, wherein said video sequence is a restored video sequence following compression by a coding method.\n\n7. The method of claim 1, wherein said video sequence is in any video format (e.g., SIF, QCIF, R601).\n\n8. The method of claim 1, whereby processing of said video sequence is performed in real time.\n\n9. The method of claim 1, whereby the step of quality evaluation of said new video frame interpolation is based on evaluation of displacement vector size for given video frame blocks that exceeds an established error threshold.\n\n10. The method of claim 1 further comprising the step of providing a means for automatic adjustment of video processing parameters to accommodate the character of motion in each source video sequence, whereby no preliminary adjustment of parameters for a particular video sequence is required.\n\n11. The method of claim 1 whereby the steps are carried out by computer software.\n\n12. The method of claim 1 whereby the steps are carried out by hardware.\n\n13. The method of claim 1 whereby the steps are carried out by any combination of software and hardware.\n\nDescription:\n\n# CROSS-REFERENCE TO RELATED APPLICATION\n\n This application claims benefit of U.S. Provisional Application Serial No. 60/405,414 entitled Method and System for Video Sequence Real-Time Motion Compensated Temporal Upsampling, filed Aug. 22, 2002.\n\n# FIELD OF INVENTION\n\n The invention relates to an improved method and system for video processing. In particular, this invention describes a method and system for motion-compensated temporal interpolation of video frames in processing video sequences such as computer video images, video games and other video effects where changes of an object's form or movement exist.\n\n# BACKGROUND OF THE INVENTION\n\n This invention relates to encoding and decoding of complex video image information including motion components that may be found in multi-media applications such as video-conferencing, video-phone, and video games. In order to be able to transfer complex video information from one machine to another, it is often desirable or even necessary to employ video compression techniques. One significant approach to achieving a high compression ratio is to remove the temporal and spatial redundancy which is present in a video sequence. To remove spatial redundancy, an image can be divided into disjoint blocks of equal size. These blocks are then subjected to a transformation (e.g., Discrete Cosine Transformation or DCT), which de-correlates the data so that it is represented as discrete frequency components.\n\n Motion film and video provide a sequence of still pictures that creates the visual illusion of moving images. Providing that the pictures are acquired and displayed in an appropriate manner, the illusion can be very convincing. In modem television systems it is often necessary to process picture sequences from film or television cameras. Processing that changes the picture rate reveals the illusory nature of television. A typical example is the conversion between European and American television standards which have picture rates of 50 and 60 Hz respectively. Conversion between these standards requires the interpolation of new pictures intermediate in time between the input pictures. Many texts on signal processing described the interpolation of intermediate samples, for a properly sampled signal using linear filtering. Unfortunately, linear filtering techniques applied to television standards conversion may fail to work. Fast moving images can result in blurring or multiple images when television standards are converted using linear filtering because video signals are under-sampled.\n\n The benefits of motion compensation as a way of overcoming the problems of processing moving images are widely recognized in the prior art. Motion compensation attempts to process moving images in the same way as the human visual system. The human visual system is able to move the eyes to track moving objects, thereby keeping their image stationary on the retina. Motion compensation in video image processing attempts to work in the same way. Corresponding points on moving objects are treated as stationary thus avoiding the problems of under sampling. In order to do this, an assumption is made that the image consists of linearly moving rigid objects (sometimes slightly less restrictive assumptions can be made). In order to apply motion-compensated processing it is necessary to track the motion of the moving objects in an image. Many techniques are available to estimate the motion present in image sequences.\n\n Motion compensation has been demonstrated to provide improvement in the quality of processed pictures. The artifacts of standard conversion using linear filtering, i.e., blurring and multiple-imaging, can be completely eliminated. Motion compensation, however, can only work when the underlying assumptions of a given subject are valid. If a subject image does not consist of linearly moving rigid objects, the motion estimation and compensation system is unable to reliably track motion resulting in random motion vectors. When a motion estimation system fails, the processed pictures can contain subjectively objectionable switching artifacts. Such artifacts can be significantly worse than the linear standards conversion artifacts which motion compensation is intended to avoid.\n\n Motion vectors are used in a broad range of video signal applications, such as coding, noise reduction, and scan rate or frame-rate conversion. Some of these applications, particularly frame rate conversion, requires estimation of the “true-motion” of the objects within a video sequence. Several algorithms have been previously proposed to achieve true-motion estimation. Algorithms have also been proposed that seek to provide motion estimation at a low complexity level. Pel-recursive algorithms that generally provide sub-pixel accuracy, and a number of block-matching algorithms have been reported that yield highly accurate motion vectors.\n\n# SUMMARY OF THE INVENTION\n\n The present invention provides a video signal processing method and system for reconstruction of a previously compressed video sequence by creation and insertion of supplementary video frames into an upsampled video sequence. Movement characteristics of an object within a video frame are evaluated and used to estimate the object's position in the supplementary video frames.\n\n Motion vectors are determined for object motion within video frames using the object's position information from the two neighboring frames, which are then used for the filling in new frames. After the creation of the new frame, a border is re-established along the edge of the frame.\n\n The present invention additionally provides a means of removal of the dark bands present along the edges of video frames by extrapolating a video frame—inserting blocks of video information beyond the borders of the real frame. This broadening of frames beyond the original frame borders also provides for better interpolation of new frames.\n\n# BRIEF DESCRIPTION OF THE DRAWINGS\n\n FIG. 1 illustrates the insertion of additional frames into the video sequence.\n\n FIG. 2 is a block diagram representation of the operation of the temporal interpolation algorithm on a video sequence.\n\n FIG. 3 illustrates a example of the movement of an object in a video film.\n\n FIG. 4(a) shows an example of a dark band along the edge of a video frame.\n\n FIG. 4(b) illustrates the filling in of the dark band along the edge of a video frame with pixels from within the frame.\n\n FIG. 5 illustrates an extrapolation of a video frame beyond its original borders.\n\n FIG. 6 illustrates the application of the present invention on an example of frames from a “Foreman” video sequence.\n\n FIG. 7 illustrates the application of the present invention on an example of frames from a “Football” video sequence.\n\n FIG. 8 illustrates details of the broadening of blocks in a frame.\n\n FIG. 9 shows the order of passing of blocks within a video frame.\n\n FIG. 10 shows the structure of a block search within a video frame.\n\n# DETAILED DESCRIPTION OF THE INVENTION\n\n The following definitions apply to the present invention:\n\n Temporal interpolation: a process of generation and insertion of one or more video frames into a source video sequence.\n\n Interpolated frame: a video frame generated by the present invention method that is inserted between the existing frames of a video sequence.\n\n Object motion: the change in location of an object over the course of a video sequence.\n\n Object deformation: the change in form of an object over the course of a video sequence.\n\n Motion vector: the difference between the coordinates of the block in the preceding frame and the coordinates of the corresponding block found in the next frame.\n\n Motion estimation: the process of locating motion vectors for all blocks in the preceding frame.\n\n Adjacent frames: sequential video frames with the numbers N and N+1 in a video sequence, as shown in FIG. 1.\n\n Fps: the number of frames per second for a video sequence. The present invention permits a variation of the value of fps by a whole number for the source video sequence.\n\n Extrapolation: the broadening of a video frame beyond its original borders by a specified number of pixels.\n\n Application of the present invention algorithm creates an arbitrary number of frames between two adjacent frames of a video sequence. Depending on the dynamic of the subject of a video sequence, the need may arise for the insertion of one frame or more video frames between two adjacent frames. The insertion of frames helps to produce smoothness of the movements of objects in the video film. There are different types of dynamics for development of the subject of a video sequence. In drawings 6 and 7, frames from the video sequences “Foreman” and “Football” are provided as examples of video sequences with a high dynamic of subject development; the objects exhibit a high degree of movement for several frames, and a significant change of scene or subject takes place.\n\n FIG. 6 shows the addition of several video frames between adjacent frames in the “Foreman” video sequence. Interpolated frames E and F were inserted between original frames D and G in order to smooth the transition from one scene to another and to make the movements of the subject more realistic.\n\n Video frames from the “Football” video sequence shown in FIG. 7 presents a somewhat different situation. There is also rapid movement of objects here, but the background scene does not change. A video frame is added in this case in order to make the movement of the objects (the falling of football players with a ball) more natural. The algorithm provides a means for:\n\n 2. Identifying and storing the dark band along the edge of the frame (if present), and removing the band from the frame as illustrated in FIG. 4(b).\n\n 3. Expanding each video frame beyond its original borders by 8 pixels from each side of the frame, as shown in FIG. 5, applying the following extrapolation procedure.\n\n 4. Splitting the next video frame into quadratic blocks, and finding the corresponding block within the previous frame in order to obtain a displacement vector for each block.\n\n 5. Decreasing the length of the displacement vector by k/n times, where n is the coefficient for increasing fps, and 0<k<n is a number relative to the previously inserted frame.\n\n 6. Determining the presence of a scene change.\n\n 7. If there was no scene change, then the interpolated frame is filled using the calculated movement vectors.\n\n 8. If there was a scene change, then the pixels of the interpolating frame are filled using values for the type of interpolation applied.\n\n 9. Restoring the dark band along the edges of each video frame if a dark band was originally present.\n\n 10. Creating the interpolated video frame and inserting it into the upsampled video sequence as the video output.\n\n In some video sequences there is a dark band along the edges of the video frames as illustrated in FIGS. 4(a) and 4(b). The following is a description of the procedure for detection and removal of defects (dark bands) along the edges of a video frame.\n\n The dark band consist of bands which we perceive as black and bands, the brightness of which differs significantly from the brightness of the primary part of the frame. The average brightness value of the black band in the YUV color system does not exceed 20, and the brightness differential for the darkened band is not less than 35. The difference between the average brightness of the dark band and the average brightness of the subsequent band of pixels, as a rule, is more than 35. It is necessary to remove these bands in order to allow extrapolation of a video frame beyond its original borders.\n\n The algorithm for detection and removal of video frame defects (dark bands) along the edges of the frame provides the following steps:\n\n 1. Calculating the average brightness values for m bands of a width of 1 pixel. 1$Average Y(x)=(∑y=0height-1Y(x,y))/height$\n\n for vertical bands 2$Average Y(y)=(∑x=0width-1Y(x,y))/width$\n\n for horizontal bands.\n\n 2. A band is considered dark if the following conditions are fulfilled: if\n\nAverageY[i]<20\n\nor\n\n(AverageY[i+1]−AverageY[i])>35.\n\n 3. The brightness values for pixels of a dark band are replaced by values for the pixels from the first non-dark band encountered, as shown in FIG. 4(b).\n\n An extrapolation algorithm is applied for predicting the brightness of pixels outside the borders of a frame of a video image following the procedure for removal of the dark band (if present.) An extrapolation a filter of length 4 is used. 1\n\n Input data: Image Input image (frame) Height Frame height Width Frame width Number_of_points Number of interpolation points I1, I2, I3, I4 Reference points k1, k2, k3, k4 Filter coefficients I1 Extrapolated point Output data: Extrapolated_Image Extrapolated image\n\n The extrapolation algorithm performs the following steps:\n\n 1. The image is transformed into YUV video format, and the algorithm is applied to each layer.\n\n 2. Four reference points are established for the filter. New reference points are then determined in relationship to these four points.\n\n 3. If the base points I1, I2, I3, I4 are established, then I0 is a point that will be extrapolated.\n\n 4. Let k1, k2, k3, k4 be the filter coefficients. The corresponding extrapolated point is then calculated by the following method: 3$I0=∑i=14Ii*ki∑i=14ki.$\n\n 5. Selection of an appropriate filter coefficient for use in the algorithm is critical. The largest coefficient k1 is selected, and is increased to the brightness value of the outermost pixel of the frame, as shown in FIG. 5.\n\n Broadening blocks within a video frame requires selection of the most appropriate (“best”) block for the given video frame. It is important to apply a metric that allows the detection a compatibility of blocks and that does not require the use of vast computer resources. The following metric is used in the present invention:\n\n where 4$SAD(Ix0,y0,Ix1,y1,block_size_x,block_size_y)=∑i=0block_size_y∑i=0block_size_xIx0+i,y0+j-I^x1+i,y1+j$\n\n where Ix0, y0 and Îx1, y1 are comparable blocks from frames I and I. The coordinates for the blocks are (x0, y0) and (x1,y1), respectively. The given coordinates are coordinates for the left upper corner of the block.\n\n block_size_x and block_size_y—are the measurements of the blocks.\n\n Y-luminance, U, V-chrominance.\n\n The following describes the procedure for splitting the frame into quadratic blocks. Prior to execution of the search, the next frame is covered with non-overlapping quadratic blocks of size N×N, where N is selected depending on the size of the frame: 2\n\n Frame size in pixels Frame format (Height) N QCIF 144 4 SIF 240 8 BT 480 16 R601 480 16\n\n During a search of the vectors a comparison is conducted for 16×16 blocks obtained from N×N blocks by means of adding to them a layer of surrounding pixels, as shown in FIG. 8. Depending on the frame sizes the following conditions are developed:\n\n N=4;\n\n if (height>200)\n\n {N=8} if (height>400); and\n\n {N=16}\n\n Both frame height and width are verified. Thus, depending on the format of the source video sequence, system parameters are automatically adjusted.\n\n Calculation of the coordinates of the lower left corner of a 16×16 block is provided as follows:\n\n Input Data: 3\n\n Input data: x, y Coordinates for the lower left corner of a N × N block x1, y2 Coordinates for the lower left corner of a 16 × 16 block width Frame width height Frame height\n\n The algorithm for calculation of the coordinates of the lower left corner of a 16×16 block:\n\n 1. x1=x−(16−N)/2;\n\n 2. y1=y−(16−N)/2;\n\n Verification is made of the following conditions:\n\n if x1<0=>x1=x;\n\n if y1<0=>y1=y;\n\n if x1+(16−N)>width−1=>x1=x−(16−N);\n\n if y1+(16−N)>height−1=>y1=y−(16−N);\n\n where x, y are coordinates for block N×N;\n\n where x1, y2 are coordinates for block 16×16;\n\n width—frame width;\n\n height—frame height.\n\n The following sequence for searching blocks for displacement vectors has been found to be optimal.\n\n 1. Beginning with a central block, an outward-spiraling by-pass of the blocks is made, as illustrated in FIG. 9.\n\n 2. The displacement vector for the remaining blocks is then calculated.\n\n An adaptive accelerated search for the displacement vectors of blocks is applied separately for each block:\n\n The algorithm for the search procedure is intended to search zones, use a vector-predictor from adjacent blocks and the preceding frame, establishing criteria for a falf-pause, and provide adaptive selection of threshholds.\n\n An aggregate of thresholds (T1, T2, T3) is used to control the course of the search operation as follows: 4\n\n Threshold Threshold designation T1 Determines the continuation or completion of the search T2 Determines if the number of subsequent examined/verified search zones is limited T3 Determines the completion of the search according to the half-pause criterion\n\n The following variables are also applied to the search operation: 5\n\n zsize The parameter for the half-pause criterion, gives the maximum number of search zones during the scanning of which a mistake in a located vector may not be corrected; znum The maximum number of search zones around the selected center (0, 0); pznum The maximum number of search zones around the main vector-predictor (median); MinZone The current number of zones in which a vector was found with minimal error; Found An indication of the fact that all vector-predictors are equal to each other, different from (0, 0), and correspond to the vector of the block positioned in the previous frame in the current position; Last An indicator of the final iteration; MinSAD The current located minimal error;\n\n The following are the initial values for the thresholds T1, T2 and T3 and the associated variables described above: 6\n\n T1 T2 T3 zsize znum pznum MinZone Found Last 4 * 256 6 * 256 14 * 256 3 4 4 0 false false\n\n The following are the algorithm steps for the search procedure:\n\n Step 1\n\n Building a rhomboid-shaped structure containing a selected number of search zones, as shown in FIG. 10.\n\n Step 2\n\n Selecting and storing adjacent blocks for the processed block, in which displacement vectors have already been found are selected and stored. The selected blocks are sorted according to the increment of errors (SAD). Blocks in which there is an error twice as large as the smallest error are eliminated. An aggregate of predictors for the search is thus created—aggregate A containing the vectors for these blocks.\n\n Step 3\n\n Calculating the threshold values. Threshold T1 is selected as the minimum from the error values (SAD) for the adjacent blocks, selected in step 2, and the error values for the block from the preceding frame in the current position of the splitting array. T2=T1+the dimension of the block in pixels. The algorithm parameters are thus initialized.\n\n Step 4\n\n Calculating the median of the vectors for the x and y coordinates for the selected adjacent blocks. If the values of all vectors in the aggregate of predictors A:\n\n 1) coincide and are different from (0,i) and (i,0), where i is a whole number; and\n\n 2) coincide with the value of the median,\n\n then the search will be conducted only in the first zone (pznum=1) and the “Found” character is specified. If only one of these conditions is fulfilled, then the search will be conducted only in the first two zones (pznum=2). The predictor forecasts the character of the movement in the given place in the interpolated frame. Due to the determination of the predictor, the system is adjusted to accommodate the determined character of movement in the given adjacent frames of the video sequence, used for the interpolation of new frames.\n\n Step 5\n\n Calculating the error (SAD) for the main predictor. If the main predictor coincides with the vector of the block positioned in the preceding frame in the same position as the main predictor, but in this case the predictor error (SAD) is less than the error for the indicated vector, or the error according to the value is less than the dimensions of the block, then skipping to the final step.\n\n Step 6\n\n Calculating the error (SAD) for all vectors in the aggregate A, and selecting with the current value the vector with the minimal error MinSAD.\n\n Step 7\n\n Verifying the condition MinSAD<T1. If the condition is satisfied, the process skips to the final step. If the current vector coincides with the vector for the block located in the previous frame in the same position, but the current minimal error in this case is less, the process also skips to the final step.\n\n Step 8\n\n If T1<Min SAD<T2, then we in fact establish the character “Last”.\n\n Step 9\n\n Constructing a given number of zones around the main predictor. Then, in the subsequent steps, processing in sequence each of the constructed zones beginning from the center.\n\n Step 10\n\n Calculating the error (SAD) for all points from each of the zones.\n\n Step 11\n\n Verifying that the result was improved within the framework of the given number of the nearest already-processed zones zsize. If the improvements were madc previously (the current zone—MinZone>size), and MinSAD<T3, then the process skips to the final step.\n\n Step 12\n\n If there is no minimal error value in the second search zone, and MinSAD<T3, then the process skips to the final step.\n\n Step 13\n\n If MinSAD<T1 or in fact is the character “Last”, then the process skips to the final step.\n\n Step 14\n\n Returning to step 8 as often as required.\n\n Step 15\n\n Shifting to processing the next furthest from the center zone, and processing to step 10.\n\n Step 16\n\n Steps 9 to 15 are repeated, but this time the center zone moves to the point (0,0).\n\n Step 17\n\n Steps 9 to 14 are repeated, but this time the center zone moves to the point, the coordinates of which are given by the best vector found up to that moment.\n\n Step 18\n\n Obtaining the optimal vector of movement for the given block, having a minimal error MinSAD.\n\n Description of the procedure for calculating the values of pixels with non-integer coordinates.\n\n The given procedure is carried out in the event that the block has a displacement vector with non-integer coordinates, that is it is displaced by a fractional number of pixels. Thus, the necessity for calculation of the values of the intermediate pixels for blocks located in the original adjacent frames of the video sequence arises. The values of the intermediate pixels are calculated with the help of a bilinear interpolation formula:\n\nI(x+dx, y+dy)=I(x, y)·(1−dx)·(1−dy)+I(x+1, ydx·(1−dy)+I(x, y+1)·(1−dx)·dy+I(x+1, y+1)·dx·dy;\n\n Where I(x, y)—is the value of the pixel,\n\n x, y—are the coordinates of the pixel.\n\n The obtained result is rounded up to the nearest whole number.\n\n The following are the algorithm steps for filling an interpolated frame on the basis of the calculated vectors of movement.\n\n 1. Superimposing on an interpolating frame the same array N×N as in the subsequent frame.\n\n 2. For each block, applying the displacement vector obtained from the adaptive accelerated search procedure.\n\n Filling in the image pixels with the following values:\n\nIinterp(x, y, vx, vy, k, n)=((n−kIprev(x+k·vx/n, y+k·vy/n)+k·Inext(x−(n−kvx/n, y−(n−kvy/n))/n, where\n\n n is the coefficient for increasing fps;\n\n 0<k<n is the number relative to the preceding, inserted frame;\n\n I_interp are the points of the interpolated frame;\n\n I_prev—are the points of the preceding frame;\n\n I_next—are the points of the subsequent frame;\n\n x,y—are the coordinates for the pixel in the interpolated frame;\n\n vx, vy—are the coordinates for the displacement vector, as determined by the adaptive accelerated search procedure.\n\n Applying a bilinear interpolation for points with a fractional vector of displacement.\n\n A video sequence can be seen as an aggregate of scenes. A scene is a part of a video sequence in which it is possible to form each subsequent frame on the basis of the previous frame with the assistance of a motion estimation procedure. If the presence of a change of scene is detected, then either the preceding or subsequent frame is duplicated.\n\n The following algorithm variables are used for determining the presence of a scene change: 7\n\n cnt_bad_blk The number of vectors with an error less than some threshold block_cnt The number of blocks into which the frame is split err[i] The error for the I block scale The coefficient for increasing fps pos The number for the inserted frame relative to the preceding frame, with which the reference is begun with zero.\n\n The following is the algorithm steps are used for determining the presence of a scene change: 8\n\n If the variable scene_is_changed has a positive value, then a change of scene is considered to have taken place. Either the preceding or the subsequent frame is then inserted as the interpolating frame, and is followed by the following: 9\n\n if(scene_is_changed) { if(pos\n\n A decision must be made as to the type of interpolation for a given segment of the video sequence. An interpolation type is implemented using motion vectors (motion compensated interpolation), or a pixel by pixel interpolation.\n\n Pixel-by-pixel interpolation is carried out using the following formula:\n\nIinterp(x, y, k, n)=((n−kIprev(x, y)+k·Inext(x, y))/n,\n\n where I_interp is the value of the interpolating pixel;\n\n I_prev—is the value of the pixel in the preceding frame;\n\n I_next—is the value of the pixel in the next frame;\n\n x, y—are the pixel coordinates;\n\n n—is the coefficient for increasing fps;\n\n k—is the number relating to the preceding, inserted frame, 0<k<n.\n\n The following variables are used in the algorithm for determining the presence of a scene change: 10\n\n cnt The number of vectors with an error less than some threshold average_v_x, Coordinates of the average vector average_v_y block_cnt The number of blocks into which a frame is split vx[i], vy[i] Coordinates of the displacement vector for block i. err[i] The error for block i max_v_x The maximum value for the x-components of a vector with an error greater than some threshold max_v_y The maximum value for the y-components of a vector with an error greater than some threshold maxv The larger of the two values max_v_x and max_v_y\n\n The average vector is calculated using the following algorithm: 11\n\n cnt=0 average_v_x=0 average_v_y=0 for (int i=0; i\n\n The following algorithm is used to determine the type of interpolation to be applied: 12\n\n max_v_y=0 for (int i=0; i15* 256[i]) { if(abs(vy[i])>max_v_y) { max_v_y=abs(vy [i]); } if(abs(vx [i])>max_v_x) { max_v_x=abs(vx [i]); } } } max_v_x=abs(max_v_x−abs(average_v_x)); max_v_y=abs(max_v·y−abs(average_v_y)); maxv=max(max_v_x, max_v_y)) not_use_MCI = (maxv>(Segm_img>height/32)&&abs(average_v·_x) <1&&abs(average_v_y)<1)\n\n notuseMCI=(maxv>(Segmimg>height/32)&&abs(averagev.x)<1&&abs(averagevy)<1)\n\n If the value of the variable not_use_MCI is positive, then the pixel-by-pixel interpolation described above is applied. Otherwise, interpolation using vectors of movement (motion compensated interpolation) will be applied.\n\n# INDUSTRIAL APPLICABILITY\n\n The present invention has applicability to the field of video processing, and more particularly to a method and system for temporal interpolation of video sequences in an electronic processing, transmission or storage medium.\n\n In compliance with the statute, the invention has been described in language more or less specific as to algorythm steps in processing video sequences. It is to be understood, however, that the invention is not limited to the specific means, features or algorythms shown or described, since the means, features and algorythms shown or described comprise preferred ways of putting the invention into effect.\n\n Additionally, while this invention is described in terms of being used for temporal interpolation of video sequences, it will be readily apparent to those skilled in the art that the invention can be adapted to other uses for other forms of digital electronic signal processing as well, and therefore the invention should not be construed as being limited to processing video sequences. The invention is, therefore, claimed in any of its forms or modifications within the legitimate and valid scope of the appended claims, appropriately interpreted in accordance with the doctrine of equivalents." ]
[ null ]
{"ft_lang_label":"__label__en","ft_lang_prob":0.8719621,"math_prob":0.9272386,"size":26980,"snap":"2019-51-2020-05","text_gpt3_token_len":6086,"char_repetition_ratio":0.16573992,"word_repetition_ratio":0.053026747,"special_character_ratio":0.24395849,"punctuation_ratio":0.085454166,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9538229,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-12-16T13:53:55Z\",\"WARC-Record-ID\":\"<urn:uuid:e67265db-8fb5-4c2c-85aa-48d60e6c3788>\",\"Content-Length\":\"84963\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:382d0f3c-7cd4-4943-bfe8-daa2df57c35d>\",\"WARC-Concurrent-To\":\"<urn:uuid:e24613a4-213e-407d-b095-ca23a99b9e39>\",\"WARC-IP-Address\":\"144.202.252.20\",\"WARC-Target-URI\":\"http://www.freepatentsonline.com/y2004/0091046.html\",\"WARC-Payload-Digest\":\"sha1:JJGSG3BJ3JU3WCUDRXJNLOMABHG4FR3J\",\"WARC-Block-Digest\":\"sha1:UFIDK6H7JLOJNO2MYDDWMEWY5ZCRH74P\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-51/CC-MAIN-2019-51_segments_1575540565544.86_warc_CC-MAIN-20191216121204-20191216145204-00293.warc.gz\"}"}
https://java.meritcampus.com/core-java-topics/modulus-operator-in-java
[ "...\n`\n\nSiva Nookala - 18 Feb 2019\n Java has one important arithmetical operator you may not be familiar with, %, also known as the modulus operator.The modulus operator, `%` returns the remainder of a division operation. e.g., `15 % 4 = 3, 7 % 3 = 1, 5 % 5 = 0`", null, "As shown above, when we divide `17` (dividend) with `3` (divisor) then the quotient is `5` and the modulus (or remainder) is `2`. The Modulus Operator allows operation for both floating point types as well as integer types. Find ModulusCODE Try it Online`class FindModulus{    public static void main(String arg[])    {        int x = 32;        double y = 36.75;                System.out.println(\"x mod 10 = \" + x % 10);        System.out.println(\"y mod 10 = \" + y % 10);        }}`OUTPUTx mod 10 = 2y mod 10 = 6.75DESCRIPTIONWhen 32 is divided by 10, the quotient will be 3 and the modulus (or remainder) will be 2. Similarly when 36.75 is divided by 10, the quotient will again be 3, where as the modulus will be 6.75. 2-min video about modulus operator\n0\nWrong\nScore more than 2 points" ]
[ null, "http://java.meritcampus.com/core-java-topics/images/Modulus-Operator-In-Java.png", null ]
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https://kanoki.org/2019/07/24/how-to-create-pandas-pivot-table/
[ "# How to create Pandas Pivot Table\n\nPivot table lets you calculate, summarize and aggregate your data. MS Excel has this feature built-in and provides an elegant way to create the pivot table from data. its a powerful tool that allows you to aggregate the data with calculations such as Sum, Count, Average, Max, and Min. and also configure the rows and columns for the pivot table and apply any filters and sort orders to the data once pivot table has been created.Coming to Python, Pandas has a feature to build Pivot table and Crosstab using the Dataframe or list of Data. In this article we will see how to use Pivot feature and what are the various options available to build a meaningful Pivot and summarize your data using pandas.\n\nLets create a dataframe of different ecommerce site and their monthly sales in different Category\n\n``````import pandas as pd\nimport numpy as np\ndf = pd.DataFrame({'site' : ['walmart', 'amazon', 'alibaba', 'flipkart','alibaba','flipkart','walmart', 'amazon', 'alibaba', 'flipkart'],\n'Product_Category' : ['Kitchen', 'Home-Decor', 'Gardening', 'Health', 'Beauty', 'Garments',\n'Gardening', 'Health', 'Beauty', 'Garments'] ,\n'fitness band','sunscreen','pyjamas'],\n'Sales' : [2000,3000,4000,5000,6000,9000,3000,2500,1020,950]})\ndf\n``````", null, "There are 4 sites and 6 different product category. We will now use this data to create the Pivot table. Before using the pandas pivot table feature we have to ensure the dataframe is created if your original data is stored in a csv or you are pulling it from the database. Read this post to find out how data can be imported and merged into a dataframe using pandas.\n\n## Create Pivot Table\n\n``````df.pivot_table( index=['Product_Category', 'Product'], values=['Sales'], columns=['site'])\n``````", null, "Important thing to note here is that attribute index is the list of rows in data and columns is the columns for the rows for which you want to see the Sales data i.e. values. So here we want to see the Product Category and Product and their sales data for each of the sites as column.\n\nBy default the aggreggate function is mean. So lets check how mean is calculated here:\n\nTake the first row Product Category: Beauty and Product: sunscreen and for site alibaba there are two rows in the above dataframe i.e. index 4 and 8. Now calculate the average of the sales data in these two rows (6000+1020)/2 = 7020/2 = 3510\n\nand that is the value under alibaba for the first row i.e. Beauty and sunscreen\n\nSimilarly for second row i.e. Product Category: Gardening and Product: digging spade there are two rows at index 2 and 6. However they both belong to unique site i.e. alibaba and walmart so their individual values are 4000 and 3000.\n\nAnd for the third row Product Category: Garments and Product: pyjamas, there are two rows at index 5 and 9 and both belongs to site flipkart and their respective sales value are 9000 and 950 and average value will be 9950/2 = 4975 and that’s the value for third row under flipkart\n\nHope you understand how the aggregate function works and by default mean is calculated when creating a Pivot table\n\n## Pandas Pivot Table Aggfunc\n\nLets see another attribute aggfunc where you can add one or list of functions so we have seen if you dont mention this param explicitly then default func is mean. Now lets check another aggfunc i.e. sum,min,max,count etc.\n\n``````df.pivot_table( index=['Product_Category', 'Product'], values=['Sales'], columns=['site'], aggfunc=min)\n``````", null, "its trying to find a minimum value of the group. For example: first row i.e. Product_Category: Beauty and Product: sunscreen the minimum sales value between the two rows in the dataframe at index 4 and 8 is 1020\n\nSimilarly for row #3 the sales value for two rows Product_Category: Garments and Product: pyjamas in the dataframe is 9000 and 950 and the minimum value out of two is 950, which is the value for the row#3 under flipkart\n\n## List of Aggfunc\n\nLets add two aggfunc in a list i.e. min and sum\n\n``````df.pivot_table( index=['Product_Category', 'Product'], values=['Sales'], columns=['site'], aggfunc=[min,sum])\n``````", null, "You can see here the two tables one is min and other is sum, enclosed in red box. Ive already explained the min table so lets understand how sum is calculated.\n\nFor row#1 Product_Category: Beauty and Product: sunscreen the two values in the above dataframe are 6000 and 1020 and their sum is 7020 which is the value under alibaba for the first row\n\n## Pivot Tables Margins\n\nNow there is another useful param in the pivot table and that is known as margin which is used for summarizing the row and column values. if margin is set to True then a row and column All is added and the aggfunc i.e. min will be apllied on Margin column All also\n\n``````df.pivot_table( index=['Product_Category', 'Product'], values=['Sales'], columns=['site'], aggfunc=[min], margins=True)\n``````", null, "For example: Row#2 there are two values 4000 and 3000. therefore the All column contains 3000 which is the min value out of two. Similarly for column Sales - alibaba there are two values 6000 and 4000 and therefore the min value out of two 4000 is value in All column\n\n## Pivot Table Margins Name\n\nYou can also rename the All column using another params which is margins_name. So here Ive replaced both the column names as Sub-total\n\n``````df.pivot_table( index=['Product_Category', 'Product'], values=['Sales'], columns=['site'], aggfunc=[min]\n, margins=True, margins_name = 'Sub-total')\n``````", null, "## Plot Pivot Tables\n\n``````%matplotlib inline\ndf.pivot_table( index=['Product_Category', 'Product'], values=['Sales'], columns=['site'], aggfunc=[min], margins=True, margins_name = 'Sub-total') .plot(kind='bar')\n``````", null, "## Export Pivot Table to Excel\n\nSo you have a nice looking Pivot table and you want to export this to an excel. Use Pandas to_csv function to export the pivot table or crosstab to csv\n\n``````pd.crosstab([df.Product_Category,df.Product],df.site,values=df.Sales,aggfunc=sum,margins=True,margins_name='Sub-Total')\n.to_csv('./cross_tab_result.xls')\n``````", null, "## Conclusion:\n\nSo we have seen how Pivot features works with any data and can be used to quickly build the pivot table using the data. Only thing you have to keep in mind that crosstab works with series, list or dataframe columns but pivot table works with the entire dataframe.\n\nTags:\n\nCategories:\n\nUpdated:" ]
[ null, "https://kanoki.org/images/2019/07/image-26.png", null, "https://kanoki.org/images/2019/07/image-27.png", null, "https://kanoki.org/images/2019/07/image-28.png", null, "https://kanoki.org/images/2019/07/image-29.png", null, "https://kanoki.org/images/2019/07/image-30.png", null, "https://kanoki.org/images/2019/07/image-31.png", null, "https://kanoki.org/images/2019/07/image-32.png", null, "https://kanoki.org/images/2019/07/image-41.png", null ]
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https://cie.co.at/publications/colorimetry-part-4-cie-1976-lab-colour-space-0
[ "# Colorimetry - Part 4: CIE 1976 L*a*b* Colour space\n\nCIE S 014-4:2007\n\nSuperseded by CIE Colorimetry - Part 4: 1976 L*a*b* Colour Space, 2nd Edition\n\nJoint ISO/CIE Standard\n\nISO 11664-4:2008(E)/CIE S 014-4/E:2007\n\nThe three-dimensional colour space produced by plotting CIE tristimulus values (X,Y,Z) in rectangular coordinates is not visually uniform, nor is the (x,y,Y) space nor the two-dimensional CIE (x,y) chromaticity diagram. Equal distances in these spaces do not represent equally perceptible differences between colour stimuli. For this reason, in 1976, the CIE introduced and recommended two new spaces (known as CIELAB and CIELUV) whose coordinates are non-linear functions of X, Y and Z. The recommendation was put forward in an attempt to unify the then very diverse practice in uniform colour spaces and associated colour difference formulae. Both these more-nearly uniform colour spaces have become well accepted and widely used. Numerical values representing approximately the magnitude of colour differences can be described by simple Euclidean distances in the spaces or by more sophisticated formulae that improve the correlation with the perceived size of differences.\n\nThe purpose of this CIE Standard is to define procedures for calculating the coordinates of the CIE 1976 L*a*b* (CIELAB) colour space and the Euclidean colour difference values based on these coordinates. The standard does not cover more sophisticated colour difference formulae based on CIELAB, such as the CMC formula, the CIE94 formula, the DIN99 formula, and the CIEDE2000 formula nor does it cover the alternative uniform colour space, CIELUV.\n\nThis Standard has been approved by CIE and ISO.\n\nThe publication is written in English. It is readily available at the National Committees of the CIE or via the CIE Webshop" ]
[ null ]
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https://www.groundai.com/project/observation-of-the-decay-b_s0rightarrow-k0overlinek0/
[ "Observation of the decay B^{0}_{s}\\rightarrow K^{0}\\overline{K}{}^{\\,0}\n\n# Observation of the decay B0s→K0¯¯¯¯¯K0\n\n###### Abstract\n\nWe measure the decay using data collected at the resonance with the Belle detector at the KEKB collider. The data sample used corresponds to an integrated luminosity of 121.4 fb. We measure a branching fraction with a significance of 5.1 standard deviations. This measurement constitutes the first observation of this decay.\n\n###### pacs:\n13.25.Hw, 14.40.Nd\n\nThe Belle Collaboration\n\nThe two-body decays , where is either a pion or kaon, have now all been observed PDG (). In contrast, the neutral-daughter decays have yet to be observed. The decay  charge-conjugate () is of particular interest because the branching fraction is predicted to be relatively large. In the standard model (SM), the decay proceeds mainly via a loop (or “penguin”) transition as shown in Fig. 1, and the branching fraction is predicted to be in the range  SM-branching (). The presence of non-SM particles or couplings could enhance this value Chang:2013hba (). It has been pointed out that asymmetries in decays are promising observables in which to search for new physics susy ().\n\nThe current upper limit on the branching fraction, at 90% confidence level, was set by the Belle Collaboration using of data recorded at the resonance Peng:2010ze (). Here, we update this result using the full data set of recorded at the . The analysis presented here uses improved tracking, reconstruction, and continuum suppression algorithms. The data set corresponds to pairs Oswald:2015dma () produced in three decay channels: , or , and . The latter two channels dominate, with production fractions of and Esen:2012yz ().\n\nThe Belle detector is a large-solid-angle magnetic spectrometer consisting of a silicon vertex detector (SVD), a 50-layer central drift chamber (CDC), an array of aerogel threshold Cherenkov counters, a barrel-like arrangement of time-of-flight scintillation counters, and an electromagnetic calorimeter comprising CsI(Tl) crystals. These detector components are located inside a superconducting solenoid coil that provides a 1.5 T magnetic field. An iron flux-return located outside the coil is instrumented to detect mesons and to identify muons. The detector is described in detail elsewhere Belle (); svd2 (). The origin of the coordinate system is defined as the position of the nominal interaction point (IP). The axis is aligned with the direction opposite the beam and is parallel to the direction of the magnetic field within the solenoid. The axis is horizontal and points towards the outside of the storage ring; the axis points vertically upward.\n\nCandidate mesons are reconstructed via the decay using a neural network (NN) technique Feindt:2006pm (). The NN uses the following information: the momentum in the laboratory frame; the distance along between the two track helices at their closest approach; the flight length in the - plane; the angle between the momentum and the vector joining the decay vertex to the IP; the angle between the pion momentum and the laboratory-frame direction in the rest frame; the distance-of-closest-approach in the - plane between the IP and the two pion helices; and the pion hit information in the SVD and CDC. The selection efficiency is 87% over the momentum range of interest. We also require that the invariant mass be within 12 MeV/ (about 3.5 in resolution) of the nominal mass PDG ().\n\nTo identify candidates, we define two variables: the beam-energy-constrained mass ; and the energy difference , where is the beam energy and and are the energy and momentum, respectively, of the candidate. These quantities are evaluated in the center-of-mass frame. We require that events satisfy  GeV/ and .\n\nTo suppress background arising from continuum production, we use a second NN Feindt:2006pm () that distinguishes jetlike continuum events from more spherical events. This NN uses the following input variables, which characterize the event topology: the cosine of the angle between the thrust axis Brandt:1964sa () of the candidate and the thrust axis of the rest of the event; the cosine of the angle between the thrust axis and the axis; a set of 16 modified Fox-Wolfram moments SFW (); and the ratio of the second to zeroth (unmodified) Fox-Wolfram moments. All quantities are evaluated in the center-of-mass frame. The NN is trained using Monte Carlo (MC) simulated signal events and background events. The MC samples are obtained using EvtGen Lange:2001uf () for event generation and Geant3 geant3 () for modeling the detector response. The NN has a single output variable () that ranges from for backgroundlike events to for signal-like events. We require , which rejects approximately 85% of background while retaining 83% of signal decays. We subsequently translate to a new variable {linenomath}\n\n C′NN=ln(CNN−CminNNCmaxNN−CNN), (1)\n\nwhere and is the maximum value of obtained from a large sample of signal MC decays. The distribution of is well modeled by a Gaussian function.\n\nAfter applying all selection criteria, approximately 1.0% of events have multiple candidates. For these events, we retain the candidate having the smallest value of obtained from the deviations of the reconstructed masses from their nominal values PDG (). According to MC simulation, this criterion selects the correct candidate % of the time.\n\nWe measure the signal yield by performing an unbinned extended maximum likelihood fit to the variables , , and . The likelihood function is defined as {linenomath}\n\n L=e−∑jYjN∏i(∑jYjPj(Mibc,ΔEi,C′iNN)), (2)\n\nwhere is the yield of component ; is the probability density function (PDF) of component for event ; runs over the two event categories (signal and background); and runs over all events in the sample (). Backgrounds arising from other and non- decays were studied using MC simulation and found to be negligible. As correlations among the variables , , and are found to be small, the three-dimensional PDFs are factorized into the product of separate one-dimensional PDFs.\n\nThe signal PDF is defined as {linenomath}\n\n Psig = fB∗0s¯¯¯¯B∗0sPB∗0s¯¯¯¯B∗0s+fB∗0s¯¯¯¯B0sPB∗0s¯¯¯¯B0s +(1−fB∗0s¯¯¯¯B∗0s−fB∗0s¯¯¯¯B0s)PB0s¯¯¯¯B0s,\n\nwhere , , and are the PDFs for signal arising from , and decays. The and PDFs are modeled with Gaussian functions, and the PDFs are each modeled with a sum of two Gaussian functions having a common mean. All parameters of the signal PDF are fixed to the corresponding MC values. The peak positions for and are adjusted according to small data-MC differences observed in a control sample of decays Esen:2012yz (). As this control sample has only modest statistics, the resolutions for , , and , and the peak position for , are adjusted for data-MC differences using a high statistics sample of decays. For background, the , , and PDFs are modeled with an ARGUS function Albrecht:1990am (), a first-order Chebyshev polynomial, and a Gaussian function, respectively. All parameters of the background PDFs except for the end point of the ARGUS function are floated in the fit.\n\nThe results of the fit are signal events and continuum background events. Projections of the fit are shown in Fig. 2. The branching fraction is calculated via {linenomath}\n\n B(B0s→K0¯¯¯¯¯K0) = (4)\n\nwhere is the fitted signal yield; is the number of events; is the branching fraction for  PDG (); and is the signal efficiency as determined from MC simulation. The efficiency is corrected by a factor for each reconstructed , to account for a small difference in reconstruction efficiency between data and simulation. This correction is estimated from a high statistics sample of decays. The factor 0.50 accounts for the 50% probability for (since is even). Inserting these values gives , where the error is statistical.", null, "Figure 2: Projections of the 3D fit to the real data: (a) Mbc in −0.11 GeV<ΔE<0.02 GeV and C′NN>0.5; (b) ΔE in 5.405 GeV/c20.5; and (c) C′NN in 5.405 GeV/c2\n\nThe systematic uncertainty on arises from several sources, as listed in Table 1. The uncertainties due to the fixed parameters in the PDF shape are estimated by varying the parameters individually according to their statistical uncertainties. For each variation the branching fraction is recalculated, and the difference with the nominal branching fraction is taken as the systematic uncertainty associated with that parameter. We add together all uncertainties in quadrature to obtain the overall uncertainty due to fixed parameters. The uncertainties due to errors in the calibration factors and the fractions are evaluated in a similar manner. To test the stability of our fitting procedure, we generate and fit a large ensemble of MC pseudoexperiments. By comparing the mean of the fitted yields with the input value, a bias of is found. We attribute this bias to our neglecting small correlations among the fitted observables. An 0.9% systematic uncertainty is assigned due to the selection; this is obtained by comparing the selection efficiencies in MC simulationand data for the control sample. We assign a 2.0% systematic uncertainty for each reconstructed ; this is determined using a sample. The uncertainty on due to the MC sample size is 0.2%. The total of the above systematic uncertainties is calculated as their sum in quadrature. In addition, there is a 10.1% uncertainty due to the number of pairs. As this large uncertainty does not arise from our analysis, we quote it separately.\n\nThe signal significance is calculated as , where is the likelihood value when the signal yield is fixed to zero, and is the likelihood value of the nominal fit. We include systematic uncertainties in the significance by convolving the likelihood function with a Gaussian function whose width is equal to that part of the systematic uncertainty that affects the signal yield. We obtain a signal significance of 5.1 standard deviations; thus, our measurement constitutes the first observation of this decay.", null, "Figure 3: Background subtracted sPlot distributions of M(π+π−) for the (a) higher momentum and (b) lower momentum K0S candidates.\n\nFigure 3 shows the background-subtracted sPlot splot () distributions of , where the selection is removed for the pair being plotted. No contribution is observed. We check this quantitatively by performing our signal fit for events in the mass sidebands of each [ and ]. The extracted signal yields, and for the higher momentum and lower momentum , respectively, are consistent with zero. We calculate the expected number of events in our signal sample using MC simulation and the measured branching fraction,  Aaij:2013uta (); the result is 0.001.\n\nIn summary, we report the first observation of the decay . The branching fraction is measured to be {linenomath}\n\n B(B0s→K0¯¯¯¯¯K0)=(19.6+5.8−5.1±1.0±2.0)×10−6,\n\nwhere the first uncertainty is statistical, the second is systematic, and the third reflects the uncertainty due to the total number of pairs. This value is in good agreement with the SM predictions SM-branching (), and it implies that the Belle II experiment Abe:2010gxa () will reconstruct over 1000 of these decays. Such a sample would allow for a much higher sensitivity search for new physics in this penguin-dominated decay.\n\nACKNOWLEDGMENTS\n\nWe thank the KEKB group for the excellent operation of the accelerator; the KEK cryogenics group for the efficient operation of the solenoid; and the KEK computer group, the National Institute of Informatics, and the PNNL/EMSL computing group for valuable computing and SINET4 network support. We acknowledge support from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan, the Japan Society for the Promotion of Science (JSPS), and the Tau-Lepton Physics Research Center of Nagoya University; the Australian Research Council; Austrian Science Fund under Grants No. P 22742-N16 and P 26794-N20; the National Natural Science Foundation of China under Contracts No. 10575109, No. 10775142, No. 10875115, No. 11175187, and No. 11475187; the Chinese Academy of Science Center for Excellence in Particle Physics; the Ministry of Education, Youth and Sports of the Czech Republic under Contract No. LG14034; the Carl Zeiss Foundation, the Deutsche Forschungsgemeinschaft and the VolkswagenStiftung; the Department of Science and Technology of India; the Istituto Nazionale di Fisica Nucleare of Italy; the WCU program of the Ministry of Education, National Research Foundation (NRF) of Korea Grants No. 2011-0029457, No. 2012-0008143, No. 2012R1A1A2008330, No. 2013R1A1A3007772, No. 2014R1A2A2A01005286, No. 2014R1A2A2A01002734, No. 2015R1A2A2A01003280 , No. 2015H1A2A1033649; the Basic Research Lab program under NRF Grant No. KRF-2011-0020333, Center for Korean J-PARC Users, No. NRF-2013K1A3A7A06056592; the Brain Korea 21-Plus program and Radiation Science Research Institute; the Polish Ministry of Science and Higher Education and the National Science Center; the Ministry of Education and Science of the Russian Federation and the Russian Foundation for Basic Research; the Slovenian Research Agency; the Basque Foundation for Science (IKERBASQUE) and the Euskal Herriko Unibertsitatea (UPV/EHU) under program UFI 11/55 (Spain); the Swiss National Science Foundation; the National Science Council and the Ministry of Education of Taiwan; and the U.S. Department of Energy and the National Science Foundation. This work is supported by a Grant-in-Aid from MEXT for Science Research in a Priority Area (“New Development of Flavor Physics”) and from JSPS for Creative Scientific Research (“Evolution of Tau-lepton Physics”).\n\n## References\n\n• (1) K. A. Olive et al. (Particle Data Group), “Review of particle physics,” Chin. Phys. C 38, 090001 (2014).\n• (2) Unless stated otherwise, charge-conjugate modes are implicitly included.\n• (3) C. H. Chen, “Analysis of decays in the PQCD,” Phys. Lett. B 520, 33 (2001); A. R. Williamson and J. Zupan, “Two body decays with isosinglet final states in SCET,” Phys. Rev. D 74, 014003 (2006); A. Ali, G. Kramer, Y. Li, C. D. Lu, Y. L. Shen, W. Wang and Y. M. Wang, “Charmless non-leptonic decays to , and final states in the pQCD approach,” Phys. Rev. D 76, 074018 (2007); C. K. Chua, “Rescattering effects in charmless decays,” Phys. Rev. D 78, 076002 (2008); K. Wang and G. Zhu, “Flavor dependence of annihilation parameters in QCD factorization,” Phys. Rev. D 88, 014043 (2013); J. J. Wang, D. T. Lin, W. Sun, Z. J. Ji, S. Cheng and Z. J. Xiao, “ decays and effects of the next-to-leading order contribution,” Phys. Rev. D 89, 074046 (2014); Q. Chang, J. Sun, Y. Yang and X. Li, “A combined fit on the annihilation corrections in decays within QCDF,” Phys. Lett. B 740, 56 (2015); H. Y. Cheng, C. W. Chiang and A. L. Kuo, “Updating decays in the framework of flavor symmetry,” Phys. Rev. D 91, 014011 (2015).\n• (4) Q. Chang, X. Q. Li and Y. D. Yang, “A comprehensive analysis of hadronic transitions in a family non-universal model,” J. Phys. G 41, 105002 (2014).\n• (5) S. Baek, D. London, J. Matias and J. Virto, “ and decays within supersymmetry,” J. High Energy Phys. 12, (2006) 019; A. Hayakawa, Y. Shimizu, M. Tanimoto and K. Yamamoto, “Searching for the squark flavor mixing in violations of and decays,” Prog. Theor. Exp. Phys. 2014, 023B04 (2014).\n• (6) C.-C. Peng et al. (Belle Collaboration), “Search for decays at the resonance,” Phys. Rev. D 82, 072007 (2010).\n• (7) C. Oswald et al. (Belle Collaboration), “Semi-inclusive studies of semileptonic decays at Belle,” Phys. Rev. D 92, 072013 (2015)\n• (8) S. Esen et al. (Belle Collaboration), “Precise measurement of the branching fractions for and first measurement of the polarization using collisions,” Phys. Rev. D 87, 031101(R) (2013).\n• (9) A. Abashian et al. (Belle Collaboration), “The Belle detector,” Nucl. Instrum. Methods Phys. Res., Sect. A 479, 117 (2002); also see the detector section in J.Brodzicka et al., “Physics achievements from the Belle experiment,” Prog. Theor. Exp. Phys. 2012, 04D001 (2012).\n• (10) Z. Natkaniec et al. (Belle SVD2 Group), “Status of the Belle silicon vertex detector,” Nucl. Instrum. Methods Phys. Res., Sect. A 560, 1 (2006).\n• (11) M. Feindt and U. Kerzel, “The NEUROBAYES neural network package,” Nucl. Instrum. Methods Phys. Res., Sect. A 559, 190 (2006).\n• (12) S. Brandt, C. Peyrou, R. Sosnowski and A. Wroblewski, “The principal axis of jets. An attempt to analyze high-energy collisions as two-body processes,” Phys. Lett. 12, 57 (1964).\n• (13) G. C. Fox and S. Wolfram, “Observables for the Analysis of Event Shapes in Annihilation and Other Processes,” Phys. Rev. Lett. 41, 1581 (1978); The modified moments used in this Letter are described in S. H. Lee et al. (Belle Collaboration), “Evidence for ,” Phys. Rev. Lett. 91, 261801 (2003).\n• (14) D. J. Lange, “The EvtGen particle decay simulation package,” Nucl. Instrum. Methods Phys. Res., Sect. A 462, 152 (2001).\n• (15) R. Brun et al., GEANT 3.21, CERN Report DD/EE/84-1, 1984.\n• (16) H. Albrecht et al. (ARGUS Collaboration), “Search for hadronic decays,” Phys. Lett. B 241, 278 (1990).\n• (17) M. Pivk and F. R. Le Diberder, “sPlot: A statistical tool to unfold data distributions”, Nucl. Instrum. Methods Phys. Res., Sect. A 555, 356 (2005).\n• (18) R. Aaij et al. (LHCb Collaboration), “Study of decays with first observation of and ,” J. High Energy Phys. 10, (2013) 143.\n• (19) T. Abe et al. (Belle II Collaboration), “Belle II Technical Design Report,” arXiv:1011.0352.\nYou are adding the first comment!\nHow to quickly get a good reply:\n• Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made.\n• Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements.\n• Your comment should inspire ideas to flow and help the author improves the paper.\n\nThe better we are at sharing our knowledge with each other, the faster we move forward.\nThe feedback must be of minimum 40 characters and the title a minimum of 5 characters", null, "", null, "", null, "" ]
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https://link.springer.com/article/10.1007/s10270-016-0516-2?error=cookies_not_supported&code=196b9083-c054-439d-b04d-191eff4be35c
[ "# A novel model-based testing approach for software product lines\n\n## Abstract\n\nModel-based testing relies on a model of the system under test. FineFit is a framework for model-based testing of Java programs. In the FineFit approach, the model is expressed by a set of tables based on Parnas tables. A software product line is a family of programs (the products) with well-defined commonalities and variabilities that are developed by (re)using common artifacts. In this paper, we address the issue of using the FineFit approach to support the development of correct software product lines. We specify a software product line as a specification product line where each product is a FineFit specification of the corresponding software product. The main challenge is to concisely specify the software product line while retaining the readability of the specification of a single system. To address this, we used delta-oriented programming, a recently proposed flexible approach for implementing software product lines, and developed: (1) delta tables as a means to apply the delta-oriented programming idea to the specification of software product lines; and (2) DeltaFineFit as a novel model-based testing approach for software product lines.\n\nThis is a preview of subscription content, log in to check access.\n\n1. 1.\n\nA straightforward embedding of FOP into DOP is illustrated, e.g., in .\n\n2. 2.\n\nIn practice, the tables can be written using any tool that can export its output to HTML, for example MS Word. FineFit ignores anything that is not part of an HTML table.\n\n3. 3.\n\nAlloy is a general purpose modeling language (in the style of Z) for reasoning about relational structures with first order logic. It has no direct concept of system states or operations, and it does not offer any tool for testing software.\n\n4. 4.\n\nNote that by definition a set does not contain duplicate elements.\n\n5. 5.\n\nIn the original paper about FineFit , we used the term fixture, which was borrowed from Fit , instead of driver. We think that driver is a more appropriate term.\n\n6. 6.\n\nA feature model defines the valid feature configurations of an SPL, i.e., the feature configurations that describe the products (see, e.g., ).\n\n7. 7.\n\nThis mechanism is similar to the Super(...) call of FOP  and to the around advice and proceed mechanisms of aspect-oriented programming (AOP)—see, e.g., [7, 25] for a comparison between DOP and AOP.\n\n8. 8.\n\nIn DeltaJ 1.5, each constraint “$$S_{i,1}$$ when $${\\texttt {P}}_{i,1},$$ $$\\ldots ,$$ $$S_{i,q_i}$$ when $${\\texttt {P}}_{i,q_i}$$;” is called “partition,” since the set of sets of delta module names $$\\{S_{i,j} \\;\\vert \\;$$ $$1\\le j\\le q_i\\}$$ is a partition of $$\\cup _{1\\le j\\le q_i} S_{i,j}$$.\n\n9. 9.\n\nThus, ruling out ambiguity.\n\n10. 10.\n\nNote that tables satisfying this case do not satisfy case 1.\n\n11. 11.\n\nThe bottom line of the cells is removed to indicate that $$c_i$$ represents a subtree rather than the content of a cell.\n\n12. 12.\n\nUnlike the $$apply$$ function, the $$prepare$$ function is aware of the row-alignment of cells in a table and provides special treatment for different kinds of cells.\n\n13. 13.\n\nRecall that the row span of the name cell defines that the first three rows of the example are condition rows and the other rows contain variable and value cells.\n\n14. 14.\n\nThis includes variables checked by the conditional operator.\n\n15. 15.\n\nRows with conditional remove operators are also moved to the right place by this rule, i.e., where the content of a cell matches the condition.\n\n16. 16.\n\nThe first row of enumeration tables, which consists of an atom name, was counted.\n\n17. 17.\n\nWe do not count the left most column containing the state variables.\n\n18. 18.\n\nIn fact, this is a forest. However, it can be transformed into a tree by adding an imaginary true as the parent of the predicates at the first row.\n\n19. 19.\n\nAdding deriving(Show) at the end of a data declaration enables printing instances on standard output.\n\n20. 20.\n\nAdding a definition for the name main makes Listing 13 a complete program.\n\n21. 21.\n\nLine 37 is a special case, where $$*$$ matches multiple condition cells.\n\n22. 22.\n\nIf replace is not substituted by insert in Line 47.\n\n## References\n\n1. 1.\n\nClements, P., Northrop, L.: Software Product Lines: Practices and Patterns. Addison Wesley Longman, Boston (2001)\n\n2. 2.\n\nPohl, K., Böckle, G., van der Linden, F.: Software Product Line Engineering-Foundations, Principles, and Techniques. Springer, Berlin (2005)\n\n3. 3.\n\nFaitelson, D., Tyszberowicz, S.S.: Data refinement based testing. Int. J. Syst. Assur. Eng. Manag. 2(2), 144–154 (2011)\n\n4. 4.\n\nde Roever, W.P., Engelhardt, K.: Data Refinement: Model-Oriented Proof Theories and Their Comparison. Cambridge Tracts in Theoretical Computer Science, vol. 46. Cambridge University Press, Cambridge (1998)\n\n5. 5.\n\n6. 6.\n\nParnas, D.L.: Tabular representation of relations. Tech. rep. 260, Research Institute of Ontario, McMaster University (1992)\n\n7. 7.\n\nBettini, L., Damiani, F., Schaefer, I.: Compositional type checking of delta-oriented software product lines. Acta Inf. 50, 77–122 (2013)\n\n8. 8.\n\nSchaefer, I., Bettini, L., Bono, V., Damiani, F., Tanzarella, N.: Delta-oriented programming of software product lines. In: Software Product Line Conference (SPLC), LNCS, vol. 6287, pp. 77–91. Springer (2010)\n\n9. 9.\n\nApel, S., Batory, D.S., Kästner, C., Saake, G.: Feature-Oriented Software Product Lines: Concepts and Implementation. Springer, Berlin (2013)\n\n10. 10.\n\nBatory, D., Sarvela, J., Rauschmayer, A.: Scaling step-wise refinement. IEEE TSE 30(6), 355–371 (2004)\n\n11. 11.\n\nSchaefer, I., Damiani, F.: Pure delta-oriented programming. In: Proceedings of the 2Nd International Workshop on Feature-Oriented Software Development, FOSD’10, pp. 49–56. ACM, New York, NY, USA (2010). doi:10.1145/1868688.1868696\n\n12. 12.\n\nKoscielny, J., Holthusen, S., Schaefer, I., Schulze, S., Bettini, L., Damiani, F.: Deltaj 1.5: delta-oriented programming for java 1.5. In: Proceedings of the 2014 International Conference on Principles and Practices of Programming on the Java Platform: Virtual Machines, Languages, and Tools, PPPJ’14, pp. 63–74. ACM, New York, NY, USA (2014). doi:10.1145/2647508.2647512\n\n13. 13.\n\n14. 14.\n\nJohansen, M.F., Haugen, O., Fleurey, F.: Properties of realistic feature models make combinatorial testing of product lines feasible. In: Proceedings of the International Conference on Model Driven Engineering Languages and Systems (MODELS), pp. 638–652. Springer, Berlin (2011)\n\n15. 15.\n\nJohansen, M.F., Haugen, O., Fleurey, F.: An algorithm for generating t-wise covering arrays from large feature models. In: Proceedings of the 16th International Software Product Line Conference, vol. 1, SPLC’12, pp. 46–55. ACM, New York, NY, USA (2012). doi:10.1145/2362536.2362547\n\n16. 16.\n\nKowal, M., Schulze, S., Schaefer, I.: Towards efficient spl testing by variant reduction. In: Proceedings of the 4th International Workshop on Variability & Composition, VariComp’13, pp. 1–6. ACM, New York, NY, USA (2013). doi:10.1145/2451617.2451619\n\n17. 17.\n\nLochau, M., Goltz, U.: Feature interaction aware test case generation for embedded control systems. Electron. Notes Theor. Comput. Sci. 264(3), 37–52 (2010)\n\n18. 18.\n\n19. 19.\n\nDamiani, F., Gladisch, C., Tyszberowicz, S.: Refinement-based testing of delta-oriented product lines. In: Proceedings of the 2013 International Conference on Principles and Practices of Programming on the Java Platform: Virtual Machines, Languages, and Tools, PPPJ’13, pp. 135–140. ACM, New York, NY, USA (2013). doi:10.1145/2500828.2500841\n\n20. 20.\n\nJackson, D.: Software Abstractions-Logic, Language, and Analysis. MIT Press, Cambridge (2012)\n\n21. 21.\n\nSpivey, J.M.: The Z Notation: A Reference Manual. Prentice Hall International, Upper Saddle River (2001)\n\n22. 22.\n\nTorlak, E., Jackson, D.: Kodkod: a relational model finder. In: Tools and Algorithms for the Construction and Analysis of Systems (TACAS), LNCS, vol. 4424, pp. 632–647. Springer (2007)\n\n23. 23.\n\nMugridge, R., Cunningham, W.: Fit for Developing Software: Framework for Integrated Tests. Prentice Education Inc., New Jersy (2005)\n\n24. 24.\n\nBatory, D.: Feature models, grammars, and propositional formulas. In: Proceedings of the International Conference on Software Product Lines (SPLC), LNCS, vol. 3714, pp. 7–20. Springer (2005)\n\n25. 25.\n\nSchaefer, I., Bettini, L., Damiani, F.: Compositional type-checking for delta-oriented programming. In: Proceedings of the Tenth International Conference on Aspect-oriented Software Development, AOSD’11, pp. 43–56. ACM, New York, NY, USA (2011). doi:10.1145/1960275.1960283\n\n26. 26.\n\nSchaefer, I., Rabiser, R., Clarke, D., Bettini, L., Benavides, D., Botterweck, G., Pathak, A., Trujillo, S., Villela, K.: Software diversity: state of the art and perspectives. Int. J. Softw. Tools Technol. Transf. 14(5), 477–495 (2012)\n\n27. 27.\n\nApel, S., Kästner, C., Grösslinger, A., Lengauer, C.: Type safety for feature-oriented product lines. Autom. Softw. Eng. 17(3), 251–300 (2010)\n\n28. 28.\n\nApel, S., Kästner, C., Lengauer, C.: Feature featherweight java: a calculus for feature-oriented programming and stepwise refinement. In: Proceedings of the 7th International Conference on Generative Programming and Component Engineering, GPCE’08, pp. 101–112. ACM, New York, NY, USA (2008). doi:10.1145/1449913.1449931\n\n29. 29.\n\nDelaware, B., Cook, W.R., Batory, D.: Fitting the pieces together: A machine-checked model of safe composition. In: Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering, ESEC/FSE’09, pp. 243–252. ACM, New York, NY, USA (2009). doi:10.1145/1595696.1595733\n\n30. 30.\n\nKrueger, C.: Eliminating the adoption barrier. IEEE Softw. 19(4), 29–31 (2002)\n\n31. 31.\n\ndo Carmo Machado, I., McGregor, J.D., Cavalcanti, Y.C., de Almeida, E.S.: On strategies for testing software product lines: a systematic literature review. Inf. Softw. Technol. 56(10), 1183–1199 (2014)\n\n32. 32.\n\nEngström, E., Runeson, P.: Software product line testing–a systematic mapping study. Inf. Softw. Technol. 53(1), 2–13 (2011)\n\n33. 33.\n\nLee, J., Kang, S., Lee, D.: A survey on software product line testing. In: Proceedings of the 16th International Software Product Line Conference, vol. 1, SPLC’12, pp. 31–40. ACM, New York, NY, USA (2012). doi:10.1145/2362536.2362545\n\n34. 34.\n\nda Mota Silveira Neto, P.A., do Carmo Machado, I., McGregor, J.D., de Almeida, E.S., de Lemos Meira, S.R.: A systematic mapping study of software product lines testing. Inf. Softw. Technol. 53(5), 407–423 (2011). Special Section on Best Papers from XP2010\n\n35. 35.\n\nSalem, K., Beyer, K., Lindsay, B., Cochrane, R.: How to roll a join: asynchronous incremental view maintenance. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, SIGMOD’00, pp. 129–140. ACM, New York, NY, USA (2000). doi:10.1145/342009.335393\n\n36. 36.\n\nUzuncaova, E., Khurshid, S., Batory, D.S.: Incremental test generation for software product lines. IEEE TSE 36(3), 309–322 (2010)\n\n37. 37.\n\nLochau, M., Lity, S., Lachmann, R., Schaefer, I., Goltz, U.: Delta-oriented model-based integration testing of large-scale systems. J. Syst. Softw. 91, 63–84 (2014)\n\n38. 38.\n\nLochau, M., Schaefer, I., Kamischke, J., Lity, S.: Incremental model-based testing of delta-oriented software product lines. In: TAP, LNCS, vol. 7305, pp. 67–82. Springer (2012)\n\n39. 39.\n\nDamiani, F., Owe, O., Dovland, J., Schaefer, I., Johnsen, E.B., Yu, I.C.: A transformational proof system for delta-oriented programming. In: Proceedings of the 16th International Software Product Line Conference, vol. 2, SPLC’12, pp. 53–60. ACM, New York, NY, USA (2012). doi:10.1145/2364412.2364422\n\n40. 40.\n\nHähnle, R., Schaefer, I.: A Liskov principle for delta-oriented programming. In: Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change International Symposium (ISoLA), Part I, LNCS, vol. 7609, pp. 32–46. Springer (2012)\n\n41. 41.\n\nDovland, J., Johnsen, E.B., Owe, O., Steffen, M.: Lazy behavioral subtyping. J. Log. Algebr. Program. 79(7), 578–607 (2010)\n\n42. 42.\n\nBeckert, B., Gladisch, C., Tyszberowicz, S., Yehudai, A.: KeYGenU: combining verification-based and capture and replay techniques for regression unit testing. Int. J. Syst. Assur. Eng. Manag. 2(2), 97–113 (2011)\n\n43. 43.\n\nDamiani, F., Padovani, L., Schaefer, I.: A formal foundation for dynamic delta-oriented software product lines. In: Proceedings of the 11th International Conference on Generative Programming and Component Engineering, GPCE’12, pp. 1–10. ACM, New York, NY, USA (2012). doi:10.1145/2371401.2371403\n\n44. 44.\n\nDamiani, F., Schaefer, I.: Dynamic delta-oriented programming. In: Proceedings of the 15th International Software Product Line Conference, vol. 2, SPLC’11, pp. 34:1–34:8. ACM, New York, NY, USA (2011). doi:10.1145/2019136.2019175\n\n45. 45.\n\nLi, Z., Harman, M., Hierons, R.M.: Search algorithms for regression test case prioritization. IEEE TSE 33(4), 225–237 (2007)\n\n## Acknowledgments\n\nWe thank the anonymous referees of PPPJ’13 for valuable comments on a preliminary version of this paper and the anonymous SoSyM referees for many insightful comments and suggestions for improving the paper.\n\n## Author information\n\nAuthors\n\n### Corresponding author\n\nCorrespondence to Ferruccio Damiani.\n\nThe authors of this paper are listed in alphabetical order. This work has been partially supported by project HyVar (www.hyvar-project.eu), which has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 644298; by ICT COST Action IC1402 ARVI (www.cost-arvi.eu); by ICT COST Action IC1201 BETTY (www.behavioural-types.eu); by Italian MIUR PRIN 2010LHT4KM Project CINA (sysma.imtlucca.it/cina); by Ateneo/CSP D16D15000360005 project RunVar; and by GIF (Grant No. 1131-9.6/2011).\n\nCommunicated by Profs. Einar Broch Johnsen and Luigia Petre.\n\n## Appendices\n\n### Appendix 1: The structure and semantics of operation tables\n\nA FineFit operation table is a predicate that specifies the behavior of an operation as a relation between the model’s state variables before the operation starts (the pre-state) and after the operation completes (the post-state). It consists of two major areas: an expression table and a precondition tree. The expression table is a set of columns, where each column defines the values of the state variables in the post-state, given their values in the pre-state. The predcondition tree consists of predicates that determine which columns to use in the definition of the post-state. For example, consider the following operation table:\n\nThe operation R has a single input parameter c? and determines the value of its two state variables x and y as follows: If c? is positive, then the operation must set x to $$x+c?$$ and y to $$x+y$$ (the right most column), otherwise, if c? is not positive, the effect of the operation depends on the current value of x. When x is not negative x and y must be set to zero and when x is negative, then x and y do not change (their value must be the same as it was when the operation started). Note that the predicate for determining whether columns 1 and 2Footnote 17 are relevant for the specification is the conjunction of the child predicates $$x<0$$ and $$x\\ge 0$$ with their parent predicate $$c? \\le 0$$.\n\nWe now define the conditions necessary for the precondition part of the table to form a tree. Consider the precondition part of the table as a matrix of cells. Then, each predicate spans one or more consecutive cells (in the same row) and the space that each predicate occupies must be included below the space that its parent (the predicate above) occupies. For example, the predicate $$x \\ge 0$$ occupies cell (2, 2) and its parent, the predicate $$c? \\le 0$$ occupies cells (1, 1), (1, 2).\n\nFormally, consider a precondition part that is arranged in a matrix of n rows by m columns of cells. The i-th row contains a sequence of predicates, each occupying a span of one or more cells. Let $$k_i$$ indicate the sequence of spans that each predicate occupies (and therefore $$k_{ij}$$ is the j-th span in the i-th row). For example, in the operation above, $$k_1 = 2,1$$ and $$k_2 = 1,1,1$$. In order for the spans to describe a valid precondition tree, three conditions must be met:\n\n1. 1.\n\nThe sum of spans in each row must be equal to the number of columns in the matrix:\n\n\\begin{aligned} \\sum _{j=1}^{|k_i|}k_{ij} = m \\end{aligned}\n\nwhere $$|k_i|$$ is the number of elements in the ith sequence of spans.\n\n2. 2.\n\nThe cells that each predicate spans must be included below the span of cells of its parent:\n\n\\begin{aligned} S_{ij} \\subseteq S_{(i-1)l} \\qquad \\hbox {for some }1\\le l\\le m \\end{aligned}\n\nwhere the span of cells of the j-th predicate in the i-th row is\n\n\\begin{aligned} S_{ij} = \\sum _{l=1}^{j-1}k_{il} + 1, \\ldots , \\sum _{l=1}^j k_{il} \\end{aligned}\n3. 3.\n\nThe last row (n) must consist of individual cells, that is\n\n\\begin{aligned} k_{nj} = 1 \\quad \\hbox {for all }1 \\le j \\le m. \\end{aligned}\n\nThis arrangement ensures that the predicates form a tree structureFootnote 18 with m leaves, all occupying the last row. The following matrix illustrates this structure:\n\nThere are three span sequences, one for each row:\n\n\\begin{aligned} k_{3}= & {} 1,1,1,1,1,1,1 \\end{aligned}\n(3)\n\\begin{aligned} k_{2}= & {} 3,2,2 \\end{aligned}\n(4)\n\\begin{aligned} k_{1}= & {} 5,2 \\end{aligned}\n(5)\n\nWe can see that each span is included below the span of its parent. For example,\n\n\\begin{aligned} S_{2,2} = \\{4,5\\} \\subseteq S_{1,1} = \\{1,2,3,4,5\\} \\end{aligned}\n\nWe say that the precondition tree is well formed when it satisfies the three conditions we have defined above. In the rest of the discussion, we always assume that we are working with well-formed precondition trees. Given a column i, we define the guard for this column as the conjunction of the leaf that occupies the i-th column and all its ancestors. Let $$\\bar{v}' = v'_1,\\ldots ,v'_l$$ be the state space vector. Let $$\\bar{c_i}$$ be the i-th column of the expression table. Then, the meaning of the operation specification is:\n\n\\begin{aligned} \\bar{v}' \\in \\{ \\bar{c_i} : 1 \\le i \\le m \\wedge guard(i) \\} \\end{aligned}\n\nThat is, the value of the state variables vector in the post-state can be equal to the value of any of the expression table columns whose guard was true in the pre-state.\n\nThe semantics of individual predicates and expressions is explained in .\n\n### Appendix 2: The algorithm that computes the $$apply$$ function\n\nWe provide (in Sect. “Executable specification in Haskell”) a Haskell executable specification of the $$apply$$ function (cf. Sect. 4.2.1) and present (in Sect. “The Java implementation”) its Java implementation. Note that in this paper all the tables resulting from delta-table application have been generated using this implementation.\n\nThe executable specification of the algorithm that computes the $$apply$$ function is provided as a program written in Haskell (Listing 13).\n\nHaskell is a functional programming language with a syntax that strongly resembles the usual mathematical notation for defining function by cases, via pattern matching. The Haskell code and the comments in Listing 13 should be almost self-explanatory. In the following, we shortly explain some technicalities. The expression contained in an ordinary table cell and in a delta-table cell containing the insert or replace operators is represented using the standard library type String (Line 3). Both ordinary tables and delta tables are represented as trees, where each node corresponds to a cell. A tree that represents a table (ordinary or delta) is expressed as a value of the recursive data type Table (Line 10)—a value of type Table describes the root cell and the list of its immediate subtrees. The data type Table has five data constructors (each describes a different kind of node): Basic, Insert, and Replace (all have arity two), Match and Remove (arity one).Footnote 19 Ordinary tables are represented by using only the data constructor Basic, while delta tables are represented by using only the other data constructors, which correspond to the delta-table operators.\n\n### Example 3\n\n(Representation of ordinary tables and delta tables in Haskell) Since ordinary tables may have more than one cell in their first row, they are always encoded by adding a top node containing the string “ROOT”. Delta tables are therefore encoded by adding a top node containing the match operator (matching the top node in the ordinary tables). The following ordinary and delta tables\n\nare respectively represented by the following values of type Table:\n\nThe Haskell function apply (Lines 1564) corresponds to the $$apply$$ function (introduced in Sect. 4.2.1) that executes the delta operations of the delta table. To improve readability, we have not modeled the behavior described in Remark 2 of Sect. 4.2.1.\n\nLine 30 declares the type of the apply function. The first argument of the function apply has type Maybe Table. The standard library Maybe data type has two constructors: the unary data constructor Just and the constant Nothing. It is used to specify optional values: A value of type Maybe Table either contains a table t (represented as Just t), or it is empty (represented as Nothing).\n\nThe standard library function concat takes two lists and returns their concatenation. The standard library function zipWith calls a given function pairwise on each member of both lists, returning a list. For the convenience of readers, zipWith’s code is as follows:\n\n### Example 4\n\n(An execution of the Haskell function apply) Consider the following delta-table application, which already has been presented at the end of Sect. 4.2.1 (recall that the numbers to the left of the cells denote the recursion step):\n\nThe above delta-table application can be defined by appending to the code in Listing 13 the following three lines:Footnote 20\n\nExecuting the main program yields\n\n### The Java implementation\n\nThe Java implementation of the algorithm that computes the $$apply$$ function is given in Listings 14 and 15. A cell of a table is represented by the Node class which extends the Vector class. Tables are represented as trees, and an object of class Node represents a cell, which is the root of a subtree, and contains (in the Vector’s elements) the reference to the roots of the immediate subtrees.\n\nThe applyPrime method (Lines 1623 in Listing 14) corresponds to the $$apply '$$ function (introduced in Sect. 4.3). It first calls (Line 19) the prepare method (which applies the rules described in Sect. 4.3) and then invokes (Line 21) the apply method (Lines 3064 in Listing 14). The apply method corresponds to the $$apply$$ function (introduced in Sect. 4.2.1 and specified in Sect. 1). When calling the apply method, the this object is the current node of the delta table, and orig and res are the current nodes of the original table and of the resulting table, respectively.\n\nThe body of the apply method implements the recursive walking of the parse tree, which is controlled by the operations in the cells of the delta table and, therefore, describes how each operation works. The first part of the method (Lines 3650) reads the operation of the current delta node and updates the content of the resulting node; Lines 4046 implement the behavior described in Remark 2 of Sect. 4.2.1. The second part of the method (Lines 5262) is responsible for the recursive call.Footnote 21 At each recursive call (Lines 4356, and 60), the method getOrCreateChild(i), which creates the nodes of the resulting table, is called; it either returns the i-th subnode if it already exists, or it creates the i-th subnode and returns it. We now continue illustrating the apply method using small examples. (An example of an execution trace of the method apply is provided in Example 5.)\n\nThe loop that begins in Line 52 iterates over the siblings of the current delta-table node. This means that if at the current level the original table has more cells than the delta table, then the additional cells are ignored. For example:\n\nIf the delta table has more cells than the original table at the current level, the last cell of the original table is repeatedly used when processing the additional operations of the delta table (Line 53). For example:\n\nIf the original table contains no cells at the current level and the current operation of the delta table is $$*$$ or $$-$$, the remaining operations of the delta table on the current branch are ignored (Line 52).\n\nIf the original table contains no cells at the current level and the current operation of the delta table is $$\\blacktriangleright$$ or $$*\\blacktriangleright$$, a cell with the value of the insert or replace operations is inserted (Line 50) in the resulting table. Line 47 maps in this case the replace operation to an insert operation. For example:\n\nIt is therefore possible to extend a table with new variable and value rows by using either the insert or replace operation.\n\nThe algorithm traverses the original table and the delta table in parallel. The recursive call in Line 54 is responsible for the operations match, remove, and replace.Footnote 22 The recursive call in Line 50 handles the insert operation, where the current node of the original table is passed as the first argument rather than the current child of the original table. Delaying the recursive step on the original table results in the described semantics of the insert operation.\n\nThe recursive call in Line 37 is used for convenience. If either the remove or the match operation occurs as the last condition in a condition hierarchy, but the condition cell c of the original table has additional subconditions (this is checked by isLastConditionNode()), the remove or the match operation is applied to all subcondition of c. For example, let AB, and C be condition cells, then\n\nThe reader may have noticed that unlike the other operations, the remove operation is not handled in the loop in Lines 5262. When a remove operation occurs, a temporary cell is created in the resulting table that is marked to be removed (Line 38). The marked cells are removed in Line 57. Removing nodes at the end of the recursion simplifies the implementation, because removing a node c pulls up its children $$c_1,\\ldots ,c_n$$ to the current level. Hence, by removing a node the number of siblings at the current level may decrease, stay constant, or increase. In the following example, we assume that A is not a condition cell.\n\n### Example 5\n\n(An execution trace of the method apply) For a complete example consider the following delta-table application, which already has been presented at the end of Sect. 4.2.1 and in Example 4 of “Executable specification in Haskell” of appendix:\n\nExecuting the method apply in (Lines 3464) will result in the following recursive invocations of the method:\n\nAt the end of the last recursion step of the apply algorithm (Line 57 in Listing 14), the method deleteTemporaryRemoveNodes is called which removes the temporary “$$-$$” node from the resulting tree res that is introduced at recursion step 5.\n\n## Rights and permissions\n\nReprints and Permissions" ]
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http://www.spectralpython.net/class_func_ref.html
[ "# Class/Function Documentation¶\n\n## File I/O¶\n\n### open_image¶\n\nopen_image(file)\n\nLocates & opens the specified hyperspectral image.\n\nArguments:\n\nfile (str):\n\nName of the file to open.\n\nReturns:\n\nSpyFile object to access the file.\n\nRaises:\n\nIOError.\n\nThis function attempts to determine the associated file type and open the file. If the specified file is not found in the current directory, all directories listed in the SPECTRAL_DATA environment variable will be searched until the file is found. If the file being opened is an ENVI file, the file argument should be the name of the header file.\n\n### ImageArray¶\n\nclass ImageArray(data, spyfile)\n\nImageArray is an interface to an image loaded entirely into memory. ImageArray objects are returned by spectral.SpyFile.load. This class inherits from both numpy.ndarray and Image, providing the interfaces of both classes.\n\n### SpyFile¶\n\nclass SpyFile(params, metadata=None)\n\nA base class for accessing spectral image files\n\n__getitem__(args)\n\nSubscripting operator that provides a numpy-like interface. Usage:\n\nx = img[i, j]\nx = img[i, j, k]\n\n\nArguments:\n\ni, j, k (int or slice object)\n\nInteger subscript indices or slice objects.\n\nThe subscript operator emulates the numpy.ndarray subscript operator, except data are read from the corresponding image file instead of an array object in memory. For frequent access or when accessing a large fraction of the image data, consider calling spectral.SpyFile.load to load the data into an spectral.image.ImageArray object and using its subscript operator instead.\n\nExamples:\n\nRead the pixel at the 30th row and 51st column of the image:\n\npixel = img[29, 50]\n\n\nband = img[:, :, 9]\n\n\nRead the first 30 bands for a square sub-region of the image:\n\nregion = img[50:100, 50:100, :30]\n\n__str__()\n\nPrints basic parameters of the associated file.\n\nload(**kwargs)\n\nLoads entire image into memory in a spectral.image.ImageArray.\n\nKeyword Arguments:\n\ndtype (numpy.dtype):\n\nAn optional dtype to which the loaded array should be cast.\n\nscale (bool, default True):\n\nSpecifies whether any applicable scale factor should be applied to the data after loading.\n\nspectral.image.ImageArray is derived from both spectral.image.Image and numpy.ndarray so it supports the full numpy.ndarray interface. The returns object will have shape (M,N,B), where M, N, and B are the numbers of rows, columns, and bands in the image.\n\n#### SpyFile Subclasses¶\n\nSpyFile is an abstract base class. Subclasses of SpyFile (BipFile, BilFile, BsqFile) all implement a common set of additional methods. BipFile is shown here but the other two are similar.\n\nclass BipFile(params, metadata=None)\n\nA class to interface image files stored with bands interleaved by pixel.\n\nopen_memmap(**kwargs)\n\nReturns a new numpy.memmap object for image file data access.\n\nKeyword Arguments:\n\ninterleave (str, default ‘bip’):\n\nSpecifies the shape/interleave of the returned object. Must be one of [‘bip’, ‘bil’, ‘bsq’, ‘source’]. If not specified, the memmap will be returned as ‘bip’. If the interleave is ‘source’, the interleave of the memmap will be the same as the source data file. If the number of rows, columns, and bands in the file are R, C, and B, the shape of the returned memmap array will be as follows:\n\ninterleave\n\narray shape\n\n‘bip’\n\n(R, C, B)\n\n‘bil’\n\n(R, B, C)\n\n‘bsq’\n\n(B, R, C)\n\nwritable (bool, default False):\n\nIf writable is True, modifying values in the returned memmap will result in corresponding modification to the image data file.\n\nread_band(band, use_memmap=True)\n\nReads a single band from the image.\n\nArguments:\n\nband (int):\n\nuse_memmap (bool, default True):\n\nSpecifies whether the file’s memmap interface should be used to read the data. Setting this arg to True only has an effect if a memmap is being used (i.e., if img.using_memmap is True).\n\nReturns:\n\nnumpy.ndarray\n\nAn MxN array of values for the specified band.\n\nread_bands(bands, use_memmap=True)\n\nReads multiple bands from the image.\n\nArguments:\n\nbands (list of ints):\n\nuse_memmap (bool, default True):\n\nSpecifies whether the file’s memmap interface should be used to read the data. Setting this arg to True only has an effect if a memmap is being used (i.e., if img.using_memmap is True).\n\nReturns:\n\nnumpy.ndarray\n\nAn MxNxL array of values for the specified bands. M and N are the number of rows & columns in the image and L equals len(bands).\n\nread_pixel(row, col, use_memmap=True)\n\nReads the pixel at position (row,col) from the file.\n\nArguments:\n\nrow, col (int):\n\nIndices of the row & column for the pixel\n\nuse_memmap (bool, default True):\n\nSpecifies whether the file’s memmap interface should be used to read the data. Setting this arg to True only has an effect if a memmap is being used (i.e., if img.using_memmap is True).\n\nReturns:\n\nnumpy.ndarray\n\nA length-B array, where B is the number of image bands.\n\nread_subimage(rows, cols, bands=None, use_memmap=False)\n\nReads arbitrary rows, columns, and bands from the image.\n\nArguments:\n\nrows (list of ints):\n\ncols (list of ints):\n\nbands (list of ints):\n\nOptional list of bands to read. If not specified, all bands are read.\n\nuse_memmap (bool, default False):\n\nSpecifies whether the file’s memmap interface should be used to read the data. Setting this arg to True only has an effect if a memmap is being used (i.e., if img.using_memmap is True).\n\nReturns:\n\nnumpy.ndarray\n\nAn MxNxL array, where M = len(rows), N = len(cols), and L = len(bands) (or # of image bands if bands == None).\n\nread_subregion(row_bounds, col_bounds, bands=None, use_memmap=True)\n\nReads a contiguous rectangular sub-region from the image.\n\nArguments:\n\nrow_bounds (2-tuple of ints):\n\n(a, b) -> Rows a through b-1 will be read.\n\ncol_bounds (2-tuple of ints):\n\n(a, b) -> Columnss a through b-1 will be read.\n\nbands (list of ints):\n\nOptional list of bands to read. If not specified, all bands are read.\n\nuse_memmap (bool, default True):\n\nSpecifies whether the file’s memmap interface should be used to read the data. Setting this arg to True only has an effect if a memmap is being used (i.e., if img.using_memmap is True).\n\nReturns:\n\nnumpy.ndarray\n\nAn MxNxL array.\n\n### SubImage¶\n\nclass SubImage(image, row_range, col_range)\n\nRepresents a rectangular sub-region of a larger SpyFile object.\n\nread_band(band)\n\nReads a single band from the image.\n\nArguments:\n\nband (int):\n\nReturns:\n\nnumpy.ndarray\n\nAn MxN array of values for the specified band.\n\nread_bands(bands)\n\nReads multiple bands from the image.\n\nArguments:\n\nbands (list of ints):\n\nReturns:\n\nnumpy.ndarray\n\nAn MxNxL array of values for the specified bands. M and N are the number of rows & columns in the image and L equals len(bands).\n\nread_pixel(row, col)\n\nReads the pixel at position (row,col) from the file.\n\nArguments:\n\nrow, col (int):\n\nIndices of the row & column for the pixel\n\nReturns:\n\nnumpy.ndarray\n\nA length-B array, where B is the number of image bands.\n\nread_subimage(rows, cols, bands=[])\n\nReads arbitrary rows, columns, and bands from the image.\n\nArguments:\n\nrows (list of ints):\n\ncols (list of ints):\n\nbands (list of ints):\n\nOptional list of bands to read. If not specified, all bands are read.\n\nReturns:\n\nnumpy.ndarray\n\nAn MxNxL array, where M = len(rows), N = len(cols), and L = len(bands) (or # of image bands if bands == None).\n\nread_subregion(row_bounds, col_bounds, bands=None)\n\nReads a contiguous rectangular sub-region from the image.\n\nArguments:\n\nrow_bounds (2-tuple of ints):\n\n(a, b) -> Rows a through b-1 will be read.\n\ncol_bounds (2-tuple of ints):\n\n(a, b) -> Columnss a through b-1 will be read.\n\nbands (list of ints):\n\nOptional list of bands to read. If not specified, all bands are read.\n\nReturns:\n\nnumpy.ndarray\n\nAn MxNxL array.\n\n### File Formats¶\n\n#### AVIRIS¶\n\nFunctions for handling AVIRIS image files.\n\nopen(file, band_file=None)\n\nReturns a SpyFile object for an AVIRIS image file.\n\nArguments:\n\nfile (str):\n\nName of the AVIRIS data file.\n\nband_file (str):\n\nOptional name of the AVIRIS spectral calibration file.\n\nReturns:\n\nA SpyFile object for the image file.\n\nRaises:\n\nspectral.io.spyfile.InvalidFileError\n\nread_aviris_bands(cal_filename)\n\nReturns a BandInfo object for an AVIRIS spectral calibration file.\n\nArguments:\n\ncal_filename (str):\n\nName of the AVIRIS spectral calibration file.\n\nReturns:\n\n#### ENVI¶\n\nENVI 1 is a popular commercial software package for processing and analyzing geospatial imagery. SPy supports reading imagery with associated ENVI header files and reading & writing spectral libraries with ENVI headers. ENVI files are opened automatically by the SPy image function but can also be called explicitly. It may be necessary to open an ENVI file explicitly if the data file is in a separate directory from the header or if the data file has an unusual file extension that SPy can not identify.\n\n>>> import spectral.io.envi as envi\n>>> img = envi.open('cup95eff.int.hdr', '/Users/thomas/spectral_data/cup95eff.int')\n\n1\n\nENVI is a registered trademark of Exelis, Inc.\n\nopen(file, image=None)\n\nOpens an image or spectral library with an associated ENVI HDR header file.\n\nArguments:\n\nfile (str):\n\nName of the header file for the image.\n\nimage (str):\n\nOptional name of the associated image data file.\n\nReturns:\n\nRaises:\n\nTypeError, EnviDataFileNotFoundError\n\nIf the specified file is not found in the current directory, all directories listed in the SPECTRAL_DATA environment variable will be searched until the file is found. Based on the name of the header file, this function will search for the image file in the same directory as the header, looking for a file with the same name as the header but different extension. Extensions recognized are .img, .dat, .sli, and no extension. Capitalized versions of the file extensions are also searched.\n\nclass SpectralLibrary(data, header=None, params=None)\n\nThe envi.SpectralLibrary class holds data contained in an ENVI-formatted spectral library file (.sli files), which stores data as specified by a corresponding .hdr header file. The primary members of an Envi.SpectralLibrary object are:\n\nspectra (numpy.ndarray):\n\nA subscriptable array of all spectra in the library. spectra will have shape CxB, where C is the number of spectra in the library and B is the number of bands for each spectrum.\n\nnames (list of str):\n\nA length-C list of names corresponding to the spectra.\n\nbands (spectral.BandInfo):\n\nSpectral bands associated with the library spectra.\n\nsave(file_basename, description=None)\n\nSaves the spectral library to a library file.\n\nArguments:\n\nfile_basename (str):\n\nName of the file (without extension) to save.\n\ndescription (str):\n\nOptional text description of the library.\n\nThis method creates two files: file_basename.hdr and file_basename.sli.\n\n### envi.create_image¶\n\ncreate_image(hdr_file, metadata=None, **kwargs)\n\nCreates an image file and ENVI header with a memmep array for write access.\n\nArguments:\n\nhdr_file (str):\n\nHeader file (with “.hdr” extension) name with path.\n\nMetadata to specify the image file format. The following parameters (in ENVI header format) are required, if not specified via corresponding keyword arguments: “bands”, “lines”, “samples”, and “data type”.\n\nKeyword Arguments:\n\ndtype (numpy dtype or type string):\n\nThe numpy data type with which to store the image. For example, to store the image in 16-bit unsigned integer format, the argument could be any of numpy.uint16, “u2”, “uint16”, or “H”. If this keyword is given, it will override the “data type” parameter in the metadata argument.\n\nforce (bool, False by default):\n\nIf the associated image file or header already exist and force is True, the files will be overwritten; otherwise, if either of the files exist, an exception will be raised.\n\next (str):\n\nThe extension to use for the image file. If not specified, the default extension “.img” will be used. If ext is an empty string, the image file will have the same name as the header but without the “.hdr” extension.\n\ninterleave (str):\n\nMust be one of “bil”, “bip”, or “bsq”. This keyword supercedes the value of “interleave” in the metadata argument, if given. If no interleave is specified (via keyword or metadata), “bip” is assumed.\n\nshape (tuple of integers):\n\nSpecifies the number of rows, columns, and bands in the image. This keyword should be either of the form (R, C, B) or (R, C), where R, C, and B specify the number or rows, columns, and bands, respectively. If B is omitted, the number of bands is assumed to be one. If this keyword is given, its values supercede the values of “bands”, “lines”, and “samples” if they are present in the metadata argument.\n\noffset (integer, default 0):\n\nThe offset (in bytes) of image data from the beginning of the file. This value supercedes the value of “header offset” in the metadata argument (if given).\n\nReturns:\n\nSpyFile object:\n\nTo access a numpy.memmap for the returned SpyFile object, call the open_memmap method of the returned object.\n\nExamples:\n\nCreating a new image from metadata:\n\n>>> md = {'lines': 30,\n'samples': 40,\n'bands': 50,\n'data type': 12}\n>>> img = envi.create_image('new_image.hdr', md)\n\n\nCreating a new image via keywords:\n\n>>> img = envi.create_image('new_image2.hdr',\nshape=(30, 40, 50),\ndtype=np.uint16)\n\n\nWriting to the new image using a memmap interface:\n\n>>> # Set all band values for a single pixel to 100.\n>>> mm = img.open_memmap(writable=True)\n>>> mm[30, 30] = 100\n\n\n### envi.save_classification¶\n\nsave_classification(hdr_file, image, **kwargs)\n\nSaves a classification image to disk.\n\nArguments:\n\nhdr_file (str):\n\nHeader file (with “.hdr” extension) name with path.\n\nimage (SpyFile object or numpy.ndarray):\n\nThe image to save.\n\nKeyword Arguments:\n\ndtype (numpy dtype or type string):\n\nThe numpy data type with which to store the image. For example, to store the image in 16-bit unsigned integer format, the argument could be any of numpy.uint16, “u2”, “uint16”, or “H”.\n\nforce (bool):\n\nIf the associated image file or header already exist and force is True, the files will be overwritten; otherwise, if either of the files exist, an exception will be raised.\n\next (str):\n\nThe extension to use for the image file. If not specified, the default extension “.img” will be used. If ext is an empty string, the image file will have the same name as the header but without the “.hdr” extension.\n\ninterleave (str):\n\nThe band interleave format to use in the file. This argument should be one of “bil”, “bip”, or “bsq”. If not specified, the image will be written in BIP interleave.\n\nbyteorder (int or string):\n\nSpecifies the byte order (endian-ness) of the data as written to disk. For little endian, this value should be either 0 or “little”. For big endian, it should be either 1 or “big”. If not specified, native byte order will be used.\n\nA dict containing ENVI header parameters (e.g., parameters extracted from a source image).\n\nclass_names (array of strings):\n\nFor classification results, specifies the names to assign each integer in the class map being written. If not given, default class names are created.\n\nclass_colors (array of RGB-tuples):\n\nFor classification results, specifies colors to assign each integer in the class map being written. If not given, default colors are automatically generated.\n\nIf the source image being saved was already in ENVI format, then the SpyFile object for that image will contain a metadata dict that can be passed as the metadata keyword. However, care should be taken to ensure that all the metadata fields from the source image are still accurate (e.g., wavelengths do not apply to classification results).\n\n### envi.save_image¶\n\nsave_image(hdr_file, image, **kwargs)\n\nSaves an image to disk.\n\nArguments:\n\nhdr_file (str):\n\nHeader file (with “.hdr” extension) name with path.\n\nimage (SpyFile object or numpy.ndarray):\n\nThe image to save.\n\nKeyword Arguments:\n\ndtype (numpy dtype or type string):\n\nThe numpy data type with which to store the image. For example, to store the image in 16-bit unsigned integer format, the argument could be any of numpy.uint16, “u2”, “uint16”, or “H”.\n\nforce (bool):\n\nIf the associated image file or header already exist and force is True, the files will be overwritten; otherwise, if either of the files exist, an exception will be raised.\n\next (str or None):\n\nThe extension to use for the image file. If not specified, the default extension “.img” will be used. If ext is an empty string or is None, the image file will have the same name as the header but without the “.hdr” extension.\n\ninterleave (str):\n\nThe band interleave format to use in the file. This argument should be one of “bil”, “bip”, or “bsq”. If not specified, the image will be written in BIP interleave.\n\nbyteorder (int or string):\n\nSpecifies the byte order (endian-ness) of the data as written to disk. For little endian, this value should be either 0 or “little”. For big endian, it should be either 1 or “big”. If not specified, native byte order will be used.\n\nA dict containing ENVI header parameters (e.g., parameters extracted from a source image).\n\nExample:\n\n>>> # Save the first 10 principal components of an image\n>>> pc = principal_components(data)\n>>> pcdata = pc.reduce(num=10).transform(data)\n>>> envi.save_image('pcimage.hdr', pcdata, dtype=np.float32)\n\n\nIf the source image being saved was already in ENVI format, then the SpyFile object for that image will contain a metadata dict that can be passed as the metadata keyword. However, care should be taken to ensure that all the metadata fields from the source image are still accurate (e.g., band names or wavelengths will no longer be correct if the data being saved are from a principal components transformation).\n\n### erdas.open¶\n\nopen(file)\n\nReturns a SpyFile object for an ERDAS/Lan image file.\n\nArguments:\n\nfile (str):\n\nName of the ERDAS/Lan image data file.\n\nReturns:\n\nA SpyFile object for the image file.\n\nRaises:\n\nspectral.io.spyfile.InvalidFileError\n\n## Graphics¶\n\n### ColorScale¶\n\nclass ColorScale(levels, colors, num_tics=0)\n\nA color scale class to map scalar values to rgb colors. The class allows associating colors with particular scalar values, setting a background color (for values below threshold), andadjusting the scale limits. The __call__ operator takes a scalar input and returns the corresponding color, interpolating between defined colors.\n\n__call__(val)\n\nReturns the scale color associated with the given value.\n\n__init__(levels, colors, num_tics=0)\n\nCreates the ColorScale.\n\nArguments:\n\nlevels (list of numbers):\n\nScalar levels to which the colors argument will correspond.\n\ncolors (list of 3-tuples):\n\nRGB 3-tuples that define the colors corresponding to levels.\n\nnum_tics (int):\n\nThe total number of colors in the scale, not including the background color. This includes the colors given in the colors argument, as well as interpolated color values. If not specified, only the colors in the colors argument will be used (i.e., num_tics = len(colors).\n\nset_background_color()\n\nSets RGB color used for values below the scale minimum.\n\nArguments:\n\ncolor (3-tuple): An RGB triplet\n\nset_range(min, max)\n\nSets the min and max values of the color scale.\n\nThe distribution of colors within the scale will stretch or shrink accordingly.\n\n### get_rgb¶\n\nget_rgb(source, bands=None, **kwargs)\n\nExtract RGB data for display from a SpyFile object or numpy array.\n\nUSAGE: rgb = get_rgb(source [, bands] [, stretch=<arg> | , bounds=<arg>]\n\n[, stretch_all=<arg>])\n\nArguments:\n\nsource (spectral.SpyFile or numpy.ndarray):\n\nData source from which to extract the RGB data.\n\nbands (list of int) (optional):\n\nOptional triplet of indices which specifies the bands to extract for the red, green, and blue components, respectively. If this arg is not given, SpyFile object, it’s metadata dict will be checked to see if it contains a “default bands” item. If it does not, then first, middle and last band will be returned.\n\nKeyword Arguments:\n\nstretch (numeric or tuple):\n\nThis keyword specifies two points on the cumulative histogram of the input data for performing a linear stretch of RGB value for the data. Numeric values given for this parameter are expected to be between 0 and 1. This keyword can be expressed in three forms:\n\n1. As a 2-tuple. In this case the two values specify the lower and upper points of the cumulative histogram respectively. The specified stretch will be performed independently on each of the three color channels unless the stretch_all keyword is set to True, in which case all three color channels will be stretched identically.\n\n2. As a 3-tuple of 2-tuples. In this case, Each channel will be stretched according to its respective 2-tuple in the keyword argument.\n\n3. As a single numeric value. In this case, the value indicates the size of the histogram tail to be applied at both ends of the histogram for each color channel. stretch=a is equivalent to stretch=(a, 1-a).\n\nIf neither stretch nor bounds are specified, then the default value of stretch defined by spectral.settings.imshow_stretch will be used.\n\nbounds (tuple):\n\nThis keyword functions similarly to the stretch keyword, except numeric values are in image data units instead of cumulative histogram values. The form of this keyword is the same as the first two forms for the stretch keyword (i.e., either a 2-tuple of numbers or a 3-tuple of 2-tuples of numbers).\n\nstretch_all (bool):\n\nIf this keyword is True, each color channel will be scaled independently.\n\ncolor_scale (ColorScale):\n\nA color scale to be applied to a single-band image.\n\nauto_scale (bool):\n\nIf color_scale is provided and auto_scale is True, the min/max values of the color scale will be mapped to the min/max data values.\n\ncolors (ndarray):\n\nIf source is a single-band integer-valued np.ndarray and this keyword is provided, then elements of source are assumed to be color index values that specify RGB values in colors.\n\nExamples:\n\nSelect color limits corresponding to 2% tails in the data histogram:\n\n>>> imshow(x, stretch=0.02)\n\n\nSame as above but specify upper and lower limits explicitly:\n\n>>> imshow(x, stretch=(0.02, 0.98))\n\n\nSame as above but specify limits for each RGB channel explicitly:\n\n>>> imshow(x, stretch=((0.02, 0.98), (0.02, 0.98), (0.02, 0.98)))\n\n\n### ImageView¶\n\nclass ImageView(data=None, bands=None, classes=None, source=None, **kwargs)\n\nClass to manage events and data associated with image raster views.\n\nIn most cases, it is more convenient to simply call imshow, which creates, displays, and returns an ImageView object. Creating an ImageView object directly (or creating an instance of a subclass) enables additional customization of the image display (e.g., overriding default event handlers). If the object is created directly, call the show method to display the image. The underlying image display functionality is implemented via matplotlib.pyplot.imshow.\n\n__init__(data=None, bands=None, classes=None, source=None, **kwargs)\n\nArguments:\n\ndata (ndarray or SpyFile):\n\nThe source of RGB bands to be displayed. with shape (R, C, B). If the shape is (R, C, 3), the last dimension is assumed to provide the red, green, and blue bands (unless the bands argument is provided). If", null, "and bands is not provided, the first, middle, and last band will be used.\n\nbands (triplet of integers):\n\nSpecifies which bands in data should be displayed as red, green, and blue, respectively.\n\nclasses (ndarray of integers):\n\nAn array of integer-valued class labels with shape (R, C). If the data argument is provided, the shape must match the first two dimensions of data.\n\nsource (ndarray or SpyFile):\n\nThe source of spectral data associated with the image display. This optional argument is used to access spectral data (e.g., to generate a spectrum plot when a user double-clicks on the image display.\n\nKeyword arguments:\n\nAny keyword that can be provided to get_rgb or matplotlib.imshow.\n\nproperty class_alpha\n\nalpha transparency for the class overlay.\n\nformat_coord(x, y)\n\nFormats pixel coordinate string displayed in the window.\n\nproperty interpolation\n\nmatplotlib pixel interpolation to use in the image display.\n\nlabel_region(rectangle, class_id)\n\nAssigns all pixels in the rectangle to the specified class.\n\nArguments:\n\nrectangle (4-tuple of integers):\n\nTuple or list defining the rectangle bounds. Should have the form (row_start, row_stop, col_start, col_stop), where the stop indices are not included (i.e., the effect is classes[row_start:row_stop, col_start:col_stop] = id.\n\nclass_id (integer >= 0):\n\nThe class to which pixels will be assigned.\n\nReturns the number of pixels reassigned (the number of pixels in the rectangle whose class has changed to class_id.\n\nopen_zoom(center=None, size=None)\n\nOpens a separate window with a zoomed view. If a ctrl-lclick event occurs in the original view, the zoomed window will pan to the location of the click event.\n\nArguments:\n\ncenter (two-tuple of int):\n\nInitial (row, col) of the zoomed view.\n\nsize (int):\n\nWidth and height (in source image pixels) of the initial zoomed view.\n\nReturns:\n\nA new ImageView object for the zoomed view.\n\npan_to(row, col)\n\nCenters view on pixel coordinate (row, col).\n\nrefresh()\n\nUpdates the displayed data (if it has been shown).\n\nset_classes(classes, colors=None, **kwargs)\n\nSets the array of class values associated with the image data.\n\nArguments:\n\nclasses (ndarray of int):\n\nclasses must be an integer-valued array with the same number rows and columns as the display data (if set).\n\ncolors: (array or 3-tuples):\n\nColor triplets (with values in the range [0, 255]) that define the colors to be associatd with the integer indices in classes.\n\nKeyword Arguments:\n\nAny valid keyword for matplotlib.imshow can be provided.\n\nset_data(data, bands=None, **kwargs)\n\nSets the data to be shown in the RGB channels.\n\nArguments:\n\ndata (ndarray or SpyImage):\n\nIf data has more than 3 bands, the bands argument can be used to specify which 3 bands to display. data will be passed to get_rgb prior to display.\n\nbands (3-tuple of int):\n\nIndices of the 3 bands to display from data.\n\nKeyword Arguments:\n\nAny valid keyword for get_rgb or matplotlib.imshow can be given.\n\nset_display_mode(mode)\n\nmode must be one of (“data”, “classes”, “overlay”).\n\nset_rgb_options(**kwargs)\n\nSets parameters affecting RGB display of data.\n\nAccepts any keyword supported by get_rgb.\n\nset_source(source)\n\nSets the image data source (used for accessing spectral data).\n\nArguments:\n\nsource (ndarray or SpyFile):\n\nThe source for spectral data associated with the view.\n\nshow(mode=None, fignum=None)\n\nRenders the image data.\n\nArguments:\n\nmode (str):\n\nMust be one of:\n\n“data”: Show the data RGB\n\n“classes”: Shows indexed color for classes\n\n“overlay”: Shows class colors overlaid on data RGB.\n\nIf mode is not provided, a mode will be automatically selected, based on the data set in the ImageView.\n\nfignum (int):\n\nFigure number of the matplotlib figure in which to display the ImageView. If not provided, a new figure will be created.\n\n### imshow¶\n\nimshow(data=None, bands=None, classes=None, source=None, colors=None, figsize=None, fignum=None, title=None, **kwargs)\n\nA wrapper around matplotlib’s imshow for multi-band images.\n\nArguments:\n\ndata (SpyFile or ndarray):\n\nCan have shape (R, C) or (R, C, B).\n\nbands (tuple of integers, optional)\n\nIf bands has 3 values, the bands specified are extracted from data to be plotted as the red, green, and blue colors, respectively. If it contains a single value, then a single band will be extracted from the image.\n\nclasses (ndarray of integers):\n\nAn array of integer-valued class labels with shape (R, C). If the data argument is provided, the shape must match the first two dimensions of data. The returned ImageView object will use a copy of this array. To access class values that were altered after calling imshow, access the classes attribute of the returned ImageView object.\n\nsource (optional, SpyImage or ndarray):\n\nObject used for accessing image source data. If this argument is not provided, events such as double-clicking will have no effect (i.e., a spectral plot will not be created).\n\ncolors (optional, array of ints):\n\nCustom colors to be used for class image view. If provided, this argument should be an array of 3-element arrays, each of which specifies an RGB triplet with integer color components in the range [0, 256).\n\nfigsize (optional, 2-tuple of scalar):\n\nSpecifies the width and height (in inches) of the figure window to be created. If this value is not provided, the value specified in spectral.settings.imshow_figure_size will be used.\n\nfignum (optional, integer):\n\nSpecifies the figure number of an existing matplotlib figure. If this argument is None, a new figure will be created.\n\ntitle (str):\n\nThe title to be displayed above the image.\n\nKeywords:\n\nKeywords accepted by get_rgb or matplotlib.imshow will be passed on to the appropriate function.\n\nThis function defaults the color scale (imshow’s “cmap” keyword) to “gray”. To use imshow’s default color scale, call this function with keyword cmap=None.\n\nReturns:\n\nAn ImageView object, which can be subsequently used to refine the image display.\n\nSee ImageView for additional details.\n\nExamples:\n\nShow a true color image of a hyperspectral image:\n\n>>> data = open_image('92AV3C.lan').load()\n>>> view = imshow(data, bands=(30, 20, 10))\n\n\nShow ground truth in a separate window:\n\n>>> classes = open_image('92AV3GT.GIS').read_band(0)\n>>> cview = imshow(classes=classes)\n\n\nOverlay ground truth data on the data display:\n\n>>> view.set_classes(classes)\n>>> view.set_display_mode('overlay')\n\n\nShow RX anomaly detector results in the view and a zoom window showing true color data:\n\n>>> x = rx(data)\n>>> zoom = view.open_zoom()\n>>> view.set_data(x)\n\n\nNote that pressing ctrl-lclick with the mouse in the main window will cause the zoom window to pan to the clicked location.\n\nOpening zoom windows, changing display modes, and other functions can also be achieved via keys mapped directly to the displayed image. Press “h” with focus on the displayed image to print a summary of mouse/ keyboard commands accepted by the display.\n\n### save_rgb¶\n\nsave_rgb(filename, data, bands=None, **kwargs)\n\nSaves a viewable image to a JPEG (or other format) file.\n\nUsage:\n\nsave_rgb(filename, data, bands=None, **kwargs)\n\n\nArguments:\n\nfilename (str):\n\nName of image file to save (e.g. “rgb.jpg”)\n\ndata (spectral.Image or numpy.ndarray):\n\nSource image data to display. data can be and instance of a spectral.Image (e.g., spectral.SpyFile or spectral.ImageArray) or a numpy.ndarray. data must have shape MxN or MxNxB. If thes shape is MxN, the image will be saved as greyscale (unless keyword colors is specified). If the shape is MxNx3, it will be interpreted as three MxN images defining the R, G, and B channels respectively. If B > 3, the first, middle, and last images in data will be used, unless bands is specified.\n\nbands (3-tuple of ints):\n\nOptional list of indices for bands to use in the red, green, and blue channels, respectively.\n\nKeyword Arguments:\n\nformat (str):\n\nThe image file format to create. Must be a format recognized by PIL (e.g., ‘png’, ‘tiff’, ‘bmp’). If format is not provided, ‘jpg’ is assumed.\n\nSee get_rgb for descriptions of additional keyword arguments.\n\nExamples:\n\nSave a color view of an image by specifying RGB band indices:\n\nsave_image('rgb.jpg', img, [29, 19, 9]])\n\n\nSave the same image as png:\n\nsave_image('rgb.png', img, [29, 19, 9]], format='png')\n\n\nSave classification results using the default color palette (note that the color palette must be passed explicitly for clMap to be interpreted as a color map):\n\nsave_image('results.jpg', clMap, colors=spectral.spy_colors)\n\n\n### view¶\n\nview(*args, **kwargs)\n\nOpens a window and displays a raster greyscale or color image.\n\nUsage:\n\nview(source, bands=None, **kwargs)\n\n\nArguments:\n\nsource (spectral.Image or numpy.ndarray):\n\nSource image data to display. source can be and instance of a spectral.Image (e.g., spectral.SpyFile or spectral.ImageArray) or a numpy.ndarray. source must have shape MxN or MxNxB.\n\nbands (3-tuple of ints):\n\nOptional list of indices for bands to display in the red, green, and blue channels, respectively.\n\nKeyword Arguments:\n\nstretch (bool):\n\nIf stretch evaluates True, the highest value in the data source will be scaled to maximum color channel intensity.\n\nstretch_all (bool):\n\nIf stretch_all evaluates True, the highest value of the data source in each color channel will be set to maximum intensity.\n\nbounds (2-tuple of ints):\n\nClips the input data at (lower, upper) values.\n\ntitle (str):\n\nText to display in the new window frame.\n\nsource is the data source and can be either a spectral.Image object or a numpy array. If source has shape MxN, the image will be displayed in greyscale. If its shape is MxNx3, the three layers/bands will be displayed as the red, green, and blue components of the displayed image, respectively. If its shape is MxNxB, where B > 3, the first, middle, and last bands will be displayed in the RGB channels, unless bands is specified.\n\n### view_cube¶\n\nview_cube(data, *args, **kwargs)\n\nRenders an interactive 3D hypercube in a new window.\n\nArguments:\n\ndata (spectral.Image or numpy.ndarray):\n\nSource image data to display. data can be and instance of a spectral.Image (e.g., spectral.SpyFile or spectral.ImageArray) or a numpy.ndarray. source must have shape MxN or MxNxB.\n\nKeyword Arguments:\n\nbands (3-tuple of ints):\n\n3-tuple specifying which bands from the image data should be displayed on top of the cube.\n\ntop (numpy.ndarray or PIL.Image):\n\nData to display on top of the cube. This will supercede the bands keyword.\n\nA color scale to be used for color in the sides of the cube. If this keyword is not specified, spectral.graphics.colorscale.defaultColorScale is used.\n\nsize (2-tuple of ints):\n\nWidth and height (in pixels) for initial size of the new window.\n\nbackground (3-tuple of floats):\n\nBackground RGB color of the scene. Each value should be in the range [0, 1]. If not specified, the background will be black.\n\ntitle (str):\n\nTitle text to display in the new window frame.\n\nThis function opens a new window, renders a 3D hypercube, and accepts keyboard input to manipulate the view of the hypercube. Accepted keyboard inputs are printed to the console output. Focus must be on the 3D window to accept keyboard input.\n\n### view_indexed¶\n\nview_indexed(*args, **kwargs)\n\nOpens a window and displays a raster image for the provided color map data.\n\nUsage:\n\nview_indexed(data, **kwargs)\n\n\nArguments:\n\ndata (numpy.ndarray):\n\nAn MxN array of integer values that correspond to colors in a color palette.\n\nKeyword Arguments:\n\ncolors (list of 3-tuples of ints):\n\nThis parameter provides an alternate color map to use for display. The parameter is a list of 3-tuples defining RGB values, where R, G, and B are in the range [0-255].\n\ntitle (str):\n\nText to display in the new window frame.\n\nThe default color palette used is defined by spectral.spy_colors.\n\n### view_nd¶\n\nview_nd(data, *args, **kwargs)\n\nCreates a 3D window that displays ND data from an image.\n\nArguments:\n\ndata (spectral.ImageArray or numpy.ndarray):\n\nSource image data to display. data can be and instance of a spectral.ImageArray or a :class:numpy.ndarray. source must have shape MxNxB, where M >= 3.\n\nKeyword Arguments:\n\nclasses (numpy.ndarray):\n\n2-dimensional array of integers specifying the classes of each pixel in data. classes must have the same dimensions as the first two dimensions of data.\n\nfeatures (list or list of integer lists):\n\nThis keyword specifies which bands/features from data should be displayed in the 3D window. It must be defined as one of the following:\n\n1. A length-3 list of integer feature IDs. In this case, the data points will be displayed in the positive x,y,z octant using features associated with the 3 integers.\n\n2. A length-6 list of integer feature IDs. In this case, each integer specifies a single feature index to be associated with the coordinate semi-axes x, y, z, -x, -y, and -z (in that order). Each octant will display data points using the features associated with the 3 semi-axes for that octant.\n\n3. A length-8 list of length-3 lists of integers. In this case, each length-3 list specfies the features to be displayed in a single octants (the same semi-axis can be associated with different features in different octants). Octants are ordered starting with the postive x,y,z octant and procede counterclockwise around the z-axis, then procede similarly around the negative half of the z-axis. An octant triplet can be specified as None instead of a list, in which case nothing will be rendered in that octant.\n\nlabels (list):\n\nList of labels to be displayed next to the axis assigned to a feature. If not specified, the feature index is shown by default.\n\nThe str() function will be called on each item of the list so, for example, a list of wavelengths can be passed as the labels.\n\nsize (2-tuple of ints)\n\nSpecifies the initial size (pixel rows/cols) of the window.\n\ntitle (string)\n\nThe title to display in the ND window title bar.\n\nReturns an NDWindowProxy object with a classes member to access the current class labels associated with data points and a set_features member to specify which features are displayed.\n\n## Training Classes¶\n\n### create_training_classes¶\n\ncreate_training_classes(image, class_mask, calc_stats=False, indices=None)\n\nCreates a :class:spectral.algorithms.TrainingClassSet: from an indexed array.\n\nArguments:\n\nimage (spectral.Image or numpy.ndarray):\n\nThe image data for which the training classes will be defined. image has shape MxNxB.\n\nclass_mask (numpy.ndarray):\n\nA rank-2 array whose elements are indices of various spectral classes. if class_mask[i,j] == k, then image[i,j] is assumed to belong to class k.\n\ncalc_stats (bool):\n\nAn optional parameter which, if True, causes statistics to be calculated for all training classes.\n\nReturns:\n\nA spectral.algorithms.TrainingClassSet object.\n\nThe dimensions of classMask should be the same as the first two dimensions of the corresponding image. Values of zero in classMask are considered unlabeled and are not added to a training set.\n\n### TrainingClass¶\n\nclass TrainingClass(image, mask, index=0, class_prob=1.0)\n__init__(image, mask, index=0, class_prob=1.0)\n\nCreates a new training class defined by applying mask to image.\n\nArguments:\n\nimage (spectral.Image or numpy.ndarray):\n\nThe MxNxB image over which the training class is defined.\n\nmask (numpy.ndarray):\n\nAn MxN array of integers that specifies which pixels in image are associated with the class.\n\nindex (int) [default 0]:\n\nif index == 0, all nonzero elements of mask are associated with the class. If index is nonzero, all elements of mask equal to index are associated with the class.\n\nclass_prob (float) [default 1.0]:\n\nDefines the prior probability associated with the class, which is used in maximum likelihood classification. If classProb is 1.0, prior probabilities are ignored by classifiers, giving all class equal weighting.\n\n__iter__()\n\nReturns an iterator over all samples for the class.\n\ncalc_stats()\n\nCalculates statistics for the class.\n\nThis function causes the stats attribute of the class to be updated, where stats will have the following attributes:\n\nAttribute\n\nType\n\nDescription\n\nmean\n\nnumpy.ndarray\n\nlength-B mean vector\n\ncov\n\nnumpy.ndarray\n\nBxB covariance matrix\n\ninv_cov\n\nnumpy.ndarray\n\nInverse of cov\n\nlog_det_cov\n\nfloat\n\nNatural log of determinant of cov\n\nsize()\n\nReturns the number of pixels/samples in the training set.\n\nstats_valid(tf=None)\n\nSets statistics for the TrainingClass to be valid or invalid.\n\nArguments:\n\ntf (bool or None):\n\nA value evaluating to False indicates that statistics should be recalculated prior to being used. If the argument is None, a value will be returned indicating whether stats need to be recomputed.\n\ntransform(transform)\n\nPerform a linear transformation on the statistics of the training set.\n\nArguments:\n\ntransform (:class:numpy.ndarray or LinearTransform):\n\nThe linear transform array. If the class has B bands, then transform must have shape (C,B).\n\nAfter transform is applied, the class statistics will have C bands.\n\n### TraningClassSet¶\n\nclass TrainingClassSet\n\nA class to manage a set of TrainingClass objects.\n\n__getitem__(i)\n\nReturns the training class having ID i.\n\n__iter__()\n\nAn iterator over all training classes in the set.\n\n__len__()\n\nReturns number of training classes in the set.\n\nadd_class(cl)\n\nAdds a new class to the training set.\n\nArguments:\n\ncl (spectral.TrainingClass):\n\ncl.index must not duplicate a class already in the set.\n\nall_samples()\n\nAn iterator over all samples in all classes.\n\ntransform(X)\n\nApplies linear transform, M, to all training classes.\n\nArguments:\n\nX (:class:numpy.ndarray):\n\nThe linear transform array. If the classes have B bands, then X must have shape (C,B).\n\nAfter the transform is applied, all classes will have C bands.\n\n## Spectral Classes/Functions¶\n\nace(X, target, background=None, window=None, cov=None, **kwargs)\n\nReturns Adaptive Coherence/Cosine Estimator (ACE) detection scores.\n\nUsage:\n\ny = ace(X, target, background)\n\ny = ace(X, target, window=<win> [, cov=<cov>])\n\nArguments:\n\nX (numpy.ndarray):\n\nFor the first calling method shown, X can be an ndarray with shape (R, C, B) or an ndarray of shape (R * C, B). If the background keyword is given, it will be used for the image background statistics; otherwise, background statistics will be computed from X.\n\nIf the window keyword is given, X must be a 3-dimensional array and background statistics will be computed for each point in the image using a local window defined by the keyword.\n\ntarget (ndarray or sequence of ndarray):\n\nIf X has shape (R, C, B), target can be any of the following:\n\nA length-B ndarray. In this case, target specifies a single target spectrum to be detected. The return value will be an ndarray with shape (R, C).\n\nAn ndarray with shape (D, B). In this case, target contains D length-B targets that define a subspace for the detector. The return value will be an ndarray with shape (R, C).\n\nA length-D sequence (e.g., list or tuple) of length-B ndarrays. In this case, the detector will be applied seperately to each of the D targets. This is equivalent to calling the function sequentially for each target and stacking the results but is much faster. The return value will be an ndarray with shape (R, C, D).\n\nbackground (GaussianStats):\n\nThe Gaussian statistics for the background (e.g., the result of calling calc_stats for an image). This argument is not required if window is given.\n\nwindow (2-tuple of odd integers):\n\nMust have the form (inner, outer), where the two values specify the widths (in pixels) of inner and outer windows centered about the pixel being evaulated. Both values must be odd integers. The background mean and covariance will be estimated from pixels in the outer window, excluding pixels within the inner window. For example, if (inner, outer) = (5, 21), then the number of pixels used to estimate background statistics will be", null, ". If this argument is given, background is not required (and will be ignored, if given).\n\nThe window is modified near image borders, where full, centered windows cannot be created. The outer window will be shifted, as needed, to ensure that the outer window still has height and width outer (in this situation, the pixel being evaluated will not be at the center of the outer window). The inner window will be clipped, as needed, near image borders. For example, assume an image with 145 rows and columns. If the window used is (5, 21), then for the image pixel at (0, 0) (upper left corner), the the inner window will cover image[:3, :3] and the outer window will cover image[:21, :21]. For the pixel at (50, 1), the inner window will cover image[48:53, :4] and the outer window will cover image[40:51, :21].\n\ncov (ndarray):\n\nAn optional covariance to use. If this parameter is given, cov will be used for all matched filter calculations (background covariance will not be recomputed in each window) and only the background mean will be recomputed in each window. If the window argument is specified, providing cov will allow the result to be computed much faster.\n\nKeyword Arguments:\n\nvectorize (bool, default True):\n\nSpecifies whether the function should attempt to vectorize operations. This typicall results in faster computation but will consume more memory.\n\nReturns numpy.ndarray:\n\nThe return value will be the ACE scores for each input pixel. The shape of the returned array will be either (R, C) or (R, C, D), depending on the value of the target argument.\n\nReferences:\n\nKraut S. & Scharf L.L., “The CFAR Adaptive Subspace Detector is a Scale- Invariant GLRT,” IEEE Trans. Signal Processing., vol. 47 no. 9, pp. 2538-41, Sep. 1999\n\n### AsterDatabase¶\n\nclass AsterDatabase(sqlite_filename=None)\n\nA relational database to manage ASTER spectral library data.\n\nclassmethod create(filename, aster_data_dir=None)\n\nCreates an ASTER relational database by parsing ASTER data files.\n\nArguments:\n\nfilename (str):\n\nName of the new sqlite database file to create.\n\naster_data_dir (str):\n\nPath to the directory containing ASTER library data files. If this argument is not provided, no data will be imported.\n\nReturns:\n\nExample:\n\n>>> AsterDatabase.create(\"aster_lib.db\", \"/CDROM/ASTER2.0/data\")\n\n\nThis is a class method (it does not require instantiating an AsterDatabase object) that creates a new database by parsing all of the files in the ASTER library data directory. Normally, this should only need to be called once. Subsequently, a corresponding database object can be created by instantiating a new AsterDatabase object with the path the database file as its argument. For example:\n\n>>> from spectral.database.aster import AsterDatabase\n>>> db = AsterDatabase(\"aster_lib.db\")\n\ncreate_envi_spectral_library(spectrumIDs, bandInfo)\n\nCreates an ENVI-formatted spectral library for a list of spectra.\n\nArguments:\n\nspectrumIDs (list of ints):\n\nList of SpectrumID values for of spectra in the “Spectra” table of the ASTER database.\n\nbandInfo (BandInfo):\n\nThe spectral bands to which the original ASTER library spectra will be resampled.\n\nReturns:\n\nThe IDs passed to the method should correspond to the SpectrumID field of the ASTER database “Spectra” table. All specified spectra will be resampled to the same discretization specified by the bandInfo parameter. See spectral.BandResampler for details on the resampling method used.\n\nget_signature(spectrumID)\n\nUsage:\n\nsig = aster.get_signature(spectrumID)\n\n\nArguments:\n\nspectrumID (int):\n\nThe SpectrumID value for the desired spectrum from the Spectra table in the database.\n\nReturns:\n\nsig (Signature):\n\nAn object with the following attributes:\n\nAttribute\n\nType\n\nDescription\n\nmeasurement_id\n\nint\n\nSpectrumID value from Spectra table\n\nsample_name\n\nstr\n\nSample from the Samples table\n\nsample_id\n\nint\n\nSampleID from the Samples table\n\nx\n\nlist\n\nlist of band center wavelengths\n\ny\n\nlist\n\nlist of spectrum values for each band\n\nget_spectrum(spectrumID)\n\nReturns a spectrum from the database.\n\nUsage:\n\n(x, y) = aster.get_spectrum(spectrumID)\n\nArguments:\n\nspectrumID (int):\n\nThe SpectrumID value for the desired spectrum from the Spectra table in the database.\n\nReturns:\n\nx (list):\n\nBand centers for the spectrum.\n\ny (list):\n\nSpectrum data values for each band.\n\nReturns a pair of vectors containing the wavelengths and measured values values of a measurment. For additional metadata, call “get_signature” instead.\n\nprint_query(sql, args=None)\n\nPrints the text result of an arbitrary SQL statement.\n\nArguments:\n\nsql (str):\n\nAn SQL statement to be passed to the database. Use “?” for variables passed into the statement.\n\nargs (tuple):\n\nOptional arguments which will replace the “?” placeholders in the sql argument.\n\nThis function performs the same query function as spectral.database.Asterdatabase.query except query results are printed to stdout instead of returning a cursor object.\n\nExample:\n\n>>> sql = r'SELECT SpectrumID, Name FROM Samples, Spectra ' +\n... 'WHERE Spectra.SampleID = Samples.SampleID ' +\n... 'AND Name LIKE \"%grass%\" AND MinWavelength < ?'\n>>> args = (0.5,)\n>>> db.print_query(sql, args)\n356|dry grass\n357|grass\n\nquery(sql, args=None)\n\nReturns the result of an arbitrary SQL statement.\n\nArguments:\n\nsql (str):\n\nAn SQL statement to be passed to the database. Use “?” for variables passed into the statement.\n\nargs (tuple):\n\nOptional arguments which will replace the “?” placeholders in the sql argument.\n\nReturns:\n\nAn sqlite3.Cursor object with the query results.\n\nExample:\n\n>>> sql = r'SELECT SpectrumID, Name FROM Samples, Spectra ' +\n... 'WHERE Spectra.SampleID = Samples.SampleID ' +\n... 'AND Name LIKE \"%grass%\" AND MinWavelength < ?'\n>>> args = (0.5,)\n>>> cur = db.query(sql, args)\n>>> for row in cur:\n... print row\n...\n(356, u'dry grass')\n(357, u'grass')\n\n\n### BandInfo¶\n\nclass BandInfo\n\nA BandInfo object characterizes the spectral bands associated with an image. All BandInfo member variables are optional. For N bands, all members of type <list> will have length N and contain float values.\n\nMember\n\nDescription\n\nDefault\n\ncenters\n\nList of band centers\n\nNone\n\nbandwidths\n\nList of band FWHM values\n\nNone\n\ncenters_stdevs\n\nList of std devs of band centers\n\nNone\n\nbandwidth_stdevs\n\nList of std devs of bands FWHMs\n\nNone\n\nband_quantity\n\nImage data type (e.g., “reflectance”)\n\n“”\n\nband_unit\n\nBand unit (e.g., “nanometer”)\n\n“”\n\n### BandResampler¶\n\nclass BandResampler(centers1, centers2, fwhm1=None, fwhm2=None)\n\nA callable object for resampling spectra between band discretizations.\n\nA source band will contribute to any destination band where there is overlap between the FWHM of the two bands. If there is an overlap, an integral is performed over the region of overlap assuming the source band data value is constant over its FWHM (since we do not know the true spectral load over the source band) and the destination band has a Gaussian response function. Any target bands that do not have any overlapping source bands will contain NaN as the resampled band value.\n\nIf bandwidths are not specified for source or destination bands, the bands are assumed to have FWHM values that span half the distance to the adjacent bands.\n\n__call__(spectrum)\n\nTakes a source spectrum as input and returns a resampled spectrum.\n\nArguments:\n\nspectrum (list or numpy.ndarray):\n\nlist or vector of values to be resampled. Must have same length as the source band discretiation used to created the resampler.\n\nReturns:\n\nA resampled rank-1 numpy.ndarray with length corresponding to the destination band discretization used to create the resampler.\n\nAny target bands that do not have at lease one overlapping source band will contain float(‘nan’) as the resampled band value.\n\n__init__(centers1, centers2, fwhm1=None, fwhm2=None)\n\nBandResampler constructor.\n\nUsage:\n\nresampler = BandResampler(bandInfo1, bandInfo2)\n\nresampler = BandResampler(centers1, centers2, [fwhm1 = None [, fwhm2 = None]])\n\nArguments:\n\nbandInfo1 (BandInfo):\n\nDiscretization of the source bands.\n\nbandInfo2 (BandInfo):\n\nDiscretization of the destination bands.\n\ncenters1 (list):\n\nfloats defining center values of source bands.\n\ncenters2 (list):\n\nfloats defining center values of destination bands.\n\nfwhm1 (list):\n\nOptional list defining FWHM values of source bands.\n\nfwhm2 (list):\n\nOptional list defining FWHM values of destination bands.\n\nReturns:\n\nA callable BandResampler object that takes a spectrum corresponding to the source bands and returns the spectrum resampled to the destination bands.\n\nIf bandwidths are not specified, the associated bands are assumed to have FWHM values that span half the distance to the adjacent bands.\n\n### Bhattacharyya Distance¶\n\nbdist(class1, class2)\n\nCalulates the Bhattacharyya distance between two classes.\n\nUSAGE: bd = bdist(class1, class2)\n\nArguments:\n\nclass1, class2 (TrainingClass)\n\nReturns:\n\nA float value for the Bhattacharyya Distance between the classes. This function is aliased to bDistance.\n\nReferences:\n\nRichards, J.A. & Jia, X. Remote Sensing Digital Image Analysis: An Introduction. (Springer: Berlin, 1999).\n\nNote\n\nSince it is unlikely anyone can actually remember how to spell “Bhattacharyya”, this function has been aliased to “bdist” for convenience.\n\n### calc_stats¶\n\ncalc_stats(image, mask=None, index=None, allow_nan=False)\n\nComputes Gaussian stats for image data..\n\nArguments:\n\nimage (ndarrray, Image, or spectral.Iterator):\n\nIf an ndarray, it should have shape MxNxB and the mean & covariance will be calculated for each band (third dimension).\n\nIf mask is specified, mean & covariance will be calculated for all pixels indicated in the mask array. If index is specified, all pixels in image for which mask == index will be used; otherwise, all nonzero elements of mask will be used.\n\nindex (int):\n\nSpecifies which value in mask to use to select pixels from image. If not specified but mask is, then all nonzero elements of mask will be used.\n\nallow_nan (bool, default False):\n\nIf True, statistics will be computed even if np.nan values are present in the data; otherwise, ~spectral.algorithms.spymath.NaNValueError is raised.\n\nIf neither mask nor index are specified, all samples in vectors will be used.\n\nReturns:\n\nGaussianStats object:\n\nThis object will have members mean, cov, and nsamples.\n\n### covariance¶\n\ncovariance(*args)\n\nReturns the covariance of the set of vectors.\n\nUsage:\n\nC = covariance(vectors [, mask=None [, index=None]])\n\n\nArguments:\n\nvectors (ndarrray, Image, or spectral.Iterator):\n\nIf an ndarray, it should have shape MxNxB and the mean & covariance will be calculated for each band (third dimension).\n\nIf mask is specified, mean & covariance will be calculated for all pixels indicated in the mask array. If index is specified, all pixels in image for which mask == index will be used; otherwise, all nonzero elements of mask will be used.\n\nindex (int):\n\nSpecifies which value in mask to use to select pixels from image. If not specified but mask is, then all nonzero elements of mask will be used.\n\nIf neither mask nor index are specified, all samples in vectors will be used.\n\nReturns:\n\nC (ndarray):\n\nThe BxB unbiased estimate (dividing by N-1) of the covariance of the vectors.\n\nTo also return the mean vector and number of samples, call mean_cov instead.\n\n### cov_avg¶\n\ncov_avg(image, mask, weighted=True)\n\nCalculates the covariance averaged over a set of classes.\n\nArguments:\n\nimage (ndarrray, Image, or spectral.Iterator):\n\nIf an ndarray, it should have shape MxNxB and the mean & covariance will be calculated for each band (third dimension).\n\nElements specify the classes associated with pixels in image. All pixels associeted with non-zero elements of mask will be used in the covariance calculation.\n\nweighted (bool, default True):\n\nSpecifies whether the individual class covariances should be weighted when computing the average. If True, each class will be weighted by the number of pixels provided for the class; otherwise, a simple average of the class covariances is performed.\n\nReturns a class-averaged covariance matrix. The number of covariances used in the average is equal to the number of non-zero elements of mask.\n\n### EcostressDatabase¶\n\nclass EcostressDatabase(sqlite_filename=None)\n\nA relational database to manage ECOSTRESS spectral library data.\n\nclassmethod create(filename, data_dir=None)\n\nCreates an ECOSTRESS relational database by parsing ECOSTRESS data files.\n\nArguments:\n\nfilename (str):\n\nName of the new sqlite database file to create.\n\ndata_dir (str):\n\nPath to the directory containing ECOSTRESS library data files. If this argument is not provided, no data will be imported.\n\nReturns:\n\nExample:\n\n>>> EcostressDatabase.create(\"ecostress.db\", \"./eco_data_ver1/\")\n\n\nThis is a class method (it does not require instantiating an EcostressDatabase object) that creates a new database by parsing all of the files in the ECOSTRESS library data directory. Normally, this should only need to be called once. Subsequently, a corresponding database object can be created by instantiating a new EcostressDatabase object with the path the database file as its argument. For example:\n\n>>> from spectral.database.ecostress import EcostressDatabase\n>>> db = EcostressDatabase(\"~/ecostress.db\")\n\n\n### FisherLinearDiscriminant¶\n\nclass FisherLinearDiscriminant(vals, vecs, mean, cov_b, cov_w)\n\nAn object for storing a data set’s linear discriminant data. For C classes with B-dimensional data, the object has the following members:\n\neigenvalues:\n\nA length C-1 array of eigenvalues\n\neigenvectors:\n\nA BxC array of normalized eigenvectors\n\nmean:\n\nThe length B mean vector of the image pixels (from all classes)\n\ncov_b:\n\nThe BxB matrix of covariance between classes\n\ncov_w:\n\nThe BxB matrix of average covariance within each class\n\ntransform:\n\nA callable function to transform data to the space of the linear discriminant.\n\n### GaussianClassifier¶\n\nclass GaussianClassifier(training_data=None, min_samples=None)\n\nA Gaussian Maximum Likelihood Classifier\n\n__init__(training_data=None, min_samples=None)\n\nCreates the classifier and optionally trains it with training data.\n\nArguments:\n\ntraining_data (TrainingClassSet):\n\nThe training classes on which to train the classifier.\n\nmin_samples (int) [default None]:\n\nMinimum number of samples required from a training class to include it in the classifier.\n\nclassify_image(image)\n\nClassifies an entire image, returning a classification map.\n\nArguments:\n\nimage (ndarray or spectral.Image)\n\nThe MxNxB image to classify.\n\nReturns (ndarray):\n\nAn MxN ndarray of integers specifying the class for each pixel.\n\nclassify_spectrum(x)\n\nClassifies a pixel into one of the trained classes.\n\nArguments:\n\nx (list or rank-1 ndarray):\n\nThe unclassified spectrum.\n\nReturns:\n\nclassIndex (int):\n\nThe index for the TrainingClass to which x is classified.\n\ntrain(training_data)\n\nTrains the classifier on the given training data.\n\nArguments:\n\ntraining_data (TrainingClassSet):\n\nData for the training classes.\n\n### GaussianStats¶\n\nclass GaussianStats(mean=None, cov=None, nsamples=None, inv_cov=None)\n\nA class for storing Gaussian statistics for a data set.\n\nStatistics stored include:\n\nmean:\n\nMean vector\n\ncov:\n\nCovariance matrix\n\nnsamples:\n\nNumber of samples used in computing the statistics\n\nSeveral derived statistics are computed on-demand (and cached) and are available as property attributes. These include:\n\ninv_cov:\n\nInverse of the covariance\n\nsqrt_cov:\n\nMatrix square root of covariance: sqrt_cov.dot(sqrt_cov) == cov\n\nsqrt_inv_cov:\n\nMatrix square root of the inverse of covariance\n\nlog_det_cov:\n\nThe log of the determinant of the covariance matrix\n\nprincipal_components:\n\nThe principal components of the data, based on mean and cov.\n\n### kmeans¶\n\nkmeans(image, nclusters=10, max_iterations=20, **kwargs)\n\nPerforms iterative clustering using the k-means algorithm.\n\nArguments:\n\nimage (numpy.ndarray or spectral.Image):\n\nThe MxNxB image on which to perform clustering.\n\nnclusters (int) [default 10]:\n\nNumber of clusters to create. The number produced may be less than nclusters.\n\nmax_iterations (int) [default 20]:\n\nMax number of iterations to perform.\n\nKeyword Arguments:\n\nstart_clusters (numpy.ndarray) [default None]:\n\nnclusters x B array of initial cluster centers. If not provided, initial cluster centers will be spaced evenly along the diagonal of the N-dimensional bounding box of the image data.\n\ncompare (callable object) [default None]:\n\nOptional comparison function. compare must be a callable object that takes 2 MxN numpy.ndarray objects as its arguments and returns non-zero when clustering is to be terminated. The two arguments are the cluster maps for the previous and current cluster cycle, respectively.\n\ndistance (callable object) [default L2]:\n\nThe distance measure to use for comparison. The default is to use L2 (Euclidean) distance. For Manhattan distance, specify L1.\n\nframes (list) [default None]:\n\nIf this argument is given and is a list object, each intermediate cluster map is appended to the list.\n\nReturns a 2-tuple containing:\n\nclass_map (numpy.ndarray):\n\nAn MxN array whos values are the indices of the cluster for the corresponding element of image.\n\ncenters (numpy.ndarray):\n\nAn nclusters x B array of cluster centers.\n\nIterations are performed until clusters converge (no pixels reassigned between iterations), maxIterations is reached, or compare returns nonzero. If KeyboardInterrupt is generated (i.e., CTRL-C pressed) while the algorithm is executing, clusters are returned from the previously completed iteration.\n\n### linear_discriminant¶\n\nlinear_discriminant(classes, whiten=True)\n\nSolve Fisher’s linear discriminant for eigenvalues and eigenvectors.\n\nUsage: (L, V, Cb, Cw) = linear_discriminant(classes)\n\nArguments:\n\nclasses (TrainingClassSet):\n\nThe set of C classes to discriminate.\n\nReturns a FisherLinearDiscriminant object containing the within/between- class covariances, mean vector, and a callable transform to convert data to the transform’s space.\n\nThis function determines the solution to the generalized eigenvalue problem\n\nCb * x = lambda * Cw * x\n\nSince cov_w is normally invertable, the reduces to\n\n(inv(Cw) * Cb) * x = lambda * x\n\nReferences:\n\nRichards, J.A. & Jia, X. Remote Sensing Digital Image Analysis: An Introduction. (Springer: Berlin, 1999).\n\n### LinearTransform¶\n\nclass LinearTransform(A, **kwargs)\n\nA callable linear transform object.\n\nIn addition to the __call__ method, which applies the transform to given, data, a LinearTransform object also has the following members:\n\ndim_in (int):\n\nThe expected length of input vectors. This will be None if the input dimension is unknown (e.g., if the transform is a scalar).\n\ndim_out (int):\n\nThe length of output vectors (after linear transformation). This will be None if the input dimension is unknown (e.g., if the transform is a scalar).\n\ndtype (numpy dtype):\n\nThe numpy dtype for the output ndarray data.\n\n__call__(X)\n\nApplies the linear transformation to the given data.\n\nArguments:\n\nX (ndarray or object with transform method):\n\nIf X is an ndarray, it is either an (M,N,K) array containing M*N length-K vectors to be transformed or it is an (R,K) array of length-K vectors to be transformed. If X is an object with a method named transform the result of passing the LinearTransform object to the transform method will be returned.\n\nReturns an (M,N,J) or (R,J) array, depending on shape of X, where J is the length of the first dimension of the array A passed to __init__.\n\n__init__(A, **kwargs)\n\nArguments:\n\nA (ndarrray):\n\nAn (J,K) array to be applied to length-K targets.\n\nKeyword Argments:\n\npre (scalar or length-K sequence):\n\nAdditive offset to be applied prior to linear transformation.\n\npost (scalar or length-J sequence):\n\nAn additive offset to be applied after linear transformation.\n\ndtype (numpy dtype):\n\nExplicit type for transformed data.\n\nchain(transform)\n\nChains together two linear transforms. If the transform f1 is given by", null, "and f2 by", null, "then f1.chain(f2) returns a new LinearTransform, f3, whose output is given by", null, "### MahalanobisDistanceClassifier¶\n\nclass MahalanobisDistanceClassifier(training_data=None, min_samples=None)\n\nA Classifier using Mahalanobis distance for class discrimination\n\n__init__(training_data=None, min_samples=None)\n\nCreates the classifier and optionally trains it with training data.\n\nArguments:\n\ntraining_data (TrainingClassSet):\n\nThe training classes on which to train the classifier.\n\nmin_samples (int) [default None]:\n\nMinimum number of samples required from a training class to include it in the classifier.\n\nclassify_image(image)\n\nClassifies an entire image, returning a classification map.\n\nArguments:\n\nimage (ndarray or spectral.Image)\n\nThe MxNxB image to classify.\n\nReturns (ndarray):\n\nAn MxN ndarray of integers specifying the class for each pixel.\n\nclassify_spectrum(x)\n\nClassifies a pixel into one of the trained classes.\n\nArguments:\n\nx (list or rank-1 ndarray):\n\nThe unclassified spectrum.\n\nReturns:\n\nclassIndex (int):\n\nThe index for the TrainingClass to which x is classified.\n\ntrain(trainingData)\n\nTrains the classifier on the given training data.\n\nArguments:\n\ntrainingData (TrainingClassSet):\n\nData for the training classes.\n\n### map_class_ids¶\n\nmap_class_ids(src_class_image, dest_class_image, unlabeled=None)\n\nCreate a mapping between class labels in two classification images.\n\nRunning a classification algorithm (particularly an unsupervised one) multiple times on the same image can yield similar results but with different class labels (indices) for the same classes. This function produces a mapping of class indices from one classification image to another by finding class indices that share the most pixels between the two classification images.\n\nArguments:\n\nsrc_class_image (ndarray):\n\nAn MxN integer array of class indices. The indices in this array will be mapped to indices in dest_class_image.\n\ndest_class_image (ndarray):\n\nAn MxN integer array of class indices.\n\nunlabeled (int or array of ints):\n\nIf this argument is provided, all pixels (in both images) will be ignored when counting coincident pixels to determine the mapping. If mapping a classification image to a ground truth image that has a labeled background value, set unlabeled to that value.\n\nReturn Value:\n\nA dictionary whose keys are class indices from src_class_image and whose values are class indices from dest_class_image.\n\n### map_classes¶\n\nmap_classes(class_image, class_id_map, allow_unmapped=False)\n\nModifies class indices according to a class index mapping.\n\nArguments:\n\nclass_image: (ndarray):\n\nAn MxN array of integer class indices.\n\nclass_id_map: (dict):\n\nA dict whose keys are indices from class_image and whose values are new values for the corresponding indices. This value is usually the output of map_class_ids.\n\nallow_unmapped (bool, default False):\n\nA flag indicating whether class indices can appear in class_image without a corresponding key in class_id_map. If this value is False and an index in the image is found without a mapping key, a ValueError is raised. If True, the unmapped index will appear unmodified in the output image.\n\nReturn Value:\n\nAn integer-valued ndarray with same shape as class_image\n\nExample:\n\n>>> m = spy.map_class_ids(result, gt, unlabeled=0)\n>>> result_mapped = spy.map_classes(result, m)\n\n\n### matched_filter¶\n\nmatched_filter(X, target, background=None, window=None, cov=None)\n\nComputes a linear matched filter target detector score.\n\nUsage:\n\ny = matched_filter(X, target, background)\n\ny = matched_filter(X, target, window=<win> [, cov=<cov>])\n\nGiven target/background means and a common covariance matrix, the matched filter response is given by:", null, "where", null, "is the target mean,", null, "is the background mean, and", null, "is the covariance.\n\nArguments:\n\nX (numpy.ndarray):\n\nFor the first calling method shown, X can be an image with shape (R, C, B) or an ndarray of shape (R * C, B). If the background keyword is given, it will be used for the image background statistics; otherwise, background statistics will be computed from X.\n\nIf the window keyword is given, X must be a 3-dimensional array and background statistics will be computed for each point in the image using a local window defined by the keyword.\n\ntarget (ndarray):\n\nLength-K vector specifying the target to be detected.\n\nbackground (GaussianStats):\n\nThe Gaussian statistics for the background (e.g., the result of calling calc_stats for an image). This argument is not required if window is given.\n\nwindow (2-tuple of odd integers):\n\nMust have the form (inner, outer), where the two values specify the widths (in pixels) of inner and outer windows centered about the pixel being evaulated. Both values must be odd integers. The background mean and covariance will be estimated from pixels in the outer window, excluding pixels within the inner window. For example, if (inner, outer) = (5, 21), then the number of pixels used to estimate background statistics will be", null, ". If this argument is given, background is not required (and will be ignored, if given).\n\nThe window is modified near image borders, where full, centered windows cannot be created. The outer window will be shifted, as needed, to ensure that the outer window still has height and width outer (in this situation, the pixel being evaluated will not be at the center of the outer window). The inner window will be clipped, as needed, near image borders. For example, assume an image with 145 rows and columns. If the window used is (5, 21), then for the image pixel at (0, 0) (upper left corner), the the inner window will cover image[:3, :3] and the outer window will cover image[:21, :21]. For the pixel at (50, 1), the inner window will cover image[48:53, :4] and the outer window will cover image[40:51, :21].\n\ncov (ndarray):\n\nAn optional covariance to use. If this parameter is given, cov will be used for all matched filter calculations (background covariance will not be recomputed in each window) and only the background mean will be recomputed in each window. If the window argument is specified, providing cov will allow the result to be computed much faster.\n\nReturns numpy.ndarray:\n\nThe return value will be the matched filter scores distance) for each pixel given. If X has shape (R, C, K), the returned ndarray will have shape (R, C).\n\n### MatchedFilter¶\n\nclass MatchedFilter(background, target)\n\nA callable linear matched filter.\n\nGiven target/background means and a common covariance matrix, the matched filter response is given by:", null, "where", null, "is the target mean,", null, "is the background mean, and", null, "is the covariance.\n\n__call__(X)\n\nApplies the linear transformation to the given data.\n\nArguments:\n\nX (ndarray or object with transform method):\n\nIf X is an ndarray, it is either an (M,N,K) array containing M*N length-K vectors to be transformed or it is an (R,K) array of length-K vectors to be transformed. If X is an object with a method named transform the result of passing the LinearTransform object to the transform method will be returned.\n\nReturns an (M,N,J) or (R,J) array, depending on shape of X, where J is the length of the first dimension of the array A passed to __init__.\n\n__init__(background, target)\n\nCreates the filter, given background/target means and covariance.\n\nArguments:\n\nbackground (GaussianStats):\n\nThe Gaussian statistics for the background (e.g., the result of calling calc_stats).\n\ntarget (ndarray):\n\nLength-K target mean\n\nwhiten(X)\n\nTransforms data to the whitened space of the background.\n\nArguments:\n\nX (ndarray):\n\nSize (M,N,K) or (M*N,K) array of length K vectors to transform.\n\nReturns an array of same size as X but linearly transformed to the whitened space of the filter.\n\n### mean_cov¶\n\nmean_cov(image, mask=None, index=None)\n\nReturn the mean and covariance of the set of vectors.\n\nUsage:\n\n(mean, cov, S) = mean_cov(vectors [, mask=None [, index=None]])\n\n\nArguments:\n\nimage (ndarrray, Image, or spectral.Iterator):\n\nIf an ndarray, it should have shape MxNxB and the mean & covariance will be calculated for each band (third dimension).\n\nIf mask is specified, mean & covariance will be calculated for all pixels indicated in the mask array. If index is specified, all pixels in image for which mask == index will be used; otherwise, all nonzero elements of mask will be used.\n\nindex (int):\n\nSpecifies which value in mask to use to select pixels from image. If not specified but mask is, then all nonzero elements of mask will be used.\n\nIf neither mask nor index are specified, all samples in vectors will be used.\n\nReturns a 3-tuple containing:\n\nmean (ndarray):\n\nThe length-B mean vectors\n\ncov (ndarray):\n\nThe BxB unbiased estimate (dividing by N-1) of the covariance of the vectors.\n\nS (int):\n\nNumber of samples used to calculate mean & cov\n\nCalculate the mean and covariance of of the given vectors. The argument can be an Iterator, a SpyFile object, or an MxNxB array.\n\n### Minimum Noise Fraction (MNF)¶\n\nmnf(signal, noise)\n\nComputes Minimum Noise Fraction / Noise-Adjusted Principal Components.\n\nArguments:\n\nsignal (GaussianStats):\n\nEstimated signal statistics\n\nnoise (GaussianStats):\n\nEstimated noise statistics\n\nReturns an MNFResult object, containing the Noise-Adjusted Principal Components (NAPC) and methods for denoising or reducing dimensionality of associated data.\n\nThe Minimum Noise Fraction (MNF) is similar to the Principal Components transformation with the difference that the Principal Components associated with the MNF are ordered by descending signal-to-noise ratio (SNR) rather than overall image variance. Note that the eigenvalues of the NAPC are equal to one plus the SNR in the transformed space (since noise has whitened unit variance in the NAPC coordinate space).\n\nExample:\n\n>>> data = open_image('92AV3C.lan').load()\n>>> signal = calc_stats(data)\n>>> noise = noise_from_diffs(data[117: 137, 85: 122, :])\n>>> mnfr = mnf(signal, noise)\n\n>>> # De-noise the data by eliminating NAPC components where SNR < 10.\n>>> # The de-noised data will be in the original coordinate space (at\n>>> # full dimensionality).\n>>> denoised = mnfr.denoise(data, snr=10)\n\n>>> # Reduce dimensionality, retaining NAPC components where SNR >= 10.\n>>> reduced = mnfr.reduce(data, snr=10)\n\n>>> # Reduce dimensionality, retaining top 50 NAPC components.\n>>> reduced = mnfr.reduce(data, num=50)\n\n\nReferences:\n\nLee, James B., A. Stephen Woodyatt, and Mark Berman. “Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform.” Geoscience and Remote Sensing, IEEE Transactions on 28.3 (1990): 295-304.\n\nclass MNFResult(signal, noise, napc)\n\nResult object returned by mnf.\n\nThis object contains data associates with a Minimum Noise Fraction calculation, including signal and noise statistics, as well as the Noise-Adjusted Principal Components (NAPC). This object can be used to denoise image data or to reduce its dimensionality.\n\ndenoise(X, **kwargs)\n\nReturns a de-noised version of X.\n\nArguments:\n\nX (np.ndarray):\n\nData to be de-noised. Can be a single pixel or an image.\n\nOne (and only one) of the following keywords must be specified:\n\nnum (int):\n\nNumber of Noise-Adjusted Principal Components to retain.\n\nsnr (float):\n\nThreshold signal-to-noise ratio (SNR) to retain.\n\nReturns denoised image data with same shape as X.\n\nNote that calling this method is equivalent to calling the get_denoising_transform method with same keyword and applying the returned transform to X. If you only intend to denoise data with the same parameters multiple times, then it is more efficient to get the denoising transform and reuse it, rather than calling this method multilple times.\n\nget_denoising_transform(**kwargs)\n\nReturns a function for denoising image data.\n\nOne (and only one) of the following keywords must be specified:\n\nnum (int):\n\nNumber of Noise-Adjusted Principal Components to retain.\n\nsnr (float):\n\nThreshold signal-to-noise ratio (SNR) to retain.\n\nReturns a callable LinearTransform object for denoising image data.\n\nget_reduction_transform(**kwargs)\n\nReduces dimensionality of image data.\n\nOne (and only one) of the following keywords must be specified:\n\nnum (int):\n\nNumber of Noise-Adjusted Principal Components to retain.\n\nsnr (float):\n\nThreshold signal-to-noise ratio (SNR) to retain.\n\nReturns a callable LinearTransform object for reducing the dimensionality of image data.\n\nnum_with_snr(snr)\n\nReturns the number of components with SNR >= snr.\n\nreduce(X, **kwargs)\n\nReduces dimensionality of image data.\n\nArguments:\n\nX (np.ndarray):\n\nData to be reduced. Can be a single pixel or an image.\n\nOne (and only one) of the following keywords must be specified:\n\nnum (int):\n\nNumber of Noise-Adjusted Principal Components to retain.\n\nsnr (float):\n\nThreshold signal-to-noise ratio (SNR) to retain.\n\nReturns a verions of X with reduced dimensionality.\n\nNote that calling this method is equivalent to calling the get_reduction_transform method with same keyword and applying the returned transform to X. If you intend to denoise data with the same parameters multiple times, then it is more efficient to get the reduction transform and reuse it, rather than calling this method multilple times.\n\n### msam¶\n\nmsam(data, members)\n\nModified SAM scores according to Oshigami, et al . Endmembers are mean-subtracted prior to spectral angle calculation. Results are normalized such that the maximum value of 1 corresponds to a perfect match (zero spectral angle).\n\nArguments:\n\ndata (numpy.ndarray or spectral.Image):\n\nAn MxNxB image for which spectral angles will be calculated.\n\nmembers (numpy.ndarray):\n\nCxB array of spectral endmembers.\n\nReturns:\n\nMxNxC array of MSAM scores with maximum value of 1 corresponding to a perfect match (zero spectral angle).\n\nCalculates the spectral angles between each vector in data and each of the endmembers. The output of this function (angles) can be used to classify the data by minimum spectral angle by calling argmax(angles).\n\nReferences:\n\n Shoko Oshigami, Yasushi Yamaguchi, Tatsumi Uezato, Atsushi Momose, Yessy Arvelyna, Yuu Kawakami, Taro Yajima, Shuichi Miyatake, and Anna Nguno. 2013. Mineralogical mapping of southern Namibia by application of continuum-removal MSAM method to the HyMap data. Int. J. Remote Sens. 34, 15 (August 2013), 5282-5295.\n\n### ndvi¶\n\nndvi(data, red, nir)\n\nCalculates Normalized Difference Vegetation Index (NDVI).\n\nArguments:\n\ndata (ndarray or spectral.Image):\n\nThe array or SpyFile for which to calculate the index.\n\nred (int or int range):\n\nIndex of the red band or an index range for multiple bands.\n\nnir (int or int range):\n\nAn integer index of the near infrared band or an index range for multiple bands.\n\nReturns an ndarray:\n\nAn array containing NDVI values in the range [-1.0, 1.0] for each corresponding element of data.\n\n### noise_from_diffs¶\n\nnoise_from_diffs(X, direction='lowerright')\n\nEstimates noise statistcs by taking differences of adjacent pixels.\n\nArguments:\n\nX (np.ndarray):\n\nThe data from which to estimage noise statistics. X should have shape (nrows, ncols, nbands).\n\ndirection (str, default “lowerright”):\n\nThe pixel direction along which to calculate pixel differences. Must be one of the following:\n\n‘lowerright’:\n\nTake difference with pixel diagonally to lower right\n\n‘lowerleft’:\n\nTake difference with pixel diagonally to lower right\n\n‘right’:\n\nTake difference with pixel to the right\n\n‘lower’:\n\nTake differenece with pixel below\n\nReturns a GaussianStats object.\n\n### orthogonalize¶\n\northogonalize(vecs, start=0)\n\nPerforms Gram-Schmidt Orthogonalization on a set of vectors.\n\nArguments:\n\nvecs (numpy.ndarray):\n\nThe set of vectors for which an orthonormal basis will be created. If there are C vectors of length B, vecs should be CxB.\n\nstart (int) [default 0]:\n\nIf start > 0, then vecs[start] will be assumed to already be orthonormal.\n\nReturns:\n\nA new CxB containing an orthonormal basis for the given vectors.\n\n### PerceptronClassifier¶\n\nclass PerceptronClassifier(layers, k=1.0)\n\nA multi-layer perceptron classifier with backpropagation learning.\n\nMulti-layer perceptrons often require many (i.e., thousands) of iterations through the traning data to converge on a solution. Therefore, it is not recommended to attempt training a network on full-dimensional hyperspectral data or even on a full set of image pixels. It is likely preferable to first train the network on a subset of the data, then retrain the network (starting with network weights from initial training) on the full data set.\n\nExample usage: Train an MLP with 20 samples from each training class after performing dimensionality reduction:\n\n>>> classes = create_training_classes(data, gt)\n>>> fld = linear_discriminant(classes)\n>>> xdata = fld.transform(data)\n>>> classes = create_training_classes(xdata, gt)\n>>> nfeatures = xdata.shape[-1]\n>>> nclasses = len(classes)\n>>>\n>>> p = PerceptronClassifier([nfeatures, 20, 8, nclasses])\n>>> p.train(classes, 20, clip=0., accuracy=100., batch=1,\n>>> momentum=0.3, rate=0.3)\n>>> c = p.classify(xdata)\n\nclassify(X, **kwargs)\n\nClassifies the given sample. This has the same result as calling input and rounding the result.\n\nclassify_image(image)\n\nClassifies an entire image, returning a classification map.\n\nArguments:\n\nimage (ndarray or spectral.Image)\n\nThe MxNxB image to classify.\n\nReturns (ndarray):\n\nAn MxN ndarray of integers specifying the class for each pixel.\n\nclassify_spectrum(x)\n\nClassifies a pixel into one of the trained classes.\n\nArguments:\n\nx (list or rank-1 ndarray):\n\nThe unclassified spectrum.\n\nReturns:\n\nclassIndex (int):\n\nThe index for the TrainingClass to which x is classified.\n\ninput(x, clip=0.0)\n\nSets Perceptron input, activates neurons and sets & returns output.\n\nArguments:\n\nx (sequence):\n\nInputs to input layer. Should not include a bias input.\n\nclip (float >= 0):\n\nOptional clipping value to limit sigmoid output. The sigmoid function has output in the range (0, 1). If the clip argument is set to a then all neuron outputs for the layer will be constrained to the range [a, 1 - a]. This can improve perceptron learning rate in some situations.\n\nFor classifying samples, call classify instead of input.\n\ntrain(training_data, samples_per_class=0, *args, **kwargs)\n\nTrains the Perceptron on the training data.\n\nArguments:\n\ntraining_data (TrainingClassSet):\n\nData for the training classes.\n\nsamples_per_class (int):\n\nMaximum number of training observations to user from each class in training_data. If this argument is not provided, all training data is used.\n\nKeyword Arguments:\n\naccuracy (float):\n\nThe percent training accuracy at which to terminate training, if the maximum number of iterations are not reached first. This value can be set greater than 100 to force a specified number of training iterations to be performed (e.g., to continue reducing the error term after 100% classification accuracy has been achieved.\n\nrate (float):\n\nThe perceptron learning rate (typically in the range (0, 1]).\n\nmomentum (float):\n\nThe perceptron learning momentum term, which specifies the fraction of the previous update value that should be added to the current update term. The value should be in the range [0, 1).\n\nbatch (positive integer):\n\nSpecifies how many samples should be evaluated before an update is made to the perceptron weights. A value of 0 indicates batch updates should be performed (evaluate all training inputs prior to updating). Otherwise, updates will be aggregated for every batch inputs (i.e., batch == 1 is stochastic learning).\n\nclip (float >= 0):\n\nOptional clipping value to limit sigmoid output during training. The sigmoid function has output in the range (0, 1). If the clip argument is set to a then all neuron outputs for the layer will be constrained to the range [a, 1 - a]. This can improve perceptron learning rate in some situations.\n\nAfter training the perceptron with a clipping value, train can be called again with clipping set to 0 to continue reducing the training error.\n\non_iteration (callable):\n\nA callable object that accepts the perceptron as input and returns bool. If this argument is set, the object will be called at the end of each training iteration with the perceptron as its argument. If the callable returns True, training will terminate.\n\nstdout:\n\nAn object with a write method that can be set to redirect training status messages somewhere other than stdout. To suppress output, set stdout to None.\n\nReturn value:\n\nReturns True if desired accuracy was achieved.\n\nNeural networks can require many iterations through a data set to converge. If convergence slows (as indicated by small changes in residual error), training can be terminated by pressing CTRL-C, which will preserve the network weights from the previous training iteration. train can then be called again with altered training parameters (e.g., increased learning rate or momentum) to increase the convergence rate.\n\n### Pixel Purity Index (PPI)¶\n\nppi(X, niters, threshold=0, centered=False, start=None, display=0, **imshow_kwargs)\n\nReturns pixel purity indices for an image.\n\nArguments:\n\nX (ndarray):\n\nImage data for which to calculate pixel purity indices\n\nniters (int):\n\nNumber of iterations to perform. Each iteration corresponds to a projection of the image data onto a random unit vector.\n\nthreshold (numeric):\n\nIf this value is zero, only the two most extreme pixels will have their indices incremented for each random vector. If the value is greater than zero, then all pixels whose projections onto the random vector are with threshold data units of either of the two extreme pixels will also have their indices incremented.\n\ncentered (bool):\n\nIf True, then the pixels in X are assumed to have their mean already subtracted; otherwise, the mean of X will be computed and subtracted prior to computing the purity indices.\n\nstart (ndarray):\n\nAn optional array of initial purity indices. This can be used to continue computing PPI values after a previous call to ppi (i.e., set start equal to the return value from a previou call to ppi. This should be an integer-valued array whose dimensions are equal to the first two dimensions of X.\n\ndisplay (integer):\n\nIf set to a postive integer, a ImageView window will be opened and dynamically display PPI values as the function iterates. The value specifies the number of PPI iterations between display updates. It is recommended to use a value around 100 or higher. If the stretch keyword (see get_rgb for meaning) is not provided, a default stretch of (0.99, 0.999) is used.\n\nReturn value:\n\nAn ndarray of integers that represent the pixel purity indices of the input image. The return array will have dimensions equal to the first two dimensions of the input image.\n\nKeyword Arguments:\n\nAny keyword accepted by imshow. These keywords will be passed to the image display and only have an effect if the display argument is nonzero.\n\nThis function can be interruped with a KeyboardInterrupt (ctrl-C), in which case, the most recent value of the PPI array will be returned. This can be used in conjunction with the display argument to view the progression of the PPI values until they appear stable, then terminate iteration using ctrl-C.\n\nReferences:\n\nBoardman J.W., Kruse F.A, and Green R.O., “Mapping Target Signatures via Partial Unmixing of AVIRIS Data,” Pasadena, California, USA, 23 Jan 1995, URI: http://hdl.handle.net/2014/33635\n\n### principal_components¶\n\nprincipal_components(image)\n\nCalculate Principal Component eigenvalues & eigenvectors for an image.\n\nUsage:\n\npc = principal_components(image)\n\n\nArguments:\n\nimage (ndarray, spectral.Image, GaussianStats):\n\nAn MxNxB image\n\nReturns a PrincipalComponents object with the following members:\n\neigenvalues:\n\nA length B array of eigenvalues\n\neigenvectors:\n\nA BxB array of normalized eigenvectors\n\nstats (GaussianStats):\n\nA statistics object containing mean, cov, and nsamples.\n\ntransform:\n\nA callable function to transform data to the space of the principal components.\n\nreduce:\n\nA method to reduce the number of eigenvalues.\n\ndenoise:\n\nA callable function to denoise data using a reduced set of principal components.\n\nget_denoising_transform:\n\nA callable function that returns a function for denoising data.\n\n### PrincipalComponents¶\n\nclass PrincipalComponents(vals, vecs, stats)\n\nAn object for storing a data set’s principal components. The object has the following members:\n\neigenvalues:\n\nA length B array of eigenvalues sorted in descending order\n\neigenvectors:\n\nA BxB array of normalized eigenvectors (in columns)\n\nstats (GaussianStats):\n\nA statistics object containing mean, cov, and nsamples.\n\ntransform:\n\nA callable function to transform data to the space of the principal components.\n\nreduce:\n\nA method to return a reduced set of principal components based on either a fixed number of components or a fraction of total variance.\n\ndenoise:\n\nA callable function to denoise data using a reduced set of principal components.\n\nget_denoising_transform:\n\nA callable function that returns a function for denoising data.\n\ndenoise(X, **kwargs)\n\nReturns a de-noised version of X.\n\nArguments:\n\nX (np.ndarray):\n\nData to be de-noised. Can be a single pixel or an image.\n\nKeyword Arguments (one of the following must be specified):\n\nnum (integer):\n\nNumber of eigenvalues/eigenvectors to use. The top num eigenvalues will be used.\n\neigs (list):\n\nA list of indices of eigenvalues/eigenvectors to be used.\n\nfraction (float):\n\nThe fraction of total image variance to retain. Eigenvalues will be included (starting from greatest to smallest) until fraction of total image variance is retained.\n\nReturns denoised image data with same shape as X.\n\nNote that calling this method is equivalent to calling the get_denoising_transform method with same keyword and applying the returned transform to X. If you only intend to denoise data with the same parameters multiple times, then it is more efficient to get the denoising transform and reuse it, rather than calling this method multilple times.\n\nget_denoising_transform(**kwargs)\n\nReturns a function for denoising image data.\n\nKeyword Arguments (one of the following must be specified):\n\nnum (integer):\n\nNumber of eigenvalues/eigenvectors to use. The top num eigenvalues will be used.\n\neigs (list):\n\nA list of indices of eigenvalues/eigenvectors to be used.\n\nfraction (float):\n\nThe fraction of total image variance to retain. Eigenvalues will be included (starting from greatest to smallest) until fraction of total image variance is retained.\n\nReturns a callable LinearTransform object for denoising image data.\n\nreduce(N=0, **kwargs)\n\nReduces the number of principal components.\n\nKeyword Arguments (one of the following must be specified):\n\nnum (integer):\n\nNumber of eigenvalues/eigenvectors to retain. The top num eigenvalues will be retained.\n\neigs (list):\n\nA list of indices of eigenvalues/eigenvectors to be retained.\n\nfraction (float):\n\nThe fraction of total image variance to retain. Eigenvalues will be retained (starting from greatest to smallest) until fraction of total image variance is retained.\n\n### RX Anomaly Detector¶\n\nrx(X, background=None, window=None, cov=None)\n\nComputes RX anomaly detector scores.\n\nUsage:\n\ny = rx(X [, background=bg])\n\ny = rx(X, window=(inner, outer) [, cov=C])\n\nThe RX anomaly detector produces a detection statistic equal to the squared Mahalanobis distance of a spectrum from a background distribution according to", null, "where x is the pixel spectrum,", null, "is the background mean, and", null, "is the background covariance.\n\nArguments:\n\nX (numpy.ndarray):\n\nFor the first calling method shown, X can be an image with shape (R, C, B) or an ndarray of shape (R * C, B). If the background keyword is given, it will be used for the image background statistics; otherwise, background statistics will be computed from X.\n\nIf the window keyword is given, X must be a 3-dimensional array and background statistics will be computed for each point in the image using a local window defined by the keyword.\n\nbackground (GaussianStats):\n\nThe Gaussian statistics for the background (e.g., the result of calling calc_stats). If no background stats are provided, they will be estimated based on data passed to the detector.\n\nwindow (2-tuple of odd integers):\n\nMust have the form (inner, outer), where the two values specify the widths (in pixels) of inner and outer windows centered about the pixel being evaulated. Both values must be odd integers. The background mean and covariance will be estimated from pixels in the outer window, excluding pixels within the inner window. For example, if (inner, outer) = (5, 21), then the number of pixels used to estimate background statistics will be", null, ".\n\nThe window are modified near image borders, where full, centered windows cannot be created. The outer window will be shifted, as needed, to ensure that the outer window still has height and width outer (in this situation, the pixel being evaluated will not be at the center of the outer window). The inner window will be clipped, as needed, near image borders. For example, assume an image with 145 rows and columns. If the window used is (5, 21), then for the image pixel at (0, 0) (upper left corner), the the inner window will cover image[:3, :3] and the outer window will cover image[:21, :21]. For the pixel at (50, 1), the inner window will cover image[48:53, :4] and the outer window will cover image[40:51, :21].\n\ncov (ndarray):\n\nAn optional covariance to use. If this parameter is given, cov will be used for all RX calculations (background covariance will not be recomputed in each window) and only the background mean will be recomputed in each window.\n\nReturns numpy.ndarray:\n\nThe return value will be the RX detector score (squared Mahalanobis distance) for each pixel given. If X has shape (R, C, B), the returned ndarray will have shape (R, C)..\n\nReferences:\n\nReed, I.S. and Yu, X., “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 38, pp. 1760-1770, Oct. 1990.\n\n### spectral_angles¶\n\nspectral_angles(data, members)\n\nCalculates spectral angles with respect to given set of spectra.\n\nArguments:\n\ndata (numpy.ndarray or spectral.Image):\n\nAn MxNxB image for which spectral angles will be calculated.\n\nmembers (numpy.ndarray):\n\nCxB array of spectral endmembers.\n\nReturns:\n\nMxNxC array of spectral angles.\n\nCalculates the spectral angles between each vector in data and each of the endmembers. The output of this function (angles) can be used to classify the data by minimum spectral angle by calling argmin(angles).\n\n### SpectralLibrary¶\n\nclass SpectralLibrary(data, header=None, params=None)\n\nThe envi.SpectralLibrary class holds data contained in an ENVI-formatted spectral library file (.sli files), which stores data as specified by a corresponding .hdr header file. The primary members of an Envi.SpectralLibrary object are:\n\nspectra (numpy.ndarray):\n\nA subscriptable array of all spectra in the library. spectra will have shape CxB, where C is the number of spectra in the library and B is the number of bands for each spectrum.\n\nnames (list of str):\n\nA length-C list of names corresponding to the spectra.\n\nbands (spectral.BandInfo):\n\nSpectral bands associated with the library spectra.\n\nsave(file_basename, description=None)\n\nSaves the spectral library to a library file.\n\nArguments:\n\nfile_basename (str):\n\nName of the file (without extension) to save.\n\ndescription (str):\n\nOptional text description of the library.\n\nThis method creates two files: file_basename.hdr and file_basename.sli.\n\n### transform_image¶\n\ntransform_image(transform, img)\n\nApplies a linear transform to an image.\n\nArguments:\n\ntransform (ndarray or LinearTransform):\n\nThe CxB linear transform to apply.\n\nimg (ndarray or spectral.SpyFile):\n\nThe MxNxB image to be transformed.\n\nReturns (ndarray or :class:spectral.spyfile.TransformedImage):\n\nThe transformed image.\n\nIf img is an ndarray, then a MxNxC ndarray is returned. If img is a spectral.SpyFile, then a spectral.spyfile.TransformedImage is returned.\n\n## Configuration¶\n\n### SpySettings¶\n\nclass SpySettings\n\nRun-time settings for the spectral module.\n\nAfter importing spectral, the settings object is referenced as spectral.settings.\n\nNoteworthy members:\n\nWX_GL_DEPTH_SIZE (integer, default 24):\n\nSets the depth (in number of bits) for the OpenGL depth buffer. If calls to view_cube or view_nd result in windows with blank canvases, try reducing this value.\n\nenvi_support_nonlowercase_params (bool, default False)\n\nBy default, ENVI headers are read with parameter names converted to lower case. If this attribute is set to True, parameters will be read with original capitalization retained.\n\nshow_progress (bool, default True):\n\nIndicates whether long-running algorithms should display progress to sys.stdout. It can be useful to set this value to False when SPy is embedded in another application (e.g., IPython Notebook).\n\nimshow_figure_size (2-tuple of integers, default None):\n\nWidth and height (in inches) of windows opened with imshow. If this value is None, matplotlib’s default size is used.\n\nimshow_background_color (3-tuple of integers, default (0,0,0)):\n\nDefault color to use for masked pixels in imshow displays.\n\nimshow_interpolation (str, default None):\n\nPixel interpolation to be used in imshow windows. If this value is None, matplotlib’s default interpolation is used. Note that zoom windows always use “nearest” interpolation.\n\nimshow_stretch:\n\nDefault RGB linear color stretch to perform.\n\nimshow_stretch_all:\n\nIf True, each color channel limits are determined independently.\n\nimshow_zoom_figure_width (int, default None):\n\nWidth of zoom windows opened from an imshow window. Since zoom windows are always square, this is also the window height. If this value is None, matplotlib’s default window size is used.\n\nimshow_zoom_pixel_width (int, default 50):\n\nNumber of source image pixel rows and columns to display in a zoom window.\n\nimshow_float_cmap (str, default “gray”):\n\nimshow color map to use with floating point arrays.\n\nimshow_class_alpha (float, default 0.5):\n\nalpha blending value to use for imshow class overlays\n\nimshow_enable_rectangle_selector (bool, default True):\n\nWhether to create the rectangle selection tool that enables interactive image pixel class labeling. On some OS/backend combinations, an exception may be raised when this object is created so disabling it allows imshow windows to be created without using the selector tool.\n\nimshow_disable_mpl_callbacks (bool, default True):\n\nIf True, several matplotlib keypress event callbacks will be disabled to prevent conflicts with callbacks from SPy. The matplotlib callbacks can be set back to their defaults by calling matplotlib.rcdefaults().\n\n## Utilities¶\n\n### iterator¶\n\niterator(image, mask=None, index=None)\n\nReturns an iterator over pixels in the image.\n\nArguments:\n\nimage (ndarray or spectral.Image):\n\nAn image over whose pixels will be iterated.\n\nReturns (spectral.Iterator):" ]
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https://bitbucket.org/sgillies/descartes
[ "# Descartes\n\nUse Shapely or GeoJSON-like geometric objects as matplotlib paths and patches", null, "Requires: matplotlib, numpy, and optionally Shapely 1.2+.\n\nExample:\n\nfrom matplotlib import pyplot\nfrom shapely.geometry import LineString\nfrom descartes import PolygonPatch\n\nBLUE = '#6699cc'\nGRAY = '#999999'\n\ndef plot_line(ax, ob):\nx, y = ob.xy\nax.plot(x, y, color=GRAY, linewidth=3, solid_capstyle='round', zorder=1)\n\nline = LineString([(0, 0), (1, 1), (0, 2), (2, 2), (3, 1), (1, 0)])\n\nfig = pyplot.figure(1, figsize=(10, 4), dpi=180)\n\n# 1\n\nplot_line(ax, line)\n\ndilated = line.buffer(0.5)\npatch1 = PolygonPatch(dilated, fc=BLUE, ec=BLUE, alpha=0.5, zorder=2)\n\n#2\n\npatch2a = PolygonPatch(dilated, fc=GRAY, ec=GRAY, alpha=0.5, zorder=1)\n\neroded = dilated.buffer(-0.3)\n\n# GeoJSON-like data works as well\n\npolygon = eroded.__geo_interface__\n# >>> geo['type']\n# 'Polygon'\n# >>> geo['coordinates'][:2]\n# ((0.50502525316941682, 0.78786796564403572), (0.5247963548222736, 0.8096820147509064))\npatch2b = PolygonPatch(polygon, fc=BLUE, ec=BLUE, alpha=0.5, zorder=2)" ]
[ null, "http://farm4.static.flickr.com/3662/4555372019_9bbed1f956_o_d.png", null ]
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https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-37/issue-1/Optimal-Stopping-and-Experimental-Design/10.1214/aoms/1177699594.full
[ "Translator Disclaimer\nFebruary, 1966 Optimal Stopping and Experimental Design\nGus W. Haggstrom\nAnn. Math. Statist. 37(1): 7-29 (February, 1966). DOI: 10.1214/aoms/1177699594\n\n## Abstract\n\nThe problem of finding Bayes solutions for sequential experimental design problems motivates the study of the following type of one-person sequential game. If the game is stopped at any stage $a$, the loss to the player is the value of a random variable (rv), say $Z_a$. If the player chooses to continue the game, he can select the next rv to be observed from a class of rv's available at that stage, thus bringing the game to one of the stages succeeding stage $a$. (The class of all stages can be pictured as a \"tree\".) At this stage the player can again choose to stop, accepting the value of the chosen rv as his loss, or he may continue by selecting one of the class of rv's now available for the next observation. The player is required to stop sometime, and his decisions at any stage must depend only on information available at that stage. A model for this situation is given in Section 4. Control variables, which correspond to stopping variables in the usual formulation of sequential games, are defined which can be used by the player to decide whether to stop or not at any stage and, if he continues, which rv to observe next. A general characterization of control variables that minimize expected loss is given, and existence of such optimal control variables is proved under conditions applicable to statistical problems. The application to finding Bayes solutions to sequential experimental design problems is given in Section 5. As a preliminary to the discussion on control variables, Section 3 provides a study of the theory of optimal stopping variables. Let $\\{Z_n, F_n, n \\geqq 1\\}$ be a stochastic process on a probability space $(\\Omega, F, P)$ with points $\\omega$. A stopping variable (sv) is a rv $t$ with values in $\\{1, 2, \\cdots, \\infty\\}$ such that $t < \\infty$ a.e. and $\\{t = n\\} \\varepsilon F_n$ for each $n$. For any such sv $t$, a rv $Z_t$ is defined by \\begin{align*}Z_t(\\omega) &= Z_n(\\omega),\\quad\\text{if} t(\\omega) = n, \\\\ &= \\infty,\\quad\\text{if} t(\\omega) = \\infty.\\end{align*} It is convenient to think of $Z_n$ as the loss after $n$ plays in a one-person sequential game and to consider the $\\sigma$-field $F_n$ as representing the knowledge of the past after $n$ plays. The problem of finding a strategy for stopping the game to minimize the expected loss corresponds to finding a minimizing sv, i.e., one which minimizes $EZ_t$ among the class of all sv's $t$. The main results in Section 3 are new characterizations of Snell's solution in to the problem of optimal stopping which generalized the well-known Arrow-Blackwell-Girshick theory in . Under the assumption that there is an integrable rv $U$ such that $Z_n \\geqq U$ a.e. for each $n$, Snell showed that when a minimizing sv $t^\\ast$ exists, it can be defined as the first integer $j$ such that $X_j = Z_j$ (or $\\infty$ if no such integer exists) where $\\{X_n, F_n, n \\geqq 1\\}$ is the maximal regular generalized submartingale relative to $\\{Z_n, F_n, n \\geqq 1\\}$. Under the additional assumption that $\\{Z_n, n \\geqq 1\\}$ is an integrable sequence, we now show that the rv's $X_n$ can be further identified as $X_n = \\operatorname{ess} \\inf_{t \\varepsilon T_n} E(Z_t \\mid F_n) \\text{a.e.},$ where $T_n$ is the class of sv's $t$ such that $t \\geqq n$. It follows that if there is a set $A_n$ in $F_n$ for each $n$ such that (i) $Z_n \\leqq E(Z_t \\mid F_n)$ a.e. on $A_n$ for each $t$ in $T_{n + 1}$, and (ii) $Z_n > \\operatorname{ess} \\inf_{t \\varepsilon T_{n + 1}} E(Z_t \\mid F_n)$ a.e. on the complement $A'_n$, then the minimizing sv $t^\\ast$ defined above is such that, for almost all points $\\omega, t^\\ast(\\omega) = n$ if and only if $\\omega \\varepsilon A_n - \\mathbf\\bigcup^{n - 1}_{i = 1} A_i$. This comes very close to stating that the player of the sequential game above should stop playing after $n$ plays if and only if there is no continuation (i.e., sv in $T_{n + 1}$) having conditional expected loss given the past which is less than the present loss $Z_n(\\omega)$. Unfortunately, such an interpretation is not valid in general since it is equivalent to stopping the game after $n$ plays if and only if $Z_n(\\omega) \\leqq \\inf_{t \\varepsilon T_{n + 1}} E(Z_t \\mid F_n)(\\omega).$ The function $\\inf_{t \\varepsilon T_{n + 1}} E(Z_t \\mid F_n)$ not only may not be measurable, but also in many cases it may be changed almost at will by choosing different versions of the conditional expectations involved. On the other hand, the interpretation above is valid, as is shown in Section 3, for the case where each $\\sigma$-field $F_n$ is generated by finitely many discrete rv's. Section 3 also contains a reasonably general development of the theory of minimizing sv's, including a more direct proof of Snell's result using the definition $X_n = \\operatorname{ess} \\inf_{t \\varepsilon T_n} E(Z_t \\mid F_n)$. Illustrations are given and comparisons are made with the approaches used by Arrow, Blackwell, and Girshick in and Chow and Robbins in and .\n\n## Citation\n\nGus W. Haggstrom. \"Optimal Stopping and Experimental Design.\" Ann. Math. Statist. 37 (1) 7 - 29, February, 1966. https://doi.org/10.1214/aoms/1177699594\n\n## Information\n\nPublished: February, 1966\nFirst available in Project Euclid: 27 April 2007\n\nzbMATH: 0202.49201\nMathSciNet: MR195221\nDigital Object Identifier: 10.1214/aoms/1177699594", null, "", null, "" ]
[ null, "https://projecteuclid.org/Content/themes/SPIEImages/Share_black_icon.png", null, "https://projecteuclid.org/images/journals/cover_aoms.jpg", null ]
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http://hackage.haskell.org/package/representable-tries-0.3.4/docs/Data-Functor-Representable-Trie.html
[ "representable-tries-0.3.4: Tries from representations of polynomial functors\n\nStability experimental ekmett@gmail.com\n\nData.Functor.Representable.Trie\n\nDescription\n\nSynopsis\n\n# Representations of polynomial functors\n\nclass (TraversableWithKey1 (BaseTrie a), Representable (BaseTrie a)) => HasTrie a whereSource\n\nAssociated Types\n\ntype BaseTrie a :: * -> *Source\n\nMethods\n\nembedKey :: a -> Key (BaseTrie a)Source\n\nprojectKey :: Key (BaseTrie a) -> aSource\n\nInstances\n\n HasTrie Bool HasTrie Char HasTrie Int HasTrie () HasTrie Any HasTrie a => HasTrie [a] HasTrie a => HasTrie (Dual a) HasTrie a => HasTrie (Sum a) HasTrie a => HasTrie (Product a) HasTrie a => HasTrie (Maybe a) HasTrie a => HasTrie (Seq a) HasTrie v => HasTrie (IntMap v) (HasTrie a, HasTrie b) => HasTrie (Either a b) (HasTrie a, HasTrie b) => HasTrie (a, b) (HasTrie k, HasTrie v) => HasTrie (Map k v) (HasTrie a, HasTrie b) => HasTrie (Entry a b) (HasTrie a, HasTrie b, HasTrie c) => HasTrie (a, b, c) (HasTrie a, HasTrie b, HasTrie c, HasTrie d) => HasTrie (a, b, c, d)\n\n# Memoizing functions\n\nmup :: HasTrie t => (b -> c) -> (t -> b) -> t -> cSource\n\nLift a memoizer to work with one more argument.\n\nmemo :: HasTrie t => (t -> a) -> t -> aSource\n\nmemo2 :: (HasTrie s, HasTrie t) => (s -> t -> a) -> s -> t -> aSource\n\nMemoize a binary function, on its first argument and then on its second. Take care to exploit any partial evaluation.\n\nmemo3 :: (HasTrie r, HasTrie s, HasTrie t) => (r -> s -> t -> a) -> r -> s -> t -> aSource\n\nMemoize a ternary function on successive arguments. Take care to exploit any partial evaluation.\n\ninTrie :: (HasTrie a, HasTrie c) => ((a -> b) -> c -> d) -> (a :->: b) -> c :->: dSource\n\nApply a unary function inside of a tabulate\n\ninTrie2 :: (HasTrie a, HasTrie c, HasTrie e) => ((a -> b) -> (c -> d) -> e -> f) -> (a :->: b) -> (c :->: d) -> e :->: fSource\n\nApply a binary function inside of a tabulate\n\ninTrie3 :: (HasTrie a, HasTrie c, HasTrie e, HasTrie g) => ((a -> b) -> (c -> d) -> (e -> f) -> g -> h) -> (a :->: b) -> (c :->: d) -> (e :->: f) -> g :->: hSource\n\nApply a ternary function inside of a tabulate\n\n# Workarounds for current GHC limitations\n\ntrie :: HasTrie t => (t -> a) -> t :->: aSource\n\nuntrie :: (t :->: a) -> t -> aSource\n\ndata a :->: b whereSource\n\nConstructors\n\n Trie :: HasTrie a => BaseTrie a b -> a :->: b\n\nInstances\n\n Semigroupoid :->: HasTrie a => MonadReader a (:->: a) HasTrie a => Monad (:->: a) Functor (:->: a) HasTrie a => Applicative (:->: a) Foldable (:->: a) Traversable (:->: a) (HasTrie m, Semigroup m, Monoid m) => Comonad (:->: m) (HasTrie m, Semigroup m) => Extend (:->: m) HasTrie e => Distributive (:->: e) Foldable1 (:->: a) Traversable1 (:->: a) Keyed (:->: a) Indexable (:->: e) FoldableWithKey (:->: a) FoldableWithKey1 (:->: a) TraversableWithKey (:->: a) TraversableWithKey1 (:->: a) HasTrie e => Representable (:->: e) Apply (:->: a) Bind (:->: a) HasTrie e => Adjunction (Entry e) (:->: e) Eq b => Eq (:->: a b) Ord b => Ord (:->: a b) (Show a, Show b) => Show (:->: a b)\n\ndata Entry a b Source\n\nConstructors\n\n Entry a b\n\nInstances\n\n Functor (Entry a) HasTrie e => Adjunction (Entry e) (:->: e) (HasTrie a, HasTrie b) => HasTrie (Entry a b)\n\nrunTrie :: (a :->: b) -> BaseTrie a bSource" ]
[ null ]
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https://proofwiki.org/wiki/Stirling_Number_of_the_Second_Kind_of_Number_with_Self
[ "# Stirling Number of the Second Kind of Number with Self\n\n## Theorem\n\n$\\displaystyle \\left\\{ {n \\atop n}\\right\\} = 1$\n\nwhere $\\displaystyle \\left\\{ {n \\atop n}\\right\\}$ denotes a Stirling number of the second kind.\n\n## Proof\n\nThe proof proceeds by induction.\n\nFor all $n \\in \\N_{> 0}$, let $P \\left({n}\\right)$ be the proposition:\n\n$\\displaystyle \\left\\{ {n \\atop n}\\right\\} = 1$\n\n### Basis for the Induction\n\n$P \\left({0}\\right)$ is the case:\n\n $\\displaystyle \\left\\{ {0 \\atop 0}\\right\\}$ $=$ $\\displaystyle \\delta_{0 0}$ Stirling Number of the Second Kind of 0 $\\displaystyle$ $=$ $\\displaystyle 1$ Definition of Kronecker Delta\n\nThis is the basis for the induction.\n\n### Induction Hypothesis\n\nNow it needs to be shown that, if $P \\left({k}\\right)$ is true, where $k \\ge 2$, then it logically follows that $P \\left({k + 1}\\right)$ is true.\n\nSo this is the induction hypothesis:\n\n$\\displaystyle \\left\\{ {k \\atop k}\\right\\} = 1$\n\nfrom which it is to be shown that:\n\n$\\displaystyle \\left\\{ {k + 1 \\atop k + 1}\\right\\} = 1$\n\n### Induction Step\n\nThis is the induction step:\n\n $\\displaystyle \\left\\{ { {k + 1} \\atop {k + 1} }\\right\\}$ $=$ $\\displaystyle \\left({k + 1}\\right) \\left\\{ {k \\atop {k + 1} }\\right\\} + \\left\\{ {k \\atop k}\\right\\}$ Definition of Stirling Numbers of the Second Kind $\\displaystyle$ $=$ $\\displaystyle \\left({k + 1}\\right) \\times 0 + \\left\\{ {k \\atop k}\\right\\}$ Stirling Number of Number with Greater $\\displaystyle$ $=$ $\\displaystyle \\left\\{ {k \\atop k}\\right\\}$ $\\displaystyle$ $=$ $\\displaystyle 1$ Induction Hypothesis\n\nSo $P \\left({k}\\right) \\implies P \\left({k + 1}\\right)$ and the result follows by the Principle of Mathematical Induction.\n\nTherefore:\n\n$\\displaystyle \\forall n \\in \\Z_{\\ge 0}: \\left\\{ {n \\atop n}\\right\\} = 1$\n\n$\\blacksquare$" ]
[ null ]
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https://hurmaninvesterarhfbb.web.app/54770/36179.html
[ "# Newton Meter (N·m): Deca Newton Meter (daN·m): Deci Newton Meter (dN·m): Centi Newton Meter (cN·m): Newton Millimeter (N·mm):. Pound. Foot Pound[force ]\n\n1 lbf = 4.4482216153 N 1 N = 0.2248089431 lbf. Example: convert 15 lbf to N: 15 lbf = 15 ×\n\n4.4482216152605 N Conversion base : 1 lbf = 4.4482216152605 N. Conversion base : 1 N = 0.22480894309971 lbf. Switch units Starting unit. Newton. meganewton (MN) N/m2 is the pressure exterted by one Newton being applied to an area of one square meter . Pounds per square foot to Newtons per metre squared table.", null, "Type in your own numbers in the form to convert the units! 11 rows A pound-inch (lb·in or lbf·in) is a unit of torque. One pound-inch is the torque created by one pound force acting at a perpendicular distance of one inch from a pivot point. This tool converts pound inch to newton meter (lb·in to nm) and vice versa. 1 pound inch ≈ 0.113 newton meter. The user must fill one of the two fields and the conversion will Pound (abbreviations: lb, or lbs, or ps): is a unit of force used in some systems of measurement including English Engineering units and the British Gravitational System.Also, it is a unit of mass used in the imperial, United States customary and other systems of measurement. Newton (abbreviation: N): is the International System of Units (SI) derived unit of force.\n\n## SG LB Jerk Minnow 12cm (6-pack). 79 SEK 49 02-Pearl n orange. 49 SEK. 49 SEK. 07-Minnow. 49 SEK. 49 SEK. 12-Perch. 49 SEK. 49 SEK. 15-Salt n Peber.\n\n30 lbs to Newton = 133.44665 Newton. 40 lbs to Newton = 177.92887 Newton 1 lbf = 4.4482216153 N 1 N = 0.2248089431 lbf.", null, "### The gauge can display force in either kilograms, imperial pounds or Newtons. It has a maximum range of 49.03 N (5 kg), with an accuracy of ±0.5%. An optional\n\nd .", null, "K v in n lig a fo rs k a re i F in la n d . Av Liisa Husu. H u r v e t v i v a d v i s e r , n ä r v i s e r n å g o t ?\nHexatronic group investor relations\n\nFAX 2696295385. SKICKA E-POST TILL  HNC210BE-L-B. kr 31.014. Netto kapacitet (L), 87.\n\nIt is equal to the torque resulting from a force of one newton 9 Pound-force inches = 1.0169 Newton metres. 1000 Pound-force inches = 112.98 Newton metres. 1000000 Pound-force inches = 112984.78 Newton metres.\nErsätta plast\n\nclaes wohlin\nenviro systems asensbruk\nflygande ö i gullivers resor\nartikel 8 dialog\n\n### Om en Sidhtommer mit n .: cast af matters s SaulTaber brödet år's optårðt vthu'n matkat Blind Med . 7 In manu . fånde f.lb. meb ( 7'3 ) f . bobft . bicfide Eth . 3.\n\n90 Pounds to … 1 lb-in is equal to 0.11298482933333 newton meter. Note that rounding errors may occur, so always check the results. Use this page to learn how to convert between pound inches and newton meters.\n\nLäka skavsår\nhur mycket är a kassan\n\n### 2 And I saw six men coming from the direction of the upper gate, which faces north, each with a deadly weapon in his hand. With them was a man clothed in\n\nForce. 1 lbf. 0.2248 lbf. 4.448 N. 1 N. Stress or Pressure.\n\n## 1 Pounds to Newtons = 4.4482. 70 Pounds to Newtons = 311.3755. 2 Pounds to Newtons = 8.8964. 80 Pounds to Newtons = 355.8577. 3 Pounds to Newtons = 13.3447. 90 Pounds to Newtons = 400.3399. …\n\noz-in, oz-in. lb-ft, lb-ft. lb-in, lb-in  Nov 18, 2012 Thus, 1 lbf, or one pound force, can be measured or converted in Newtons. For example, 1 lbf = 0.45359237 kg × 9.80665 m/s² = 4.448 N,  Newton Meter (N·m): Deca Newton Meter (daN·m): Deci Newton Meter (dN·m): Centi Newton Meter (cN·m): Newton Millimeter (N·mm):. Pound. Foot Pound[force ]  MKS Unit Equivalent. Force.\n\nThere are 0.113 newton meters in a inch pound. or 1 in lb = 0.113 nm. Inch Pounds. Newton Meters." ]
[ null, "https://picsum.photos/800/600", null, "https://picsum.photos/800/616", null, "https://picsum.photos/800/618", null ]
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http://forum-mcat.nextsteptestprep.com/viewtopic.php?f=12&t=2805
[ "## Chemistry End of Chapter 3 ?s\n\nchibean\nPosts: 11\nJoined: Fri Jan 03, 2020 12:10 pm\n\n### Chemistry End of Chapter 3 ?s\n\nFor this problem:\nHow many ions are expected to be present in a 1 L solution of 1.5 M NaCl? Assume ions present due to the dissociation of water are negligible.\nCould you explain the steps to solve this in more detail?\nNS_Tutor_Mathias\nPosts: 616\nJoined: Sat Mar 30, 2019 8:39 pm\n\n### Re: Chemistry End of Chapter 3 ?s\n\n1 mole of anything = 6.022 * 10^23 individual particles\n\nSo:\n1.5 M * 1L = 1.5 mol NaCl\n\nEach NaCl dissociates into 2 ions, so\n1.5 mol NaCl * 2 = 3 mol ions\n\nEach mol is a count of 6.022 * 10^23 particles\n3 mol ions * 6.022 * 10^23 particles/mol = 18 * 10^23 = 1.8 * 10^24 ions" ]
[ null ]
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https://mathoverflow.net/questions/275124/filtering-mixed-discrete-and-continous/287491
[ "# Filtering Mixed Discrete and Continous\n\nSuppose I have signal process $\\lambda_t$ following the dynamics \\begin{equation} \\begin{aligned} \\zeta_t&=\\mu^{\\zeta}(t,{\\zeta}_t)dt+\\sigma^{\\zeta}(t,{\\zeta}_t)dW^{\\zeta}_t\\\\ \\xi_t&=\\mu^{\\xi}(t,\\xi_t)dt+\\sigma^{\\xi}(t,{\\xi}_t)dW^{\\xi}_t\\\\ \\frac{\\partial p_t}{\\partial t}&= A p_t\\\\ \\end{aligned} \\end{equation} where $p_t^1\\triangleq \\mathbb{P}\\left(\\lambda_t={\\zeta}\\right)$, and $p_t^2\\triangleq \\mathbb{P}\\left(\\lambda_t={\\xi}\\right)$, are the states of the continuous-time finite state Markov process $\\lambda_t$ (with state space $\\mathbb{S}\\triangleq \\{\\zeta_t,\\xi_t\\}$).\n\nMoreover, my observation process follows the dynamics $$dY_t = a(t,\\lambda_t,Y_t)dt + b(t,\\lambda_t,Y_t)d\\tilde{W}_t,$$ where $\\tilde{W}_t$ and $W_t$ are correlated Brownian motions. I know there are explicit solutions to the \"The Kushner–Stratonovich Equation\" for the general semi-martingale case (see citation below) as well as the particular KS Equation for finite-state continous-time Markov process. My question is how can I (is it known) how to combine these to obtain the explicit KS equation for the filtering problem I have stated. (Can I just combine the general KS solutions for both with the KS equations for the Markov process goverened by $A$?\n\nCohen, Samuel N.; Elliott, Robert J., Stochastic calculus and applications, Probability and Its Applications. New York, NY: Birkhäuser/Springer (ISBN 978-1-4939-2866-8/hbk; 978-1-4939-2867-5/ebook). xxiii, 666 p. (2015). ZBL1338.60001.)\n\nI'm only going to answer the special case $b\\equiv 1$ and $\\bar W$ independent from the signal noise because I'm not familiar enough with the case of multiplicative/correlated observation noise. However, as far as I have been able to glean from the literature, adding those generalizations is pretty straightforward, if a little technical.\n\nWith this restriction, quite generally, suppose that our Markov signal process is called $X_t$ with an infinitesimal generator $\\mathcal{A}$, and our observations are given by\n\n$$dY_t=a(t,X_t,Y_t)dt+d\\bar W_t, \\quad Y_0=0$$\n\nWe can find a reference measure $\\tilde P$ that is absolutely continuous to the original measure $P$ such that\n\n$$L_t\\doteq\\frac{dP}{d\\tilde P}\\Bigg\\vert_{\\mathcal{F}_t}=\\exp\\left[\\int_0^ta(s,X_s,Y_s)dY_s-\\frac{1}{2}\\int_0^ta(s,X_s,Y_s)^2ds\\right]$$\n\nand $Y_t$ is a Wiener process under $\\tilde P$. We may then write down a Zakai equation for the conditional expectation $\\rho_t[\\varphi]=\\mathbb{\\tilde E}[L_t\\varphi(X_t)|\\mathcal{F}^Y_t]$:\n\n$$d\\rho_t[\\varphi]=\\rho_t[\\mathcal{A}\\varphi]dt+\\rho_t[a\\varphi]dY_t,$$\n\nwhere $\\varphi$ is a measurable function with $\\mathbb{E}[\\varphi(X_t)]<\\infty$ that is in the domain of $\\mathcal{A}$.\n\nTo get back to the conditional expectations that you need for filtering, you use the Kallianpur-Striebel formula\n\n$$\\mathbb{E}[\\varphi(X_t)|\\mathcal{F}^Y_t]=\\frac{\\rho_t[\\varphi]}{\\rho_t}.$$\n\nThe expectation $\\mathbb{E}[\\varphi(X_t)|\\mathcal{F}^Y_t]$ satisfies a Kushner-Stratonovich equation which you can obtain from the Zakai equation (if required) by means of stochastic calculus.\n\nLet's go back to the specific example that you provide. I have to modify the notation a little because $\\lambda_t$ as you defined it is not Markov. Let's call the signal process $X_t$, given by\n\n$$X_t=\\begin{pmatrix}\\zeta_t\\\\\\xi_t\\\\\\alpha_t\\end{pmatrix}\\in \\mathbb{R}\\times\\mathbb{R}\\times\\{1,2\\},$$\n\nso the process that you want to filter is now given by $\\lambda_t=\\psi(X_t)$, where\n\n$$\\psi(\\zeta,\\xi,1)=\\zeta, \\quad \\psi(\\zeta,\\xi,2)=\\xi.$$\n\nThe infinitesimal generator of $X_t$ (which is now Markov) is given by (please verify)\n\n\\begin{multline} \\mathcal{A}\\varphi(\\zeta,\\xi,\\alpha)=\\mu^{\\zeta}(t,\\zeta)\\partial_{\\zeta}\\varphi(\\zeta,\\xi,\\alpha)+\\mu^{\\xi}(t,\\xi)\\partial_{\\xi}\\varphi(\\zeta,\\xi,\\alpha)\\\\ +\\frac{1}{2}\\sigma^{\\zeta}(t,\\zeta)^2\\partial^2_{\\zeta}\\varphi(\\zeta,\\xi,\\alpha) +\\frac{1}{2}\\sigma^{\\xi}(t,\\xi)^2\\partial^2_{\\xi}\\varphi(\\zeta,\\xi,\\alpha)\\\\ +A^{\\ast}_{\\alpha,1}\\varphi(\\zeta,\\xi,1)+A^{\\ast}_{\\alpha,2}\\varphi(\\zeta,\\xi,2), \\end{multline}\n\nwhere $A^{\\ast}$ is the transpose of the transition rate matrix $A$. From here on, you should be able to use the standard machinery in order to try to find approximate solutions to the Zakai equation." ]
[ null ]
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https://www.indiabix.com/non-verbal-reasoning/analytical-reasoning/discussion-315
[ "# Non Verbal Reasoning - Analytical Reasoning - Discussion\n\nDiscussion Forum : Analytical Reasoning - Section 1 (Q.No. 11)\n11.\n\nFind the number of triangles in the given figure.", null, "21\n23\n25\n27\nExplanation:\n\nThe figure may be labelled as shown.", null, "The simplest triangles are ABL, BCD, DEF, FGP, PGH, QHI, JQI, KRJ and LRK i.e. 9 in number.\n\nThe triangles composed of two components each are OSG, SGQ, SPI, SRI, KSQ, KMS, FGH, JHI and JKL i.e. 9 in number.\n\nThere is only one triangle i.e. KSG which is composed of four components.\n\nThe triangles composed of five components each are NEI, ANI, MCG and KCO i.e. 4 in number.\n\nThe triangles composed of six components each are GMK and KOG i.e. 2 in number.\n\nThere is only one triangle i.e. AEI composed of ten components.\n\nThere is only one triangle i.e. KCG composed of eleven components.\n\nTherefore, Total number of triangles in the given figure = 9 + 9+1 + 4 + 2+1 + 1 = 27.\n\nDiscussion:\n6 comments Page 1 of 1.\n\nPRANAY said:   2 years ago\nPlease anyone explain this in detail.\n\nRajashree said:   4 years ago\nHow to solve this?\n\nVivek said:   5 years ago\nThe total outer nodes+ inner node * 2 + 2.\n13 + 2 * 6 + 2 = 27.\n\nSuraj tharu said:   5 years ago" ]
[ null, "https://www.indiabix.com/_files/images/non-verbal-reasoning/analytical-reasoning/15.png", null, "https://www.indiabix.com/_files/images/non-verbal-reasoning/analytical-reasoning/15-1.png", null ]
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https://www.beyondgrader.com/java/2d-array-max-subarray-sum/challen@illinois.edu/2021.8.0
[ "### 2D Array Max Subarray Sum\n\nGeoffrey Challen // 2021.8.0\n\nWrite a method `maxSubarraySum` that, given a non-rectangular two-dimensional `int` array, returns the sum of the subarray that sums to the largest value.\n\nSo given the following array, with each subarray on a separate line:\n\nYou would return 7.\n\n`assert` that the passed array is not `null`. However, if the passed array is not `null` it will contain no empty subarrays.\n\nOne hint for this problem is that you may need both an `int` variable to store the max and a `boolean` variable to record whether the maximum value has been initialized. Once you have summed each subarray, check whether either your `boolean` value is `false` or the sum is larger than the largest you've seen so far. After you check the sum of the first subarray, set your `boolean` value to `true`. Another approach is to use the counter that you use to proceed through each subarray to determine whether you have initialized the max value." ]
[ null ]
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http://www.contrib.andrew.cmu.edu/~ryanod/?p=1085
[ "# §6.5: Highlight: Fooling ${\\mathbb F}_2$-polynomials\n\nRecall that a density $\\varphi$ is said to be $\\epsilon$-biased if its correlation with every ${\\mathbb F}_2$-linear function $f$ is at most $\\epsilon$ in magnitude. In the lingo of pseudorandomness, one says that $\\varphi$ fools the class of ${\\mathbb F}_2$-linear functions:\n\nDefinition 46 Let $\\varphi : {\\mathbb F}_2^n \\to {\\mathbb R}^{\\geq 0}$ be a density function and let $\\mathcal{C}$ be a class of functions ${\\mathbb F}_2^n \\to {\\mathbb R}$. We say that $\\varphi$ $\\epsilon$-fools $\\mathcal{C}$ if $\\Bigl|\\mathop{\\bf E}_{\\boldsymbol{y} \\sim \\varphi}[f(\\boldsymbol{y})] – \\mathop{\\bf E}_{{\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[f({\\boldsymbol{x}})]\\Bigr| \\leq \\epsilon$ for all $f \\in \\mathcal{C}$.\n\nTheorem 30 implies that using just $O(\\log(n/\\epsilon))$ independent random bits, one can generate a density which $\\epsilon$-fools the class of $f : {\\mathbb F}_2^n \\to \\{-1,1\\}$ with $\\deg_{{\\mathbb F}_2}(f) \\leq 1$. A natural problem in the field of derandomization is: How many independent random bits are needed to generate a density which $\\epsilon$-fools all functions of ${\\mathbb F}_2$-degree at most $d$? A naive hope might be that $\\epsilon$-biased densities automatically fool functions of ${\\mathbb F}_2$-degree $d > 1$. The next example shows that this hope fails badly, even for $d = 2$:\n\nExample 47 Recall the inner product mod $2$ function, $\\mathrm{IP}_{n} : {\\mathbb F}_2^n \\to \\{0,1\\}$, which has ${\\mathbb F}_2$-degree $2$. Let $\\varphi : {\\mathbb F}_2^n \\to {\\mathbb R}^{\\geq 0}$ be the density of the uniform distribution on the support of $\\mathrm{IP}_n$. Now $\\mathrm{IP}_n$ is an extremely regular function (see Examples 4), and indeed $\\varphi$ is a roughly $2^{-n/2}$-biased density (see the exercises). But $\\varphi$ is very bad at fooling at least one function of ${\\mathbb F}_2$-degree $2$, namely $\\mathrm{IP}_n$ itself: $\\mathop{\\bf E}_{{\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[\\mathrm{IP}_n({\\boldsymbol{x}})] \\approx 1/2, \\qquad \\mathop{\\bf E}_{\\boldsymbol{y} \\sim \\varphi}[\\mathrm{IP}_n(\\boldsymbol{y})] = 1.$\n\nThe problem of using few random bits to fool $n$-bit, ${\\mathbb F}_2$-degree-$d$ functions was first taken up by Luby, Veličković and Wigderson [LVW93]. They showed how to generate a fooling distribution using $\\exp(O(\\sqrt{d \\log(n/d) + \\log(1/\\epsilon)}))$ independent random bits. There was no improvement on this for $14$ years, at which point Bogdanov and Viola [BV07] achieved $O(\\log(n/\\epsilon))$ random bits for $d = 2$ and $O(\\log n) + \\exp(\\mathrm{poly}(1/\\epsilon))$ random bits for $d = 3$. In general they suggested that ${\\mathbb F}_2$-degree-$d$ might be fooled by the sum of $d$ independent draws from a small-bias distribution. Soon thereafter Lovett [Lov08] showed that a sum of $2^d$ independent draws from a small-bias distribution suffices, implying that ${\\mathbb F}_2$-degree-$d$ functions can be fooled using just $2^{O(d)} \\cdot \\log(n/\\epsilon)$ random bits. More precisely, if $\\varphi$ is any $\\epsilon$-biased density on ${\\mathbb F}_2^n$ he showed that $\\Bigl|\\mathop{\\bf E}_{\\boldsymbol{y}^{(1)}, \\dots, \\boldsymbol{y}^{(2^d)} \\sim \\varphi}[f(\\boldsymbol{y}^{(1)} + \\cdots + \\boldsymbol{y}^{(2^d)})] – \\mathop{\\bf E}_{{\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[f({\\boldsymbol{x}})]\\Bigr| \\leq O(\\epsilon^{1/4^d}).$ In other words, the $2^d$-fold convolution $\\varphi^{* 2^d}$ density fools functions of ${\\mathbb F}_2$-degree $d$.\n\nThe current state of the art for this problem is Viola’s Theorem, which show that the original idea from [BV07] works: summing $d$ independent draws from an $\\epsilon$-biased distribution fools ${\\mathbb F}_2$-degree-$d$ polynomials.\n\nViola’s Theorem Let $\\varphi$ be any $\\epsilon$-biased density on ${\\mathbb F}_2^n$, $0 \\leq \\epsilon \\leq 1$. Let $d \\in {\\mathbb N}^+$ and define $\\epsilon_d = 9\\epsilon^{1/2^{d-1}}$. Then the class of all $f : {\\mathbb F}_2^n \\to \\{-1,1\\}$ with $\\deg_{{\\mathbb F}_2}(f) \\leq d$ is $\\epsilon_d$-fooled by the $d$-fold convolution $\\varphi^{* d}$. I.e., $\\Bigl|\\mathop{\\bf E}_{\\boldsymbol{y}^{(1)}, \\dots, \\boldsymbol{y}^{(d)} \\sim \\varphi}[f(\\boldsymbol{y}^{(1)} + \\cdots + \\boldsymbol{y}^{(d)})] – \\mathop{\\bf E}_{{\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[f({\\boldsymbol{x}})]\\Bigr| \\leq 9\\epsilon^{1/2^{d-1}}.$\n\nIn light of Theorem 30, Viola’s Theorem implies that one can $\\epsilon$-fool $n$-bit functions of ${\\mathbb F}_2$-degree $d$ using only $O(d\\log n) + O(d 2^d \\log(1/\\epsilon))$ independent random bits.\n\nThe proof of Viola’s Theorem is an induction on $d$. To reduce the case of degree $d+1$ to degree $d$, Viola makes use of a simple concept: directional derivatives.\n\nDefinition 48 Let $f : {\\mathbb F}_2^n \\to {\\mathbb F}_2$ and let $y \\in {\\mathbb F}_2^n$. The directional derivative $\\Delta_y f : {\\mathbb F}_2^n \\to {\\mathbb F}_2$ is defined by $\\Delta_y f(x) = f(x+y) - f(x).$ Over ${\\mathbb F}_2$ we may equivalently write $\\Delta_y f(x) = f(x+y) + f(x)$.\n\nAs expected, taking a derivative reduces degree by $1$:\n\nFact 49 For any $f : {\\mathbb F}_2^n \\to {\\mathbb F}_2$ and $y \\in {\\mathbb F}_2^n$ we have $\\deg_{{\\mathbb F}_2}(\\Delta_y f) \\leq \\deg_{{\\mathbb F}_2}(f) -1$.\n\nIn fact, we’ll prove a slightly stronger statement:\n\nProposition 50 Let $f : {\\mathbb F}_2^n \\to {\\mathbb F}_2$ have $\\deg_{{\\mathbb F}_2}(f) = d$ and fix $y, y’ \\in {\\mathbb F}_2^n$. Define $g :{\\mathbb F}_2^n \\to {\\mathbb F}_2$ by $g(x) = f(x+y) – f(x+y’)$. Then $\\deg_{{\\mathbb F}_2}(g) \\leq d-1$.\n\nProof: In passing from the ${\\mathbb F}_2$-polynomial representation of $f(x)$ to that of $g(x)$, each monomial $x^S$ of maximal degree $d$ is replaced by $(x+y)^S – (x+y’)^S$. Upon expansion the monomials $x^S$ cancel, leaving a polynomial of degree at most $d-1$. $\\Box$\n\nWe are now ready to give the proof of Viola’s Theorem.\n\nProof of Viola’s Theorem: The proof is by induction on $d$. The $d = 1$ case is immediate (even without the factor of $9$) because $\\varphi$ is $\\epsilon$-biased. Assume that the theorem holds for general $d \\geq 1$ and let $f : {\\mathbb F}_2^n \\to \\{-1,1\\}$ have $\\deg_{{\\mathbb F}_2}(f) \\leq d+1$. We split into two cases, depending on whether the bias of $f$ is large or small.\n\nCase 1: $\\mathop{\\bf E}[f]^2 > \\epsilon_d$. In this case, \\begin{align*} &\\quad \\sqrt{\\epsilon_d} \\cdot \\Bigl|\\mathop{\\bf E}_{\\boldsymbol{z} \\sim \\varphi^{*(d+1)}}[f(\\boldsymbol{z})] – \\mathop{\\bf E}_{{\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[f({\\boldsymbol{x}})]\\Bigr| \\\\ &< |\\mathop{\\bf E}[f]| \\cdot \\Bigl|\\mathop{\\bf E}_{\\boldsymbol{z} \\sim \\varphi^{*(d+1)}}[f(\\boldsymbol{z})] – \\mathop{\\bf E}_{{\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[f({\\boldsymbol{x}})]\\Bigr| \\\\ &= \\Bigl|\\mathop{\\bf E}_{{\\boldsymbol{x}}’ \\sim {\\mathbb F}_2^n, \\boldsymbol{z} \\sim \\varphi^{*(d+1)}}[f({\\boldsymbol{x}}')f(\\boldsymbol{z})] – \\mathop{\\bf E}_{{\\boldsymbol{x}}’, {\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[f({\\boldsymbol{x}}')f({\\boldsymbol{x}})]\\Bigr| \\\\ &= \\Bigl|\\mathop{\\bf E}_{\\boldsymbol{y} \\sim {\\mathbb F}_2^n, \\boldsymbol{z} \\sim \\varphi^{*(d+1)}}[f(\\boldsymbol{z} + \\boldsymbol{y})f(\\boldsymbol{z})] – \\mathop{\\bf E}_{\\boldsymbol{y}, {\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[f({\\boldsymbol{x}} + \\boldsymbol{y})f({\\boldsymbol{x}})]\\Bigr| \\\\ &= \\Bigl|\\mathop{\\bf E}_{\\boldsymbol{y} \\sim {\\mathbb F}_2^n, \\boldsymbol{z} \\sim \\varphi^{*(d+1)}}[\\Delta_{\\boldsymbol{y}}f(\\boldsymbol{z})] – \\mathop{\\bf E}_{\\boldsymbol{y}, {\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[\\Delta_{\\boldsymbol{y}}f({\\boldsymbol{x}})]\\Bigr| \\\\ &\\leq \\mathop{\\bf E}_{\\boldsymbol{y} \\sim {\\mathbb F}_2^n}\\Bigl[\\Bigl|\\mathop{\\bf E}_{\\boldsymbol{z} \\sim \\varphi^{*(d+1)}}[\\Delta_{\\boldsymbol{y}}f(\\boldsymbol{z})] – \\mathop{\\bf E}_{{\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[\\Delta_{\\boldsymbol{y}}f({\\boldsymbol{x}})\\Bigr|\\Bigr]. \\end{align*} For each outcome $\\boldsymbol{y}=y$ the directional derivative $\\Delta_{y} f$ has ${\\mathbb F}_2$-degree at most $d$ (Fact 49). By induction we know that $\\varphi^{* d}$ $\\epsilon_d$-fools any such polynomial and it follows (exercise) that $\\varphi^{* (d+1)}$ does too. Thus each quantity in the expectation over $\\boldsymbol{y}$ is at most $\\epsilon_d$ and we conclude $\\Bigl|\\mathop{\\bf E}_{\\boldsymbol{z} \\sim \\varphi^{*(d+1)}}[f(\\boldsymbol{z})] – \\mathop{\\bf E}_{{\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[f({\\boldsymbol{x}})]\\Bigr| \\leq \\frac{\\epsilon_d}{\\sqrt{\\epsilon_d}} = \\sqrt{\\epsilon_d} = \\tfrac13 \\epsilon_{d+1} \\leq \\epsilon_{d+1}.$\n\nCase 2: $\\mathop{\\bf E}[f]^2 \\leq \\epsilon_d$. In this case we want to show that $\\mathop{\\bf E}_{\\boldsymbol{w} \\sim \\varphi^{*(d+1)}}[f(\\boldsymbol{w})]^2$ is nearly as small. By Cauchy–Schwarz, \\begin{multline*} \\mathop{\\bf E}_{\\boldsymbol{w} \\sim \\varphi^{*(d+1)}}[f(\\boldsymbol{w})]^2 = \\mathop{\\bf E}_{\\boldsymbol{z} \\sim \\varphi^{* d}}\\Bigl[\\mathop{\\bf E}_{\\boldsymbol{y} \\sim \\varphi}[f(\\boldsymbol{z}+\\boldsymbol{y})]\\Bigr]^2 \\leq \\mathop{\\bf E}_{\\boldsymbol{z} \\sim \\varphi^{* d}}\\Bigl[\\mathop{\\bf E}_{\\boldsymbol{y} \\sim \\varphi}[f(\\boldsymbol{z}+\\boldsymbol{y})]^2\\Bigr] \\\\= \\mathop{\\bf E}_{\\boldsymbol{z} \\sim \\varphi^{* d}}\\Bigl[\\mathop{\\bf E}_{\\boldsymbol{y}, \\boldsymbol{y}' \\sim \\varphi}[f(\\boldsymbol{z}+\\boldsymbol{y})f(\\boldsymbol{z}+\\boldsymbol{y}')]\\Bigr] = \\mathop{\\bf E}_{\\boldsymbol{y}, \\boldsymbol{y}’ \\sim \\varphi}\\Bigl[\\mathop{\\bf E}_{\\boldsymbol{z} \\sim \\varphi^{* d}}[f(\\boldsymbol{z}+\\boldsymbol{y})f(\\boldsymbol{z}+\\boldsymbol{y}')]\\Bigr]. \\end{multline*} For each outcome of $\\boldsymbol{y}=y$, $\\boldsymbol{y}’=y’$, the function $f(\\boldsymbol{z}+y)f(\\boldsymbol{z}+y’)$ is of ${\\mathbb F}_2$-degree at most $d$ in the variables $\\boldsymbol{z}$, by Proposition 50. Hence by induction we have \\begin{align*} \\mathop{\\bf E}_{\\boldsymbol{y}, \\boldsymbol{y}’ \\sim \\varphi}\\Bigl[\\mathop{\\bf E}_{\\boldsymbol{z} \\sim \\varphi^{* d}}[f(\\boldsymbol{z}+\\boldsymbol{y})f(\\boldsymbol{z}+\\boldsymbol{y}')]\\Bigr] & \\leq \\mathop{\\bf E}_{\\boldsymbol{y}, \\boldsymbol{y}’ \\sim \\varphi}\\Bigl[\\mathop{\\bf E}_{{\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[f({\\boldsymbol{x}}+\\boldsymbol{y})f({\\boldsymbol{x}}+\\boldsymbol{y}')]\\Bigr] + \\epsilon_d\\\\ &= \\mathop{\\bf E}_{{\\boldsymbol{x}} \\sim {\\mathbb F}_2^n}[\\varphi * f({\\boldsymbol{x}})^2] +\\epsilon_d\\\\ &= \\sum_{\\gamma \\in {\\widehat{{\\mathbb F}_2^n}}} \\widehat{\\varphi}(\\gamma)^2 \\widehat{f}(\\gamma)^2 +\\epsilon_d\\\\ &\\leq \\widehat{f}(0)^2 + \\epsilon^2 \\sum_{\\gamma \\neq 0} \\widehat{f}(\\gamma)^2 + \\epsilon_d\\\\ &\\leq 2\\epsilon_d + \\epsilon^2, \\end{align*} where the last step used the hypothesis of Case $2$. We have thus shown $\\mathop{\\bf E}_{\\boldsymbol{w} \\sim \\varphi^{*(d+1)}}[f(\\boldsymbol{w})]^2 \\leq 2\\epsilon_d + \\epsilon^2 \\leq 3\\epsilon_d \\leq 4 \\epsilon_d,$ and hence $|\\mathop{\\bf E}[f(\\boldsymbol{w})]| \\leq 2 \\sqrt{\\epsilon_d}$. Since we are in Case 2, $|\\mathop{\\bf E}[f]| \\leq \\sqrt{\\epsilon_d}$, and so $\\Bigl| \\mathop{\\bf E}_{\\boldsymbol{w} \\sim \\varphi^{*(d+1)}}[f(\\boldsymbol{w})] – \\mathop{\\bf E}[f] \\Bigr| \\leq 3 \\sqrt{\\epsilon_d} = \\epsilon_{d+1},$ as needed. $\\Box$\n\nWe end this section by discussing the tightness of parameters in Viola’s Theorem. First, if we ignore the error parameter then the result is sharp: Lovett and Tzur [LT09] showed that the $d$-fold convolution of $\\epsilon$-biased densities cannot in general fool functions of ${\\mathbb F}_2$-degree $d+1$. More precisely, for any $d \\in {\\mathbb N}^+$, $\\ell \\geq 2d+1$ they give an explicit $\\frac{\\ell}{2^n}$-biased density on ${\\mathbb F}_2^{(\\ell+1)n}$ and an explicit function $f : {\\mathbb F}_2^{(\\ell+1)n} \\to \\{-1,1\\}$ of degree $d+1$ for which $\\Bigl|\\mathop{\\bf E}_{\\boldsymbol{w} \\sim \\varphi^{* d}}[f(\\boldsymbol{w})] – \\mathop{\\bf E}[f]\\Bigr| \\geq 1-\\frac{2d}{2^n}.$ Regarding the error parameter in Viola’s Theorem, it is not known whether the quantity $\\epsilon^{1/2^{d-1}}$ can be improved, even in the case $d = 2$. However obtaining even a modest improvement to $\\epsilon^{1/1.99^d}$ (for $d$ as large as $\\log n$) would constitute a major advance since it would imply progress on the notorious problem of “correlation bounds for polynomials” — see [Vio09a].\n\n### 10 comments to §6.5: Highlight: Fooling ${\\mathbb F}_2$-polynomials\n\n•", null, "Avishay Tal\n\nIn definition 48, it seems that it should be written “\\Delta_y f(x) = f(x+y)-f(x)”.\n\n•", null, "Ryan O'Donnell\n\nAbsolutely, thanks.\n\n•", null, "Chung Hoi Wong\n\nHello, seems that in definition 48, all the z’s should be changed to y.\n\n•", null, "Ryan O'Donnell\n\nThanks, fixed.\n\n•", null, "Noam Lifshitz\n\nIn example 47, “(see )” is written, without any references.\n\n•", null, "Ryan O'Donnell\n\nThanks! It should point to exercise 7 of this chapter.\n\nPS: one more correction and you’ll have caught up to the mighty LYT! Keep them coming", null, "•", null, "Gautam Kamath\n\nThe word “the” is duplicated in the statement of Viola’s Theorem\n\n•", null, "Ryan O'Donnell\n\nThanks!\n\n•", null, "Matt Franklin\n\nThere might be a small typo in Case 2 of the proof of Viola’s Theorem\n(next to last line on p. 152 of the book).\nShould there be an extra pair of parentheses around $\\phi \\star f(x)$\n\n•", null, "Ryan O'Donnell\n\nThanks — I think that kind of parenthesis-free notation for convolution is okay; on the other hand, it can’t really hurt if I add it." ]
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https://www.varsitytutors.com/ged_math-help/median
[ "# GED Math : Median\n\n## Example Questions\n\n← Previous 1 3 4\n\n### Example Question #1 : Median\n\nFind the median of the following set of numbers:", null, "", null, "", null, "This set of numbers does not have a median.", null, "", null, "", null, "Explanation:\n\nThe median of a set of numbers is the number that is in the middle of the set when the numbers are written in increasing order (from smallest to largest).\n\nWhen a list of numbers includes an even quantity, like the list in this question, you need to find the mean (average) of the two numbers in the middle.\n\nThe steps to find the median of this number set are below:\n\n1. Rewrite the list of numbers so that the numbers are written in increasing order", null, "2. Identify the number(s) in the middle of the set", null, "3. Since there are two numbers in the middle of the set, find the average of the two numbers", null, "### Example Question #2 : Median\n\nGiven the data set", null, ", which of the following quantities are equal to each other/one another?\n\nI: The mean\n\nII: The median\n\nIII: The mode\n\nI and III only\n\nII and III only\n\nI,  II, and III\n\nI and II only\n\nII and III only\n\nExplanation:\n\nThe median of a data set with an odd number of elements is the middle value when the set is arranged in ascending order; the middle value of this nine-element set is the fifth value, 5.\n\nThe mode of a data set is the most frequently occurring element. Here, only 5 appears multiple times, so it is the mode.\n\nThe mean of a data set is the sum of its elements divided by the number of elements, which here is 9:", null, "The mean and the mode are equal, but the mean is different. The correct answer is \"II and III only\".\n\n### Example Question #3 : Median\n\nWhich of the following elements can be added to the data set", null, "so that its median remains unchanged?\n\nI:", null, "II:", null, "III:", null, "II and III only\n\nI, II, and III\n\nI and II only\n\nI and III only\n\nI, II, and III\n\nExplanation:\n\nThe median of a data set with an even number of elements is the mean of the two elements that are in the middle when the elements are arranged in ascending order, such as this one. The middle elements of this ten-element set are both 5, so this is the median.\n\nThe median of a data set with an odd number of elements is the element that is in the middle when the elements are arranged in ascending order.\n\nIf 0 is added to the set, it becomes", null, ".\n\nIf 5 is added, it becomes", null, ".\n\nIf 100 is added, it becomes", null, ".\n\nIn all cases, the middle element is 5, so the median remains 5. The correct choice is \"I, II, and III\".\n\n### Example Question #1 : Median\n\nVeronica went to the mall and bought several gifts for her sister's birthday. The prices of the items were the following:\n\n$8,$10, $6,$7, $10,$2, $5,$3, $4 What is the median price of the gifts? Possible Answers:$6\n\n$8$2\n\n$4.50$10\n\n$6 Explanation: To find the median, first reorder the prices in ascending order:$2,  $3,$4, $5,$6, $7,$8, $10,$10\n\nThe median is the middle numbein a data set.\n\nHere, the middle number in this set of nine prices is the 5th number, which is $6.$2,  $3,$4, $5,$6, $7,$8, $10,$10\n\n### Example Question #1 : Median\n\nPhilip went to a family reunion this weekend and met his extended cousins, whose ages were the following:\n\n10, 21, 7, 8, 19, 20, 8, 15, 17, and 13.\n\nWhat is the median age?\n\n10\n\n17\n\n13\n\n14\n\n8\n\n14\n\nExplanation:\n\nThe median is the middle number in a data set.\n\nTo find the median, rearrange the numbers in ascending order and find the middle number:\n\nGiven: 10, 21, 7, 8, 19, 20, 8, 15, 17, 13.\n\nIn order: 7, 8, 8, 10, 13, 15, 17, 19, 20, 21.\n\nSince the data set has 10 numbers, which is even, the median is the average of the two middle numbers. The two numbers in the middle of this data set are 13 and 15. Their average, or the number in between them, is 14.\n\nTherefore the median age is 14.\n\n### Example Question #6 : Median\n\nUse the following data set of test scores to answer the question:", null, "Find the median.", null, "", null, "", null, "", null, "", null, "", null, "Explanation:\n\nTo find the median score, we will first arrange the numbers in ascending order. Then, we will find the number in the middle of the set.\n\nSo, given the set", null, "we will arrange the numbers in ascending order (from smallest to largest). So, we get", null, "Now, we will find the number in the middle.", null, "We can see that it is 84.\n\nTherefore, the median score of the data set is 84.\n\n### Example Question #7 : Median\n\nA Science class took an exam. Here are the scores of 9 students:", null, "Find the median score.", null, "", null, "", null, "", null, "", null, "", null, "Explanation:\n\nTo find the median score, we will first arrange the scores in ascending order. Then, we will find the score in the middle of the set.\n\nSo, given the set", null, "we will first arrange them in ascending order (from smallest to largest). So, we get", null, "Now, we will find the score in the middle of the set", null, "we can see that it is 84.\n\nTherefore, the median score is 84.\n\n### Example Question #8 : Median\n\nA class took a Math exam.  Here are the test scores of 11 students.", null, "Find the median.", null, "", null, "", null, "", null, "", null, "", null, "Explanation:\n\nTo find the median of a data set, we will first arrange the numbers in ascending order. Then, we will find the number in the middle of the set. So, given the set", null, "we will arrange the numbers in ascending order (from smallest to largest). We get", null, "Now, we will find the number in the middle of the set.", null, "Therefore, the median of the data set is 82.\n\n### Example Question #9 : Median\n\nSolve for the median:", null, "", null, "", null, "", null, "", null, "", null, "", null, "Explanation:\n\nReorder the numbers from least to greatest.", null, "In an odd set of data, the median is the central number of this data set.\n\nThe answer is:", null, "### Example Question #10 : Median\n\nIdentify the median:", null, "", null, "", null, "", null, "", null, "", null, "", null, "Explanation:\n\nReorder all the numbers in chronological order.", null, "The median of an even set of numbers is the average of the central two numbers.", null, "The median is:", null, "← Previous 1 3 4\n\n### All GED Math Resources", null, "" ]
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https://websupergoo.com/helpcr/source/2-examples/decfile.htm
[ "", null, "Decrypting Files", null, "This example shows how to decrypt a file.\n Setup", null, "First we'll set up some convenient variables containing our password, a path to the file we want to decrypt and a path to the location we want the decrypted file saved. ```thePass = \"pittabread\" theInPath = \"c:\\mysecurefolder\\myfile.txt\" theOutPath = \"c:\\myfolder\\myfile.txt\"```\n Crypto", null, "Next we create an ABCCrypto Crypto object and ask it to decrypt the file using the password we provided as a key. ```Set theCrypto = Server.CreateObject(\"ABCCrypto2.Crypto\") theCrypto.License = \"30-day-trial\" theCrypto.Password = thePass theCrypto.DecryptFile(theInPath, theOutPath)```" ]
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https://www.b4x.com/android/forum/threads/corrupted-array-values.50166/
[ "", null, "", null, "Bug? Corrupted array values\n\nDiscussion in 'B4J Bugs & Wishlist' started by Simon Kinsey v2, Feb 2, 2015.\n\n1. Simon Kinsey v2MemberLicensed User\n\nI seem to be having problems with this easter egg game, that I think is due to an array of egg objects getting corrupted. The game works fine, until the values in the array are reset to their original values with the reset button. The code gives an error message when array item (0,0) changes from its initial value, set up in a nested loop. The error occurs in the Egg_stack module.\n\nThe code is modified from a prototype I wrote and tested in C, so I know that this part of the program works OK when compiled in C.\n\nI have attached the source code.\n\nFile size:\n5.9 KB\nViews:\n62\nFile size:\n222.3 KB\nViews:\n55\n2. Simon Kinsey v2MemberLicensed User\n\nNo, this isn't a problem of 'aliasing', where a variable refers to an object rather than being an object. The only code where the array of objects changes value is here:\nCode:\nFor n=0 To colourCount - 1\n\nFor m=0 To EGGSEND\n\nDim c, t As Int\nt =  {calculated value\n}\nc =  {calculated value}\nstack(n,m).Initialize(t, c)\nnext\nnext\nwhere {calculated value} is a function that calcultes the value from a string, not relevant to this problem.\n\nStepping through the code, initially, stack(0,0) has the correct value of (0,0). However, sometimes, when n=1 and m=0, stack(0,0) returns to its previous value of (1,2). This is not a consistent problem. At other times I have run the code and stack(0,0) keeps its value. It depends on whether the player of the game got very far in the game before restarting it.\n\n3. Simon Kinsey v2MemberLicensed User\n\nHowever, the following does work:\n\nCode:\nFor n=0 To colourCount - 1\n\nFor m=0 To EGGSEND\n\nDim c, t As Int\n\nDim e as Egg\nt =  {calculated value\n}\nc =  {calculated value}\ne.Initialize(t,c)\nstack(n,m) = e\nnext\nnext\nso it seems that when an object is initialized, the previous values are not properly destroyed beforehand. Is this right?\n\n4. This is actually the same issue as the one I was referring.\n\nOnly when you call Dim e As Egg you are creating a new instance.\n\n5. Simon Kinsey v2MemberLicensed User\n\nSo you cannot re-initialise the same instance to new values? OK, thank you Erel.\n\n6. You can. But you aren't creating a new instance so all the references still point to the same single object.\n\n7. Simon Kinsey v2MemberLicensed User\n\nYes, Erel, I see you are right. The flaw in my code is that in C I was able to move data structures - eggs - where in Java and B4J I move references to objects. When the game restarts I only re-instantiate some of the array elements, and these are now pointing to different objects. There is no bug." ]
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https://ask.metafilter.com/81306/Help-me-with-triangle-math
[ "# Help me with triangle math.January 17, 2008 9:36 PM   Subscribe\n\nTriangles.math.Filter: Its been a while since algebra - I am trying to calculate the linear length of material required to fabricate a repeating V pattern out of sheet metal in various lengths.\n\nThe metal, after being bent looks like:\n\na/\\/\\/\\/\\/\\/\\/\\/\\/b\n\nThe height of the wave will be constant, 50mm. The bend angles will be 90degrees.\n\nWhat I am trying to figure out is how many mm of initial material to be used in various lengths of Wave. For example, if AB=1000mm, then how much would the original material be flattened out.\n\nIn practice, both the height and length will vary, so my overall goal is to put this into excel with the various finished sizes, that give me length of material required to fabricate.\n\nThanks\nposted by dripped to Work & Money (8 answers total)\n\nMultiply by the square root of 2.\n\nheight doesnt matter\nposted by vacapinta at 9:40 PM on January 17, 2008 [1 favorite]\n\nSince the angle is 90 degrees, the distance (in the direction of A to B) of each linear piece is also 50 mm. The length of the piece is 50/cos(45 degrees) = about 70.17 mm.\n\nSo the length of metal is 50/cos(45 degrees) mm per linear piece, and each linear piece covers 50 mm toward B, so you need\n\nAB*[50/cos(45 degrees)]/50\n= AB/cos(45 degrees)\n\nSince the 50 cancels, this will work no matter the height and length, so long as the 90 degree angle remains constant.\nposted by kjackelen05 at 9:54 PM on January 17, 2008\n\nExpanding a bit on what vacapinta said, you can think of each wave as being a triangle with one 90° angle (and thus two 45° angles).\n\nCheck out this picture on wikipedia and imagine it's your triangle. The 90° corner would be \"C\" here. And the length of your hypotenuse (h) would be the 'linear length' of your triangle in the wave, while the sum of lines a and b would equal the length of the actual metal strip.\n\nThe ratio of the hypotenuse (h) and a is the sine of the angle in the corner (45°) and the ratio of and the ratio of the hypotenuse (h) and b is cosine(45°). So the ratio between the length of the metal strip to the linear length of the sine(45°)+cos(45&deg);. And it just so happens that the sine and cosine of 45° are both √(2)/2. So double that and you get √2.\n\nAssuming I'm remembering my high school trig correctly :)\nposted by delmoi at 10:05 PM on January 17, 2008\n\nI didn't do any calculations to get that answer. Once I read 90 degrees and saw his picture I realized it was like setting up a row of square frames. So its just a matter of measuring the length of a diagonal of a square: sqrt(2)\n\nThe height doesnt matter for the same reason that it doesnt matter how many steps you build in a staircase. You still need the same amount of material. Or why going from 3rd and 22nd st to 4th and 18th is the same distance no matter how many blocks you cut over when you are walking there.\nposted by vacapinta at 10:20 PM on January 17, 2008 [1 favorite]\n\nvacapinta is correct.\nposted by Opposite George at 10:45 PM on January 17, 2008\n\nThanks guys!\nposted by dripped at 11:08 PM on January 17, 2008\n\nJust wanted to point out that dividing by 0.707107 (which is really 1/√2) is the same as multiplying by √2.\nposted by yeoz at 5:18 AM on January 18, 2008\n\n... and to further complicate, all these solutions assume you're able to bend sharp geometric angles into your sheet metal, which in real life you can't and will approximate with arcs (this picture shows it well, along with a calculator you might use if you end up caring about this effect).\n\nThe upshot is that the length of material along the real-life arc is going to be different than the length to the (imaginary) vertex. The discrepancy might not make any difference if you're only doing a few bends, or if you're going to trim to finished size after bending, but if you're doing a continuous piece of siding for a wall and need it to come out correct to 1/16\", you might want to take this into account.\n\n(At a minimum, the effect means that using all the digits in .707107 is a misleading amount of precision for you...)\nposted by range at 5:47 AM on January 18, 2008\n\n« Older Being a class-A busybody on an effort budget   |   Vista Windows Explorer default opening window? Newer »" ]
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https://physics.stackexchange.com/questions/56158/liouvilles-theorem-and-gravitationally-deflected-lightpaths/56161#56161
[ "# Liouville's theorem and gravitationally deflected lightpaths\n\nIt is customary in gravitational lensing problems, to project both the background source and the deflecting mass (e.g. a background quasar, and a foreground galaxy acting as a lens) in a plane.\n\nThen, the lensing problem can be regarded as a mapping between the unlensed source plane, and the lensed image plane. In such transformations, the Jacobian evaluated at a point of the source plane, expresses how an infinitesimal area located around that point increases.\n\nLens mass and mass distribution, relative positions and distances involved give rise to different scenarios. The special case in which the distortions are too small to be resolved by telescopes, is called microlensing regime. Typically, a dark, unseen object like a floating planet, happens to cross transversally in front of a background star.\n\nThe image of the background star suffers amplification and distortions that are unresolved, but a change in brightness is detected, with a very typical light curve shape. The measured light curve of a microlensing event can be related to physical parameters of the problem, because the change in brightness of a lensed image can be modelled simply by dividing the area of the lensed image by that of the unlensed source image. If that can be done, it is because the mean surface flux of the image equals that of the source.\n\nThat is, gravitational lensing can make a tiny source appear bigger in the sky but in plain terms, every square inch of the image has the same brightness of every square inch of the source. Here comes my question, because that seems to me rather counter-intuitive and, when I try to find a rigorous justification to it, I find the same arcane sentence in each book, in each review, in each paper I have seen:\n\n'Because of Liouville's theorem, gravitational lensing conserves surface brightness'\n\n(... and therefore the magnification is found by dividing the subtended area of the image by that of the source). Every single author I have read, drops that sentence as if it were something very obvious, and quickly goes into other questions.\n\nI have tried to trace-back the origin of the idea, by consulting the bibliography of every book or document in which that thing is stated. Interestingly, I have recognized sort of a fingerprint of obscure points like this one, a patter that is repeated in many of the documents, as if some authors didn't understand and merely copied from each other, developing and personalizing only the parts they understand in between.\n\nI have rigorously developed and resolved every one of the dark points in that pattern, but this one remains unresolved. Is it perhaps something obvious? How is Liouville's theorem applied to photons along null geodesics? I will accept an appropiate link or paper reference as a good answer.\n\n• See also (related but definitely not a duplicate) physics.stackexchange.com/q/31534 - Qmechanic's answer to that question might be helpful to you. Mar 7, 2013 at 8:46\n• @Nathaniel, thanks (+1). It seem at first sight related. I'll have a closer look, maybe it puts me on the right track. Mar 7, 2013 at 17:07\n\nThis question is pretty old but I figured other people than OP might be interested in the answer. I had the exact same question, so I asked my advisor and he told me that the answer can be found in two places. First (and unsurprisingly), there's a full derivation in Misner, Thorne and Wheeler, particularly on sections 22.5 and 22.6. There's also an attempt at a more intuitive explanation in Schutz's Gravity from the Ground Up, a very interesting book which attempts to explain GR with only high school math. I'm not actually sure if the explanation given there makes sense, but it has helped me arrive at one that does.\n\nIf I had to summarize the whole thing, I'd say that the conservation of surface brightness (or specific intensity, or étendue, or...) is the result of an exchange between position space volume and momentum space volume, or between solid angle and area.\n\nI won't reproduce all the math because it can be found in MTW, but the basic idea is as follows. The first important fact is that you can think of light propagation as a bunch of \"classical\" photons moving along null geodesics, and the number of photons is conserved.\n\nThis photon picture allows you to use kinetic theory, as developed in section 22.6 of MTW, where Liouville's theorem is derived: given a bunch of $N$ particles, the volume $\\mathcal{V}$ in phase space occupied by them, given by the product $\\mathcal{V} = \\mathcal{V}_x \\mathcal{V}_p$ of the volume in 3-space and in momentum space as measured by a locally inertial observer, is Lorentz invariant and constant along the world line. Therefore, the number density $\\mathcal{N} = N/\\mathcal{V}$ is constant as well. I won't go through the proof since it is much easier to just look at the pictures in MTW, but this is the key step which has all the counterintuitive consequences.\n\nThe last step is relating the number density to the specific intensity or surface brightness $I_\\nu$, the amount of energy per unit time, area of detector, frequency, and solid angle of the photons' momenta. This is a pretty standard derivation, and it makes sense. The result is that $\\mathcal{N} = h^{-4} I_\\nu/\\nu^3$.\n\nThe frequency can change in crazy ways along the geodesic, but as long as there's no cosmological redshift between the source and the observer the net change will be zero, so for our purposes $I_\\nu$ is constant, which is the result we needed. A given piece of solid angle from the observer's perspective receives the same flux no matter if there's a lens in the way or not.\n\nAs you've noticed, this has fairly counterintuitive consequences. From conservation of energy you would (wrongly) expect a bigger image to be dimmer, because a given solid angle of observation covers less of the lensed image than of the unlensed image. But the image also appears closer, so it's also brighter. Or, to put it in more careful terms, given a point on the source, light from a larger solid angle will reach the observer than if there were no lens (hope this sentence makes sense!). Conservation of phase space volume guarantees that these two effects exactly cancel each other out.\n\nIf anyone is interested in thinking through this, I have some advice. You must be careful when drawing 2D pictures, because using solid angles is essential: it can happen that images get compressed in one direction and stretched in the other, so in a 2D diagram the image would look smaller while it's actually larger. Also, you have to consider light rays emanating in a given solid angle from a point on the source as well as light rays emanating from an area on the source which converge on a point at the observer with some solid angle. This is the tradeoff between solid angle and area I mentioned earlier.\n\nWhat they're referring to is a property of light from geometrical optics. The conserved property is \"entendue\" (see wikipedia article), and the constancy of brightness can be demonstrated in a bunch of ways (Hamiltonian optics i.e. Liouville's theorem, second law of thermodynamics as above, etc.).\n\n• You are pointing in the very right direction (+1), the conservation of éntendue is the non-relativistic analogous to this, but what I am looking for is the derivation in the context of gravitational lensing with general relativity, geodesics and so on, where you cannot for instance happily say \"energy is conserved across here and there...\". I know this issue is waiting for me, I will have to stop sooner or later and spend the necessary time to try to understand and derive it by myself. But I would like to see the original derivation all authors are ignoring, because now I am short on time. Sep 3, 2013 at 23:00\n• Yeah, I figured you were looking for the real derivation. I wish I could help but my classical-mechanics-fu is not so strong. Sep 4, 2013 at 6:29\n• (I would guess that the original non-relativistic proof (if you can find it), if put into strictly hamiltonian terms, should work well enough because the hamiltonian mechanics can be used also to model relativistic motion, with suitable choice of coordinates.) Sep 4, 2013 at 6:41\n• By the way, ouch, the correct spell is \"étendue\". It is a frech word meaning sort of \"extended\" or \"extensive\". I also spelled it wrong in my comment above, which cannot be edited any more (unlike your answer, that can be edited easily) Sep 4, 2013 at 12:18\n\nThis is just the second law of thermodynamics.\n\nSuppose you have some big source black body and some little target black body. You build a whole bunch of geometric optics to focus light from the source to the target (e.g. mirrors behind the target and lenses in front of it). Eventually an observer standing on the target looks up and sees light from the source at every point in the sky.\n\nAll this incoming light heats the target blackbody up until it's radiating light away as fast as it's coming in. That means the target comes up to the same temperature as the source. If you could increase the flux per unit solid angle, the target would get hotter than the source, breaking the second law.\n\nIs this enough, or did you want a technical analysis of the lensing equation?\n\n• I am afraid it is not enough. (i) I don't grasp why the observer standing at the target cannot simply receive less light from, say, the mirrors standing behind, but more light from the front lens, thus having parts of the image brighter at the cost of having other parts of the image darker. (ii) In real problems, the observer receives only part of the flux, the rest goes to another directions. I don't see why a spatially extended image cannot be dimmer (that would be my guess). So the technical analysis of the lensing equation would be fantastic, if you feel like writing it... Mar 7, 2013 at 5:19\n• I had read too about the violation of the 2nd principle if you could concentrate light in too small a point with a magnifying glass, but I don't see how that translates to this question. That is why I want to find the mathematical details, that will surely give rise to the correct understanding. Mar 7, 2013 at 5:44\n• The second-law argument does work. For example if there is less light from the mirror behind the black body, just paint the back of it perfect white. Re: Liouville's theorem in geometric optics; I might have some time for that later on but I can't just do it in a couple minutes off the top of my head like this above argument. Mar 7, 2013 at 18:15" ]
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https://www.scribd.com/doc/51645490/C-Notes-Complete
[ "You are on page 1of 145\n\n# C C++\n\n## #include <stdio.h> #include <iostream.h>\n\nFunction <printf cout. in are objects\nScanf cout << “Hello”;\nInsertion operator\nOr\nInsertors\ncout << “value =” <<a;\nNo need of format specefiers\nIn C++\n\n## Scanf ( “%d”, &a); cin >>a;\n\nExtraction operator\nOr\nExtractor\ncin >>a >>b;\n\n## for next line\n\nPrintf ( “Hello \\n user”); cout << “Hello \\n User”;\nOr\ncout << “Hello” <<end<<”User”;\n\n## In C, default return type in C++ default return\n\nis “void” type is an “integer”\n\nHISTORY OF C++\n\nYear  1982\nDeveloped  Bjarne stroustrap\nLab  Bell Labs\nCompany  At & T\n\n## Easy & fast programming in C++ closely models sear world\n\nC. Logics can be easily developed problems Developed.\nCLASSES AND OBJECTS\n\n## In structure of C :- only data can be member of structure and not functions\n\nAll member of structure of are public by default\nIn class of C++ data + functions accessing those data are member of class and All\nmember of class are private by default\n\nclass stud\n{\nint roll;\nfloat par;\npublic:\nvoid get( );\nvoid show( );\n};\nvoid stud : : get( )\n{\ncout << “enter roll. Grade and per”;\ncin>>roll>> grade >> per;\n}\nvoid stud : : show\n{\ncout <<roll << “ “<< grade << “ “ <<per<< end1;\n}\n\n## void main( ) roll\n\n{ get\nstud s;\ns. get ( ); s show grade\ns. show( );\n}\nPer\n\nFunction are never seplicated there is only one copy of function no matter now\nmany objects are created only once memory is allocated to functions for all objects\nwhere as multiple copies of data are created for multiple objects.\n: : Scope resolution operator helps compiler to identify functions of which class if\ntwo classes have the same name.\n\n## Q. 1 wap to add two numbers give by user\n\n{\nint a, b, c;\npublic :\nvoid get( );\nvoid sum( );\nvoid show( );\n};\nvoid add : : get ( )\n{\ncout << “Enter no”;\ncin >> a >>b;\n}\nvoid add : : sum( )\n{\nc= a+b;\n}\nvoid add : : show( )\n{\ncout << “Numbers are = “<< a << “ “ << b;\ncout << “sum =” <<c;\n}\nvoid main( )\n{\nobj. get( );\nobj.sum( );\nobj. show( );\ngetch( );\n}\nC++ (Terminology) OOPs (Terminology)\n1. objects instances\n2. data members properties & attributes\n3. member function methods & behaviors\n4. function call message passing\n\n## BASIC PRINCIPLES OF OOP\n\nENCAPSULATION\nWord has been derived from a word capsule which means multiple\nmedicines packed in one single unit. Similarly in a software there are two major\nunit data & functions acting on that data since functions and data are related\nentities it is advisable to score them within a single unit. Thus according to oop’s\nEncapsulation means building or wrapping up of data members and f n acting on\nthose data members with in a single unit. Since a class allows us to hold data &\nfunctions within it are say that it supports the principle of encapsulation.\n\nPOLYMORPHISM\nThe word polymorphism is derived from combination of two words\npoly mcarning multiple and morph means the ability to have multiple forms. In\nother words if an entity can acquire multiple forms in different situation we say\nthat its behaviors is polymorphic for eg in c++ it is possible for programmer to\nredife operator ‘+’ in such a way that it can be used to add two integers as well as\nat the same time it can add two abject or two strings. So it a programmer define +\nto behave in above mentioned member we say that + behaves polymorphically .\nIn C++, polymorphism is implemented in two way:-\n\n## (1). Compile time polymorphism :- function overloading and operator\n\n(ii). RunTime polymorphism:- Virtual functions, pure virtual functions\nAbstract classes\n(iii). Inheritance :- to inherit means to acquire properties and fea of an\nExisting entity into a newly created entity Like a\nchild acquire properties of his or has Parent, similary when designing\nsoftware if a programmer wishes then he can acquire the fratures (data and\nmember function) of an existing class in his own class with the help of\ninheritance. The class which gets inherited is known as bass class and the\nclass. Thus by inheriting the members of base class it becomes possible to\naccess them through the objects of derive class. The major advantage offered\nby principle of Inheritance is “rcusability and reliability”\nCREATING PARAMETERIZED FUNCTIONS WITHIN CLASSES\n\nclass Emp\n{\nint age;\nchar name;\nfloat salary;\npublic:\nvoid set (int, char *, float);\nvoid show( )\n};\nvoid Emp: : set (int I, char *j, float K)\n{\nage =I;\nstrcpy(name, j);\nsalary =k;\n}\nvoid Emp : : show( )\n{\ncout <<age << “ “ <<name << “ “ salary;\n}\n\nASSIGNMENT\nWap to create a class c/a string. The class must contain a character\narray of size 20. class should have following fucnitons:\n\n## (1). Getstring( ) which accept string as parameter + stores it in array str.\n\n(2). Show string( ) which display the string stored in str[].\n(3). Reversestring ( ) which reverses the string stored in str[].\n(4). Add string( ) which accepts string as parameter and if possible\nConcatenates in str.\n\n## Write an object oriented program to calculate the factorial of a given by user.\n\nProvide separated function for initialization, inputting, calculating and display.\n\nclass Fact\n{\nlong int f;\nint n;\npublic:\nvoid init( );\nvoid getno( );\nvoid calculate( );\nvoid display( );\n};\nvoid Fact : : init( )\n{\nf=1;\n}\nvoid Fact : : getno( )\n{\ncout << “Enter a number”;\ncin >> n;\n}\nvoid Fact : : calculate( )\n{\nint i;\nfor (i=1; i<=n; i++)\nf=f * i;\n}\nvoid Fact : : display( )\n{\ncout << “ Number =” << n;\ncout << “factorial=” <<f;\n}\nvoid main( )\n{\nFact obj ;\nobj. init( );\nobj.getno( );\nobj.get calculate( );\nobj. display( );\n}\n\nCONTRUCTOR\nConstructor :- constructor are special member f n of a class with the following\nproperties\n\n## 1. They have the same name as that of the class\n\n2. They don’t have any return type not even void\n3. They are automatically called as soon as the object of class is created i.e. their\ncalling is implicit.\n4. They can’t be declared as static\n5. They can’t be declared as virtual\n\nAny class which does not contain any constructor then compiler from itself\nsupplier a constructor but it is hidden. for programmer these constructors are\ndefault\n\nclass fact\n{\n\n}\nConstructors are automatically called even when not declared, at that time\ndefault constructors are called. Default contractors are destroyed as soon as we\ndeclare constructor\n\nExample :-\nclass fact\n{\nint n;\nlong int f;\npublic:\nfact( )\n{\nf=1;\n}\nvoid getno( );\nvoid calculate( );\nvoid display( );\n};\nvoid fact : : getno( )\n{\ncout << “enter a no”;\nan >> n;\n}\nvoid fact : : calculate( )\n{\nint i;\nfor (i=1; i<=n; i++)\nf= f * i;\n}\nvoid fact : : display ( )\n{\ncout << “no=” <<n<<end1;\ncout << “factorial=” <<f;\n}\nvoid main( )\n{\nfact obj;\nobj.getno( );\nobj.calculate( );\nobj.display( );\n}\n\nPARAMETERIZED CONSTRUCTOR\n\nclass Emp\n{\nint age;\nchar name;\nfloat sal;\npublic:\nEmp (int, char *, float);\nvoid show( );\n};\nEmp : : Emp (int i, char *j, float k)\n{\nage =i;\nstrcpy (name, j);\nsal=k;\n}\nvoid Emp : : show( )\n{\ncout << age << “ “ << name<< ‘ ‘ <<sal;\n}\n\nvoid main( )\n{\nEmp e(101,”Amit”,6000.00) ;\ne.show( );\ngetch( );\n}\n\nvoid show (int, int, int)\n1. No of Arguments void show (int)\n2. Type of Argument void show (double)\n3. Order of argument void show (int, double)\nvoid show (double, int)\n\nint show ( )\nNot allowed (return type differs)\nvoid show( )\n\nCompiler does not consider return type, other wise constructor can never be\noverloaded as they have no returns type. Function overloading allows some\nfunction name in same scope but there should be some different in functions.\n\n## void vol (int);\n\nvoid vol (int, int, int );\nvoid main ( )\n{\nint choice;\ncout << “select a figure”;\ncout << “ (1) cube \\n (2) cuboid”;\ncin >> choice;\nSwitch (choice)\n{\ncase 1:\nint s;\ncout << “enter side of cube”;\ncin >> s;\nvol (s);\nbreak;\ncase 2:\nint l, b, h;\ncout << “Enter l, b and h of values ”;\ncin >> l >> b >> h;\nvol (l,b, h);\nbreak;\ndefault:\ncout << “wrong choice”;\n}\nvoid vol (int s)\n{\ncout << “value of cube =” << s*s*s;\n}\nvoid vol (int l, int b, int h)\n{\ncout << “value of cuboard=” << l*b*h;\n}\n\n## 1. Overload of remembering the name is transformed from programmer to\n\ncompiler\n2. it develops symmetry & incrrases the readability of program\n\nclass Box\n{\nint l, b,h;\npublic:\nBox( ); //constructor for user defined Box\nBox (int); //constructor for\nBox(int,int,int);\nvoid show( );\n};\nBox : : Box( )\n{\ncout << “enter l, b and h of box”;\ncin >> l >> b>> h;\n}\nBox : : Box(int s)\n{\nl=b=h=s;\n}\nBox : : Box (int i, int j, int k)\n{\nl=i;\nb=j;\nh=k;\n}\nvoid Box : : show( )\n{\ncout << l << “ “ <<b<< “ “ << h;\n}\nvoid main ( )\n{\nBox B1;\nBox B2 (5,7,11);\nBox B3 (10);\nB1. show( );\nB2. show ( );\nB3. show ( );\n}\n\nCOPY CONSTRUCTOR\n\nIt is a special constructor of a class which accepts the reference of the object of its\nour class as a parameter. It is called by c++ compiler in there situations\n\n1. when ever programmer created an object and at the same time passes another\nobject of the same class as a parameter\n2. whenever a f n accepts an object as a parameter by value.\n3. whenever a f n returns an object by value.\n\nReference variable:-\n\n## void main( ) backs of pointer\n\n{ 1. occupy 2 bytes of memory\nint a = 10; 2. will be initialized by garbage\nint *p; 3. necessary to initialize before their use\np=&a;\ncout <<*p <<end1; 4. very careful in using indirection operator\n}\n\n## void main ( ) Advantage of Reference variable\n\n{\nint a=10; 1. we can have n reference variables of one\nint &p=a; variable\ncout <<a<< “ “ <<p;\ncout << &a << “ “ <<&p; 2. both variables get interlocked on each\nOther\n3. does not require any memory space it\nOnly reuse the memory of any variable\n\n## Reference variable is a technology provided by c++ which allow a programmer to\n\ncreate a new variable which stores (not holds) the memory location of an existing\nvariable. In other words, reference variable is a technique using which programmer\ncan allot multiple mames to same memory location.\n\nPOINTER TO REMEMBER\n1. int &p = a;\n2. int &p =b;\nmultiple declarations for the variable p.\n3. In case of array, always use pointer. Reference variable can not work with\narray\n4. we can not make array of reference variable\n\nC C C++\n\n## pass by value pass by reference pass by reference\n\nvoid swap (int, int); void swap (int*, int*); void swap (int&,int&);\nvoid main( ) void main( ) void main( )\n{ { {\nint a,b; int a,b; int a,b;\ncout << “Enter2 number”; cout << “Enter2no”; clrscr( ) ;\ncin >>a>>b; cin >> a >> b;\nswap (a,b); swap (&a, &b) cout << “enter2no”)\ncout << a << “ “ <<b; cout <<a<< “ “<<b; cin>>a>>b;\n} } swap (a,b);\nvoid swap(int p,intq) void swap (int*p, int*q) cout <<a<< “ “<<b;\n{ {\nint temp; int temp; }\ntemp =p; temp=*p; void swap(int&p,int&q)\np=q; *p=*q; {\nq=temp; *q= temp; int temp;\n} } temp=p;\np=q;\nq=temp;\n}\n\n## Note :- By call it is not possible to call whether it is call by value or call by\n\nreference\n\nQ. WAP to use a function call maximum which accepts an integer array of size\npure as an argument & retirns the largest & smallest element of that array to main.\nWithout changing the arcginal position of element of ht array.\n\n## void Maximum (int a[], int &, int&)\n\nvoid main( )\n{\nint a, i, large, small;\nfor (i=0; i<5; i++)\n{\ncout << “enter elements of array”;\ncin >>a[i];\n}\nMaximum (a, large, small);\ncout << “maximum element=” <<large;\ncout << “smallert element=” <<small;\n}\nvoid Maximum (int a, int &max, int & min)\n{\nmax = a;\nmin=a;\nfor(int i=1; i<5; i++)\n{\nif (*(a+i) >max)\nmax=* (a+i);\nelse if (* (a+i) <min)\nmin = *(a+i);\n}\n}\n\nclass Box\n{\nint l, b, h;\npublic:\nBox( );\nBox (int);\nBox (int, int, int);\nBox (Box &);\nvoid show( );\n};\nBox : : Box( )\n{\ncout << “enter l, b and h of Box”;\ncin >> l >> b >> h;\n}\nBox : : Box (int S)\n{\nl=b=h=s;\n}\nBox : : Box (Box &p)\n{\nl=p.l;\nb=p.b;\nh=p.h;\n}\nBox : : Box (int i, int j, int k)\n{\nl=i;\nb=j;\nh=k;\n}\nvoid Box : : show( )\n{\ncout << l << “ “ << b << “ “ << h;\n}\nvoid main ( )\n{\nBox B1;\nBox B2 (10);\nBox B2 (5,7,11);\nBox B4 (B1);\nB1. show( );\nB2.show( );\nB3.show( );\nB4.show( );\n}\n\n## Box B2 (10) Box B2 = 10;\n\nBox B4 (B1) Box B4 = B;\nCall for copy constructor\n\nBox B4;\nB4 = B1\nNo call for copy constructor use of assignment operator other\n\nDEFUALT ARGUMENTS\n\n## void printline (char =’*’, int=1);\n\nvoid main ( )\n{\nprintline ( ); // printline (‘*’, 1)\nprintline (‘#’); // printline ( ‘#’, 1)\nprintline (‘!’, 10) // printline (‘!’, 10);\n}\nvoid printline (char ch, int n)\n{\nfor (int i=1 i<=n; i++)\ncout <<ch;\n}\n\n## printline(50);  printline (ASCII of 50, 1);\n\nclass Stud\n{\nint age;\nfloat per;\npublic:\nStud (int, char, float); // stud (int=o, char= ‘ ; float=0.0);\nvoid get( );\nvoid show( );\n};\nStud : : Stud (int I, char j, float k)\n{\nage =I;\nper =k;\n}\nvoid Stud : : get( )\n{\ncout << “Enter age, grade & per ‘;\ncin >> age >> grade >> per;\n}\nvoid Stud : : show( )\n{\ncout<< “ age = “<<age<<” \\n grade= “<<grade<<” \\n per = “<<per;\n}\nvoid main( )\n{\nStud t ( 15, ‘A’, 75);\nStud p;\np. get( );\nt. show( );\np. show( );\n}\n\nNote:- In class at same time it is not possible to have default argument constructor\nand default constructor.\n\nDESTRUCTOR\n\nAre special member f n of class which have same name as that of the class but\npreceded with the symbol of field ( ). They are automatically called whenever an\nobject goes out of scope & is about to be destroyed\n\n## When an object is created first function which is automatically called is\n\nconstructor & when object ends its life a last function is called to free occupied\nmemory is destructor\n\nclass Emp\n{\nint age;\nchar name ;\nfloat sal;\npublic:\nEmp( );\n~Emp( );\nvoid show( );\n};\nEmp : : Emp( )\n{\ncout << “Enter age, name & sal”;\ncin >> age >> name >> sal;\n}\nvoid Emp : : show( ) Note\n{ A class by default has 3 built in\ncout <<age << “name <<sal; fucntion\n} 1. Constructor\nEmp : :~ Emp( ) 2. copy constructor\n{ 3. Destructor\ncout << “Object destroyed”;\n} Note:- Destructor are always called in\nvoid main( ) reverse order.\n{\nEmp e, f,;\ne. show( );\nf. show( );\n}\n\nCreate a class c/o student having 3 data members (i) for storing Roll no. (ii) for\nstoring name (iii) for storing marks in subjects\n\nThe member for storing name should be char *, member for storing marks should\nbe int *;\n\n## Create constructor of class which prompts user to enter length of name,\n\nallocates sufficient memory & accepts the name from user. The same constructor\nasks the user how many subject he want to enter, againallocate sufficient memory\nfor that & accepts the marks given by user. Finally provide appropriate member f n\nwhich display % & grade of student. At the end define the destructor in proper\nmanner to deallocate memory allocated by constructor.\n\nclass student\n{\nint roll ,n;\nchar * name, grade;\nint *marks;\nfloat per;\npublic:\nstudent ( );\nvoid get();\nvoid calculate( );\nvoid show( );\n~student( );\n}\n\nstudent : : student ( )\n{\ncout << “how many letters”;\ncin >>n;\nname = (char *) malloc ((n+1) * sizeof (char));\nif (name= = NULL)\nexit(1);\nget( );\n}\nvoid student : : get( )\n{\ncout << “ enter roll no”;\ncin >> roll ;\ncout << “enter name”;\ncin >> name;\ncout << “how many subject are there enter ”;\ncin >>n;\nmarks=(int*)malloc(n*2);\nfor (i=0; i<n; i++)\n{\ncout << “Enter marks”;\ncin >> * (marks+i);\n}\n}\n\n## void student : : calculate ( )\n\n{\nper=0;\nfor (int i=0; i<n; i++)\nper+= * (marks+i)\nper =per/n;\nif (per >=75)\nelse if (per >=60)\nelse\n}\nstudent : :~ student ( )\n{\nfree (name);\nfree(marks);\n}\nvoid student : : show( )\n{\ncout<<” Name = “<<name<<”\\n roll number = “<<roll;\nfor(int i=0;i<n;i++)\ncout<<”\\n “<<marks[i];\n}\nvoid main( )\n{\nsudent s;\ns. calculate( );\ns. show( );\n}\nNote In C++, cin don’t accepts the space\nvoid main( )\n{\nchar str;\ncout << “enter name”;\ncin >> str;\n}\ncin.getline (str, 80); //Enter Key /9.\n\n## Prototype of get line ( )\n\nvoid get line (char *, int );\nMember of istream header file\n\nCOMPARISION BETWEEN\nCONSTRUCTOR & DESTRUCTOR\n\nCONSTRUCTOR\n1. are special member f n of a class having same name as that of the class.\n2. constructors are automatically called as soon as object of class is created i.e.\ntheir calling is implicit\n3. constructors can be parameterized.\n4. since constructors accepts parameters they can be overloaded & thus a class\ncan have multiple constructors.\n5. constructors are called in the orders in which objects are created.\n6. constructors can not be inherited.\n7. constructors can not be declared as virtual.\nDESTRUCTOR\n\n1. are special member fn of class having same name as that of the class but\npreceded with the symbol of tilde\n2. a destructor is also automatically called but whenever an object is about to be\ndestructor or goes out of scope thus these calling is implicit.\n3. we can not pass parameters to a destructor.\n4. as they don’t accept parameters we can not overload then, thus a class can not\nhave multiple destructor\n5. calling of destructor is always done in reverse or des of the creation of objects.\n6. inheriting of destructor is also not possible\n7. we can have virtual destructor in a class.\n\nINLINE FUCNTIONS\n\nInline functions\nAre those fn whose call is replaced by their body during\ncompilation. Declaring a fn as inline has two major advantages.\n\n1. compiler does not has to leave the calling fn as it finds definition of fn being\ncalled their it self.\n2. The overhead of maintaing the stack in belureen function call is seduced\nThus declaring a function as inline increases the execution\nSpeed, s\\reuces execution time & thus enhances the overall efficiency of program.\nBut two ptr must be considered before declaring a fn as inline\n\n1. The definition of inline function must appear before its call i.e. if a non\nmember fn is to be made inline then its declaration & definition must appear\nabout main as a single unit.\n2. the body of inline fn must be short & small\n3. it should not contain any complex keywords or keyword or statement like for,\nif, while, dowhile do,\nif any of the aboul sules are violated then compiler ignores the\nkeyword inline an treats the fn as offline or normal one. Moreover a\nclass can have two kinds of inline functions\n\n## 1.Implicit Inline :- fn efined within the body of class\n\n2. Explicit Inline :- fn declared within the class but defined outside the class\npreceded with the keyword inline.\n\n## Thus at the end we can say, that declaring a fn as line is a request\n\nmade by programmer which the later may agrel or deny.\n\nclass Emp\n{\nchar name ;\nint age;\nfloat sal;\npublic :\nvoid get( )\nImplicit {\nInline cout << “enter age, name and sal”;\ncin >> age >> name >> sal;\n}\nvoid show( );\n};\n\n## Explicit inline void Emp : : show( )\n\nInline {\ncout << age << “ “<<name << “ “<<sal;\n}\n\nvoid main( )\n{\nEmp E;\nint i\nfor (i=0; i<5; i++)\nE[i]. get( );\nfor (i=0; i<5; i++)\nE[i] show( );\n}\n\nSTORAGE CLASSES\n\n## Storage class decides the following\n\n1. Default value\n2. life (persistence)\n3. scope (accessibility)\n\n## storage default life scope\n\n1.auto garbage limited to limited to declaration block\n(automatic) their\nDeclaration\nBlock\n2. static Zero throughout “\nThe program\n3. register garbage same as auto same as auto\n4. global zero throughout throughout the program\nThe program\n\nAuto Static\nvoid display ( ); void display ( );\nvoid main( ) void main ( )\n{ {\ndisplay( ); display( );\ndisplay ( ); display ( );\ndisplay ( ); display( );\n} }\nvoid display( ) void display( )\n{ {\nint a;\ncout <<a<<end1; static int a;\na++; cout <<a<<end1;\n} a++;\n}\no/p 3 garbage values will be o/p  0\ngenerated 1\n2\n\nSTATIC DATA\nStatic data members with in the class\nclass data\n{\nint a ;\nStatic int b;\n}; 0 b\n\na a\nint data : : b;\nd1 D2\n\nstatic variable don’t wait for creation of object, before object creation memory is\nallocated for then\nof class\n1. A static data member has a single copy shared amongst each object of that\nclass. On other hand if a class has non static data then every object of that\nclass has its own copy of non static data\n2. static data members arrive in memory even before objects of the class get\ncreated. Because of their feature it becomes necessary to redefine them outside\nthe class so that they can be given space in RAM without the help of object.\n3. since they are not related to any particular object of the class & have a single\ncopy in the entire class, a static data member never contributes in the size of\nobject. In other words size of object is always calculated by summing up sizes\nof non static members.\nWAP to create a class c/o student having two data members roll & count. The\nmember roll should keep the roll no. allocated to every student object while the\nmembers count should keep track of total no. of student objects currently in the\nmemory. Finally provide appropriate members fn to initialize & display the values\nof Roll no. & count.\n\nclass Student\n{\nint roll;\nstatic int count;\npublic:\nStudent (int i)\n{\nroll=i;\n++ count ;\n}\nvoid show( )\n{\ncout << “Roll no=” <<roll<<end1;\ncout << “total objects alive =” <<count;\n}\n~Student ( )\n{\n--count;\n}\n};\nint student : : count;\nvoid main( )\n{\nStudent S=10;\nStudent P=20;\nStudent Q=30;\nS. show( );\nP. show( );\nQ. show( );\n{\nStudent X = 40;\nStudent Y=50;\nX. show( );\nY. show( );\n}\n}\n\nSTATIC FUCNITON\n\nSyntax\n<class-name> : : <function-name>(list of arguments);\n\n## RESTRICTIONS ON STATIC FUNCTIONS\n\n1. static f n can never access non –static data numbers. But reverse is true.\n2. Constructor & Destructor can never be made or declared as static.\n3. If a static function is defined outside the class the keyword static cannot be\nused.\n\nclass student\n{\nint roll;\nstatic int count;\npublic :\nstudent (int i)\n{\nroll =i;\n++count;\n}\nvoid show( )\n{\ncout<<roll;\n\n}\nstatic void total_student()\n{\ncout << “total objects alive =” <<count;\n}\n~student ( )\n{\n--count;\n}\n};\nint student : : count;\nvoid main( )\n{\nstudent S(10). P(20), Q(30);\nS.show();\nP.show();\nQ.show();\nstudent : : total_student( );\n{\nstudent X(40), Y(50);\nX. show( );\nY.show();\nstudent::total_student();\n}\nstudent::total_student();\n\n}\nstudent::total_student();\ngetch( );\n}\n\nProgram :- Create a class c/a employa having data members for storing age, name\n& id. Also provide another member which stores the next id which will be\nallocated to next incoming object. Provide a member f n to initialize this variable\nwith its initial value & also provide appropriate constructor & member f n for\ninitialized all other members of class.\n\nclass Emp\n{\nint age;\nchar name ;\nint id;\nstatic int nid;\npublic:\nstatic void init( )\n{\nid=nid;\n}\nEmp( )\n{\ncout << “enter age, name”;\ncin >> age >> name;\nid=nid;\n++nid;\n}\nvoid show( )\n{\ncout <<age<<name<<id;\n}\n};\n\nSolution :-\n\nclass Employee\n{\nint age;\nchar name;\nint id;\nStatic int nextid;\npublic:\nEmployee (int, char *);\nstatic void initid( );\nvoid show( );\nstatic void getnextid( );\n~Employee( );\n};\n\n## int Employee :: nextid;\n\nEmployee : : Employee (int I, char * j)\n{\nage =I;\nstrcpy (name,j);\nid = nextid++;\n}\nvoid Employee : : initid( )\n{\nnextid=1;\n}\nvoid Employee :: show( )\n{\ncout <<age<< “ “<name << “ “<< id<<end1;\n}\nvoid Employce : : getnextid( )\n{\ncout << “next employee will be given id=” <<nextid;\n}\nEmployce : : ~Employee( )\n{\n--nextid;\n}\nvoid main( )\n{\nEmployee : : initid( );\nEmployee : : getnextid( );\n{\nEmployee e (25, “Rahul”);\nEmployee f (30, “vipin”);\ne. show( );\nf. show( );\nEmployee : : getnextid( );\n}\nEmployee : : getnextid( );\n}\n\n“this” POINTER\n\nclass Emp\n{\nint age;\nchar name;\nfloat sal;\npublic :\nvoid get( )\n{\ncout << “enter age, name & sal”:\ncin >> age>> name>>sal;\ncout << “Address of calling object =” <<this;\n}\nvoid show( )\n{\ncout <<age<<name<<sal;\ncout << “Address of calling object=” <<this;\n}\n};\nvoid main( )\n{\nEmp E, F;\nE.get( );\nF.get( );\nE.show( );\nF.show( );’\n}\n\n## 1. Every member f n of class has this pointer.\n\n2. No need to declare & initialize this pointer. Compiler initialize it with base\naddress of calling object.\n\n“this” POINTER\n\n## This is a special pointer available in every member foundation of a class except\n\nstatic member f n of a class. Whenever the object of a class puts a call to any non\nstatic member f n of that class, the this pointer available in that member f n\nimplicitly starts pointing to the address of calling object. Thus we can say that a\n“this” pointer always points or referr to the aurrent object.\n\n## By default class has 3 “this” pointer.\n\nConstructor copy constructor Destructor\n\n## Accessing Data Member Using ‘this”\n\nclass Box\n{\nint l, b, h;\npublic:\nBox( );\nBox (int, int, int );\nBox (Box &);\nvoid show( );\n};\nBox : : Box( )\n{\ncout << “Enter l, b, and h”;\ncin >> l >> b >> h;\n}\nBox : : Box (int I, int j, int k)\n{\nl=i; /* can also be written as thisl=l;\nb=j; thisb=b;\nh=k; thish=h;*/\n\n## Box : : Box (Box &p)\n\n{\nl=p.l;\nb=p.b;\nh=p.h\n/* can also be written as *this=p;*/\n\n}\nvoid Box : : show( )\n{\ncout << l << “ “ <<b<< “ “ <<h;\ncout << this  l << thisb << this h;\n}\nvoid main( )\n{\nBox B1;\nBox B2 (5, 7, 11);\nBox B3 = B1;\nB1. show( );\nB2. show( );\nB3. show( );\n}\n\nLIMITATIONS OF THIS\n\n## This pointer always points to calling object thus it can not be\n\nincremented or decremented. It is a constant pointer.\n\nBox = *q;\nQ = this+1 // valid\nQ=++this // not valid\n\n## USING THE “const” KEYWORD\n\n1. Const variable\n2. pointer to const C\n3. const pointer\n4. const pointer to const variable\n5. const parameters\n6. “ data members of class\n7. “ member f n of a class\n\n“const” Variables\nvoid main ( )\n{\nconst float pi=3.14;\n}\nConst variable are initialized at the pt of declaration menns they are read\nonly variables & their values can not be manipulated.\n\n## cout <<pi++ ; // not valid.\n\n“const” Pointer\nconst int *p;// read as “p is a pointer to a const int”\nvoid main( )\n{\nint b=50;\nint a =10;\nconst int *p; // can also be written as “int const *p”\np= &a;\n*p=20;// invalid operation\np=&b;// valid operation\n}\n\n## int * const P :- is a const pointer to an integer means P can’t be incremented or\n\ndecremented. Thus they are initialized at the time of declaration.\n(“this” pointer comes in the above category.)\n\nvoid main( )\n{\nint b=50;\nint a=10;\nint * const p=&a;\n*p=20;\np=&b\n\n“const” parameter:\nWhen the const modifier is used with a pointer parameter in a function's\nparameter list, the function cannot modify the variable that the pointer\npoints to.\nFor example,\n\n## int strlen(const char *p)\n\nHere the strlen function is prevented from modifying the string that p points to.\n\n## Working on the same theme the prototype of a copy constructor is actually\n\n<classname>(<classname> &<referencename>)\n\nFor example:\nBox (const Box &p) // prototype of default copy constructor\n{\nL=p.l;\nB=p.b\nH=p.h\n}\nConst data members of class\nMany times we have some data members in a class whose values should remain\nunaltered after they are initialized. To create such members we can use the “const”\nkeyword in front of their declaration and such members are called “const”\nmembers. But they must be initialized using initializers.\nFor example\n\nclass circle\n{\nconst float pi; // This is invalid\n};\n\nRather the class should have a constructor to initialize “pi” using initializers.\n\nclass circle\n{\nconst float pi; // This is invalid\npublic:\ncircle (int r) : pie (3.14)\n{\n}\n};\n\n## if we want that a f n that should not be allowed tochange value of class\n\nmember than we make them const.\n\nclass circle\n{\nint r;\nfloat a;\npublic:\nvoid get (int x)\n{\nr=x;\n}\nvoid cal_area ( )\n{\na=r * r * 3.14;\n}\nvoid show ( ) const\n{\ncount << “Rad=” <<r << “\\t Area=” <<a;\n}\nif const is there then value of r will not be changed other wise at last it\nwill be incremented.\n\nINITIALIZERS\n\nInializers\nIt is a special syntax allotted by C++ used to initialize there thing .\n\n## 1. const data members with in the class.\n\n2. reference variables\n3. calling parameterized constructor of the base class from derived class.\n\n## They are always written in front of the constructors\n\nOrders of initializes must be same as that of the order of data members\n\nclass Data\n{\nint x, y;\npublic:\nData (int I, int j): x(i), y(j)\n{\n\n}\nData (int I, int j): x(i+j), y(x+i)\n{\n}\n};\n\nIst case\nData D(5,10)\nX=5\nY=10\nData D(7,14)\nX=21\nY=28\n\n## PASSING OBEJCTS USING POINTER\n\nclass Box\n{\nint l, b, h;\npublic:\nvoid get( )\n{\n// same as previous\n}\nint compare (Box *);\n};\nint Box : : compare (Box *p)\n{\nint x, y;\nx= l*b*h;\ny=P l* pb* ph;\nif (x= = y)\nreturn (0);\nif (x>y)\nreturn (1);\nelse\nreturn (-1);\n}\nvoid main( )\n{\nBox B1, B2;\nB1. get( );\nB2. get( );\nB1. show( );\nB2. show( );\nint ans ;\nans = B1. compare (&B2);\nif (ans= =0)\ncout<<” both are equal “;\nelse if (ans==1)\ncout<<”b1 is greater than b2”;\nelse\ncout<<”b2 is greatyer than b1”;\n}\n\n## Passing Objects by Reference Using Reference Variable\n\nclass Box\n{\nint l, b, h;\npublic:\nvoid get( )\n{\n// same as previous;\n}\nvoid show( )\n{\n// same as previous;\n}\nint compare (Box &);\n};\nint Box : : compare (const Box &P)\n{\nint x, y;\nx = l*b*h;\ny = p.l*p.b*p.h;\nif (x==y)\nreturn (0);\nelse if (x > y)\nreturn (1);\nelse\nreturn (-1);\n}\n\nvoid main( )\n{\nBox B1, B2;\nB1.get( );\nB2.get( );\nB1.show( );\nB2.show( );\nint ans;\nans=B1. compare (B2);\nif (ans= =0)\ncout<<” both are equal “;\nelse if (ans==1)\ncout<<”b1 is greater than b2”;\nelse\ncout<<”b2 is greatyer than b1”;\n}\n\nFRIEND FUNCTIONS\n\n## A friend function is a special function which despite of not being\n\nmember f n of class has full access to the private, protected members of the\nclass.\n\n## 1. if a Function is to be made friend of a class then it has to be declared within\n\nthe body of the class preceded with the keyword friend.\n2. whenever a friend f n is defined neither, the name of class nor scope\nresolution operator appears in its definition. Moreour the keyword friend\nalso does not appear.\n3. whenever a friend fn is called, neither the name of the object nor dot\noperator appears toward its left. It may however accept – the object as a\n4.\n5.\n6. parameter whose members it wants to access.\n7. It does not matter in which diction of class we declare a friend function as\nwe can always access it from any portion of program\n8. if a friend fn want to manipulate the values of data members of an object it\nveds the reference of object to be passed as parameter\n\n## Example:- // Demonstrating the use of friend function\n\nclass student\n{\nint roll;\nfloat per;\npublic:\nvoid get( );\nfriend void show (student);\n};\nvoid student : : get( )\n{\ncout << “enter roll. Grade & per”;\ncin >> roll>> grade>>per;\n}\nvoid show (student P)\n{\ncout << p.roll<< “ “ << p.grade<< “ “ <<P.per;\n}\nvoid main( )\n{\nstudent S;\nS. get( );\nshow(S);\n}\n\n## Demonstrating the use of friend function\n\nclass student\n{\nint roll;\nfloat per;\npublic:\nfriend void get (student &);\nvoid show( );\n};\nvoid get (student & P)\n{\ncout << enter roll, grade & per”;\ncin >> P.roll >> P.grade >> P.per;\n}\nvoid student : : show( )\n{\ncout << roll << grade << per;\n}\nvoid main( )\n{\nStudent S;\nvoid get(S);\ns. show( );\n}\n\nclass student\n{\nint roll;\nfloat per;\npublic:\nfriend void get (student *P);\nvoid show( );\n};\nvoid get (student *q)\n{\ncout << “enter roll, grade & per”;\ncin >>q  roll >> q grade >> q per;\n}\nvoid student : : show ( )\n{\ncout << roll << grade << per;\n}\nvoid main( )\n{\nStudent s;\nGet (&s);\ns.show( );\n}\n\n## A FUNCTION BEING FRIEND OF TWO CLASSES\n\nclass Beta; // forward declaration\nclass Alpha\n{\nint x;\npublic:\nvoid get( )\n{\ncout << “enter x=”;\ncin >> x;\n}\nfriend void compare (Alpha, Beta);\n};\nclass Beta\n{\nint y;\npublic:\nvoid set( )\n{\ncout << “enter y=”;\ncin >>y;\n}\nfriend void compare (Alpha, Beta);\n}\nvoid compare (Alpha A, Beta B)\n{\nif (A, X > B. Y)\ncout << “greater=” << A, X;\nelse if (B.Y> A.X)\ncout << “Greater=” << B.Y;\nelse\ncout << “equal”;\n}\nvoid main( )\n{\nAlpha Obj1;\nBeta Obj2;\nObj1. get( );\nObj2. set( );\nCompare (obj1, obj2);\n}\nADDING TWO OBJECTS OF A CLASS USING MEMEBR FUCNTIONS\n\nclass Distance\n{\nint feet;\nint in cher;\npublic:\nvoid get( )\n{\ncout << “enter feet & inches”;\ncin >> feet >> inches;\n}\nDistance add (Distance &)\nvoid show( )\n{\ncout << “feet=” << feet<< “inches=” <<inches;\n}\n};\nDistance Distance : : add (Distance &P)\n{\nDistance temp;\nTemp. feet = feet +P. feet;\nTemp.inches= inches + P.inches;\nif (temp. inches>=12)\n{\ntemp. feer += temp. inches/12;\ntemp. inches % =12\n}\nReturn (temp);\n}\nvoid main( )\n{\nDistance D1, D2, D3;\nD1. get( );\nD2, get( );\nD3 = D1. add (d2);\nD3. show( );\n}\nclass Distance\n{\nint feet, inches;\npublic:\nvoid get( 0\n{\n// same as previous\n}\nvoid add (distance &, distance &);\nvoid show( )\n{\n// same as previous;\n}\n};\nvoid distance : : add (distance &d1, distance &d2)\n{\nFeet = d1. feet + d2, feet;\nInches = d1. inches + d2, inches\nif (inches >12)\n{\nFeet = feet + inches /12;\nInches % = 12;\n}\n}\nvoid main( )\n{\nDistance d1, d2, d3; d1.get( );, d2.get( );\nD3, add (d1, d2);\nD3. show( );\n}\n\n## ADDING TWO AF CLASS UISNG PRIED FUNCTION\n\nclass distance\n{\nint feet;\nint inches;\npublic:\nvoid get( )\n{\nvoid << “enter feet & inches”;\ncin >>feet >> inches;\n}\nvoid show( )\n{\n//same\n}\nfriend distance add (distance, distance ):\n};\ndistance add (distance P, distance Q)\n{\ndistance temp;\ntemp. feet = P.feet +Q.feet;\ntemp. inches= P.inches + Q.inches;\nif (temp. inches>=12)\n{\ntemp.feet += temp. inches/12;\ntemp. inches % = 12;\n}\nreturn (temp);\n}\nvoid main( )\n{\ndistance D1, D2, D3;\nD1.get( );\nD2.get( );\nD3 = add (D1, D2)\nD3.show( );\n}\n\n## It is a mechanism using which a programmer can make built in\n\noperator of C++ act on objects of user defined classes much in the same way like\nthey act on variables of primitive data type. Thus we can say by using operator\noverloading a programmer can enhance the working range of built in operator from\nprimitive type to non primitive type also.\n\n## The major advantage offered by operator overloading is the simplicity\n\nin readability of the call i.e. f n call which are given using operator overloading are\nmuch more easy to interpret as compared to conventional function calls.\n\n## 1. Making use of member function\n\n2. making use of friend function.\n\nSyntax:-\n<ret-type> operator <op-symbol> (arguments);\nfriend (ret-type> operator <op-symbol> (<arguments>);\n\n## Overloading Of Unary Operator As Member Function Of The class\n\n(Pre Increment)\n\nclass counter\n{\nint count;\npublic:\ncounter( )\n{\ncount =0;\n}\ncounter (int c)\n{\ncount = c\n}\nvoid operator ++( );\nvoid show( )\n{\ncout << count <<end1;\n}\n};\nvoid counter : : operator ++ ( )\n{\n++ count;\n}\nvoid main( )\n{\ncounter a = 10; // Single parameterize constractor\na.show( );\n++ a; // a. operator + +( );\na. show( );\n}\n\nclass Counter\n{\nint count;\npublic:\nCounter( )\n{\nCount = 0;\n}\nCounter (int c)\n{\ncount = c;\n}\nCounter operator ++ ( );\nvoid show( )\n{\ncout << count;\n}\n};\nCounter &\nOr\nCounter Counter : : operator ++ ( )\n{\nCounter temp;\n++ count; or\ntemp. count = count; ++ count\nreturn (temp); return (* this);\n}\nvoid main( )\n{\nCounter c1 = 10, c2;\nC1. show( );\nC2 = ++ c1;\nC1. show( );\nC2. show ( );\n}\n\n## Overloading Of Post Increment Operator Using Member Function Of this class\n\nclass Counter\n{\nint count;\npublic:\nCounter( )\n{\ncount=0;\n}\nCounter(int C)\n{\ncount = c;\n}\nCounter operator ++ (int);\nvoid show( )\n{\ncout << count;\n}\n};\nCounter Counter : : operator ++ (int)\n{\nCounter temp;\nTemp. count = count++;\nreturn (temp);\n}\nvoid main( )\n{\nCounter c1 = 10, c2;\nC2 = c1++;\nC1. show( );\nC2. show( );\n}\n\n## Overloading Unary Operator As friend Of The class\n\nclass counter\n{\nint count;\npublic:\ncounter( 0\n{\ncount=0;\n}\ncounter (int i)\n{\ncount = I;\n}\nvoid show( )\n{\ncout << count << end1;\n}\nfriend void operator ++ (counter &);\n};\n\n## void operator ++ (counter &C)\n\n{ Or\n++ c.count counter temp;\nreturn (C); temp. count = c.count++;\n} return (temp);\n\nvoid main ( )\n{\ncounter C1 = 10, C2;\nC1. show ( );\nC2. = ++C2;\nC1. show ( );\nC2. show( );\n}\n\nNote:- when unary operators are overloading using member function then\ndon’t accept any argument but when they are overloaded using friend fn then they\nhave one argument of type object (class).\n\nclass counter\n{\nint count;\npublic:\nfriend counter operator ++ (counter &);\ncounter\n{\ncount =0;\n}\ncounter (int i)\n{\ncount = i;\n{\nvoid show( )\n{\ncout << count << end1;\n}\n};\n\n## counter operator ++(counter &c)\n\n{\ncounter temp;\ntemp.count=++c.ount;\nreturn(temp);\n}\n\nvoid main()\n{\ncounter c1=10,c2;\nc1.show();\nc2=++c1;\nc1.show();\nc2.show();\ngetch();\n}\n\n## Overloading Binary Operators As Member Functions Of The class\n\nD3 = D1 + D2;\nD3 = D1. operator + (D2);\n\nclass Distance\n{\nint feet, inches;\npublic:\nvoid get( )\n{\nCount << “enter feet and inches”;\ncin >> feet >> inches;\n}\nvoid show( )\n{\ncout << feet << inches;\n}\nDistance operator + (Distance);\n};\nDistance Disttacne : : operator + (Distance P)\n{\nDistance t;\nt.feet = feet + P. feet\nt. inches = inches + P. inches;\nif (t.inches>= /12)\n{\nt.feet + = t.inches/12;\nt.inches % = 12;\n}\nReturn (t);\n}\nvoid main ( )\n{\nDistance D1, D2, D3;\nD1, get( );\nD2, get( );\nD3 = D1+D2;\nD3. show( );\nGetch( );\n}\n\nclass Distance\n{\nint feet, inches;\npublic:\nvoid get( )\n{\ncout << “enter feet and inches”:\ncin >> feet>> inches;\n}\nvoid show( )\n{\ncout << feet<< inches;\n}\nfriend Distance operator + (Distance, Distance);\n};\nDistance Operator + (Distance p, Distance Q)\n{\nDistance t;\nt.feet = P.feet +Q.feet;\nt.inches = P.inches+ Q.inches;\nif (t. feet>=12)\n{\nt.feet = t.feet +t.inches/12;\nt.inches % = 12;\n}\nReturn (t);\n}\nvoid main ( )\n{\nDistance D1, D2, D3;\nD1.get( );\nD2.get( );\nD3=D1+d2;\nD3.show( );\n}\n\nAssignment:-\nD2 = D1+n\nD3 = n + D1\n\nclass Distance\n{\nint feet, inches;\npublic:\nvoid get( )\n{\n// same as previous\n}\nvoid show( )\n{\n// same as previous\n}\nDistance Distance : : operator + (int n)\n{\nDistance temp;\nTemp feet = feet + n;\nTemp. inches = inches + n;\nif (temp. inches > = 12)\n{\nTemp. feet + = temp. inches / 12;\nTemp. inches % = 12;\n}\nReturn (temp);\n}\nDistance operator + (int P, Distance Q)\n{\nDistance temp;\nTemp.feet = P+Q.feet;\nTemp. inches= P+Q. inches;\nif (temp. inches> = 12)\n{\nTemp.feet = temp. feet + temp. inches/12;\nTemp. inches % = 12;\n}\nReturn (temp);\n}\nvoid main( )\n{\nDistance D1, D2, D3;\nint n;\ncout << “enter an integer”;\ncin >> n;\nD1. get ( );\n\n## I D2 = D1+n; // can be done using member fn & friend fn\n\nD2. show( );\ncout << “Enter an integer”;\ncin >>n;\nII D3 = n+D1; // not possible through member fn\nD3. show( );\n}\n\nNote:-\nII can not be done using member function becoz n is an integer and only an\nobject can call function not integer. So I call can be made using member function\nas well as friend function but II can be done only friend function\n\n## Assignment :- D1+=D2  using member fn & friend fn\n\nif (D1= = D2)\nCompiler if (D1.operator = = (D2) )\n\nclass Distance\n{\nint feet, inches;\npublic:\nvoid get( )\n{\ncout << enter feet & inches”;\ncin >> feet >> inches;\n}\nvoid show( )\n{\ncout << feet << “ “ << inches;\n}\nint operator = = (distance D);\n};\nint Distance : : operator = = (Distance D)\n{\nint x, y;\nX= feet &12 + inches;\nY = D. feet *12 + D.inches;\nif (x= = y)\nReturn (1);\nelse\nReturn (0);\n}\nvoid main( 0\n{\nDistance D1, D2;\nD1. get( );\nD2. get( );\nD1. show( );\nD2.show( );\nif (D1= = D2)\ncout << “equal”;\nelse\ncout << “not equal”;\n}\n\nAssignment:-\nModify the above program so that now your cosle prents either of\nthere message\ni. Object are equal.\nii. Dl is greater.\niii. D2 is greater\nUse minimum possible operator\n\nclass string\n{\nchar str;\npublic:\nstring ( )\n{\nstr = ‘\\0’;\n}\nstring (char *P);\n{\nstrcpy (str, P);\n}\nvoid show( )\n{\ncout << str;\n}\nstring operator + (string S)\n};\nstring string : : operator + (string S)\n{\nstring t;\nint i, j;\nfor (i=0; str[i]; i++)\nt.str[i] = str[i];\nfor (j=0; s.str[j]; i++, j++)\nt.str[i] = s.str[j];\nreturn (t);\n}\nvoid main( )\n{\nstring s1= “Hello”;\nstring s2 = “How are you ?”;\nstring s3;\ns3 = s1 +s2;\ns1. show( );\ns2. show( );\ns3. show( );\n}\n\nAssignment :-\nWAP which compares two string objects & checks which one is\ngreater.\n\n## Operators Which Can Not Be Overloaded\n\n1> .(dot operator)  member access operator (already overloaded)\n2> :: (scope resolution operator) it already works on classes we overload\nonly those operators which works on primitine and not on non prin\n3> ?: (conditional Operator) It requires there arguments overloading of\noperators atmost can take 2 argument\n4> *.  Pointer to member operator\n5> Size of operator\n\n## DYNAMIC MEMORY ALLOCATION (DMA)\n\nMALLOC NEW\n1. It is a function It is a operator\n2. Necessary to include the header file header file not required\n3. It takes no, of bytes as parameter It takes no. of elements\nRequired as parameter\n4. brings memory from local heap bring has no such memory store\n5. Maximum memory that malloc New has no such memory\nB\ncan allocate = 64K limitation\n6. Malloc retirens the address of New automatically converts\nAllocate block in from of (void*) according to the datatype given\nThus its return value has to be type thus no need to explicitly type\nCasted accordingly cast its return value.\n7. when malloc fails it returns null when new fails it returns zero\n8. Malloc only creates a new block on the other hand operator new\nBut not call the constructor of the can allocate memory as well as\nclass for initialized the data member call the class for initializing the\nOf that block members of block created. Such\nConstructors are called dynamic\nConstructors.\n\n## (i) new <data – type>\n\n(ii) new <data-type> [int];\n\nAllocations Of New :-\n1. int * P; new allows programmer to initialize the memory\nP= new int (10); allocated but malloc does not provide nay such feature\ncout << *P;\ndelete P;\n\n2000 10\n\nP 2000 2002\n\n## 2. int *P; Here value 10 denotes that\n\nP=new int ; programmer is interested in creating memory for\ndelete [ ] P; elements\n\n3000 ..\n\nP 3000\n\n## 1. delete <ptr-name>; // deletes one block of data type\n\n2. delete [ ] <ptr-name>; // deletes whole allocated memory\n\n## Eg:- float *P;\n\ndelete P; // four bytes free\n\n## Delete is a request to OS it does not work immediately where as new creates\n\nmemory immediately\n\nWAP which creates a dynamic array of user defined size Accept values from user\nin that array & then find largest element in that array along with its position.\nFinally it should display the largest element & delete the array.\n\nvoid main ( )\n{\nint n;\ncout << “How many integers”;\ncin >> n;\nint *p;\np= new int [n];\nif (p= = 0)\n{\ncout << “Insufficient memory”;\ngetch( );\nexit(1);\n}\nfor (int i=0; i<n; i++)\n{\ncout << “enter element”;\ncin >> * (p+i);\n}\nint max = *p;\nint pos = 0;\nfor (i=1; i<n; i++)\n{\nif (*(p+i) > max)\n{\nmax = *(p+i);\npos = i;\n}\n}\ncout << “largest element=” << max;\ncout << “Position =” << pos;\ngetch( );\ndelete [ ] p;\n}\n\nWAP to create a class c/a string having a character pointer P. provide constructor\nin the class which accept int as parameter and allocates dynamic array of characters\nof special size. Now provide following member fn in the class.\n\n## 1. get string  which prompts users to enter string in dynamic array.\n\n2. show string  which display the string\n3. Reverse string  which accept an object as parameter and copies the\nReverse of string stored in calling object in object\nPassed as parameter.\nclass string\n{\nchar *p;\nint n;\npublic :\nstring (int);\nstring ( );\nvoid showstr( );\nvoid reversestr ( string &);\nvoid getstr( );\n};\nstring : : string (int size)\n{\np = new char [size +1];\nif (p= =0)\nexit (1);\nn = size +1;\n}\n\n## void string : : getstr( )\n\n{\ncout << “enter string”;\ncin. getline (p,n);\n}\nvoid string : : reversestr (string &s)\n{\nint i, j;\nfor (i=0, j=strlen (p)-1;j>=0; j--, i++)\n{\ns.p[i] = p[j];\n}\ns. p[i] = ‘\\0’;\n}\n\n## void string : : showstr( )\n\n{\ncout << p << end1;\n}\nstring : : string ( )\n{\ndelete [ ]p;\ncout << “ Array destroyed”;\n}\nvoid main ( )\n{\nint n;\ncout << “How may character”;\ncin >> n\nstring S1 =n;\nstring S2 =n;\nS1. getstr ( );\nS1. reverse str(S2);\nS1. show str( );\nS2. show str( );\n}\n\nDYNAMIC OBJECTS\n\n## Allocating memory for class with the help of new.\n\nclass Emp\n{\nint age;\nchar name;\nfloat sal;\npublic:\nvoid get( )\n{\ncout << “enter age, name & sal”;\ncin >> age >> name >> sal;\n}\nvoid show( )\n{\ncout << age << “ “ << name<< “ “ <<sal;\n}\n};\nvoid main( )\n{\nEmp *P;\nP=new Emp();\nif (P= =0)\n{\ncout << “Memory Insufficient”;\nexit (1);\n}\n\nP get( );\nP show( );\ngetch ( );\ndelete p;\n}\n\n## ARRAY OF DYNAMIC OBJECTS\n\nModify the previous code so that now your program creates an array of a objects\nwhere n is given by user. Then it should accept values for each object and finally\ndisplay them.\n\nclass Emp\n{\nint age;\nchar name;\nfloat sal;\nvoid get( )\n{\ncout << “enter are, name and sal”;\ncin >> age>> name>> sal;\n}\nvoid show( )\n{\ncout <<age<< name<<sal<<end1;\n}\n};\nvoid main( )\n{\nEmp *p;\nint i, n;\ncout << “how many employees?”;\ncin >> n;\np=new Emp[n];\nif ( p = =0)\n{\ncout << “error in crating objects”;\nexit (1);\n}\nfor (i=0; i<n; i++)\n(p+i)  get( ); or p[i]. get( );\nfor (i=0; i<n; i++)\n(p+i)  show( ); or p[i]. show( );\ngetch( );\ndelete[ ]p;\n}\n\nEnhance the above program so that after accepting records of n employees. Your\nprogram prompts the user to enter another name & display the record of that\nemployee only. if employee name is not available then program should display the\n\nclass Emp\n{\nint age;\nchar name ;\nfloat sal;\npublic:\nvoid get( )\n{\ncout << “enter age, name and sal”;\ncin >> age>> name>> sal;\n}\nvoid show( )\n{\ncout << age<< name << sal;\n}\nint compare (char *);\n};\nvoid main ( )\n{\nEmp *p;\nint n;\ncout << “how many employees?”;\ncin >> n;\nP= new emp [n];\nif (p = =0)\n{\ncout << “Insufficient Memory”;\nexit (1);\n}\n\n## for (i=0; i<n; i++0\n\n(p+i)  get( ); or p[i]. get( );\ncout << “enter name to search”;\nchar str ;\ncin. ignore( );\ncin.getline (str, 20);\nfor (i=0; i<n; i++)\n{\nif ( (p+i) compare (str) = = 0)\n{\n(p+i)  show ( );\nbreak;\n}\n}\nif (i = = n)\ngetch ( );\ndelete [ ] p;\n}\nint compare (char *p)\n{\nreturn (strcmp ( name.p) );\n}\nDYNAMIC COSNTRUCTORS\n\nclass Emp\n{\nint age;\nchar age;\nchar name ;\nfloat sal;\npublic;\nEmp( )\n{\ncout << “enter age, name and sal;\ncin >> age >> name >> sal;\n}\nEmp (int I, char *j , float k)\n{\nAge = I;\nStrcpy (name, j);\nSal = k;\n}\nvoid show( )\n{\ncout << age << “ “ name “ “ << sal;\n}\n~Emp( )\n{\ncout << “Object destroyed”;\n}\n};\nvoid main ( )\n{\nEmp *P, *q;\nP=new Emp;\nQ=new Emp (30, “vineet”, 200000);\np show( );\nq show( );\ndelete q;\ndelete p;\n}\nNote:- The above Program will not call the destructor of the class even at the\ntermination. This is because in C++ memory which is allocated using new can only\nbe deal located using delete and calling of destructor is only made when memory\ngets deallocated. Since in above code, call for delete is not present so memory\nblock still remains in RAM & might be collected in future through garbage\ncollection. Thus if memory is freed for a dynamic object it can only be done\nthrough delete operator.\n\nINHERITANCE\n\nA B A\n\nC B C\n\nA\nB C\n\n## Derived class Mode of Inheritance Base class\n\nclass Box\n{\nint l, b, h;\npublic:\nvoid get( )\n{\ncout << “enter l, band”;\ncin >> l>>b>>h;\n}\nvoid show( )\n{\ncout <<l<< “ “ << b<< “ “ << h;\n}\n};\nclass carton : public Box\n{\nchar type;\npublic:\nvoid set( )\n{\ncout << “enter material name”;\ncin.getline (type, 20);\n}\nvoid display ( )\n{\ncout << “material =” << type;\n}\n};\nvoid main( )\n{\ncarton obj;\nobj. get( );\nobj. set( );\nobj. show( );\nobj. display( );\n}\n\n## When a base class is inheritance in public mode then :-\n\n1. All the public members of base class become public members of derived\nclass i.e. they can be accessed through the function of derived class as well\nas by objects of derived class.\n2. All the protected members of base class become protected members of\nderive class & thus can only be accessed through functions of derived class\nbut not by the object (main) of derived class.\n3. All private members of base remain private to their own class & thus can\nneither be accessed through functions of derive class nor by object of derive\nclass.\n\nEARLY BINDING:-\nIt is a prours which is executed during compilation in which\ncompiler binary the fn body with the fn call i.e. even before program starts\nexecuting decision regarding the fn call and fn body to be executed are taken by the\ncompiler since this happens before execution, we can say it is early binding. Early\nbinding is always an action until & unless keyword virtual is used infront of the fn\nreturn type. It is done on the basis of there criterias:-\n\n## (i) function name or\n\n(ii) calling object type or\n(iii) function parameter type\n\n## in other words early binding is always in action in normal fn calls,\n\nThe major benefit of early binding is speed of execution i.e. function\ncalls which are bound using early binding get executed at a fastest speed as\ncompared to late binding fn calls.\n\nOVERRIING\nThe function overriding is a term which is used when a derive class\ncontains a function with the same prototype as its bass class. In other words, fn\nprovided by base class has same prototype in derive class but in different body.\n\nScope must be different always in same class\nBy the classes related by means at single level\nInheritance\n\nMust be same .\n\n## When base class is inheritance in protected mode then:-\n\n1. All public members of base class becomes protected member of derive class\ni.e. they can be accessed only through function of derived class but not by\nobject of derive class.\n2. All protected members of base class become protected members of derive\nclass i.e. they two can only be accessed through functions of derived class\nbut not by object of derive class.\n3. All private members of base class remains private to their own class & thus\nneither accessible through the object nor through functions of derive class.\n\nclass Num\n{\nprotected:\nint a, b;\npublic:\nvoid get( )\n{\ncout << “enter two numbers”;\ncin >> a >> b;\n}\nvoid show( )\n{\ncout << “Numbers are =”;\ncout <<a << “ “ << b;\n}\n};\n\n## class AddNum : protected Num\n\n{\nProtected :\nint c;\npublic:\nvoid set( )\n{\nget( );\n}\n{\nc=a+b;\n}\nvoid display( )\n{\nshow( );\ncout << “sum=” <<c;\n}\n};\nvoid main ( )\n{\nobj. set( );\nobj. display( );\n}\n\n## When a base class is inheritance in private mode then:-\n\n1. All the public members of base class become private members of derive\nclass i.e. they can be only accessed through fn of derive class but not by the\nobjects of derive class.\n2. All the protected members of base become private members of derive class\ni.e. they two can only be accessed through the function of derive class but\nnot by objects of derive class.\n3. All private members of base class remain private to their own class & thus\ncan neither be accessed by fn nor by objects of derives class.\n\nclass Num\n{\nprotected :\nint a, b;\npublic:\nvoid get( )\n{\ncout << “enter a and b”;\ncin >> a >> b;\n}\nvoid show( )\n{\ncout << “a = “<<a<<end1;\ncout << “b= “<< b<< end1;\n}\n};\n\n## class AddNum: private Num\n\n{\nprotected :\nint c;\npublic:\nvoid set( )\n{\nget( );\n}\n{\nc=a+b;\n}\nvoid display ( )\n{\nshow( );\ncout << “sum = “<<c;\n}\n};\nvoid main( )\n{\nObj.set( );\nObj. display( );\n}\n\n## Note:- At single level Inheritance private & protected inheritance seems to be\n\nsimilar.\n\nMULTILEVEL INHERITANCE\n\nclass Num\n{\nprotected:\nint a, b;\npublic:\nvoid get( )\n{\ncout << “enter a nad b”:\ncin >> a >> b;\n}\nvoid show( )\n{\ncout << “a=” <<a<<end1;\ncout << “b=” <<b<<end1;\n}\n};\nclass AddNum : public Num\n{\nprotected :\nint c;\npublic :\nvoid set( )\n{\nget( );\n}\nvoid display( )\n{\nshow( );\ncout << “sum=” <<c;\n}\n{\nc=a+b;\n}\n};\n\n## class DiffNum : public AddNum\n\n{\nint d;\npublic:\nvoid accept( )\n{\nset( );\n}\nvoid diff( )\n{\nd= a – b;\n}\nvoid print( )\n{\ndisplay ( );\ncout << “Difference=” <<d;\n}\n};\nvoid main( )\n{\nDiffNum obj;\nobj. accept( );\nobj. diff( );\nobj. print( );\n}\nProgram\n\nclass counter\n{\nprotected:\nint count;\npublic:\nvoid init (int a)\n{\ncount =a;\n}\nvoid operator ++( )\n{\ncount ++;\n}\nvoid show( )\n{\ncout << “count=” <<count;\n}\n};\nclass DecCounter : public counter\n{\npublic:\nvoid operator - -( )\n{\n- - count;\n}\n};\nvoid main( )\n{\nDeccounter D;\nD.int(10);\nD.show( );\n++D;\nD.show( );\n- - D;\nD. show( );\n}\nAssignment :-\nCreate a class c/a array which contains an integer array of size 10. The\nclass should have two member functions called get arr and showarr, which should\naccept values in the array & display its values respectively. Now create a derive\nclass of array c/a sortarr, the class should accept a string as parameter if string\ncontains ‘asc’ then sorting should be done in ascending order & if it contains\n“desc” sorting should be done in descending order.\n\nFinally create the function main which should contain menu drive\ninterface for the user having 5 options:-\n\n(i) input\n(ii) display\n(iii) sort in ascending\n(iv) sort in descending\n(v) quit\n\n## Solution  #include <iostream.h>\n\n#include <stdlib.h>\nclass Array\n{\nprotected:\nint a;\npublic:\nvoid get( );\nvoid display( );\n};\nvoid Array : : get( )\n{\nint i;\ncout << “enter array elements”;\nfor (i=0; i<5; i++)\ncin >>a[i];\n}\nvoid Array : : display ( )\n{\nfor (int i=0; i<5; i++)\ncout << “\\n elements are =” << a[i];\n}\nclass sortarr : public Array\n{\npublic:\nvoid ascsort( );\nvoid descsort( );\n};\nvoid sortarr : : ascsort ( )\n{\nint i, j, t;\nfor (i=0; i<5; i++)\n{\nfor (j=0; j<4; j++)\n{\nif (a [j] > a [j+1])\n{\nt = a [j];\na [j] = a [j+1];\na [j+1] = t;\n}\n}\n}\n}\nvoid sortarr : : descsort( )\n{\nint i, j, t;\nfor (i=0; i<5; i++)\n{\nfor (j=0; j<4; j++)\n{\nif (a[j] <a [j+1])\n{\nt=a[j];\na[j]=a[j+1]\na[j+1] = t;\n}\n}\n}\n}\n\nvoid main( )\n{\nclrscr( );\nsortarr sr;\nint ch=0;\ndo\n{\ncout << “\\n \\t Enter (1) for Input data”;\ncout << “\\n \\t Enter (2) for Display Data”;\ncout << “\\n \\t Enter (3) sort in Ascending order”;\ncout << “\\n \\t Enter (4) sort in Descending order”;\ncout << “\\n \\t Enter (5) for quit”;\ncout << “enter choice”;\ncin >> ch;\nclrscr( );\nswitch (ch)\n{\ncase 1:\nsr.get( );\nbreak;\ncase 2:\nsr.display( );\nbreak;\ncase 3:\nsr.ascsort( );\nbreak;\ncase 4: sr.descsort( );\nbreak;\ncase 5:\nexit(1)\n}\n} while (ch !=5)\ngetch( );\n}\n\nMULTIPLE INHERITANCE\n\nclass base1\n{\nprotected:\nint a;\npublic:\nvoid get( )\n{\ncout << “enter a =”;\ncin >>a;\n}\nvoid show( )\n{\ncout <<a << end1;\n}\n};\nclass base2\n{\nprotected:\nint b;\npublic:\nvoid set( )\n{\ncout << “enter b=”;\ncin >> b;\n}\nvoid display( )\n{\ncout <<b << end1;\n}\n};\n\n## class drv : public base1, public base2\n\n{\nint c;\npublic :\nvoid accept ( )\n{\nget( );\nset( );\n}\nvoid add ( )\n{\nc = a+b;\n}\nvoid print( )\n{\nshow( );\ndisplay( );\ncout << “sem =” <<c;\n}\n};\nvoid main( )\n{\ndrv obj;\nobj. accept( );\nobj. print( );\n}\n\nProgram :-\nBase & derive having the function with same name & arguments.\n\nclass base1\n{\nprotected:\nint a;\npublic:\nvoid get( )\n{\ncout << “enter a=”;\ncin >>a;\n}\nvoid show( )\n{\ncout << a << end1;\n}\n};\nclass base2\n{\nprotected:\nint b;\npublic:\nvoid set( )\n{\ncout << “enter b=”;\ncin >> b;\n}\nvoid show( )\n{\ncout <<b<<end1;\n}\n};\n\n## class drv : public base1, public base2\n\n{\nint c;\npublic:\nvoid accept( )\n{\nget( );\nset( );\n}\n{\nc= a+b;\n}\nvoid print( )\n{\nWill show show( ); base1: : show( );\nError ambiguity base2: : show( );\nerror\n}\n};\nvoid main( )\n{\nDrv obj;\nobj. accept( );\nobj. print( );\n}\n\n## Role Of Constructor & Destructor In Inheritance\n\nAs a basic rule in inheritance, it a base class contains a constructor and\ndestructor as well as derive class also contains constructor & destructor, then when\nobject of derive class is created, constructor of base class gets executed first\nfollowed by constructor of derive class and the destructor is called in reverse order\ni.e. the destructor of derive class is called first followed by the destructor of base\nclass.\n\nThus we can say constructor are always called in the order of inheritance and\ndestructor are called in reverse order.\n\nclass Base\n{\npublic :\nBase ( )\n{\ncout << In base’s constructor” <<end1;\n}\n~Base( )\n{\ncout << In base’s destructor “<<end1;\n}\n};\n\n## class drv: public Base\n\n{\npublic:\ndrv( )\n{\ncout << “In derive’s const “<<end1;\n}\n~drv( )\n{\ncout << “In derives destructor “<<end1:\n}\n};\nvoid main( )\n{\n{\ndrv obj;\n}\ngetch( );\n}\n\nPARAMETERIES CONSTRUCTOR\n\nclass Base\n{\nProtected:\nint a, b;\npublic:\nBase (int I, int j)\n{\na = I;\nb = j;\n}\nvoid show( )\n{\ncout << a << “ “ << b;\n}\n};\nclass drv : public base\n{\nint c;\npublic:\ndrv ( ): Base (10, 20)\n{\nc=a+b;\n}\nvoid show( )\n{\nBase : : show( );\ncout << “sum = “ << c;\n}\n};\nvoid main( )\n{\ndrv obj1\ndrv obj2\nobj1. show( );\nobj2. show( );\n}\n\n## int a; int b; int c;\n\nBase1 (int); base2 (int);\nvoid show( ); void display();\n\nclass Base1\n{\nprotected:\nint a;\npublic:\nBase (int i)\n{\na = i;\n}\nvoid show( )\n{\ncout << a;\n}\n};\nclass Base2\n{\nprotected :\nint b;\npublic:\nBase2 (int j)\n{\nb = j;\n}\nvoid display( )\n{\ncout << b;\n}\n};\n\n## class drv: public Base1, public Base2\n\n{\nprotected :\nint c;\npublic:\ndrv (int p, int q) : Base1 (p), Base2(q)\n{\nc=a+b;\n}\nvoid print ( )\n{\nshow ( );\ndisplay( );\ncout << “their sum =” << c;\n}\n};\nvoid main( )\n{\ndrv obj1 (10, 20);\ndrv obj2 (20, 70);\nobj1. print ( );\nobj2. print( );\n}\n\nNote :-\nif the constructor of base class is parameterized then\n\n## (i) derive class must have constructor\n\n(ii) Base class constructor should be called in derives constructor.\n\n## Constructor Calling In Multilevel Inheritance\n\nclass Base1\n{\nprotected:\nint a ;\npublic:\nBase1(int i)\n{\na=i;\n}\nvoid show( )\n{\ncout << a << end1;\n}\n};\nclass Base2: public Base1\n{\nprotected:\nint b;\npublic:\nBase2 (int i, int j): Base1(i)\n{\nb=j;\n}\nvoid display( )\n{\ncout << b << end1;\n}\n};\nclass drv : public Base1, public Base2\n{\nprotected:\nint c;\npublic:\ndrv (int x, int y) : base2 (x, y)\n{\nc=a+b;\n}\nvoid print ( )\n{\nshow( );\ndisplay ( );\ncout << “their sum=” << c;\n}\n};\nvoid main ( )\n{\ndrv obj(10, 20);\ndrv obj (50, 60);\nobj1. print( );\nobj2. print( );\n}\n\nNote\nConstructors can not be inherited :-\n\n## Constructors are special member fn which are exclusively used for\n\ninitialing private data members of class. Now since the private data of class is not\npassed on to its derive classes, so the functions which explicitly initialize then\n(constructor) are not inherited. Same is the case with destructor,\n\nHIERARCHIAL INHERITANCE\n\nclass Num\n{\nprotected :\nint a, b:\npublic :\nNum (int i, int j)\n{\na = i;\nb = j;\n}\nvoid show( )\n{\ncout << “a =” << a;\ncout << “b=” << b;\n}\n};\nclass AddNum : public Num\n{\nint c;\npublic:\nAddNum (int i, int j) : Num (i, j)\n{\nc=a+b;\n{\nvoid show( )\n{\nNum : : show( );\ncout << “ sum =” <<c;\n}\n};\n\n## class Diffnum : public Num\n\n{\nint d;\npublic :\nDiffNum (int x, int y) : Num (x, y)\n{\nd= a – b;\n}\nvoid show( )\n{\nNum : : show( );\ncout << “ Difference =” << d;\n}\n};\nvoid main( )\n{\nDiffNUm diffobj (30, 70);\ndiffobj. Show( );\n}\n\nHYBRID INHERITANCE\n\nclass Base\n{\npublic:\nint a;\n};\nclass drv1 : virtual public Base\n{\npublic:\nint b;\n};\nclass drv2: virtual pubic Base\n{\npublic:\nint c;\n};\nclass drv3 : public drv1, public drv2\n{\npublic:\nint d;\n};\nvoid main( )\n{\ndrv3 obj;\nobj. a =10;\nobj. b = 20\nobj. c =30;\nobj.d= obj a + obj.b + obj. c;\ncout << “sum =” << obj,d;\n}\n\nGRATING ACCESS\n\nclass Data\n{\nprivate :\nint a;\nProtected :\nint b;\npublic :\nint c;\n};\nclass drv : protected Data\n{\npublic:\nData : : C // bring C in public mode instead of protected\n};\nForeg:-\nclass Bank\n{\npublic :\nvoid deposit( );\nvoid withdraw( );\nvoid int-cal( );\n};\nclass saving – acct: Private Bank\n{\npublic :\nBank : : deposit;\nBank : : with draw;\n};\n\nPOLYMORPHISM\n\nclass Base\n{\npublic:\nvoid show( )\n{\ncout << “In base ‘& show”;\n}\n};\nclass drv : public Base\n{\npublic :\nvoid show( )\n{\ncout << “In drv’s show”;\n}\n};\nvoid main( )\n{\nBase b, *ptr;\ndrv d;\nptr = & b;\nptr  show( );\nptr = & d;\nptr  show( );\n}\n\n## VIRTUAL FUNCTION & MULTILEVEL INHERITANCE\n\nclass Base\n{\npublic:\nvirtual void show( )\n{\ncout << “In base’s show”;\n}\n};\nclass drv1 : public Base\n{\npublic :\nvoid show( )\n{\ncout << “In drv1’s show”;\n}\n};\nclass drv2: public drv1\n{\npublic:\nvoid show( )\n{\ncout << “In drv2’s show”;\n}\n};\nvoid main( )\n{\nBase * ptr, b;\ndrv1 d1;\ndrv2 d2;\nptr = & b;\nptr  show( );\nptr = & d1;\nptr  show( );\nptr = &d2;\nptr  show( );\n}\n\nNote :-\nTop level class ptr can access member of lower level class. Virtual is\nmandaroty in base show as function is originally from base.\n\nVirtual functions are those functions for which no decision regarding the call\nand the definition to be executed is token by the compiler during compilation. In\nother words, if a fn is preceded with keyword virtual then if never become the past\nof early binding & compiler delays its binding until runtime. Thus all the decisions\nregarding the call and the body to be executed are taken at runtime. These\ndecisions are not based on the type of caller (as was the case with early binding)\nbut on the bases of contents of the caller. if the calling pointer is storing the address\nof base class object then base’s version of virtual function will be executed & if it\npointing to an object of derive class then derive version of virtual fn is executed.\n\nBut to fully uses the potential of virtual function the derive class while\ngiving its own body /definition for virtual fn must keep its prototype same as the\nbase class i.e. derive should override the virtual function of base class if it wants to\nplace its own definition of virtual function.\n\nThis is because pointer of base class can access only those function of\nderive class which are overridden in the derive class but not those which are\nhidden or added by derive class.\n\n## class A VTABLE FOR A\n\n{\nint a; &A : : f2( )\npublic:\nvoid f1( ) &A : : f3( )\n{\n}\nvirtual void f2( ) obj1 VPTR a\n{\n}\n}; VTABLE FOR B\nclass B : public A &B : : f2( )\n{ int x;\npublic: &A : f3( )\nvoid f4( );\nvoid f2( ); obj2 VPTR\n};\n\nvoid main( )’\n{ size of class A = 4 bytes\nA obj1;\nA *ptr; 2 bytes for variable a\nptr = & obj1; 2 bytes for VPTR\nptr  f2( );\nptr = & obj2;\nptr  f3( );\nptr  f4( );\n} This will not be executed since this is not virtual & compiler\nwill go to Early binding table for base class A where there is no function f4.\n\nVTABLE:-\nfor every class in C++ which contains at least one virtual function a\nspecial look up table for storing addresses of there virtual function is created which\nis known as VTABLE or virtual Table. Thus in short VTABLE is a table\ncontaining addresses of virtual functions of the class as well as virtual function of\nbase class if they are not overridden. This table is then consulted by the compiler\nwhen a call for virtual function is encountered during runtime.\n\n## VVPTR:- (Virtual void Pointer)\n\nfor every class which contains a virtual function special pointer is\ncreated by the compiler known as VVPTR, which is used for pointing to the virtual\ntable. Thus every object of class containing virtual function has its size\nincremented by2. Now whenever compiler encounters call for virtual function\nthrough a pointer, it first refers to the address of object to which pointer is pointing\nfrom their it reads the address contained in VVPTR of the object the which is the\naddress of VTABLE of class. Lastly within the VATBLE it executes the virtual\nfunction called. Thus virtual function enhances the size of code as reduces the\nexecution speed but provides a flexible way of designing program which can\nrespond to the changes which accur at run time.\n\nclass Data\n{\nint a ;\nData (int );\npublic:\nstatic Data getObject( );\nvoid show( );\n};\nData : : Data (int i)\n{\na = i;\n}\nData Data : : getobject( )\n{\nData D(10);\nreturn (D);\n}\nvoid Data : : show( )\n{\ncout << a;\n}\nvoid main( )\n{\nData D = Data. getobject( );\nD. show( );\n}\nNote\nWhen only one object of class is created then that type of class is c/a single\nto n class.\n\nPolymorphism\nCompile Time Polymorphism Run Time Polymorphism\n3. Early Binding 3. Late Binding\n\n## Virtual Inheritance avoids multiple copies\n\nPolymorphism\n(converts early binding to late binding)\n\nclass Figure\n{\nprotected:\nint dim1, dim2;\npublic :\nvoid get( )\n{\ncout << “enter 1st & 2nd dimension”;\ncin >> dim1>> dim2;\n}\n};\nclass Rectangle : public Figure\n{\npublic:\nvoid area( )\n{\ncout << “area of Rectangle=”;\ncout << dim1 * dim2;\n}\n};\n\n## class Triangle : public Figure\n\n{\npublic:\nvoid area( )\n{\ncout << “area =” <<’5 * dim1 *dim2;\n}\n};\nvoid main( )\n{\nFigure *p;\nFigure F;\np = &F;\np get( );\np area( );\nRectangle R;\np=&R;\np get( );\np area( );\nTriangle T;\np = & T;\np get( );\np area( );\n}\n\n## Def:- if a virtual functions declarations is equated with zero then it is\n\nknown as pure virtual fn. In other words, it we do not want to provide any\ndefinition for a virtual fn wee can equate with zero then it will be known as pure\nvirtual fn. Thus by definition a pure virtual fn is one which has no body define with\nin its own class or base class.\n\nABSTRACT CLASS\n\n1. if a class contain at least one pure virtual fn than it is known as abstract Base\nclass.\n2. we can never create any object of abstract class but we can always create its\npointers. This is because whenever an object of a class containing virtual\nfunction is created a VTABLE is setup by the complete, which stores the\naddress of virtual function has no body it can not have memory address &\nthus it can not places in VTABLE. So to prevent any accidental calls to a\nnon-existing pure virtual function compiler prohibits the creation of an\nobject of an abstract class.\n3. An class which extends an Abstract Base class must provide its own\ndefinition of pure virtual fn available in its base class other wise the class\nitself would be created as an abstract class & then it too can not be\ninstantiated.\n\nNote:- Constructors can not be virtual since link between VPTR VTABLE\nis made by constructor\n\nVirtual Destructor:-\n\nclass Base\n{\nprotected:\nint *p;\npublic: base( )\n{\np=new int ;\ncout << “ p constructed!”;\n}\nvirtual ~ base\n{\ndelete [ ] p;\ncout << “memory deallocated”;\n}\n};\nclass drv : public Base\n{\nint *q;\npublic :\ndrv( )\n{\nq = new int ;\ncout << “ q constructed”;\n}\n~drv( )\n{\ndelete [ ] q;\ncout << “memory deallocated for q”;\n}\n};\nvoid main( )\n{\nBase *ptr;\nptr = new drv;\ndelete ptr;\n}\n\nDestructor can not be declared as pure virtual. Since it is not possible to leave\nempty body of destructor since at the time of call of destructor same activity must\nbe performed.\n\n## FILE – HANDLING (STREAM I/O)\n\nFlow of data\n\nif flow of data is from program towards device then o/p stream eg. cout . is used.\n\nif flow of data is from device to program then i/p stream eg cin is used.\n\nC++ Program\n\n## 1. Of Stream:- if a class whose objects can be created for writing data to\n\nsecondary memory of file.\n2. It Stream:- is a class whose objects can be created for reading data\nfrom file.\n\n## Step  1 Create the object of “Of stream” class\n\nEg:- Of stream obj;\nStep 2 Connect the object with file on your system.\nStep 3 write the data through the object in file.\nStep 4 Close the file.\n\n## Step 1 (a) Of stream obj;\n\nStep 2 obj. Open ( “Data. Txt”);\nStep 3 (a) obj << “Hello”;\n(b) obj. put (‘H’);\nStep 4 obj.close ( );\n\n## 1 (a) Of stream Obj ( “Data, txt”);\n\n2(a) Of stream obj; clas static data\nBj.open (“Data. Txt”, ios : : app)\nFile Opening Mode\n\n## 1. Create the object of “ifstream” class.\n\n2. Connect the object with the file on your system & check whether file is\navailable or not.\n3. Read the data through object from file.\n4. class file.\n\nC++ IMPLEMENTATION\n1. if stream obj\n2. (a) Obj. Open ( “Data.txt);\nOr\nif stream obj;\nObj.open ( “Data. Txt”, ios : : in);\nOr\nif stream obj (“Data.txt”, ios: : in);\n\n## Creating Object Of fstream class\n\n1. fstream obj;\nObj.open ( “Data.text”, ios : : out | ios : : in);\n2. Using Constructor\nFstream obj ( “Data.txt”, ios : : out | ios : : in);\n\nWAP to create a file c/a “message. text” Accept a line of text from user & write it\nin file character by character\n\n#include <stdlib.h>\n#include <iostream.h>\n#include <fstream.h>\n#include <conio.h>\n\nvoid main ( )\n{\nOf stream out ( “Message. Text”);\nif (!out)\n{\ncout << “file can not be opened”;\nGetch ( );\nexit(1);\n}\nchar str;\ncout << “enter a line of text”;\ncin.qetline (str, 80);\nint i=0;\nWhile (str[i])\n{\nOut.put (str[i]);\nI++;\n}\ncout << “file written successfully”;\nGetch ( );\nOut. Close( );\n}\n\n1\nNote:- isopen ( ) Returns 1 if conceted\n0\n1\nFail( ) Retruns I if not connected\n0\n\nQue:- WAP to open the file created by the previous program. Read it on character\nby character basic & display its contents on the screen.\n\nSolution :-\nvoid main ( )\n{\nif stream in ( “message.txt”);\nif (! in)\n{\ncout << “filoe can not be opened”;\nexit(1);\n}\nchar ch;\nWhile (! In.enf( ) )\n{\nCh=in.get( );\ncout << ch;\n}\nGetch( );\nIn.close( );\n}\n\nQue:- WAP to open a file c/a “Data.txt”. Accept a line of text from user & write it\non file character by character basic and it in the same program read file and print\nits content on the screen .\n\nvoid main( )\n{\nFstream Nisha ( “Data”, ios: : out | ios: : in);\nif (! Nisha)\n{\ncout << “error opening file”;\nexit(1);\n}\nchar str ;\ncout << “enter a line of text”;\ncin.get line (str, 80);\nint i=0;\nchar ch;\nWhile (str [i])\n{\nCh=str[i];\nObj.put (ch);\nI++;\n}\nObj.seekg (0);\nWhile (! Obj.eof( ) )\n{\nCh=obj.get( );\ncout << ch;\n}\nGetch( );\nObj. close( );\n}\n\nQue  Assume there is a file c/a “Message. Txt” containing certain line of txt.\nCreate a file c/a “Message2.txt, and copy the contents of “messages.txt” into it.\nBefore coping the character must be converted upper to lower & vice versa &\nspaces must be skipper. Finally display the contents of “Message2 txt”.\n\nvoid main( )\n{\nif stream obj1 ( “Messages.txt”);\nStream obj2 (“Message2. txt”, ios : : out | ios : : in);\nchar ch;\nif (! Obj1)\n{\ncout << sourcl file cant be opened”;\nexit(1);\n}\nWhile (! Obj1, eof)\n{\nCh = obj1. get( );\nif (ch! = 32)\n{\nif (ch>=65 & & ch<=90)\nCh=ch+32;\nelse if (ch > =97 && ch <=122)\nCh=ch-32;\n}\nObj2.put(ch);\n}\nObj2.seekg(0);\nWhile ( ! obj2. eof( ) )\n{\nCh=obj2. get( );\ncout << ch;\n}\nGetch( );\nObj2.closs( );\nObj1. close( );\n}\n\n## READING AND WRITING STRINGS\n\nvoid main ( )\n{\nFstream obj ( “Data.txt”, ios : : out | ios : : in);\nif !(!obj)\n{\ncout << “error”;\nexit (1);\n}\nchar text ;\ncout << “how many lines”;\nint n;\ncin >> n;\ncout << “Enter “ << n<< “lines each terminated by enter : “<<end1;\nfor (in i=1; i<=n; i++)\n{\ncin.get line (text, 80);\nObj << text << end1;\n}\nObj. seekg (0);\ncout << “File written press nay key to read”;\nGecth( 0;\nWhile (!obj.eof( ) )\n{\nObj.getline (text,80);\ncout << text << end1;\n}\nGecth( );\nObj.close( );\n}\nvoid main( )\n{\nFstream obj ( “Data.txt”, ios : : out | ios : : in);\nif (!obj)\n{\ncout << “error”;\nexit(1);\n}\nchar text ;\ncout << “enter lines and press enter on new line to stop”;\nWhile (1)\n{\ncin.getline (text, 80);\nif (strlen (text) = =0)\nBreak;\nObj << text << end1;\n}\n}\n\n## 1. ios : : out (Default mode of ofstream)\n\nif file is not existing then it is created. Otherwise data gets\nErased and pointer is placed at the beginning.\n\n2. ios : : in\nif file is existing, pointer is placed a the beginning otherwise\nerror is generated.\n3. ios : : app\nIt can not alter previous contents but can add new content at the\nEnd of file.\n\n## 4. ios : : ate (ate stands for at the end)\n\nAllows updations as well as adding of new data.\nIn this pointer can move forward and backward.\n\n5. ios : : trunc\nNeeded only in fstream.\nUsed with ios : : out\nTruncate previous data & brings pointer to beginning.\n\n6. ios : : nerplace\nUsed with ios : : out if file is existing do not replace it otherwise\ncreate it.\n\n7. ios : : nocreate\nUsed with ios : : out if file is existing overwrite it otherwise do not\ncreate it.\n\n8. ios : : binary\nif we want to write data in binary form.\n\nBINARY I /O\n\n## void write (char *, int )\n\nOf stream address of variable no of bytes to be written\nMember whose data is to be\nWritten\n\nint a = 23091 ;\nObj, write (( char *) &a, size of (int) );\nchar b = ‘x’;\nObj.seekg(0);\nint c;\nObj. read ( (char *) &c, size of (int ) );\ncout << c;\nchar d;\nObj. read (&d, size of (char) );\ncout <<d;\nint read (char *, int)\nAddress of variable whose data is to be stored after reading\nFrom file\nOn successfully reading from file it returns 1 on error it return 0.\n\n## Reading and writing class objects\n\nclass Emp\n{\nint age;\nchar name ;\nfloat sal;\npublic:\nvoid get( )\n{\ncout << “ enter age, name and sal”;\ncin >> age >> name>> sal;\n}\nvoid show( )\n{\ncout << age << “ “ <<name << “ “ sal <<end1;\n}\n};\n\nvoid main ( )\n{\nEmp E;\nFstream obj ( “Records.dat”, ios : : out | ios : : in | ios : : trunc| ios : :\nBinary );\nif (! Obj)\n{\ncout << “error in opening file”;\nexit (1);\n}\nE.get( );\nObj.write ((char *) &E, size of (Emp) );\nObj.seekg(0);\nEmp F;\nObj.read ((char *) &F, size of (Emp));\nF.show( );\nGetch( );\nObj.close( );\n}\n\n## Reading And Writing Multiple Objects\n\nvoid main ( )\n{\nEmp E;\nFstream obj ( “Record.dat’, ios: : out| ios : : in | ios : : trunc | ios : :\nBinary);\nif (! Obj)\n{\nOut << “error in opening file”;\nexit (1);\n}\nchar choice;\n\n{\ne.get( );\nobj.write ((char *) &E, size of (Emp));\ncout << “Any More (Y/N)”;\ncin.ignore( );\ncin >> choice;\n} while ( ch = = ‘y’);\nObj. seekg (0);\nWhile (opj.read (char *) &E, size of (emp))\nE.show( );\ngetch( );\nobj.close( );\n}\nTypical Feature Of Read:-\n\n## Note :- if in any ane program, we want to read file successively to eof\n\nwe must clear the flag\nObj. seekg (0);\nObj. clear( );\n\nQ. WAP to write multiple records of type emp in a file. Accept a name from user\n& display the record of the employee by searching it with in the file and display\nthe appropriate message\n\nvoid main( )\n{\nint flat = 0\nEmp E;\nFstream obj1 ( “Ekta, txt”, ios : : out | ios : : in | ios : : trunc |\nIos : ; binary);\nif (! Obj)\n{\ncout << “error”;\nexit(1);\n}\nchar ch;\nDo\n{\nE.get( );\nObj.write ( ( char *) &E, size of (Emp));\ncout << “Ant more (y/n)”;\ncin . ignore ( );\n} while (ch = = ‘Y’);\nchar name ;\ncout << “enter name to search”;\ncin >> name;\nObj. seekg(0);\n\n## While (obj.read (char *) &E, size of (emp))\n\n{\nif ( ! ( E = = name) )\n{\nE. show( );\nFlag =1;\nBreak;\n}\n}\nif ( ! flag) or if (flag = =0)\n{\nObj1. close( );\n}\nint operator = = (char * ptr)\n{\nReturn (strcmp (name, ptr) );\n}\n}\n\nRANDOM I/O\n\nQ. Assume there is a file c/a “Record.dat” which contains several records of type\nemp. WAP to open this file & read the last record.\n\nvoid main ( )\n{\nifstream in ( “Records.dal”, ios : : in | ios : : binary);\nif ( ! in)\n{\ncout << “error”;\nexit (1);\n}\nIn.seekg (-1 * size of (emp), ios : : end);\nEmp E;\nIn. read ( ( char *) &E, size of (emp));\nE.show( );\nIn.close( );\nGetch( );\n}\n\n## Note:- Prototype of seekg( )\n\nvoid seekg (int, int)\nNo, of position of movement\nBytes to ios : : beg\nMove ios : : cur\nIos : : end\n\nWAP to accept a name from user. Search the record of that employee in\n“Records.dat” & Add a new record at its position by accepting it from user\n\nvoid main( )\n{\nchar temp;\nEmp E;\nFstream in ( “Records.dat”, ios : : in | ios : : ate| ios : : binary);\ncout << “enter name to update”;\ncin >> temp;\nIn.seekg(0);\nWhile (in.read ( (char *) &E, size of (emp) )\n{\nif ( ( E = =temp) = =0)\n{\nE.get( );\nIn.seekg (-1 * size of (Emp), ios : : cur);\nIn.write ( ( char *) &E, size of (emp));\nBreack;\n}\n}\nIn.clear( );\nIn.seekg(0);\nWhile (in.read ( ( char *) &E, size of (Emp) )\nE.show( );\nint operator = =(char *n)\n{\nReturn (strcmp (temp, ptr) );\n}\n}\n\nAssignment :-\n\n## 1. WAP to delete record given by user from file.\n\n2. WAP to update just the salary of employee whose name given by user.\nTEMPLATES\n\n## Templates are a technique provide by C++ using which a programmer\n\ncan define a single function in which last to be carried out is mentioned but the\ndatatype is not mentioned. During runtime by looking at the call of the function &\nits parameter type the compiler generates specific version of that function to act\naccording to parameter passed.\n\n## Thus in other words, we can say templates are generic functions\n\nwhich at the time of creation are only told what to do & during run time they\nbecome aware on what type of argument it has to be done.\n\n## Templates are of two type\n\n(i) Function Template\n(ii) class Template\n\n## Syntax Function Template\n\nTemplate <class <type-name> > <return – type>\n< function-name> (< type-name> <arg-name>)\n\nExample\n\n## Template <class T>\n\nvoid display ( T n)\n{\ncout << n << end1;\n}\nvoid main ( )\n{\nint a =10;\nchar b = ‘x’;\nfloat c= 11.5;\nDisplay (a);\nDisplay (b);\nDisplay (c);\n}\n\nWrite a function template c/a swap which accepts two parameters & swaps then.\nParameters passed can be two integer, two floats two class. Finally two swapped\nvalues must be displayed in the function main\n\n## Template <class T>\n\nvoid swap ( T &a, T &b)\n{\nT temp;\nTemp = a;\nA= b;\nB = temp;\n}\nvoid main ( )\n{\nint a, b;\ncout << “enter two integers”;\ncin >> a >> b;\nSwap (a, b);\ncout << ‘a= “<<a<< “b=” <<b<<end1;\ncout << “enter two character”;\nchar p, q;\ncin >> p >>q;\nSwap (p, q);\ncout << “p= “<<p<<’q = “<<q <<end1;\nfloat x, y;\ncout << “enter two float numbers”;\ncin >> x>>y;\nSwap (x, y);\ncout << “x= “<<x<< “y= “<<y << end1;\n}\n\nQ. write a function template which accepts two values as an argument & return\nmaximum amongst then.\n\n## Template <class T>\n\nT greater ( T &P, T 7q)\n{\nif ( p > q)\nReturn (p);\nelse\nReturn (q);\n}\nvoid main ( )\n{\nint a, b;\ncout << “enter two integers:”;\ncin >> a >> b;\ncout << “ maximum = “<< greater (a, b);\ncout << “Enter two charr:”\nchar x, y;\ncin >> x >>y;\ncout << “maximum =” << greater (x, y);\n}\n\nWrite a function template c/a greatest which accepts an array as an argument &\nreturns the largest element of that array. The array passed can be of different type\nand different size.\n\n## Template <class T>\n\nT greatest ( T *a, int n)\n{\nint I;\nT max = *a;\nfor (i=1; i<n ; i++)\n{\nif ( * (a+i) > max)\nMax = * (a+i);\n}\nReturn (max);\n}\nvoid main ( )\n{\nint arr[ ] = {7, 11, 2, 3, 4, 8};\nfloat brr[ ] = { 10.4, 11.3, 6.5, 8.2};\ncout << “max int =” << max (arr, 6);\ncout “max float=” << max (brr, 4);\n}\nCLASS TEMPLATE\n\n## Template <class T>\n\nclass Data\n{\nT a;\nT b;\nT c;\npublic:\nvoid get( )\n{\ncin >> a >> b;\n}\nvoid add ( )\n{\nC = a+b;\n}\nvoid show( )\n{\ncout << “ values are =” << a << end1;\ncout << b;\ncout << “sum=” <<c;\n}\n};\nvoid main ( )\n{\nData <int> obj1;\nData ,double> bj2;\ncout << “enter two int”;\ncout << “enter two doubles”;\nObj1, get( );\nObj1. show( );\nObj2. show( );\n}\nclass Template With Different Generic Type\n\n## Template <class T1, class T2>\n\nclass Data\n{\nT1, a;\nT2, b;\npublic:\nvoid get( )\n{\ncin >> a >>b;\n}\nvoid show( )\n{\ncout << “values are =” << a << “and “<< b<< end1;\n}\n};\nvoid main( )\n{\nData <int, float> obj1;\nData <double, char> obj2;\ncout << “enter an int and float”;\nObj1.get( );\nObj1.show( );\ncout << “enter double and char”;\nObj2.get( )\nObj2.show( );\n}\n\nQ. write a class template for a class called stack and implement three basic\noperations of stack push, pop and peek. The stack can be of int, char and flots.\n\nTemplate <class>\nclass stack\n{\nT arr [S];\nint tos;\npublic:\nStack ( )\n{\nTos = -1;\n}\nT pop ( );\n};\nTemplate <class T>\nvoid stack <T> : : push (T n)\n{\nif (tos = = 4)\n{\ncout << “stack overflow”;\nReturn;\n}\n++ tos\nArr [tos] = n;\n}\nTemplate <class T>\nT stack <T> : : pop ( )\n{\nif (tos = = -1)\n{\ncout << “stack under floaw”;\nReturn(-1)\n}\nReturn (arr [tos - -]);\n}\n\nvoid main ( )\n{\nStack <int> sa;\nint n;\nStack <char> s2;\nchar ch;\nfor (int i=1; i<=6; i++)\n{\ncout << “enter int to be pushed”;\ncin >> n;\nS1.push(n);\n}\nfor (int i=1; I,6; i++)\ncout << “ element popped =” << s1.pop( );\n}\n\nclass string\n{\nchar *p;\npublic:\nstring (int);\nvoid set string (char *);\nvoid resetstrign ( char *);\nvoid display ( );\nstring( );\n};\nstring : : string (int n)\n{\nP=new int [n+1];\n}\nvoid string : : setstring (char *str)\n{\nStrcpy (p, str);\n}\nvoid string : : resetstring (char *s)\n{\nStrcpy (p, s);\n}\nvoid string : : display ( )\n{\ncout << p <<end1;\n}\nstring : : string( )\n{\nDelete [ ] p;\ncout << “Memory deallocated”;\n}\nvoid main ( )\n{\nstring s1 = 20;\nstring s2 = 20;\nS11. setstring ( “Hello User”);\nS2=s1;\nS1. display ( );\nS2. display ( ); void string : : operator (string\nS1. resetstring ( “Welcome”); &s)\nS1. display ( ); {\nS2. display( ); strcpy 9p, s.p);\n} x=s.x;\n}\n\nNote:-\nwhen ever the class is containing dynamic pointer then it is necessary to\n\n## At the time of overloading = it is necessary to pass parameter by reference.\n\n( eg ( string & s) ).\n\nNote :-\nfor cascading of assignment operator it is necessary that return type must be\nstring\n\n## string string : : operator = (string &s)\n\n{\nstring (p, s, p);\nX= s,x;\nReturn (* this);\n}\n\nNote :-\nQ. why do we overload assignment operator?\n\nAns:- The default assignment operator provided by C++ is used for copying one\nobject of class to another but it copies the value bit by bit i.e. the value contained in\nevery bit of source object are copied in every bit of destination object. This\nbehaviour is perfect if source cosle is not containing any pointer to dynamically\nalloveated block, but if it is so then the default equl to (=) operator will simply\ncopy the address stored in the pointer with in source object to pointer in the\ndestination object. This will make two the defferent pointers point to same location\nwhich may cause multiple problems like the destructor calling delete operator for\nthe same memory to overcome this, a programmer should overload the equal (=)\noperator & provide his own definition for copying the data pointer by pointer of\nsource object rather than address.\n\n## Prototy Of << operator:-\n\nfriend ostream & operator << (ostream & cout, strudent &p);\n\nclass Emp\n{\nint age;\nchar name ;\nfloat sal;\npublic:\nfriend istream & operator >> (istream 7, Emp&);\nfriend ostream & operator << (ostream &, Emp &);\n};\nIstream & operator >> (istream & in, Emp & p)\n{\nIn >> P.age >> P.sal >> P.name;\nReturn (in);\n}\nOstream & operator << (ostream & out, Emp &P)\n{\nOut << P.age << “ “ << P.name<< “ “ << p.sal;\nReturn out;\n}\nvoid main( )\n{\nEmp E, F;\ncout << “ Enter age, name and sal”;\ncin >> E;\ncout << “Enter age, name & sal;\ncin >> F;\ncout << E << end1;\ncout << F <<end1;\n\n## Q. What is the need of overloading insertion and extraction operator?\n\nAns:- In C++, all primitive types like int, float, char etc. are display on console\nusing predefined object cout along with insertion operator (<<).\nNow if programmer wishes to use the same way of displaying objects of his\nclass on screen then he has to overload insertion and extraction operator so that\nthey can be derictly used with user defined objects.\n\n## Q. Why don’t we overload insertion and extraction operator as member function?\n\nAns:- Insertion and Extraction operator are binary operator and if binary operator\nis overloaded as member function of class then a compulsion is their to keep the\nobject of same class towards left of operator while calling. Thus if it is done then\nwll would become P << cout where P is object af class. Now this violotes the\nregular symmetry of insertion operator and so it must be overloaded as friend\nfunction.\n\n## Q. Equal Operator can never be overloaded as friend function.\n\nAns:- Since equal operator should return the reference of calling object (to support\ncoscading). It has to be overloaded as member function as friend function do not\nhave any calling object\n\nNote:-\nEqual operator if defined by base class never inherited by derive class\nbecause it needs the same members in source as well as destination object.\n\nTYPE CONVERSION\n\nType conversion is the process using a programmer can convert value from\nprimitive to non primitive and vice versa as well as from one object of class to\nanother object of different class.\nThus type conversion falls in three categories:-\n1> Basic to User Defined\n2> User Defined to Basic (Primitive )\n3> User Defined to User Defined\n\n## Constructor (Basic type\n\n{\n// steps to convert basic to user defined\n}\n\n## Operator primitive type of C++ ( ) return type\n\n{\n// steps to convert basic to user defined\nReturn (<basic – val>);\n}\n\n## Example User To Basic & Basic To User\n\nclass Meter\n{\nfloat length;\n\npublic :\nMeter ( )\n{\nLength = 0.0;\n}\nMeter (float cm)\n{\nLength = CM/100;\n}\nOperator float( )\n{\nFlaot ans= length * 100.0;\nReturn (ans);\n}\nvoid accept meters( )\n{\ncout << “enter length in (int meters)”;\ncin >> length ;\n}\nvoid show meter( )\n{\ncout << “In meter = “ << length ;\n}\n};\nvoid main ( )\n{\nMeter M1 = 250;\nMeter M2;\nM2.accept Meter( );\nfloat cm = m2; // float CM= M2. operator float( );\ncout << “250 Meters when converted”;\nM1. show Meter( ); M2. show Meter( );\ncout “ when converted to cms =” << cm\n}\n\nProgram :- Asignment\n\nclass string\n{\nchar str ;\npublic:\nstring (int n)\n{\nItoa (n, str, 10);\n}\nstring( )\n{\nStr = ‘\\0’;\n}\nOperator int( )\n{\nK=1;\nI= strlen (str);\nWhile (i>=0)\n{\nSum = sum + (str [i] -48)* k\nK * = 10;\nI - -;\n}\n\n## 1. Conversion Routine In Source Object:-\n\nOperator <destination_class-name> ( )\n{\n//routines to convert source to destination\nReturn ( <destination – object>);\n}\n\n## 2. Conversion Routine In Destination Object:-\n\nConstructor ( <source – class – name>)\n{\n//routines to convert source to diction\n}\n\n## Conversion From User Defined To User Defined By Placing Conversion Routine\n\nIn Source Object\n\n{\npublic:\n{\n}\n{\n}\nvoid show( )\n{\ncout << “Radian =” << rad << end1;\n}\n};\nclass Degree\n{\nfloat deg;\npublic:\nDegree( )\n{\nBeg = 0.0;\n}\nOperator Radian ( )\n{\nfloat x = deg * PI/180;\nRadian obj = x;\nReturn (obj );\n}\nvoid show( )\n{\ncout << “Degree=” << deg;\n}\nvoid getdagree( )\n{\ncout << “ enter angle in degree=”;\ncin >> deg;\n}\n};\nvoid main ( )\n{\nDegree D;\nd. getdegree( );\nR=D;\nD. show( );\nR. show( );\n}\n\nclass Degree\n{\nfloat deg;\npublic:\nDegree( )\n{\nDeg =0.0;\n}\nvoid show( )_\n{\ncout << “Degrees=” << deg;\n}\nvoid getdata( )\n{\ncout << “enter angle in degrees”;\ncin >> deg;\n}\nFlaot getDree( )\n{\nReturn (deg);\n}\n};\n{\npublic:\n{\nRad = obj.get Dece ( ) * PI/180;\n}\n{\n}\nvoid show( )\n{\ncout << “ Radian +” << rad;\n}\n};\nvoid main( )\n{\nDegree D;\nD.getData( );\nRadian R = D; // Radian R (D);\nD. show( );\nR.show( );\n}\n\nAssignment:-\n\n#include <iostream.h>\n#include <conio.h>\nclass hour\n{\nfloat time;\npublic:\nHour( )\n{\nTime = 0.0;\n}\nHour (double x)\n{\nTime =x/3600;\n}\nOperator float( )\n{\nfloat x = time * 3600;\nReturn(x);\n}\nvoid accept( )\n{\ncout << “enter time in hours”;\ncin >> time;\n}\nvoid show( )\n{\ncout << “in hour =” << time;\n}\n};\nvoid main( )\n{\nHour t1=3600;\nHour t2;\nT2. accept( );\nfloat x = t2;\ncout << “3600 when converted to hours=”;\nT1.show( );\nT2.shjow( );\ncout << “when converted to sconds =” <<x;\nGetch( );\n}\n\n## CONSOLE I/O OPERATIONS\n\nUnformatted Formatted\n\n1. Unformatted I/O\n(a) istream & get (char &) can be\n(b) int get( ) called by cin\n(c) istream & get (char *, int)\n\nExample :- cin.get(ch);\nCan read only characters\ncin=cin.get( );\n\n## Istream & getline (char *, int num, char delimiter);\n\nIstream & getline (char *, int num);\n\nExample :-\n\nvoid main ( )\n{\nchar ch;\ncout << “enter text and press enter to stop”;\ncin. Get (ch);\nWhile (ch ! =’\\n’)\n{\ncout << ch;\ncin.get(ch);\n}\nGetch( );\n}\n\nvoid main ( )\n{\nchar country , capital ;\ncout << “enter country name:”;\ncin.get (country,20); cin.getline (country, 20);\ncout << “enter capital”;\ncin.getline (capital, 20);\ncout << “contry is “<< country << end1;\ncout << “its capital = “<< capital << end1;\n}\n\n## This will terminate after 19 character or on pressing *, nothing\n\nwill happen on pressing enter rey.\n\n## Functions Of Ostream class\n\n(a) ostream & put (char)\n(b) ostream & write (char *, int)\nThis will start from base add upto no. of integers given.\n\nExample :-\nvoid main( )\n{\nchar str[ ] = { “programming “};\nint I;\nfor (i=0; i<strlen (str); i++)\n{\ncout.put (str [i]);\ncout << end1;\n}\nfor (i=1; i<=strlen(str); i++)\n{\ncout.write (str, i);\ncout << end1;\n}\nfor (i=strlen (str) ; i>=1; i--)\n{\ncout.write (str, i);\ncout << end1;\n}\n}\n\nFORMATTED I/O\n\nFunction Description\n\n## (i)width( ) specifies required no of fields to be used for displaying\n\nthe output. This fn is used for alignment.\n(ii) precision( ) specifies the no of values to be displayed after decimal pt\n\n## (iii) fill( ) specifies a character to be used to fill the unused area of\n\nfield. By default the fulling is done using spaces.\n\n(iv) setf( ) sets the format flag to be used while displaying the\noutput.\n\n(v) unsetf( ) clears the flogs set using setf & restones the default\nsettling.\n\n## Prototype:- int width( )\n\nReturns the current width.\nint width (int)\nOld width aurrent width\n\nvoid main( )\n{\ncout. Width(4);\ncout << 123;\ncout.width(4);\ncout. << 39;\ncout.width(2);\ncout << 2345\n}\n\nSetting Precision\n\n## Prototype :-int precision ( ) By default\n\nint precision (int) precision is of 6 pt.\n\nvoid main ( )\n{\ncout precision (2);\ncout << 2.23 << end1;\ncout << 5. 169 << end1;\ncout << 4.003 << end1;\n}\nOutput  2,23\n5.17\n4\n\nFilling :-\nint fill( )\nint fill (char)\n\nvoid main( )\n{\ncout.file ( ‘*’);\ncout.precision(2);\ncout.width(6);\ncout << 12.53;\ncout.width(6);\ncout << 20.5;\ncout. width(6);\ncout << 2;\n}\no/p  * 12.53* * * 20.5 * * * * * 2\n\nNote\nThere is no need to set fill and precision again and again while width flag\nmust be set again and again.\n\n## Flag-value (1st argument) Bit field (2nd Arguments Description\n\nIos : : left ios : : adjust field justifies the output on\nleft side.\nIos : : right ios : : adjeist field justifies the output in\nsight align mennes.\nIos : : internal ios : : adjust field passing accurs between\nthe sign or base indicator\n& the value when the\nvalue fails to fill the\nentire width\nios : : dec ios : : base field display the data in decimal\nconversion\nios : : oct “ display the data in actor\nios : : hax “ display the data in hexadecimal\nios : : scientific ios : : float field user exponential floating\nnotation\nios : : fixed “ user normal floating notation\n\nExample :-\n\nvoid main ( )\n{\ncout.self (ios : ; left, ios : : adjust field);\ncout.fill (‘*’);\ncout. precision (2);\ncout.width (6);\ncout << 12.53;\ncout.width (6);\ncout << 20.5;\ncout.witdth (6);\ncout <<2;\n}\n12.53 * 20.5 * * 2 * * * * *\n\nvoid main ( )\n{\ncout.self (ios : : internal | ios : : adjust field );\ncout.field (‘*);\ncout. precision (2);\ncout. width (10);\ncout << -420.53;\n}\nOutput : - - * * * 4 2 0 . 5 3\n\n## Displaying Trailing Zeros & Plus Sign\n\nLong self (long – setbits)\n(i) ios : : show pint (for trailing zeros)\n(ii) ios : : show pos (for plus sign)\nvoid main ( )\n{\ncout.setf (ios : : show point);\ncout.precision (2);\ncout << 20.55 << end1; output\ncout << 55.55 << end1; 20.55\ncout << 20.40 << end1; 55.55\n} 20.40\n\nExample :-\nvoid main ( )\n{\ncout.setf (ios : : showpoint);\ncout.setf (ios : : show pos);\ncout.setf (ios : : internal, ios : : adjust field);\ncout.precison(3);\ncout.width (10);\ncout << 420.53;\n}\nOutput \n+ * 4 2 0 . 5 3 0\n\n## Formatting Data Using Manipulators\n\nNon Parameterised Manipulators\n\nManipulator Description\n\n1. end1 terminates the line and transfers the cursor to next row.\n2. dec set conversion base to 10.\n3. bex set the conversion field to 16.\n4. oct set the conversion field to 8\n5. flush flushes the output screen\n\nExample :-\n1. WAP to read a number in decimal and display it in hxadecimal.\nvoid main ( )\n{\ncout << “ enter number”;\ncin a;\ncin >> a;\ncout << “ no is = “ << n << end;\ncout << “ It’s hexadecimal value= “ << hex<<n;\ncout. self (ios : : show base);\ncout << a;\n}\nOutput:- no is = 64\nIts hexaslccimal value = 40 0x 40\n\n## Example :- void main ( )\n\n{\nint n;\ncout << “enter number”;\ncin >> nex>> n;\ncout << “number= “ << n;\n}\n\nParameterised Manipulators\n\nManipulator Description\n\n## 1. setw (int) sets the field width\n\n2. setprecision (int) sets number of digits to be displayed after decimal\npint\n3. setfil (char) sets the character to be field\n4. setbase (int) set the conversion base. Here possible value of int\nare 8.10 and 16.\n5. setiosflag (long) sets the format flag.\n6. resetiosflag (long) resets the format flag.\n\nExample :-\nvoid main ( )\n{\nint n = 100;\ncout << hex <<n << “ “ << dec <<n << end1;\nfloat f = 122.3434;\ncout << f << end1;\ncout << setprecision (3);\ncout << f << end1;\ncout << setiosflag (ios : : internal } ios : : show base);\ncout << hex << n << end1;\ncout << setiosfloag (ios : : scientifie) << f << end1;\n}\n\nOutput:- 64 100\n122.34339 9\n122. 343\n0x0064\n1. 2 2 3 c + 0. 2\n\nclass Array\n{\nint *P;\nint n;\npublic :\nArray (int size)\n{\nP=new int [size];\nN=size;\n}\nint & operator [ ] (int i)\n{\nReturn (* (p+i) );\n}\nvoid fil (int pos, int value)\n{\n* (p+pos) = value;\n}\nArray ( )\n{\nDelete [ ]p;\n}\n};\nvoid main ( )\n{\nArray obj(5);\nint x;\nfor (int i=0; i<5; i++)\n{\ncin >> x;\nObj.fill (I, x); or obj [i] = x\n}\nfor (I = 0; i<5; i++)\n{\ncout << obj [i];\n}\n}\n\nclass Box\n{\nint l, b, h;\npublic:\nBox operator ( ) (int, int, int);\nvoid get( )\n{\ncout << “enter l, b and h”;\ncin >> l >> b >> h;\n}\nvoid show( )\n{\ncout << l, << “ “ << b << “ “ << h;\n}\n};\nBox Box : : Operator (int l, int b, int h)\n{\nBox temp;\nTemp.l = l + this  l;\nTemp.b = b + this  b;\nTemp.h = h + this  h;\nReturn (temp);\n}\nvoid main ( )\n{\nBox B1;\nB1.get( );\nBox B2;\nB2 = B1 (5, 3, 9); // B2 = B1. operator ( ) (5, 3, 9);\nB1. show( );\nB2. show( 0;\n}\n\n## Steps Required for Designing Graphics Mode Program\n\n1> Concert the display monitor from text mode to graphics mode.\n2> Perform the required graphics operation like filling coloring, drawing etc.\n3> Finally close the graphics mode and restore character mode.\n\n## 1. void init gragraph (int *, int *, char *)\n\nDriver resolution path of BGI file\nOr\nMode\n2. Drawing using built in functions.\n3. void close graph( );\nvoid restorecrtmode( );\n\nProgram:- WAP to couvert monitor display mode from char to pixel and print\nwelcome message.\n\n#include <graphics.h>\n#include <conio.h>\n#include <stdlib.h>\nvoid main( )\n{\nint gd, gm, ec;\nGd= DETECT;\nInttgraph (&gd, &gm, “C:\\\\TC\\\\BGI”);\nEc= graphresult( );\nif (ec!=grOk) or (ec!=0)\n{\nPrintf (“error in initialising”);\nexit(1);\n}\nCleardevice\nOuttext ( “welcome to graphics”);\nGetch( );\nClosegraph( );\nRestarectmode( );\n}\nNote:-\n\n## int getmaxx( ); returns the maximum value of x – coordinate.\n\nint getmaxy( ):- returns the minimum value of if – coordinate.\n\nAssignment:- Modify the above code so that now code display the\nMessage at the necter of screen.\n\nvoid main ( )\n{\nint gd, gm, ec;\nGd = DETECT;\nInitgraph (&gd, & gm, “C:\\\\TC\\\\BGI”);\nEc=graphresult( );\nif (ec ! = grOk)\n{\nPrintf ( “error”);\nexit(1);\n}\nCleardevice( );\nint a = getmaxx( );\nint b = getmaxy( );\nOuttextry ( ( a/2), (b/2), “welcome to graphics”);\nGetch( );\nClosegraph( );\nRestorecrtmode( );\n}\n\nQ. WAP to accept user name & then convert screen to graphics mode & display\nthe name given by user at the center of the screen on char at a time.\n\nvoid main ( )\n{\nchar str\nint gd, gm, ec;\nGd = DETECT;\nInitgraph (&gd, &gm, “c:\\\\TC\\\\BGI”);\nEc = graphresult( );\nif (ec!=grOk)\n{\nPrintf ( “error”);\nexit(1);\n}\nCleardevicl ( ); printf (“enter name”);\nMove to (getmaxx( ) /2, getmaxy( )/2);\nfor (i=0; str[i]; i++)\n{\nSpritf (msg, “%c”, str[i]);\nOut text (msg);\nDelay (1000);\n}\n}\nChanging the font style and size\n\n## 1. void settextstyle (int font, int dir, int charsize)\n\ndescription of parameters\n\n## font  default – FONT (0)\n\nTRIPLEX – FONT (1)\nSMALL – FONT (2)\nSANS – SERIT – FONT (3)\nGOTHIC – FONT (4)\nSCRIPT – FONT (5)\nSIMPLEX – FONT (6)\nPRIPLEX – SCRIPT – FONT (7)\nCOMPLEX – FONT (8)\nEUROPEAN – FONT (9)\nBOLD – FONT (10)\n\n## Dir  horiz – dir (0)\n\nVert – dir (1)\n\nCharsize :- 1 to 10\n1 being size of 8 x 8 pixel.\n\nProgram:-\nvoid main( )\n{\nchar * font – style [ ] = { “ Defualt – font” ----, “BOLD-\nFONT”);\nint gd, gm, ec; char msg;\nGd = DETECT;\nInitgraph (&gd, &gm, “C: \\\\ TC\\\\ BGI”);\nEc = graphresult ( );\nif (ec ! = 0)\n{\nPrintf ( “error”);\nexit (1);\n}\nCleardevicl ( )\nMove to (getmaxx( )/2, getmaxy( )/2);\nfor (1=0; i<=10; i++)\n{\nPrintf (msg = “shiv - %s”, font-style [i]);\nSettext style (I, 0, 1);\nOuttextry (getmaxx( )/2, getmaxy( )/2, msg);\nGetch( );\nCleardevicl ( );\n}\nCleardevicl( );\nRestorecrtdevicl ( );\n}\n\nDRAWING IN GRAPHICS\n1. for Drawing Line:-\n\na. void line (int x1, int y1, int x2, int y2)\nb. void linerel (int, int)\nc. void line to (int, int)\n\nExample :-\nvoid maiN ( )\n{\nint gd, gm, ec, x, y;\nGd = DETECT;\nInitgraph ( &gd, &gm, “ c: \\\\TC\\\\BGI”);\nEc = graphresult( );\nif (ec ! = 0)\n{\nPrintf ( “error”);\nexit (1);\n}\nCleardevicl ( );\nX=getmaxx( )/2; y=getmaxy( )/2\nLine (0, 0, x, y);\nGetch ( );\nClosegraph( );\nRestorertmode( );\n\nQ.WAP which draws a line from 20, 30 to 100 pixels further & display the\nwordinate of line at its end point.\n\nvoid main ( )\n{\nint gd, gm, ec, x, y;\nGd = DETECT;\nInitgraph (&gd, & gm, “c: \\\\TC\\\\BGI”);\nEc = graphresult ( );\nif (ec! = 0)\n{\nPrintf ( “error”);\nexit (1);\n}\nCleardericl ( );\nMove to (20, 30);\nSprintf (msg, “%d %d”, get x( ), gety( ) );\nOuttext (msg);\nLinerel (100, 100);\nSprintf (msg, “%d %d”, getx( ), get y( ) );\nOuttext (msg);\n}\n\n## Style:- No Constant meaning\n\n0 SOLID-LINE solid line\n1 DOTTED –LINE\n2 CENTER-LINE line withdot & dash\n3 DASHED-LINE line with dashes\n4 USERBIT-LINE\n\n## Pattern:- It is always zero except when first parameter is userbit line.\n\nThickners:- 1 – THICK-WIDTH\n3 – NORM-WIDTH\n\nvoid main( )\n{\nint gd, gm, ec, I;\nchar * style [ ] = { “solidline}, Dotted-line”,----, “Dashedline”};\nGd = DETECT;\nInitgraph ( &gd, & gm, “C: \\\\TC\\\\BGI”);\nEc=graphresult( );\nif (ec != grok)\n{\nPrintf ( “error”);\nexit(1);\n}\nDeardvice( );\nfor (i=0; i<4; i++)\n{\nSpritf (msg, “%s in normal width”, style[i]);\nOuttext (getmaxx( )/2-20, getmaxy( )/2-10, msg);\nSetlinestyle (I,0, NORM-WIDTH);\nLine (getmaxx( )/2-50, getmaxy( )/2 +20, getmaxx( )/2 +50,\nGetmaxy( )/2 +100);\nGetch( );\nCleardevicl( );\n}\nRestorectdevicl( );\n}\n\n## Defining Pattens in User Defined Way\n\nIo define a user bit line wee have to build a sixbit pattern. In this pattern\nwheever a bit is one the curresposing pixel in the line is drawn in the current\ndrawing colour.\nfor eg:- 65535\nor\nsetline style (4, OXFFFF, NORM-WIDTH);\nthis will draw a solid line,\nsetlinestyle (4, Ox3333, NORM-WIDTH)\nthis will draw a dashed line.\n\n## 1. void arc (int x, int y, intstangle, int endangle, int rad)\n\n2. void piesline (int x, int y, int stangle, int endangle, int rad)\n\n## 3. void circle (int x, int y, int rad)\n\n4. void rectangle (int left, int top, int right, int bottom)\n5. void bar (int left, in top, int right, int bottom)\n\n## Filling Image With Different Patterns\n\nvoid set color (int)\nvoid setfillstyle (int pattern, int style)\n\n## Name value Result\n\n1. Empty Fill 0 Background color\n\n## 2. SOLID-FILL 1 Solid Filling\n\n3. LINE-FILE 2 --------------\n-------------\n\n4. LISLASH_FILE 3\n\n5. SLASH-FILL 4\n\n6 BKLASH-FILE 5\n\n7. LTBKSLASH-FILE 6\n\nHATCH-FILE 7\n\nXHATCH-FILE 8\n\nINTERLEAVE-FILE 9\n\nWIDE-DOT-FILE 10\n\nCLOSE-DOT-FILE 11\n\nWAP which draws a sectangle with white outline & red fill color & should display\nthe fitling in all of the twelve patterns one at a time. Also name of pattern should\nbe displayed with in the rectangle.\n\nvoid main ( )\n{\nint gd, gm, ec, left, right, top, bottom, I;\nchar * style [ ] = { “EMPTY-FILE”, “SOLID-FILE”, ----,\nCLOSED-DOT-FILE”};\nGd=DETECT\nInitgraph (&gd, &gm, “C:\\\\TC\\\\BGI”);\nEc=grapgresult( );\nif (ec !=0)\n{\nPrintf (“error”);\nexit (1);\n}\nCleardevicl ( );\nLeft = getmaxx( )/2-100;\nTop=getmaxy( )/2-100\nRight – getmaxx( )/2+100;\nBottom=getmaxy( )/2;\nfor(i=0; i<12; i++)\n{\nSetfill style (I, RED);\nBar(left, top, right bottom);\nRectangle (left, top, right, bottom);\nOuttextxy (left+50, top+50, style[i]);\n}\n}\n\n## 1. void floodfill (int x, int y, int boundary)\n\n(x, y)  a point with in the figure which has to be filled using flodfill also known\n\n## Filling Circles With Different Pattern\n\nvoid main ( )\n{\nchar * pattern = { “EMPTY-FILE”, --------, “CLOSE-DOT-FILE”};\nint gd, gm, ec, x, y, 0;\nGd=DETECT;\nInitgraph (&gd, &gm, “C:\\\\TC\\\\BGI”);\nEc=graphresult( );\nif (ec! = grOk)\n{\nPrintf ( “error”);\nexit(1);\n}\nCleardevice( );\nX=getmaxx( )/2; y=getmaxy( )/2;\nfor (i=0; i<12; i++)\n{\nSetcolor (GREEN);\nCircle (x, y, 100);\nSetfill style (I, RED);\nFloodfill (x, y, GREER);\nSetcolor (WHITE);\nOuttextxy (x-50, y-50, patter[i]);\nGetch( );\nCleardivice( );\n}\nGetch ( );\nRestorecrtdevice( );\n}\n\n## 1. void getline (int, int, int, int, void *)\n\n2. int imagesize (int, int, int, int)\n3. void putimage (int, int, void *, int option)\n\n## void getimage (int, int, int, int, void *)\n\ncopies the bit image of specified\npostion in memoery\n\n## 1st parameter indicates  left coordinates\n\n2nd parameterer  to “\nrd\n3 parameter  right “\nth\n4 parameter  bottom “\n5th parameter  pointer to an array large enough to store bit pattern.\n\n## void putimage [int x, int y, void * are, int iption)\n\nCopies or outputs the image pattern form memoery to the specified\nportion on the screen.\n\n## X = starting left coordinate\n\nY = starting top coordinate\nArr = maner un which color of the resultant pixel is to be decided, taking into\nconsideration the pixels stored in memory & the pixels stores on screen.\n\n## int image size (int, int, int, int);-\n\nReturns no. of bytes required to store the image, on any kind of error\nreturn -1\n\nvoid main ( )\n{\nint gd, gm, ec;\nchar * buffer, msg ;\nint size -0f-image;\nGd = DETECT;\nInitgraph ( &gd, &gm, “C:\\\\TC\\\\BGI”);\nEc = graphresult( );\nif (ec ! =0)\n{\nPrintf (“error”);\nexit (1);\n}\nRectangle (150, 150, 200, 200);\nSize-of-image = image size (150, 150, 200, 200);\nif (size-of-image = = -1)\n{\nOuttextxy (getmaxx( )/2, getmaxy( )/2, “Error”);\nGetch( );\nClose graph( );\nexit(1);\n}\nBuffer = (char *) malloc (size-of-image * size of (char) );\nif (buffer = = NULL)\n{\nOuttextxy (getmaxx( )/2, getmaxy( )/2, “Can not allocate memory”);\nGecth( );\nexit(1);\nClose graph( );\n}\nGetimage (150, 150, 200, 200, buffer);\nLine (200, 220, 220, 220);\nPutimage (175, 200, buffer, COPY-PUT);\nGetch( );\nClosegraph( );\nRestore crtdevice( );\n}\n\n## VALUES CREEN EMORY OUTPUT\n\nCOPY-PUT ON ON ON\nOFF ON ON\nON OFF OFF\nOFF OFF OFF\n\nXOR-PUT ON ON OFF\nOFF ON ON\nON OFF ON\nOFF OFF OFF\n\nOR-PUT ON ON ON\nOFF ON ON\nON OFF ON\nOFF OFF OFF\n\nAND-PUT ON ON ON\nOFF ON OFF\nON OFF OFF\nOFF OFF OFF\n\nTHE END" ]
[ null ]
{"ft_lang_label":"__label__en","ft_lang_prob":0.64908606,"math_prob":0.9365731,"size":64921,"snap":"2019-35-2019-39","text_gpt3_token_len":19308,"char_repetition_ratio":0.17490026,"word_repetition_ratio":0.12776831,"special_character_ratio":0.35587868,"punctuation_ratio":0.22624072,"nsfw_num_words":0,"has_unicode_error":false,"math_prob_llama3":0.9839813,"pos_list":[0],"im_url_duplicate_count":[null],"WARC_HEADER":"{\"WARC-Type\":\"response\",\"WARC-Date\":\"2019-09-16T13:45:35Z\",\"WARC-Record-ID\":\"<urn:uuid:c565cf9d-b0cb-4cac-aa5e-eff28fc4201b>\",\"Content-Length\":\"464185\",\"Content-Type\":\"application/http; msgtype=response\",\"WARC-Warcinfo-ID\":\"<urn:uuid:c75b69fb-323b-4518-b4db-3f0b790bb95a>\",\"WARC-Concurrent-To\":\"<urn:uuid:eebd73cd-f7ab-443a-a2fd-10f9cf4de32f>\",\"WARC-IP-Address\":\"151.101.250.152\",\"WARC-Target-URI\":\"https://www.scribd.com/doc/51645490/C-Notes-Complete\",\"WARC-Payload-Digest\":\"sha1:CHR656DQ6HSFSZXDFFX3R5G35ZSEXHKS\",\"WARC-Block-Digest\":\"sha1:Z7YRW5S6V4BNMXTY2DJ3DUIJW3GC4JOH\",\"WARC-Identified-Payload-Type\":\"application/xhtml+xml\",\"warc_filename\":\"/cc_download/warc_2019/CC-MAIN-2019-39/CC-MAIN-2019-39_segments_1568514572556.54_warc_CC-MAIN-20190916120037-20190916142037-00475.warc.gz\"}"}
https://arxiv.org/abs/1307.6610
[ "math.ST\n\n# Title:Information bounds for inverse problems with application to deconvolution and Lévy models\n\nAbstract: If a functional in an inverse problem can be estimated with parametric rate, then the minimax rate gives no information about the ill-posedness of the problem. To have a more precise lower bound, we study semiparametric efficiency in the sense of Hájek-Le Cam for functional estimation in regular indirect models. These are characterized as models that can be locally approximated by a linear white noise model that is described by the generalized score operator. A convolution theorem for regular indirect models is proved. This applies to a large class of statistical inverse problems, which is illustrated for the prototypical white noise and deconvolution model. It is especially useful for nonlinear models. We discuss in detail a nonlinear model of deconvolution type where a Lévy process is observed at low frequency, concluding an information bound for the estimation of linear functionals of the jump measure.\n Comments: To appear in Annales de l'Institut Henri Poincaré Subjects: Statistics Theory (math.ST); Probability (math.PR) MSC classes: 60G51, 60J75, 62B15, 62G20, 62M05 Cite as: arXiv:1307.6610 [math.ST] (or arXiv:1307.6610v2 [math.ST] for this version)\n\n## Submission history\n\nFrom: Mathias Trabs [view email]\n[v1] Wed, 24 Jul 2013 23:37:54 UTC (39 KB)\n[v2] Tue, 6 May 2014 14:37:14 UTC (40 KB)" ]
[ null ]
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http://infolab.stanford.edu/~ullman/cs345notes/CS345_Midterm_2003_Solutions.html
[ "### Problem 1:\n\na) True\nConsider visiting the rows in the permuted order. The first time you see a one in any of the two columns, the column C1 \\/ C2 will also have a one. Consequently, the first (minimum) row number which corresponds to the min hash value for any of the two columns will also be the min hash for C1 \\/ C2.\n\nb) False\nConsider the following permuted order or rows: 1) 1 0 2) 0 1 3) 1 1 Under this permutation the minhash for C1 and C2 are 1 and 2, while that for C1 /\\ C2 is 3.\n\nc) True\nFollows directly from part a)\n\nd) True\nSince h(C1) = h (C2), the first row (under the permuted order) that has a 1 in C1 also has a 1 in C2. Therefore, by definition the column C1 /\\ C2 also has a 1 in this row. The result follows.\n\n### Problem 2:\n\na) True\nh(i) = lambda sumk A(i,k) a(k) h(j) = lambda sumk A(j,k) a(k) Out(i) subseteq Out(j) implies that whenever A(i,k) is 1, A(j,k) is also 1. This coupled with the fact that a(k)'s are positive gives the result.\n\nb) False\nConsider the following figure. In the figure Out(i) subset Out(j), while p(i) > p(j)", null, "c) True\np(i) = (1-f)(sumk M(i,k) p(k)) + f p(j) = (1-f)(sumk M(j,k) p(k)) + f where 'f' is the fudge factor and M is the matrix that has entry M(i,k) = 1/d iff k points to i and k has degree 'd'. In(i) subseteq In(j) implies that if M(i,k) = 1/d > 0, then M(j,k) = 1/d > 0. This coupled with the fact that p(k)'s are positive gives the result\n\nd) False\nInfact the opposite is true, namely a(j) <= a(i). This follows from same reasoning as in a) with A replaced by AT and lambda replace by mu\n\n### Problem 3:\n\nThere are exactly 3 stable models: {p1,p2},{q1,p2} and {q1,q2}.  One may arrive at the answer by applying the GL-transform to all 16 candidate models but the following observations might relieve one of that tedium:\n(1) Since we have pi:-NOT qi and qi:-NOT pi (for both i=1 and i=2), exactly one of pi and qi belongs in a stable model.\n(2) If p1 is part of a model, then so is p2.\n\nObservation (1) reduces the number of candidate models to check down to just 4, and observation (2) rules out the candidate {p1, q2}. The three possibilities left all turn out to be stable.\n\n#### Common errors:\n\nAll errors had low support but the following two stood out:\n1. Not considering all the possibilities and providing only a subset of the answer.\n2. Believing that {p1,q2} is stable.\n\nIf the provided solution was a subset of the correct solution, your score was 15*Sim_Jaccard(Correct Solution, Your Solution).\nIf you provided a superset of the correct solution, but had used Observation (1), you lost 3 points.  Otherwise, you scored min(5*#correct models in solution, 15-max(5*#wrong models in solution,10)).\n\n### Problem 4:\n\n(a) There are 100,000-choose-2 or about 5*109 frequent pairs. These occur 100 times each, for a total of 5*1011 occurrences. The number of frequent-infreqent pairs is 1011, and these occur 10 times each, for a total of 1012 occurrences. Finally, there are 1,000,000-choose-2 or about 5*1011 infrequent pair occurrences, for a total of 2*1012 occurrences.\n\n(b) Boy is my face red. I was imagining that pairs had to occur isolated in baskets, so one could argue just by counting the pairs in which each item participated that the support threshold had to be between 2*106 and 2*107. However, there are many other possibilities. There actually is no upper bound at all, since there could be an indefinite number of baskets with one item. For example, here's how you could explain the given data with a support threshold of one google (10100):\n\n1. One basket with all the infrequent items.\n2. 100 baskets with all the frequent items.\n3. For each pair of a frequent and an infrequent item, 10 baskets containing exactly those two items.\n4. For each frequent item, one google minus 10,000,100 baskets containing only that item.\n\nOn the other hand, I'm having trouble getting the exact lower bound at all. Here's as far as I've gotten. Suppose a collection of market-baskets was uniform, in the sense that it consists of k baskets, each with i infrequent items and f frequent items. Then we can place upper bounds on some functions of k, i, and f by counting the total number of pairs of various types. The fact that there are only 1,000,000-choose-2 infrequent-infrequent pair occurrences says that ki2/2 = 10^12/2. (note: throughout we'll use n2/2 as the approximation to n-choose-2). Similarly, the fact that there are 1012 frequent-infrequent pair occurrences says kif = 1012, and the fact that there are 1012/2 frequent-frequent pair occurrences says kf2 = 1012/2. We conclude that i = f, and k = 1012/i2. Finally, note that s, the support, must be bigger than the number of times any infrequent item occurs, which is s > ki/106, or s > 106/i.\n\nOur apparent conclusion is that i should be as large as possible, which is 100,000. The reason is that i = f, and there are only 100,000 frequent items. that gives s = 11, and there were actually 4 people who gave 11 as the lower bound. However, unless someone can take this one step further, I declined to give them extra credit for that answer. The reason is that I doubt we can get specific baskets that meet all the conditions.\n\nTo begin, we could divide the 106 infrequent items into 10 groups of 100,000, and create one basket with each group and all the frequent items. However, k = 1012/i2, so we need another 90 baskets, each with 100,000 infrequent items. We cannot completely avoid reusing some of the pairs of infrequent items from the original ten groups, so we violate the condition that each pair of infrequent items appears exactly once. If we lower i, there is hope we could arrange the infrequent items into groups of i so that no pair appears more than once. There is also the option to have some baskets with one infrequent item and many frequent items, which gives us further flexibility. Anyway --- if anybody has some thoughts about the best design of the baskets, please let me know.\n\nGiven the state of this part of the problem, I decided to give everyone credit, and to ignore the problem completely.\n\n(c) The answer is simply the answer to (a) divided by 107, or 200,000.\n\n#### Common errors:\n\n4A: Forgetting that the number of pairs of n items is n-choose-2, not N2. (-2)\n\n4B: Omitting the frequent pairs in the calculation of (c). Note that the given support 107 is so large, that the frequent pairs distribute essentially randomly in buckets, just like the infrequent pairs. (-2)\n\n### Problem 5:\n\n(a)\n Round 0 Round 1 Round 2 Round 3 Truth Value p(1) 0 0 0 0 False p(2) 0 1 1 1 True p(3) 0 1 1 1 True p(4) 0 1 0 0 False c(1) 0 0 0 0 False c(2) 0 0 0 0 False c(3) 0 0 0 0 False c(4) 0 1 1 1 True\n\n(b)", null, "(c) Stratum 0: { p(1), c(1),c(2), c(3)} Stratum 1: {p(2),p(3),c(4)} Stratum 2: {p(4)}\n\n(d)  The program+EDB is locally stratified for all values of n.  The only negative arcs in the dependency graph are from p(x) to c(x). Since, all arcs from c(x) are to a p(y) where y<x, there can be no cycles (and, consequently, no cycles with negative arcs) in the dependency graph.  Therefore, the program+EDB is locally stratified.\n\n#### Common Errors:\n\n1.Forgetting to make inferences from positive facts derived in a round. Specifically, when you infer  p(2) to be true in Round 1, you need to use the rule c(4):-p(2) to infer that c(4) is true.  (-1)\n\n2.The singular form of \"strata\" is \"stratum\". (-0)\n\nEach part carries 4 points. You lose one point for each mistake (but you aren't docked points for cascading errors). An incorrect explanation in part (d) costs you 2 points.\n\n### Problem 6:\n\n(a) When we expand, we get:\n\n`q(A,B,C,D) :- e(A,B) & e(B,C) & e(A,C) & e(B,C) & e(C,D) & e(B,D)`\n\nIn all expansions, A, B, C, and D from the query have to map to themselves, because they appear in the head, in that order, in both the query and solutions. In this case, e(A,D) from the query has to map to some subgoal in the expansion above, but there is no such subgoal. Hence, \"solution\" (a) is not contained in the query, and is not a solution.\n\n(b) Here, the expansion is:\n\n`q(A,B,C,D) :- e(A,B) & e(B,C) & e(A,C) & e(B,C) & e(C,D) & e(B,D) & e(A,C) & e(C,D) & e(A,D)`\n\nNow, there is a target for each of the 6 query subgoals, so we have a solution. It is also minimal, because if we eliminate any of the three views, since there is a common e-subgoal in the expansions of any two of these views, only five of the six query subgoals could be covered.\n\n(c) This is also a minimal solution. The expansion is:\n\n`q(A,B,C,D) :- e(A,B) & e(B,C) & e(A,C) & e(A,E) & e(E,D) & e(A,D) & e(B,F) & e(F,D) & e(B,D) & e(G,C) & e(C,D) & e(G,D)`\n\nThe identity map again shows this expansion is contained in the query. Since the subgoals of the expansion that have E, F, or G are useless as the possible target of a query subgoal, we see that there are only six subgoals of the expansion that could be a target. Thus, if we remove any view-subgoal from the proposed solution, we cannot cover the query, which proves we have a minimal solution.\n\n(d) Another minimal solution, with expansion:\n\n`q(A,B,C,D) :- e(A,B) & e(B,C) & e(A,C) & e(B,C) & e(C,D) & e(B,D) & e(B,A) & e(A,D) & e(B,D)`\n\nA subgoal from the expansion of each of the three view subgoals is needed, so we cannot eliminate any view subgoals. Specifically, e(A,B) comes only from v(A,B,C), e(C,D) comes only from v(B,C,D), and e(A,D) comes only from v(B,A,D).\n\n#### Common error:\n\n6A: A number of people thought that the LMSS theorem said the number of subgoals in the expansion could not exceed the number of subgoals in the query. Rather, it is the number of subgoals in the solution before expansion that cannot exceed the number in the query. You lost 6 points if this affected your answer to more than one part. In some cases, people lost only 4 points, if for some reason this (false) theorem was invoked only once.\n\n### Problem 7:\n\nConsider any set of three items (A,B,C) All pairs of items are frequent and hence (A,B), (B,C) and (A,C) are frequent in the sample. Hence, by definition (A,B,C) is in negative border. Consider any item set with 4 or more items. No item set with 3 items or more is frequent in the sample. Hence, no item set with 4 or more items will be part of the negative border. In other words, the negative border is exactly all the triples of items. Number of triples = 10*9*8/1*2*3 = 120\n\n#### Common errors:\n\n7A: incorrect calculation of number of triples out of 10 or not calculating them at all. (-4)\n\n7B: having all pairs as part of the negative border (-8)\n\n7C: no explanation (-8)\n\n### Problem 8:\n\n(a)\n\n`Q2: panic :- a(X,Y) & a(U,V) & X<Y & X=V & Y=UQ1: panic :- a(A,B) & a(C,D) & A!=B & A=D & B=C`\n\n(b)\n\nX->A; Y->B; U->A; V->B\nX->A; Y->B; U->C; V->D\nX->C; Y->D; U->A; V->B\nX->C; Y->D; U->C; V->D\n\n(c)\n\n`A!=B & A=D & B=C => A<B & A=B & A=B OR A<B & A=D & B=C OR C<D & A=D & B=C OR C<D & C=D & C=D`\n\n(d)\n\n`A!=B & A=D & B=C => (A<B OR B<A) & A=D & B=C => A<B & A=D & B=C OR B<A & A=D & B=C => A<B & A=D & B=C OR C<D & A=D & B=C => entire right side of (c)`\n\n#### Common errors:\n\n8A: In part (d), using \"backwards\" logic. That is, a common mistake was to use the left side of (c) to derive something true like \"A<B OR B<A\" from the right side and then declare that because the right side derives truth, it must itself be true. That's not correct. For example, I can prove from \"2+2=5\" that \"5=5.\" Just because my conclusion is true doesn't mean I started with a true statement. (-2)\n\n8B: In (b), a number of people omitted the two containment mappings that send both relational subgoals of Q2 to the same subgoal. (-2)\n\n### Problem 9:\n\na) True.\nSince the Web graph is undirected, A = AT. Thus AAT = ATA and hence we get that h and a vectors are identical and satisfy h = lambda mu AAT h\n\nb) False.\nThe following simple example of an undirected graph has M(2,1) != M(1,2)", null, "#### Common errors:\n\n9A: for the pagerank matrix M[i,j], claiming that M[i,j] =1 whenever there is a link from j to i, or just claiming that M[i,j] = M[j,i] without explanation. (-5)\n\n9B: little or no explanation for the hubbiness and authority problem. (-5)" ]
[ null, "http://infolab.stanford.edu/~ullman/cs345notes/2b.gif", null, "http://infolab.stanford.edu/~ullman/cs345notes/5b.gif", null, "http://infolab.stanford.edu/~ullman/cs345notes/9b.gif", null ]
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https://medschool.ucsd.edu/research/actri/education/crest-program/course_descriptions/pages/biostatisticsii.aspx
[ "# Biostatistics II : Florin Vaida, Ph.D.\n\nObjectives: the students will conduct more advanced regression-based statistical analyses, including: simple linear regression and correlation analysis; multiple linear regression; logistic regression; Cox proportional hazards models.  Issues of model diagnostics and analysis of residuals, model comparison and model building, and strategies for univariable and multivariable analyses will be discussed.  The analyses will be conducted in SPSS.\n\n## Course Content\n\nTopic\n\nContent\n\nCorrelation and simple linear\nregression\n\nCorrelation and simple linear regression\n\nInference for simple linear\nregression\n\nModel formulation and estimation in simple linear regression; examination of residuals and model assumptions; prediction and explained variation\n\nMultiple Linear Regression\n\nmultiple linear regression: model assumptions and interpretation; diagnostics;\n\nModel building\n\nmodel comparison and model building in multiple linear regression; polynomial regression; ANCOVA and ANOVA as multiple linear regression\n\nRegression for proportions\n\nLogistic regression for binary outcomes; model interpretation; comparing several groups\n\nLogistic regression\n\nMultiple logistic regression; model building and model comparison\n\nSurvival analysis\n\nKaplan-Meier curves and log-rank test\n\nCox proportional hazards\nmodel\n\nRegression methods for survival data: Cox proportional hazards model\n\n2016 Syllabus" ]
[ null ]
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https://www.bystudin.com/250-ml-of-a-5-molar-and-100-ml-of-a-0-8-molar-sulfuric-acid-solution-are-mixed-the-resulting-volume-was/
[ "# 250 ml of a 5 molar and 100 ml of a 0.8 molar sulfuric acid solution are mixed. The resulting volume was\n\n250 ml of a 5 molar and 100 ml of a 0.8 molar sulfuric acid solution are mixed. The resulting volume was added up to 3 liters. Find the molar concentration of the equivalent of the resulting solution", null, "Finding the molar concentration of the equivalent of the resulting solution is carried out according to the scheme:\nM (H2SO4) = 98 g / mol;\nfeq (H2SO4) = 1/2 = 0.5;\nME (H2SO4) = fEq (H2SO4). M (H2SO4) = 0.5. 98 = 49 g / mol.\n1) Calculate the total amount of sulfuric acid when mixing solutions, we get:\nn (H2SO4) = (5. 0.25) + (0.8. 0.1) = 1.33 mol.\nFrom here\n2) Find the mass of sulfuric acid:\nm (H2SO4) = n (H2SO4). M (H2SO4) = (1.33. 98) = 130.34 g.\n3) Determine the molar concentration of the equivalent of sulfuric acid in a 3-liter solution of it according to the formula:\nCH (H2SO4) = mH2SO4) / [ME (H2SO4). V] where\nCH (H2SO4) – molar concentration of the equivalent of a solution of sulfuric acid (mol / l); V is the volume of the total acid solution.\nCH (H2SO4) = 130.34 / (49.3) = 0.8866 mol / L (mol · L-1).\nAnswer: CH (H2SO4) = 0.8866 mol / l", null, "Remember: The process of learning a person lasts a lifetime. The value of the same knowledge for different people may be different, it is determined by their individual characteristics and needs. Therefore, knowledge is always needed at any age and position." ]
[ null, "https://www.bystudin.com/wp-content/uploads/2020/06/biol-17.jpg", null, "https://www.bystudin.com/img.jpg", null ]
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https://www.proprofs.com/quiz-school/story.php?title=Electricity-Quiz-1
[ "# An Electricity Trivia Quiz! Ultimate Questions!\n\n10 Questions | Total Attempts: 22270", null, "", null, "Settings", null, "", null, "Electricity is a part of our daily life. Humans cannot survive without it. Have you ever imagined how electricity is produced and what's the science behind it? Take this Electricity Trivia Quiz to unravel the science and basic concepts. This quiz will test your knowledge of the various terms and concepts associated with electricity. All the best!\n\n• 1.\nA simple device that opens and closes an electrical unit is called a(an):\n• A.\n\nVolt\n\n• B.\n\nDischarge\n\n• C.\n\nSwitch\n\n• D.\n\nAmpere\n\n• 2.\nThe unit used for measuring electrical current is called a(an):\n• A.\n\nElectrode\n\n• B.\n\nVolt\n\n• C.\n\nDischarge\n\n• D.\n\nAmpere\n\n• 3.\nThis is the unit of measurement for measuring electrical pressure or EMF?\n• A.\n\nSwitch\n\n• B.\n\nVolt\n\n• C.\n\nAmpere\n\n• D.\n\nElectrode\n\n• 4.\nMaterial that electricity can travel through easily is called a\n• A.\n\nConductor\n\n• B.\n\nInsulator\n\n• C.\n\nRelay\n\n• D.\n\nResistor\n\n• 5.\nA circuit is a path taken by a current. A path with no breaks is called a\n• A.\n\nClosed-circuit\n\n• B.\n\nCapacitor\n\n• C.\n\nCircuit Breaker\n\n• D.\n\nVariable Resistor\n\n• 6.\nIf there is a break in the path the current follows, the circuit is incomplete. A break in the path is called an\n• A.\n\nOpen circuit\n\n• B.\n\nParallel Circuit\n\n• C.\n\n• D.\n\nElectromotive Force\n\n• 7.\nMaterials that do not allow electrical charges to pass through it easily are called\n• A.\n\nInsulator\n\n• B.\n\nConductor\n\n• C.\n\nVoltage\n\n• D.\n\nWaveform\n\n• 8.\nLightning is caused by the build-up of electrical charges in a cloud, also known as static electricity.\n• A.\n\nTrue\n\n• B.\n\nFalse\n\n• 9.\nParallel circuits have only one path.\n• A.\n\nTrue\n\n• B.\n\nFalse\n\n• 10.\nThe current always travels from the negative electrode to the positive electrode.\n• A.\n\nTrue\n\n• B.\n\nFalse\n\nRelated Topics", null, "Back to top" ]
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https://scholarworks.sjsu.edu/faculty_rsca/3003/
[ "## Faculty Research, Scholarly, and Creative Activity\n\n#### Title\n\nPermanental sums of graphs of extreme sizes\n\n6-1-2021\n\nArticle\n\n#### Publication Title\n\nDiscrete Mathematics\n\n344\n\n6\n\n#### DOI\n\n10.1016/j.disc.2021.112353\n\n#### Abstract\n\nLet G be a simple undirected graph. The permanental sum of G is equal to the permanent of the matrix I+A(G), where I is the identity matrix and A(G) is an adjacency matrix of G. The computation of permanental sum is #P-complete. In this paper, we compute the permanental sum of graphs of small sizes and derive explicit formulae for the permanental sum of graphs of large sizes. The results from small sizes are used as evidence to support a conjecture on an upper bound of the permanental sum of a graph in terms of its size. The results from large sizes are obtained by a new technique of employing rook's polynomial via the Principle of Inclusion and Exclusion.\n\n11761056" ]
[ null ]
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https://books.google.gr/books?id=g6p5fKAHYT0C&qtid=f67e79d0&lr=&hl=el&source=gbs_quotes_r&cad=5
[ "Αναζήτηση Εικόνες Χάρτες Play YouTube Ειδήσεις Gmail Drive Περισσότερα »\nΕίσοδος\n Βιβλία Βιβλία 1 - 10 από 134 για Multiply the divisor, thus increased, by the last figure of the root; subtract the....", null, "Multiply the divisor, thus increased, by the last figure of the root; subtract the product from the dividend, and to the remainder bring down the next period for a new dividend. 5. Double the whole root already found for a new divisor, and continue the...", null, "Putnam's Arithmetic - Σελίδα 196\nτων Rufus Putnam - 1849 - 264 σελίδες\nΠλήρης προβολή - Σχετικά με αυτό το βιβλίο", null, "## A Concise System of Arithmetic: Peculiarly Adapted to the Use of ..., Μέρη 1-2\n\nA. Melrose (Teacher) - 1795 - 128 σελίδες\n...divifor, thus increafed, by the laft. figure placed iu the root : Subtract the produft from the drwdend, and to the remainder annex the next period for a new dividend ; with which proceed in the fame manner, and'fo on,. till all the periods are ufed. I Required the...", null, "## Arithmetic Modernised: Or, A Complete System of Arithmetic, Adapted to ...\n\nJohn Davidson, Robert Scott (writing master) - 1818 - 172 σελίδες\n...divisor. The tum of these three parts will be the complete divisor, which multiply by the last figure of the root, subtract the product from the dividend, and to the remainder bring down the next part for a new dividend. Proceed in the same manner as before to find the divisor...", null, "## A system of practical arithmetic\n\n...both in the root and on the right of the Disisor; also by it multiply the Divisor thus completed, and subtract the Product from the Dividend, and to the...Remainder annex the next period for a new Dividend. To the completed Divisor add the figure last put in the root ; the sum is a new Divisor, with which...", null, "## An Elementary Treatise on Algebra: To which are Added Exponential Equations ...\n\nBenjamin Peirce - 1837 - 276 σελίδες\n...be placed at the right of the divisor. Multiply the divisor, thus augmented, by the last figure of the root, subtract the product from the dividend, and to the remainder bring down the next period for a new dividend. Double' the root now found for a new divisor and continue...", null, "## An Elementary Treatise on Algebra: To which are Added Exponential Equations ...\n\nBenjamin Peirce - 1837 - 288 σελίδες\n...be placed at the right of the divisor. Multiply the divisor, thus augmented, by the last figure of the root, subtract the product from the dividend, and to the remainder bring down the next period for a new dividend. Double the root now found for a new divisor and continue...", null, "## An Elementary Treatise on Algebra, Theoretical and Practical: With Attempts ...\n\n...divisor's place, and the divisor will be completed. Multiply the complete divisor by the last term of the root, subtract the product from the dividend, and to the remainder connect the three next terms, and proceed as before. For (by Art. 37,) the cube of a+b is a»+ 3a2¿>...", null, "## A Practical Treatise on Arithmetic: Wherein Every Principle Taught is ...\n\nGeorge Leonard (Jr.) - 1839 - 347 σελίδες\n...the root already found, and also at the right of the divisor. Multiply the divisor thus increased, by the last figure in the root, subtract the product from the whole dividend, and bring down the two next figures, and so on ; but if the product be greater than...", null, "## A system of arithmetic\n\n...to the right ; add together these two lines for the complete divisor; multiply the sum by the second figure in the root ; subtract the product from the dividend, and to the remainder annex the third period for a new dividend. Place the square of the second figure of the root under the complete...", null, "## Higher Arithmetic: Designed for the Use of High Schools, Academies, and Colleges\n\nGeorge Roberts Perkings - 1841 - 252 σελίδες\n...the result will be the TRUE DIVISOR. Multiply the true divisor by this second figure of the root, and subtract the product from the dividend, and to the remainder annex the next period,for a SECCUD DIVIDEND. . ft IV. To the last TRUE DIVISOR, add the Jastfgure of the root, for...", null, "## A Practical Treatise on Arithmetic ...: Combining the Useful Properties of ...\n\nGeorge Leonard (jr.) - 1841 - 340 σελίδες\n...the root already found, and also at the right of the divisor. Multiply the divisor thus increased, by the last figure^ in the root, subtract the product from the whole dividend, and bring down the two next figures, and so on ; but if the product be greater than..." ]
[ null, "https://books.google.gr/googlebooks/quote_l.gif", null, "https://books.google.gr/googlebooks/quote_r.gif", null, "https://books.google.gr/books/content", null, "https://books.google.gr/books/content", null, "https://books.google.gr/books/content", null, "https://books.google.gr/books/content", null, "https://books.google.gr/books/content", null, "https://books.google.gr/books/content", null, "https://books.google.gr/books/content", null, "https://books.google.gr/books/content", null, "https://books.google.gr/books/content", null, "https://books.google.gr/books/content", null ]
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https://f0nzie.github.io/machine_learning_compilation/detailed-study-of-principal-component-analysis.html
[ "# 4 Detailed study of Principal Component Analysis\n\n• Datasets: decathlon2\n\n• Algorithms:\n\n• PCA\n\nhttp://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/112-pca-principal-component-analysis-essentials/\n\nPrincipal component analysis (PCA) allows us to summarize and to visualize the information in a data set containing individuals/observations described by multiple inter-correlated quantitative variables. Each variable could be considered as a different dimension. If you have more than 3 variables in your data sets, it could be very difficult to visualize a multi-dimensional hyperspace.\n\nPrincipal component analysis is used to extract the important information from a multivariate data table and to express this information as a set of few new variables called principal components. These new variables correspond to a linear combination of the originals. The number of principal components is less than or equal to the number of original variables.\n\nThe information in a given data set corresponds to the total variation it contains. The goal of PCA is to identify directions (or principal components) along which the variation in the data is maximal.\n\nIn other words, PCA reduces the dimensionality of a multivariate data to two or three principal components, that can be visualized graphically, with minimal loss of information.\n\n# install.packages(c(\"FactoMineR\", \"factoextra\"))\nlibrary(FactoMineR)\nlibrary(factoextra)\n#> Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa\ndata(decathlon2)\n# head(decathlon2)\n\nIn PCA terminology, our data contains :\n\n• Active individuals (in light blue, rows 1:23) : Individuals that are used during the principal component analysis.\n\n• Supplementary individuals (in dark blue, rows 24:27) : The coordinates of these individuals will be predicted using the PCA information and parameters obtained with active individuals/variables\n\n• Active variables (in pink, columns 1:10) : Variables that are used for the principal component analysis.\n\n• Supplementary variables: As supplementary individuals, the coordinates of these variables will be predicted also. These can be:\n\n• Supplementary continuous variables (red): Columns 11 and 12 corresponding respectively to the rank and the points of athletes.\n• Supplementary qualitative variables (green): Column 13 corresponding to the two athlete-tic meetings (2004 Olympic Game or 2004 Decastar). This is a categorical (or factor) variable factor. It can be used to color individuals by groups.\n\nWe start by subsetting active individuals and active variables for the principal component analysis:\n\ndecathlon2.active <- decathlon2[1:23, 1:10]\n#> X100m Long.jump Shot.put High.jump X400m X110m.hurdle\n#> SEBRLE 11.0 7.58 14.8 2.07 49.8 14.7\n#> CLAY 10.8 7.40 14.3 1.86 49.4 14.1\n#> BERNARD 11.0 7.23 14.2 1.92 48.9 15.0\n#> YURKOV 11.3 7.09 15.2 2.10 50.4 15.3\n\n## 4.1 Data standardization\n\nIn principal component analysis, variables are often scaled ( i.e. standardized). This is particularly recommended when variables are measured in different scales (e.g: kilograms, kilometers, centimeters, …); otherwise, the PCA outputs obtained will be severely affected.\n\nThe goal is to make the variables comparable. Generally variables are scaled to have i) standard deviation one and ii) mean zero.\n\nThe function PCA() [FactoMineR package] can be used. A simplified format is:\n\nlibrary(FactoMineR)\nres.pca <- PCA(decathlon2.active, graph = FALSE)\nprint(res.pca)\n#> **Results for the Principal Component Analysis (PCA)**\n#> The analysis was performed on 23 individuals, described by 10 variables\n#> *The results are available in the following objects:\n#>\n#> name description\n#> 1 \"$eig\" \"eigenvalues\" #> 2 \"$var\" \"results for the variables\"\n#> 3 \"$var$coord\" \"coord. for the variables\"\n#> 4 \"$var$cor\" \"correlations variables - dimensions\"\n#> 5 \"$var$cos2\" \"cos2 for the variables\"\n#> 6 \"$var$contrib\" \"contributions of the variables\"\n#> 7 \"$ind\" \"results for the individuals\" #> 8 \"$ind$coord\" \"coord. for the individuals\" #> 9 \"$ind$cos2\" \"cos2 for the individuals\" #> 10 \"$ind$contrib\" \"contributions of the individuals\" #> 11 \"$call\" \"summary statistics\"\n#> 12 \"$call$centre\" \"mean of the variables\"\n#> 13 \"$call$ecart.type\" \"standard error of the variables\"\n#> 14 \"$call$row.w\" \"weights for the individuals\"\n#> 15 \"$call$col.w\" \"weights for the variables\"\n\nThe object that is created using the function PCA() contains many information found in many different lists and matrices. These values are described in the next section.\n\n## 4.2 Eigenvalues / Variances\n\nAs described in previous sections, the eigenvalues measure the amount of variation retained by each principal component. Eigenvalues are large for the first PCs and small for the subsequent PCs. That is, the first PCs corresponds to the directions with the maximum amount of variation in the data set.\n\nWe examine the eigenvalues to determine the number of principal components to be considered. The eigenvalues and the proportion of variances (i.e., information) retained by the principal components (PCs) can be extracted using the function get_eigenvalue() [factoextra package].\n\nlibrary(factoextra)\neig.val <- get_eigenvalue(res.pca)\neig.val\n#> eigenvalue variance.percent cumulative.variance.percent\n#> Dim.1 4.124 41.24 41.2\n#> Dim.2 1.839 18.39 59.6\n#> Dim.3 1.239 12.39 72.0\n#> Dim.4 0.819 8.19 80.2\n#> Dim.5 0.702 7.02 87.2\n#> Dim.6 0.423 4.23 91.5\n#> Dim.7 0.303 3.03 94.5\n#> Dim.8 0.274 2.74 97.2\n#> Dim.9 0.155 1.55 98.8\n#> Dim.10 0.122 1.22 100.0\n\nThe sum of all the eigenvalues give a total variance of 10.\n\nThe proportion of variation explained by each eigenvalue is given in the second column. For example, 4.124 divided by 10 equals 0.4124, or, about 41.24% of the variation is explained by this first eigenvalue. The cumulative percentage explained is obtained by adding the successive proportions of variation explained to obtain the running total. For instance, 41.242% plus 18.385% equals 59.627%, and so forth. Therefore, about 59.627% of the variation is explained by the first two eigenvalues together.\n\nUnfortunately, there is no well-accepted objective way to decide how many principal components are enough. This will depend on the specific field of application and the specific data set. In practice, we tend to look at the first few principal components in order to find interesting patterns in the data.\n\nIn our analysis, the first three principal components explain 72% of the variation. This is an acceptably large percentage.\n\nAn alternative method to determine the number of principal components is to look at a Scree Plot, which is the plot of eigenvalues ordered from largest to the smallest. The number of component is determined at the point, beyond which the remaining eigenvalues are all relatively small and of comparable size (Jollife 2002, Peres-Neto, Jackson, and Somers (2005)).\n\nThe scree plot can be produced using the function fviz_eig() or fviz_screeplot() [factoextra package].\n\nfviz_eig(res.pca, addlabels = TRUE, ylim = c(0, 50))", null, "From the plot above, we might want to stop at the fifth principal component. 87% of the information (variances) contained in the data are retained by the first five principal components.\n\n## 4.3 Graph of variables\n\nResults A simple method to extract the results, for variables, from a PCA output is to use the function get_pca_var() [factoextra package]. This function provides a list of matrices containing all the results for the active variables (coordinates, correlation between variables and axes, squared cosine and contributions)\n\nvar <- get_pca_var(res.pca)\nvar\n#> Principal Component Analysis Results for variables\n#> ===================================================\n#> Name Description\n#> 1 \"$coord\" \"Coordinates for the variables\" #> 2 \"$cor\" \"Correlations between variables and dimensions\"\n#> 3 \"$cos2\" \"Cos2 for the variables\" #> 4 \"$contrib\" \"contributions of the variables\"\n\nThe components of the get_pca_var() can be used in the plot of variables as follow:\n\n• var$coord: coordinates of variables to create a scatter plot • var$cos2: represents the quality of representation for variables on the factor map. It’s calculated as the squared coordinates: var.cos2 = var.coord * var.coord.\n• var$contrib: contains the contributions (in percentage) of the variables to the principal components. The contribution of a variable (var) to a given principal component is (in percentage) : (var.cos2 * 100) / (total cos2 of the component). Note that, it’s possible to plot variables and to color them according to either i) their quality on the factor map (cos2) or ii) their contribution values to the principal components (contrib). The different components can be accessed as follow: # Coordinates head(var$coord)\n#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5\n#> X100m -0.851 -0.1794 0.302 0.0336 -0.194\n#> Long.jump 0.794 0.2809 -0.191 -0.1154 0.233\n#> Shot.put 0.734 0.0854 0.518 0.1285 -0.249\n#> High.jump 0.610 -0.4652 0.330 0.1446 0.403\n#> X400m -0.702 0.2902 0.284 0.4308 0.104\n#> X110m.hurdle -0.764 -0.0247 0.449 -0.0169 0.224\n# Cos2: quality on the factore map\nhead(var$cos2) #> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> X100m 0.724 0.032184 0.0909 0.001127 0.0378 #> Long.jump 0.631 0.078881 0.0363 0.013315 0.0544 #> Shot.put 0.539 0.007294 0.2679 0.016504 0.0619 #> High.jump 0.372 0.216424 0.1090 0.020895 0.1622 #> X400m 0.492 0.084203 0.0804 0.185611 0.0108 #> X110m.hurdle 0.584 0.000612 0.2015 0.000285 0.0503 # Contributions to the principal components head(var$contrib)\n#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5\n#> X100m 17.54 1.7505 7.34 0.1376 5.39\n#> Long.jump 15.29 4.2904 2.93 1.6249 7.75\n#> Shot.put 13.06 0.3967 21.62 2.0141 8.82\n#> High.jump 9.02 11.7716 8.79 2.5499 23.12\n#> X400m 11.94 4.5799 6.49 22.6509 1.54\n#> X110m.hurdle 14.16 0.0333 16.26 0.0348 7.17\n\nIn this section, we describe how to visualize variables and draw conclusions about their correlations. Next, we highlight variables according to either i) their quality of representation on the factor map or ii) their contributions to the principal components.\n\n## 4.4 Correlation circle\n\nThe correlation between a variable and a principal component (PC) is used as the coordinates of the variable on the PC. The representation of variables differs from the plot of the observations: The observations are represented by their projections, but the variables are represented by their correlations (Abdi and Williams 2010).\n\n# Coordinates of variables\nhead(var$coord, 4) #> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> X100m -0.851 -0.1794 0.302 0.0336 -0.194 #> Long.jump 0.794 0.2809 -0.191 -0.1154 0.233 #> Shot.put 0.734 0.0854 0.518 0.1285 -0.249 #> High.jump 0.610 -0.4652 0.330 0.1446 0.403 To plot variables, type this: fviz_pca_var(res.pca, col.var = \"black\")", null, "The plot above is also known as variable correlation plots. It shows the relationships between all variables. It can be interpreted as follow: • Positively correlated variables are grouped together. • Negatively correlated variables are positioned on opposite sides of the plot origin (opposed quadrants). • The distance between variables and the origin measures the quality of the variables on the factor map. Variables that are away from the origin are well represented on the factor map. ## 4.5 Quality of representation The quality of representation of the variables on factor map is called cos2 (square cosine, squared coordinates) . You can access to the cos2 as follow: head(var$cos2, 4)\n#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5\n#> X100m 0.724 0.03218 0.0909 0.00113 0.0378\n#> Long.jump 0.631 0.07888 0.0363 0.01331 0.0544\n#> Shot.put 0.539 0.00729 0.2679 0.01650 0.0619\n#> High.jump 0.372 0.21642 0.1090 0.02089 0.1622\n\nYou can visualize the cos2 of variables on all the dimensions using the corrplot package:\n\nlibrary(corrplot)\ncorrplot(var$cos2, is.corr=FALSE)", null, "It’s also possible to create a bar plot of variables cos2 using the function fviz_cos2() [in factoextra]: # Total cos2 of variables on Dim.1 and Dim.2 fviz_cos2(res.pca, choice = \"var\", axes = 1:2)", null, "Note that, • A high cos2 indicates a good representation of the variable on the principal component. In this case the variable is positioned close to the circumference of the correlation circle. • A low cos2 indicates that the variable is not perfectly represented by the PCs. In this case the variable is close to the center of the circle. For a given variable, the sum of the cos2 on all the principal components is equal to one. If a variable is perfectly represented by only two principal components (Dim.1 & Dim.2), the sum of the cos2 on these two PCs is equal to one. In this case the variables will be positioned on the circle of correlations. For some of the variables, more than 2 components might be required to perfectly represent the data. In this case the variables are positioned inside the circle of correlations. In summary: • The cos2 values are used to estimate the quality of the representation • The closer a variable is to the circle of correlations, the better its representation on the factor map (and the more important it is to interpret these components) • Variables that are closed to the center of the plot are less important for the first components. It’s possible to color variables by their cos2 values using the argument col.var = \"cos2\". This produces a gradient colors. In this case, the argument gradient.cols can be used to provide a custom color. For instance, gradient.cols = c(“white”, “blue”, “red”) means that: • variables with low cos2 values will be colored in “white” • variables with mid cos2 values will be colored in “blue” • variables with high cos2 values will be colored in red # Color by cos2 values: quality on the factor map fviz_pca_var(res.pca, col.var = \"cos2\", gradient.cols = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), repel = TRUE # Avoid text overlapping )", null, "Note that, it’s also possible to change the transparency of the variables according to their cos2 values using the option alpha.var = “cos2”. For example, type this: # Change the transparency by cos2 values fviz_pca_var(res.pca, alpha.var = \"cos2\")", null, "## 4.6 Contributions of variables to PCs The contributions of variables in accounting for the variability in a given principal component are expressed in percentage. • Variables that are correlated with PC1 (i.e., Dim.1) and PC2 (i.e., Dim.2) are the most important in explaining the variability in the data set. • Variables that do not correlated with any PC or correlated with the last dimensions are variables with low contribution and might be removed to simplify the overall analysis. The contribution of variables can be extracted as follow : head(var$contrib, 4)\n#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5\n#> X100m 17.54 1.751 7.34 0.138 5.39\n#> Long.jump 15.29 4.290 2.93 1.625 7.75\n#> Shot.put 13.06 0.397 21.62 2.014 8.82\n#> High.jump 9.02 11.772 8.79 2.550 23.12\n\nThe larger the value of the contribution, the more the variable contributes to the component.\n\nIt’s possible to use the function corrplot() [corrplot package] to highlight the most contributing variables for each dimension:\n\nlibrary(\"corrplot\")\n\ngrp <- as.factor(res.km$cluster) # Color variables by groups fviz_pca_var(res.pca, col.var = grp, palette = c(\"#0073C2FF\", \"#EFC000FF\", \"#868686FF\"), legend.title = \"Cluster\")", null, "## 4.9 Dimension description In the section (???)(pca-variable-contributions), we described how to highlight variables according to their contributions to the principal components. Note also that, the function dimdesc() [in FactoMineR], for dimension description, can be used to identify the most significantly associated variables with a given principal component . It can be used as follow: res.desc <- dimdesc(res.pca, axes = c(1,2), proba = 0.05) # Description of dimension 1 res.desc$Dim.1\n#> $quanti #> correlation p.value #> Long.jump 0.794 6.06e-06 #> Discus 0.743 4.84e-05 #> Shot.put 0.734 6.72e-05 #> High.jump 0.610 1.99e-03 #> Javeline 0.428 4.15e-02 #> X400m -0.702 1.91e-04 #> X110m.hurdle -0.764 2.20e-05 #> X100m -0.851 2.73e-07 #> #> attr(,\"class\") #> \"condes\" \"list \" # Description of dimension 2 res.desc$Dim.2\n#> $quanti #> correlation p.value #> Pole.vault 0.807 3.21e-06 #> X1500m 0.784 9.38e-06 #> High.jump -0.465 2.53e-02 #> #> attr(,\"class\") #> \"condes\" \"list \" ## 4.10 Graph of individuals Results The results, for individuals can be extracted using the function get_pca_ind()[factoextra package]. Similarly to the get_pca_var(), the function get_pca_ind() provides a list of matrices containing all the results for the individuals (coordinates, correlation between individuals and axes, squared cosine and contributions) ind <- get_pca_ind(res.pca) ind #> Principal Component Analysis Results for individuals #> =================================================== #> Name Description #> 1 \"$coord\" \"Coordinates for the individuals\"\n#> 2 \"$cos2\" \"Cos2 for the individuals\" #> 3 \"$contrib\" \"contributions of the individuals\"\n\n# Coordinates of individuals\nhead(ind$coord) #> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> SEBRLE 0.196 1.589 0.642 0.0839 1.1683 #> CLAY 0.808 2.475 -1.387 1.2984 -0.8250 #> BERNARD -1.359 1.648 0.201 -1.9641 0.0842 #> YURKOV -0.889 -0.443 2.530 0.7129 0.4078 #> ZSIVOCZKY -0.108 -2.069 -1.334 -0.1015 -0.2015 #> McMULLEN 0.121 -1.014 -0.863 1.3416 1.6215 # Quality of individuals head(ind$cos2)\n#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5\n#> SEBRLE 0.00753 0.4975 0.08133 0.00139 0.268903\n#> CLAY 0.04870 0.4570 0.14363 0.12579 0.050785\n#> BERNARD 0.19720 0.2900 0.00429 0.41182 0.000757\n#> YURKOV 0.09611 0.0238 0.77823 0.06181 0.020228\n#> ZSIVOCZKY 0.00157 0.5764 0.23975 0.00139 0.005465\n#> McMULLEN 0.00218 0.1522 0.11014 0.26649 0.389262\n# Contributions of individuals\nhead(ind$contrib) #> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> SEBRLE 0.0403 5.971 1.448 0.0373 8.4589 #> CLAY 0.6881 14.484 6.754 8.9446 4.2179 #> BERNARD 1.9474 6.423 0.141 20.4682 0.0439 #> YURKOV 0.8331 0.463 22.452 2.6966 1.0308 #> ZSIVOCZKY 0.0123 10.122 6.246 0.0547 0.2515 #> McMULLEN 0.0155 2.431 2.610 9.5506 16.2949 ## 4.11 Plots: quality and contribution The fviz_pca_ind() is used to produce the graph of individuals. To create a simple plot, type this: fviz_pca_ind(res.pca)", null, "Like variables, it’s also possible to color individuals by their cos2 values: fviz_pca_ind(res.pca, col.ind = \"cos2\", gradient.cols = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), repel = TRUE # Avoid text overlapping (slow if many points) )", null, "Note that, individuals that are similar are grouped together on the plot. You can also change the point size according the cos2 of the corresponding individuals: fviz_pca_ind(res.pca, pointsize = \"cos2\", pointshape = 21, fill = \"#E7B800\", repel = TRUE # Avoid text overlapping (slow if many points) )", null, "To change both point size and color by cos2, try this: fviz_pca_ind(res.pca, col.ind = \"cos2\", pointsize = \"cos2\", gradient.cols = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), repel = TRUE # Avoid text overlapping (slow if many points) )", null, "To create a bar plot of the quality of representation (cos2) of individuals on the factor map, you can use the function fviz_cos2() as previously described for variables: fviz_cos2(res.pca, choice = \"ind\")", null, "To visualize the contribution of individuals to the first two principal components, type this: # Total contribution on PC1 and PC2 fviz_contrib(res.pca, choice = \"ind\", axes = 1:2)", null, "## 4.12 Color by a custom continuous variable As for variables, individuals can be colored by any custom continuous variable by specifying the argument col.ind. For example, type this: # Create a random continuous variable of length 23, # Same length as the number of active individuals in the PCA set.seed(123) my.cont.var <- rnorm(23) # Color individuals by the continuous variable fviz_pca_ind(res.pca, col.ind = my.cont.var, gradient.cols = c(\"blue\", \"yellow\", \"red\"), legend.title = \"Cont.Var\")", null, "## 4.13 Color by groups Here, we describe how to color individuals by group. Additionally, we show how to add concentration ellipses and confidence ellipses by groups. For this, we’ll use the iris data as demo data sets. Iris data sets look like this: head(iris, 3) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3.0 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa The column “Species” will be used as grouping variable. We start by computing principal component analysis as follow: # The variable Species (index = 5) is removed # before PCA analysis iris.pca <- PCA(iris[,-5], graph = FALSE) In the R code below: the argument habillage or col.ind can be used to specify the factor variable for coloring the individuals by groups. To add a concentration ellipse around each group, specify the argument addEllipses = TRUE. The argument palette can be used to change group colors. fviz_pca_ind(iris.pca, geom.ind = \"point\", # show points only (nbut not \"text\") col.ind = iris$Species, # color by groups\npalette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\naddEllipses = TRUE, # Concentration ellipses\nlegend.title = \"Groups\"\n)", null, "To remove the group mean point, specify the argument mean.point = FALSE. If you want confidence ellipses instead of concentration ellipses, use ellipse.type = “confidence”.\n\n# Add confidence ellipses\nfviz_pca_ind(iris.pca, geom.ind = \"point\", col.ind = iris$Species, palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), addEllipses = TRUE, ellipse.type = \"confidence\", legend.title = \"Groups\" )", null, "Note that, allowed values for palette include: • “grey” for grey color palettes; • brewer palettes e.g. “RdBu”, “Blues”, …; To view all, type this in R: RColorBrewer::display.brewer.all(). • custom color palette e.g. c(“blue”, “red”); and scientific journal palettes from ggsci R package, e.g.: “npg”, “aaas”, * “lancet”, “jco”, “ucscgb”, “uchicago”, “simpsons” and “rickandmorty”. For example, to use the jco (journal of clinical oncology) color palette, type this: fviz_pca_ind(iris.pca, label = \"none\", # hide individual labels habillage = iris$Species, # color by groups\naddEllipses = TRUE, # Concentration ellipses\npalette = \"jco\"\n)", null, "## 4.14 Graph customization\n\nNote that, fviz_pca_ind() and fviz_pca_var() and related functions are wrapper around the core function fviz() [in factoextra]. fviz() is a wrapper around the function ggscatter() [in ggpubr]. Therefore, further arguments, to be passed to the function fviz() and ggscatter(), can be specified in fviz_pca_ind() and fviz_pca_var().\n\nHere, we present some of these additional arguments to customize the PCA graph of variables and individuals.\n\n### 4.14.1 Dimensions\n\nBy default, variables/individuals are represented on dimensions 1 and 2. If you want to visualize them on dimensions 2 and 3, for example, you should specify the argument axes = c(2, 3).\n\n# Variables on dimensions 2 and 3\nfviz_pca_var(res.pca, axes = c(2, 3))\n# Individuals on dimensions 2 and 3\nfviz_pca_ind(res.pca, axes = c(2, 3))", null, "", null, "Plot elements: point, text, arrow The argument geom (for geometry) and derivatives are used to specify the geometry elements or graphical elements to be used for plotting.\n\n1. geom.var: a text specifying the geometry to be used for plotting variables. Allowed values are the combination of c(“point”, “arrow”, “text”).\n• Use geom.var = “point”, to show only points;\n• Use geom.var = “text” to show only text labels;\n• Use geom.var = c(“point”, “text”) to show both points and text labels\n• Use geom.var = c(“arrow”, “text”) to show arrows and labels (default).\n\nFor example, type this:\n\n# Show variable points and text labels\nfviz_pca_var(res.pca, geom.var = c(\"point\", \"text\"))", null, "# Show individuals text labels only\nfviz_pca_ind(res.pca, geom.ind = \"text\")", null, "## 4.15 Size and shape of plot elements\n\n# Change the size of arrows an labels\nfviz_pca_var(res.pca, arrowsize = 1, labelsize = 5,\nrepel = TRUE)\n# Change points size, shape and fill color\n# Change labelsize\nfviz_pca_ind(res.pca,\npointsize = 3, pointshape = 21, fill = \"lightblue\",\nlabelsize = 5, repel = TRUE)", null, "", null, "## 4.16 Ellipses\n\n# Add confidence ellipses\nfviz_pca_ind(iris.pca, geom.ind = \"point\",\ncol.ind = iris$Species, # color by groups palette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"), addEllipses = TRUE, ellipse.type = \"confidence\", legend.title = \"Groups\" ) # Convex hull fviz_pca_ind(iris.pca, geom.ind = \"point\", col.ind = iris$Species, # color by groups\npalette = c(\"#00AFBB\", \"#E7B800\", \"#FC4E07\"),\naddEllipses = TRUE, ellipse.type = \"convex\",\nlegend.title = \"Groups\"\n)", null, "", null, "## 4.17 Group mean points\n\nWhen coloring individuals by groups (section (???)(color-ind-by-groups)), the mean points of groups (barycenters) are also displayed by default.\n\nTo remove the mean points, use the argument mean.point = FALSE.\n\nfviz_pca_ind(iris.pca,\ngeom.ind = \"point\", # show points only (but not \"text\")\ngroup.ind = iris$Species, # color by groups legend.title = \"Groups\", mean.point = FALSE)", null, "## 4.18 Axis lines fviz_pca_var(res.pca, axes.linetype = \"blank\")", null, "## 4.19 Graphical parameters To change easily the graphical of any ggplots, you can use the function ggpar() [ggpubr package] ind.p <- fviz_pca_ind(iris.pca, geom = \"point\", col.ind = iris$Species)\nggpubr::ggpar(ind.p,\ntitle = \"Principal Component Analysis\",\nsubtitle = \"Iris data set\",\ncaption = \"Source: factoextra\",\nxlab = \"PC1\", ylab = \"PC2\",\nlegend.title = \"Species\", legend.position = \"top\",\nggtheme = theme_gray(), palette = \"jco\"\n)", null, "## 4.20 Biplot\n\nTo make a simple biplot of individuals and variables, type this:\n\nfviz_pca_biplot(res.pca, repel = TRUE,\ncol.var = \"#2E9FDF\", # Variables color\ncol.ind = \"#696969\" # Individuals color\n)", null, "Note that, the biplot might be only useful when there is a low number of variables and individuals in the data set; otherwise the final plot would be unreadable.\n\nNote also that, the coordinate of individuals and variables are not constructed on the same space. Therefore, in the biplot, you should mainly focus on the direction of variables but not on their absolute positions on the plot.\n\nRoughly speaking a biplot can be interpreted as follow: * an individual that is on the same side of a given variable has a high value for this variable; * an individual that is on the opposite side of a given variable has a low value for this variable.\n\nfviz_pca_biplot(iris.pca,\ncol.ind = iris$Species, palette = \"jco\", addEllipses = TRUE, label = \"var\", col.var = \"black\", repel = TRUE, legend.title = \"Species\")", null, "In the following example, we want to color both individuals and variables by groups. The trick is to use pointshape = 21 for individual points. This particular point shape can be filled by a color using the argument fill.ind. The border line color of individual points is set to “black” using col.ind. To color variable by groups, the argument col.var will be used. To customize individuals and variable colors, we use the helper functions fill_palette() and color_palette() [in ggpubr package]. fviz_pca_biplot(iris.pca, # Fill individuals by groups geom.ind = \"point\", pointshape = 21, pointsize = 2.5, fill.ind = iris$Species,\ncol.ind = \"black\",\n# Color variable by groups\ncol.var = factor(c(\"sepal\", \"sepal\", \"petal\", \"petal\")),\n\nlegend.title = list(fill = \"Species\", color = \"Clusters\"),\nrepel = TRUE # Avoid label overplotting\n)+\nggpubr::fill_palette(\"jco\")+ # Indiviual fill color\nggpubr::color_palette(\"npg\") # Variable colors", null, "Another complex example is to color individuals by groups (discrete color) and variables by their contributions to the principal components (gradient colors). Additionally, we’ll change the transparency of variables by their contributions using the argument alpha.var.\n\nfviz_pca_biplot(iris.pca,\n# Individuals\ngeom.ind = \"point\",\nfill.ind = iris$Species, col.ind = \"black\", pointshape = 21, pointsize = 2, palette = \"jco\", addEllipses = TRUE, # Variables alpha.var =\"contrib\", col.var = \"contrib\", gradient.cols = \"RdYlBu\", legend.title = list(fill = \"Species\", color = \"Contrib\", alpha = \"Contrib\") )", null, "## 4.21 Supplementary elements Definition and types As described above (section (???)(pca-data-format)), the decathlon2 datasets contain supplementary continuous variables (quanti.sup, columns 11:12), supplementary qualitative variables (quali.sup, column 13) and supplementary individuals (ind.sup, rows 24:27). Supplementary variables and individuals are not used for the determination of the principal components. Their coordinates are predicted using only the information provided by the performed principal component analysis on active variables/individuals. Specification in PCA To specify supplementary individuals and variables, the function PCA() can be used as follow: res.pca <- PCA(decathlon2, ind.sup = 24:27, quanti.sup = 11:12, quali.sup = 13, graph=FALSE) ## 4.22 Quantitative variables Predicted results (coordinates, correlation and cos2) for the supplementary quantitative variables: res.pca$quanti.sup\n#> $coord #> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> Rank -0.701 -0.2452 -0.183 0.0558 -0.0738 #> Points 0.964 0.0777 0.158 -0.1662 -0.0311 #> #>$cor\n#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5\n#> Rank -0.701 -0.2452 -0.183 0.0558 -0.0738\n#> Points 0.964 0.0777 0.158 -0.1662 -0.0311\n#>\n#> $cos2 #> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> Rank 0.492 0.06012 0.0336 0.00311 0.00545 #> Points 0.929 0.00603 0.0250 0.02763 0.00097 Visualize all variables (active and supplementary ones): fviz_pca_var(res.pca)", null, "Note that, by default, supplementary quantitative variables are shown in blue color and dashed lines. Further arguments to customize the plot: # Change color of variables fviz_pca_var(res.pca, col.var = \"black\", # Active variables col.quanti.sup = \"red\" # Suppl. quantitative variables ) # Hide active variables on the plot, # show only supplementary variables fviz_pca_var(res.pca, invisible = \"var\") # Hide supplementary variables fviz_pca_var(res.pca, invisible = \"quanti.sup\")", null, "", null, "", null, "Using the fviz_pca_var(), the quantitative supplementary variables are displayed automatically on the correlation circle plot. Note that, you can add the quanti.sup variables manually, using the fviz_add() function, for further customization. An example is shown below. # Plot of active variables p <- fviz_pca_var(res.pca, invisible = \"quanti.sup\") # Add supplementary active variables fviz_add(p, res.pca$quanti.sup$coord, geom = c(\"arrow\", \"text\"), color = \"red\")", null, "## 4.23 Individuals Predicted results for the supplementary individuals (ind.sup): res.pca$ind.sup\n#> $coord #> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> KARPOV 0.795 0.7795 -1.633 1.724 -0.7507 #> WARNERS -0.386 -0.1216 -1.739 -0.706 -0.0323 #> Nool -0.559 1.9775 -0.483 -2.278 -0.2546 #> Drews -1.109 0.0174 -3.049 -1.534 -0.3264 #> #>$cos2\n#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5\n#> KARPOV 0.0510 4.91e-02 0.2155 0.2403 0.045549\n#> WARNERS 0.0242 2.40e-03 0.4904 0.0809 0.000169\n#> Nool 0.0290 3.62e-01 0.0216 0.4811 0.006008\n#> Drews 0.0921 2.27e-05 0.6956 0.1762 0.007974\n#>\n#> $dist #> KARPOV WARNERS Nool Drews #> 3.52 2.48 3.28 3.66 Visualize all individuals (active and supplementary ones). On the graph, you can add also the supplementary qualitative variables (quali.sup), which coordinates is accessible using res.pca$$quali.supp$$coord. p <- fviz_pca_ind(res.pca, col.ind.sup = \"blue\", repel = TRUE) p <- fviz_add(p, res.pca$quali.sup$coord, color = \"red\") p", null, "Supplementary individuals are shown in blue. The levels of the supplementary qualitative variable are shown in red color. ## 4.24 Qualitative variables In the previous section, we showed that you can add the supplementary qualitative variables on individuals plot using fviz_add(). Note that, the supplementary qualitative variables can be also used for coloring individuals by groups. This can help to interpret the data. The data sets decathlon2 contain a supplementary qualitative variable at columns 13 corresponding to the type of competitions. The results concerning the supplementary qualitative variable are: res.pca$quali\n#> $coord #> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> Decastar -1.34 0.122 -0.0379 0.181 0.134 #> OlympicG 1.23 -0.112 0.0347 -0.166 -0.123 #> #>$cos2\n#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5\n#> Decastar 0.905 0.00744 0.00072 0.0164 0.00905\n#> OlympicG 0.905 0.00744 0.00072 0.0164 0.00905\n#>\n#> $v.test #> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 #> Decastar -2.97 0.403 -0.153 0.897 0.72 #> OlympicG 2.97 -0.403 0.153 -0.897 -0.72 #> #>$dist\n#> Decastar OlympicG\n#> 1.41 1.29\n#>\n#> \\$eta2\n#> Dim.1 Dim.2 Dim.3 Dim.4 Dim.5\n#> Competition 0.401 0.0074 0.00106 0.0366 0.0236\n\nTo color individuals by a supplementary qualitative variable, the argument habillage is used to specify the index of the supplementary qualitative variable. Historically, this argument name comes from the FactoMineR package. It’s a french word meaning “dressing” in english. To keep consistency between FactoMineR and factoextra, we decided to keep the same argument name\n\nfviz_pca_ind(res.pca, habillage = 13,\npalette = \"jco\", repel = TRUE)", null, "Recall that, to remove the mean points of groups, specify the argument mean.point = FALSE.\n\n## 4.25 Filtering results\n\nIf you have many individuals/variable, it’s possible to visualize only some of them using the arguments select.ind and select.var.\n\n# Visualize variable with cos2 >= 0.6\nfviz_pca_var(res.pca, select.var = list(cos2 = 0.6))\n# Top 5 active variables with the highest cos2\nfviz_pca_var(res.pca, select.var= list(cos2 = 5))\n# Select by names\nname <- list(name = c(\"Long.jump\", \"High.jump\", \"X100m\"))\nfviz_pca_var(res.pca, select.var = name)\n# top 5 contributing individuals and variable\nfviz_pca_biplot(res.pca, select.ind = list(contrib = 5),\nselect.var = list(contrib = 5),\nggtheme = theme_minimal())", null, "", null, "", null, "", null, "When the selection is done according to the contribution values, supplementary individuals/variables are not shown because they don’t contribute to the construction of the axes.\n\n## 4.26 Exporting results\n\nExport plots to PDF/PNG files The factoextra package produces a ggplot2-based graphs. To save any ggplots, the standard R code is as follow:\n\n# Print the plot to a pdf file\npdf(\"myplot.pdf\")\nprint(myplot)\ndev.off()\n\nIn the following examples, we’ll show you how to save the different graphs into pdf or png files.\n\nThe first step is to create the plots you want as an R object:\n\n# Scree plot\nscree.plot <- fviz_eig(res.pca)\n# Plot of individuals\nind.plot <- fviz_pca_ind(res.pca)\n# Plot of variables\nvar.plot <- fviz_pca_var(res.pca)\npdf(file.path(data_out_dir, \"PCA.pdf\")) # Create a new pdf device\nprint(scree.plot)\nprint(ind.plot)\nprint(var.plot)\ndev.off() # Close the pdf device\n#> png\n#> 2\n\nNote that, using the above R code will create the PDF file into your current working directory. To see the path of your current working directory, type getwd() in the R console.\n\nTo print each plot to specific png file, the R code looks like this:\n\n# Print scree plot to a png file\npng(file.path(data_out_dir, \"pca-scree-plot.png\"))\nprint(scree.plot)\ndev.off()\n#> png\n#> 2\n# Print individuals plot to a png file\npng(file.path(data_out_dir, \"pca-variables.png\"))\nprint(var.plot)\ndev.off()\n#> png\n#> 2\n# Print variables plot to a png file\npng(file.path(data_out_dir, \"pca-individuals.png\"))\nprint(ind.plot)\ndev.off()\n#> png\n#> 2\n\nAnother alternative, to export ggplots, is to use the function ggexport() [in ggpubr package]. We like ggexport(), because it’s very simple. With one line R code, it allows us to export individual plots to a file (pdf, eps or png) (one plot per page). It can also arrange the plots (2 plot per page, for example) before exporting them. The examples below demonstrates how to export ggplots using ggexport().\n\nExport individual plots to a pdf file (one plot per page):\n\nlibrary(ggpubr)\nggexport(plotlist = list(scree.plot, ind.plot, var.plot),\nfilename = file.path(data_out_dir, \"PCA.pdf\"))\n#> file saved to ../output/data/PCA.pdf\n\nArrange and export. Specify nrow and ncol to display multiple plots on the same page:\n\nggexport(plotlist = list(scree.plot, ind.plot, var.plot),\nnrow = 2, ncol = 2,\nfilename = file.path(data_out_dir, \"PCA.pdf\"))\n#> file saved to ../output/data/PCA.pdf\n\nExport plots to png files. If you specify a list of plots, then multiple png files will be automatically created to hold each plot.\n\nggexport(plotlist = list(scree.plot, ind.plot, var.plot),\nfilename = file.path(data_out_dir, \"PCA.png\"))\n#> \"../output/data/PCA%03d.png\"\n#> file saved to ../output/data/PCA%03d.png\n\n## 4.27 Export results to txt/csv files\n\nAll the outputs of the PCA (individuals/variables coordinates, contributions, etc) can be exported at once, into a TXT/CSV file, using the function write.infile() [in FactoMineR] package:\n\n# Export into a TXT file\nwrite.infile(res.pca, file.path(data_out_dir, \"pca.txt\"), sep = \"\\t\")\n# Export into a CSV file\nwrite.infile(res.pca, file.path(data_out_dir, \"pca.csv\"), sep = \";\")\n\n## 4.28 Summary\n\nIn conclusion, we described how to perform and interpret principal component analysis (PCA). We computed PCA using the PCA() function [FactoMineR]. Next, we used the factoextra R package to produce ggplot2-based visualization of the PCA results.\n\nThere are other functions [packages] to compute PCA in R:\n\n1. Using prcomp() [stats]\nres.pca <- prcomp(iris[, -5], scale. = TRUE)\nres.pca <- princomp(iris[, -5], cor = TRUE)\nlibrary(ade4)\n#>\n#> The following object is masked from 'package:FactoMineR':\n#>\n#> reconst\nres.pca <- dudi.pca(iris[, -5], scannf = FALSE, nf = 5)\n1. Using epPCA() [ExPosition]\nlibrary(ExPosition)\nres.pca <- epPCA(iris[, -5], graph = FALSE)\nfviz_eig(res.pca) # Scree plot\nfviz_pca_var(res.pca) # Graph of variables", null, "", null, "", null, "" ]
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https://uk.answers.yahoo.com/question/index?qid=20200330103638AAgzNZH
[ "# I have \\$20 and I have to add 5.37 percent for a tip. How do i specifically find the answer? How much money do i need to add?\n\nRelevance\n\n20 x 5.37/100 = 1.074\n\n• If chef only has \\$20 he might need \"to dine and dash.\"\n\n•", null, "Log in to reply to the answers\n• I have \\$20 and I have to add 5.37 percent for a tip.\n\nI need to add \\$1.07 for the tip.\n\n• If chef only has \\$20 he might need \"to dine and dash.\"\n\n•", null, "Log in to reply to the answers\n• \\$20 x 1.0537 = \\$21.08\n\n•", null, "Log in to reply to the answers\n• You have \\$20, but you haven't told us how much the bill is. There is no way to compute a tip.\n\nBTW, a tip below 10% (and some would say 15%) would be taken as an insult to the server. If you're going to insult your server, why do you care if it's 5% or 5.37%?\n\n• If chef only has \\$20 he might need \"to dine and dash.\" {I need a life !! }\n\n•", null, "Log in to reply to the answers\n• Multiply .0537 by 20.\n\n•", null, "Log in to reply to the answers\n• To figure it out, you'd need to multiply the bill's amount by the percent tip you want to leave. After you've done the multiplication, you'd get your tip amount. But first you need to convert that percentage figure into a decimal. So to convert a percent to a decimal, you'd divide the percent number it by ______. That would move your decimal point ___ places to the ______.\n\nFYI, some of the others who've posted answers to your question gave you how much total you'd end up paying. So the amount of the bill 𝐩𝐥𝐮𝐬 the tip. And several presumed the amount of the bill itself is \\$20. With the way your question is actually worded, it doesn't sound that way.\n\nIf \\$20 is the total amount you have to spend, that's not necessarily the amount of the bill. So the bill could be \\$11. Or it could be \\$8.50. The point is the bill amount may actually be unknown. And if that's the case, which is what it sounds like to me, then you can still calculate the tip and total you'll end up paying. But it will use algebra because you'll have variables. Either way, the steps to getting the right answers are the same!\n\nSource(s): I've taught math and many other subjects. And have helped lots of students out with problems. Providing them with helpful guidance so they can better understand and figure things out.\n• Yes, if you were spending the entire \\$20, which includes both the bill and tip amounts added together, you'd divide the \\$20 amount to spend by ( 1 + % tip ). That's because the \\$20 total would include the bill's amount and the tip, which is calculated off the bill's amount.\n\n•", null, "Log in to reply to the answers\n• multiply 5.37 times 2 and then divide by 100 and multiply by 10. round answer. 1.07\n\n• or .537 x 2, to bypass your other steps (except the rounding part)\n\n•", null, "Log in to reply to the answers\n• lol.  5.37% is not mandatory. So just leave a dollar. {They probably overcharged you anyway. No math skills - get taken advantage of.}\n\n•", null, "Log in to reply to the answers\n• \"Percent\" are 1/100th.\n\nSo percent are directly equivalent to cents on the dollar or pence on the pound.\n\nAdd \\$0.0537 (5.37 cents) to each dollar:\n\n\\$10 would be \\$0.537, double that for \\$20 = \\$1.074\n\nTo the nearest cent, \\$1.07\n\nAdding that to \\$20 = \\$21.07\n\nWorking with values other than money, you can still use the same methods, it's just numbers after all, regardless of them representing dollars, metres or joules etc.\n\n•", null, "Log in to reply to the answers\n• the tip you give will be \\$1.07. a percent of a number is a portion of it, like a part. for example, 5.37 percent of 100 is 5.37\n\n•", null, "Log in to reply to the answers" ]
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https://cs.stackexchange.com/questions/40120/problem-with-understanding-a-recursion-tree
[ "# Problem with Understanding a Recursion Tree\n\nConsider the recursion tree:\n\n$T(p) = 3T(\\frac{2p}{8}) + 2T(\\frac{p}{8}) + O(p)$.\n\nI determined that there are at most $1 + log_{4}\\ p$ levels, because the longest simple path from root to leaf is $p \\rightarrow \\frac{2p}{8} \\rightarrow \\frac{4p}{16} \\rightarrow \\frac{8p}{32} \\rightarrow\\ ...$.\n\nThis means that the time complexity is $O(p\\ log\\ p)$.\n\nNow, I stopped all my leaves in the above recursion tree have $T(1)$, but say I have the condition $T(0) + O(1)$. Does this change my solution somehow?\n\n• There is no \"time complexity\" here, just a recurrence (class). – Raphael Mar 6 '15 at 6:53\n• I don't understand your last paragraph: I can't parse it as a sentence. – David Richerby Mar 6 '15 at 8:51\n\n• Welcome and thank you for posting but I can't understand what you're saying at all. What do you mean by \"a mathematically formal description of a recursive function\"? What other kind of description would there be? What do you mean by \"Which every function maps...\"? That seems to be a sentence fragment. Why are you trying to express $T(0)$ in terms of some function you've not defined? What wouldn't change the time complexity from what to what? – David Richerby Mar 6 '15 at 8:49" ]
[ null ]
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https://studylib.net/doc/10908403/problem-40-cm--
[ "# Problem 40 cm !", null, "```Problem\n4.\nA circular wire loop 40 cm in diameter has 100- ! resistance and lies in a horizontal plane. A\nuniform magnetic field points vertically downward, and in 25 ms it increases linearly from\n5.0 mT to 55 mT. Find the magnetic flux through the loop at (a) the beginning and (b) the end of the\n25 ms period. (c) What is the loop current during this time?\n(d) Which way does this current flow?\nSolution\n(a) As in the previous solution,\n! B = B &quot; A = 14 # d 2 B = 14 #( 40 cm) 2 (5 mT) = 6.28 \\$ 10%4 Wb at t1 = 0, and\n&quot;3\n(b) 6.91 ! 10\nWb at t2 = 25 ms. (c) Since the field increases linearly,\n!3\nd !B =dt = &quot;! B =&quot;t = (6.91 # 0.628) \\$ 10 Wb=25 ms = 0.251 V. From Faraday’s law, this is equal to\nthe magnitude of the induced emf, which causes a current I = E=R = 0.251 V=100 ! = 2.51 mA in the\nloop. (d) The direction must oppose the increase of the external field downward, hence the induced field is\nupward and I is CCW when viewed from above the loop.\nProblem\n6.\nA conducting loop of area A and resistance R lies at right angles to a spatially uniform magnetic field.\nAt time t = 0 the magnetic field and loop current are both zero. Subsequently, the current increases\n2\n2\naccording to I = bt , where b is a constant with the units A/s . Find an expression for the magnetic\nfield strength as a function of time.\nSolution\nThe induced current and the derivative of the magnetic field strength are related as in the previous problem,\n2\n3\ndB=dt = IR=A = (bR=A)t . Integration yields B(t ) = (bR=A)t =3, where B(0) = 0 was specified.\nProblem\n9.\nA square wire loop of side l and resistance R is pulled with constant speed v from a region of no\nmagnetic field until it is fully inside a region of constant, uniform magnetic field B perpendicular to the\nloop plane. The boundary of the field region is parallel to one side of the loop. Find an expression for\nthe total work done by the agent pulling the loop.\nSolution\nThe loop can be treated analogously to the situation analyzed in Section 31-3, under the heading “Motional\nEMF and Lenz’s Law”; instead of exiting, the loop is entering the field region at constant velocity. All\nquantities have the same magnitudes, except the current in the loop is CCW instead of CW, as in Fig. 3113. Since the applied force acts over a displacement equal to the side-length of the loop, the work done can\n2\nbe calculated directly: Wapp = Fapp ! l = (IlB)l = Il B. But,\n2 3\nI = E=R = d !B =dt =R = d=dt(Blx)=R = Blv=R, as before, so Wapp = B l v=R. [Alternatively, the work\ncan be calculated from the conservation of energy:\nI = Blv=R, Pdiss = I 2 R = (Blv ) 2=R, and Wapp = Pdisst = [(Blv )2 =R](l=v ).]\nProblem\n14. A square wire loop 3.0 m on a side is perpendicular to a uniform magnetic field of 2.0 T. A 6-V light\nbulb is in series with the loop, as shown in Fig. 31-45. The magnetic field is reduced steadily to zero\nover a time !t. (a) Find !t such that the light will shine at full brightness during this time. (b) Which\nway will the loop current flow?\nFIGURE 31-45\nProblem 14.\nSolution\n(a) To shine at full brightness, the potential drop across the bulb must be 6 V. This is\nequal to the induced emf, if we neglect the resistance of the rest of the loop circuit. From\nFaraday’s law, E = ! d &quot;B =dt = !d (BA)=dt = A #B =#t. Thus,\n2\n!t = A !B =E = (3 m) (2 T)=6 V = 3 s. (b) The direction of current opposes the decrease of\nB into the page, and thus must act to increase B into the page. From the right-hand rule,\nthis corresponds to a clockwise current in Fig. 31-45.\nProblem\n17. A square conducting loop of side s = 0.50 m and resistance R = 5.0 ! moves to the right with speed\nv = 0.25 m/s. At time t = 0 its rightmost edge enters a uniform magnetic field B = 1.0 T pointing\ninto the page, as shown in\nFig. 31-46. The magnetic field covers a region of width w = 0.75 m. Plot (a) the current and (b) the\npower dissipation in the loop as functions of time, taking a clockwise current as positive and covering\nthe time until the entire loop has exited the field region.\nSolution\nLet x be the distance between the right side of the loop and the left edge of the field region. Take\nt = 0 when x = 0, so that x = v t. The loop enters the field region at t = 0, is completely within the\nregion for t between l=v = 2 s and w=v = 3 s, and is out of the region for t ! (w + l)=v = 5 s.\nFIGURE\n31-46 Problem 17 Solution.\n2\n2\nThe area of loop overlapping the field region increases linearly from 0 to l , stays constant at l , then\ndecreases to 0 between these times. (We use l for side length to avoid confusion with time units.). Thus,\n#0\n#0,\nt '0\n%\n%\n0't '2\n%%v t=l\n%%0.5t,\n! B = BA = Bl 2 \\$1\n= 0.25 Wb \\$1,\n2't '3\n%(w + l &quot; vt )=l\n%0.5(5 &quot; t), 3 ' t ' 5\n%\n%\n%&amp;0\n%&amp;0,\n5' t\n(We substituted the given numerical values and used SI units for flux, with time t in seconds, see solution\nto Problem 3.)\nProblem 17 Solution.\n(a) The induced current (positive clockwise) is given by Faraday’s and Ohm’s laws:\nI = !\n1 d&quot;B\nR dt\n\\$\n&amp;\n&amp;\n&amp;\n= 25 mA %\n&amp;\n&amp;\n&amp;\n'\n0,\n!1,\nt # 0\n0# t # 2\n0,\n+1,\n2# t # 3\n3# t #5\n0,\n5# t\n(b) The power dissipated, I 2 R, is (&plusmn;25 mA) 2 (5 !) = 3.13 mW when the current is not zero.\nProblem\n22. A magnetic field is described by B = B0 sin ! t kˆ , where B0 = 2.0 T and ! = 10 s . A conducting\n&quot;1\n2\nloop with area 150 cm and resistance 5.0 ! lies in the x-y plane. Find the induced current in the loop\n(a) at t = 0 and (b) at t = 0.10 s.\nSolution\nUsing the current given in the next solution, we find (a)\nI(0) = !&quot;B0 A=R = !(10 s!1 )(2 T)(150 cm2 )=(5 #) = !60 mA, and (b)\nI(0.1 s) = !(60 mA) cos[(10 s!1)(0.1 s)] = !60 mAcos(1 radian) = !32.4 mA.\nProblem\n24. A car alternator consists of a 250-turn coil 10 cm in diameter in a magnetic field of 0.10 T. If the\nalternator is turning at 1000 revolutions per minute, what is its peak output voltage?\nSolution\nThe peak output voltage of an electric generator, like the one depicted in Fig. 31-15, was found in Example\n2\n31-6 to be Epeak = 2! f NBA, where A = 14 !d is the loop area in this case. Numerically,\nEpeak = 2!(1000=60 s)(250) &quot; (0.1 T) 14 !( 0.1 m)2 = 20.6 V.\nProblem\n27. Figure 31-49 shows a pair of parallel conducting rails a distance l apart in a uniform magnetic field B.\nA resistance R is connected across the rails, and a conducting bar of negligible resistance is being\npulled along the rails with velocity v to the right. (a) What is the direction of the current in the resistor?\n(b) At what rate must work be done by the agent pulling the bar?\nFIGURE 31-49\nProblem 27.\nSolution\n(a) The force on a (hypothetical) positive charge carrier in the bar, qv ! B, is upward in Fig. 31-49, so\ncurrent will circulate CCW around the loop containing the bar, the resistor, and the rails (i.e., downward in\nthe resistor). (The force per unit positive charge is the motional emf in the bar.) Alternatively, since the area\nenclosed by the circuit, and the magnetic flux through it, are increasing, Lenz’s law requires that the\ninduced current oppose this with an upward induced magnetic field. Thus, from the right-hand rule, the\ninduced current must circulate CCW. (Take the positive sense of circulation around the circuit CW, so that\nthe normal to the area is in the direction of B, into the page.) (b) In Example 31-4, which analyzed the same\nsituation, the current in the bar was found to be I = E =R = Blv=R. Since this is perpendicular to the\nmagnetic field, the magnetic force on the bar is Fmag = IlB (to the left in Fig. 31-49). The agent pulling\nthe bar at constant velocity must exert an equal force in the direction of v, and therefore does work at the\n2\nrate F ! v = IlBv = (Blv ) =R. (Note: The conservation of energy requires that this equal the rate energy is\n2\n2\ndissipated in the resistor (we neglected the resistance of the bar and the rails), I R = (Blv=R) R.)\nProblem\n28. The resistor in the preceding problem is replaced by an ideal voltmeter. (a) To which rail should the\npositive meter terminal be connected to if the meter is to indicate a positive voltage? (b) At what rate\nmust work be done by the agent pulling the bar?\nSolution\n(a) The motional emf (mentioned in part (a) of the previous solution) is upward in the moving bar, and so\nacts like the positive terminal of a battery. Thus, the positive terminal of the voltmeter should be connected\nto the top rail in Fig. 31-49. (b) When an ideal voltmeter replaces the resistor, no current flows (since its\nresistance is infinite), and no work must be done moving the bar. (However, during a brief instant when\ncharge is separating in the bar, work is done.)\nProblem\n30. A toroidal coil of square cross section has inner radius a and outer radius b. It consists of N turns of\nwire and carries a time-varying current I = I 0 sin ! t . A single-turn wire loop encircles the toroid,\npassing through its center hole as shown in Fig. 31-50. Find an expression for the peak emf induced in\nthe loop.\nFIGURE 31-50\nProblem 30.\nSolution\nThe magnetic flux through the loop is the same as the flux in one turn of the toroid. From Equation 30-12,\nB = &micro;0 NI=2! r, and we can take strips of area, dA = (b ! a) dr, over the square cross-section of the\ntoroid, so\n!B =\n&micro;0 NI(b &quot; a)\n2#\n\\$\nb\na\ndr\n&micro; NI\n% b(\n= 0\n(b &quot; a) ln' * .\n&amp; a)\nr\n2#\nFrom Faraday’s law and the given sinusoidal current, the induced emf is proportional to\ndI =dt = !I 0 cos !t, so (Ei) peak = [&micro;0 N! I 0 (b &quot; a)=2# ] ln(b=a).\nProblem\n34. A rectangular conducting loop of resistance R, mass m, and width w falls into a uniform magnetic field\nB, as shown in Fig. 31-52. If the loop is long enough and the field region has a great enough vertical\nextent, the loop will reach a terminal speed. (a) Why? (b) Find an expression for the terminal speed. (c)\nWhat will be the direction of the loop current as the loop enters the field?\nFIGURE\n31-52 Problem 34.\nSolution\n(a) As long as the flux through the loop is changing, there is an upward magnetic force on the induced\ncurrent in the bottom wire, which may cancel the downward force of gravity on the loop. (b) The flux\nthrough the loop is proportional to the vertical distance it falls into the field region (as shown added to Fig.\n31-52), ! B = BA = Bwy, so Faraday’s and Ohm’s laws give a magnetic force proportional to the vertical\nspeed, F = IwB = Ei =R wB = !d &quot; B =dt wB=R = v w 2 B2=R. When this equals mg, the terminal speed is\n2 2\n(\n)\nv t = mgR=w B . (c) The flux of B is positive (into the plane of Fig. 31-52) for clockwise circulation\naround the loop, so the induced current must be negative, or counterclockwise. (The motional emf,\ndqv ! B, on positive charge carriers in the bottom wire, is to the right, and the magnetic force on them,\nIdl ! B, is upward.)\nProblem\n36. The induced electric field 12 cm from the axis of a solenoid with 10 cm radius is 45 V/m. Find the\nrate of change of the solenoid’s magnetic field.\nSolution\nThe geometry of the induced electric field from the solenoid is described in Example 31-9, where\n2!r E = &quot;d(!R2 B)=dt =\n!R2 dB=dt . Thus, dB=dt = 2r E =R2 = 2(12 cm)( 45 V/m)=(10 cm)2 = 1.08 &quot; 103 T/s = 1.08 T/ms. (The\nsign of dB=dt and the direction of the induced electric field are related by Lenz’s law.)\nProblem\n38. Figure 31-53 shows a top view of a tokamak. The magnetic field in the center is confined to a circular\narea of radius 50 cm, and during a pulse it increases at the rate of 5.1 T/ms. (a) What is the\nmagnitude of the induced electric field in the tokamak, 1.2 m from the center of the field region in\nFig. 31-53? (b) What is the field direction? (c) If a proton circles the tokamak once at this radius, going\nwith the electric field, how much energy does it gain?\nFIGURE\n31-53 Problem 38.\nSolution\n(a) Assume that the induced electric field is axially symmetric, so that the lines of E encircle the magnetic\nfield region. Then Faraday’s law (as in Example 31-9) gives\n! E &quot; dl = 2#rE = \\$d%\nB =dt\n2\n2\n= \\$d(#R B)=dt = \\$#R dB=dt, so E =\n(R2=2r ) dB=dt = (50 cm)2 (5.1 T/ms)=(2 ! 1.2 m) = 531 V/m. (b) E must be CCW in Fig. 31-53 in order\nto oppose the increase of magnetic field into the page. (c) The induced emf is\nE = 2!r E = 2!(1.2 m)(531 V/m) = 4.01 kV so a proton (of charge e) would gain energy\ne E = 4.01 keV = 6.41 ! 10&quot;1 6 J in one complete circuit.\nProblem\n41. Figure 31-56 shows a magnetic field pointing into the page; the field is confined to a layer of thickness\nh in the vertical direction but extends infinitely to the left and right. The field strength is increasing\nwith time: B = bt, where b is a constant. Find an expression for the electric field at all points outside\nthe field region. Hint: Consult Example 30-5.\nFIGURE\n31-56 Problem 41.\nSolution\nA changing magnetic field acts as a source for an induced electric field, just like a current density is a\nsource for a magnetic field. In fact, Faraday’s law (for a loop fixed in space),\n!\n!\nloop E\n&quot; dl = #d \\$ B =dt = !surface (% B=%t ) &quot; dA, is analogous to Amp&egrave;re’s law,\nloop B\n&quot; dl = &micro;0 I encircled = !surface &micro;0 J &quot; dA (compare E and ! B=! t with B and &micro;0 J ). The geometry of\nthe source and symmetry of the field in Fig. 31-56 is similar to that in Fig. 30-25 for an infinite current\nsheet. The induced electric field, E, should have the same magnitude above and below the source region for\n! B=! t, and should circulate in a CCW sense so as to oppose the increase of flux into the page (i.e., CCW\ncirculation is out of the page, opposite to the normal to an area into the page). For the rectangular loop\n!\nloop E\n&quot; dl = 2El = !area ( &quot;B=&quot;t ) dA = ( &quot;B=&quot;t )lh, or E =\n1\n2\n&quot;B=&quot;t h =\n1\n2\nbh.\nProblem\n50. A copper disk 90 cm in diameter is spinning at 3600 rpm about a conducting axle through its center,\nas shown in\nFig. 31-59. A uniform 1.5-T magnetic field is perpendicular to the disk, as shown. A stationary\nconducting brush maintains contact with the disk’s rim, and a voltmeter is connected between the\nbrush and the axle. (a) What does the voltmeter read? (b) Which voltmeter lead is positive?\nFIGURE\n31-59 Problem 50.\nSolution\nThe result of the previous problem can be used, although the motional emf argument makes more sense in\nthe case of a disk. (b) Since ω is parallel to B (rather than anti-parallel as in the previous problem) the rim\nis positive relative to the axle. (v ! B = +v Brˆ in Fig. 31-59.) (a) The voltmeter reading equals the\nmagnitude of the induced emf (since no current flows if the voltmeter’s resistance is very large)\nE = 12 ! R2 B = 12 (3600 &quot; 2#=60 s)(0.45 m)2 (1.5 T) = 57.3 V.\nProblem\n58. A circular wire loop of resistance R and radius a lies with its plane perpendicular to a uniform\nmagnetic field. The field strength changes from an initial value B1 to a final value B2. Show, by\nintegrating the loop current over time, that the total charge that moves around the ring is\nq=\n! a2\n(B2 &quot; B1 ).\nR\nNote that this result is independent of how the field changes with time.\nSolution\nThe magnitude of the induced current is I = E=R = !d &quot; B =dt =R = ( #a2 =R) dB=dt , since for a fixed\ncircular loop perpendicular to B, ! B = BA. In terms of the charge moving through the circuit during the\ninterval the magnetic field is changing, I = dq=dt, therefore\nq = !21 dq = ( &quot; a2=R) # !21 dB = (&quot; a2 =R)(B2 \\$ B1 ).\n```" ]
[ null, "https://s2.studylib.net/store/data/010908403_1-384ee5b8d6bd36c918f7fe5c58850471.png", null ]
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https://pypi.org/project/lrgs/
[ "Linear Regression by Gibbs Sampling\n\n## Project description", null, "", null, "", null, "## LRGS: Linear Regression by Gibbs Sampling\n\nCode implementing a Gibbs sampler to deal with the problem of multivariate linear regression with uncertainties in all measured quantities and intrinsic scatter. Full details can be found in this paper, the abstract of which appears below. (The paper describes an implementation in the R language, while this package is a port of the method to Python.)\n\nKelly (2007, hereafter K07) described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modeled by a flexible mixture of Gaussians rather than assumed to be uniform. Here I extend the K07 algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Second, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamically relaxed galaxy clusters as a function of their mass and redshift. An implementation of the Gibbs sampler in the R language, called LRGS, is provided.\n\nFor questions, comments, requests, problems, etc. use the GitHub issues.\n\n### Status\n\nLRGS for Python is currently in alpha. It has not been fully vetted, and some features of the R version are not implemented (see VERSION.md).\n\n### Installation\n\n#### Automatic\n\nInstall from PyPI by running pip install lrgs.\n\n#### Manual\n\nDownload lrgs/lrgs.py and put it somewhere on your PYTHONPATH. You will need to have the numpy and scipy packages installed.\n\n### Usage and Help\n\nDocumentation is sparse at this point, but an example notebook can be found here.\n\n## Project details\n\nThis version", null, "0.0.2" ]
[ null, "https://warehouse-camo.cmh1.psfhosted.org/3f01e16a36f2636af1959652456de7aab4f88d3d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6173636c2d313630322e3030352d626c75652e7376673f636f6c6f72423d323632323535", null, "https://warehouse-camo.cmh1.psfhosted.org/10389e9a0624bdf5d3d033c9f280b0987a132b22/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c7267732e737667", null, "https://warehouse-camo.cmh1.psfhosted.org/12fb4ddb7f4affce5c0a7890dcbdb6ca0a4605b3/68747470733a2f2f696d672e736869656c64732e696f2f707970692f6c2f6c7267732e737667", null, "https://pypi.org/static/images/blue-cube.e6165d35.svg", null ]
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https://gmatclub.com/forum/if-a-b-and-c-are-nonzero-numbers-and-a-b-c-which-of-t-167601.html
[ "Summer is Coming! Join the Game of Timers Competition to Win Epic Prizes. Registration is Open. Game starts Mon July 1st.\n\n It is currently 23 Jul 2019, 07:34", null, "### GMAT Club Daily Prep\n\n#### Thank you for using the timer - this advanced tool can estimate your performance and suggest more practice questions. We have subscribed you to Daily Prep Questions via email.\n\nCustomized\nfor You\n\nwe will pick new questions that match your level based on your Timer History\n\nTrack\n\nevery week, we’ll send you an estimated GMAT score based on your performance\n\nPractice\nPays\n\nwe will pick new questions that match your level based on your Timer History\n\n#### Not interested in getting valuable practice questions and articles delivered to your email? No problem, unsubscribe here.", null, "", null, "# If a, b, and c are nonzero numbers and a + b = c, which of t\n\nAuthor Message\nTAGS:\n\n### Hide Tags\n\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 56366\nIf a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\n1\n3", null, "00:00\n\nDifficulty:", null, "", null, "", null, "5% (low)\n\nQuestion Stats:", null, "89% (01:04) correct", null, "11% (01:09) wrong", null, "based on 497 sessions\n\n### HideShow timer Statistics", null, "The Official Guide For GMAT® Quantitative Review, 2ND Edition\n\nIf a, b, and c are nonzero numbers and a + b = c, which of the following is equal to 1 ?\n\n(A) (a - b)/c\n(B) (a - c)/b\n(C) (b - c)/a\n(D) (b - a)/c\n(E) (c - b)/a\n\nProblem Solving\nQuestion: 99\nCategory: Arithmetic Operations on rational numbers\nPage: 74\nDifficulty: 550\n\nGMAT Club is introducing a new project: The Official Guide For GMAT® Quantitative Review, 2ND Edition - Quantitative Questions Project\n\nEach week we'll be posting several questions from The Official Guide For GMAT® Quantitative Review, 2ND Edition and then after couple of days we'll provide Official Answer (OA) to them along with a slution.\n\nWe'll be glad if you participate in development of this project:\n2. Please vote for the best solutions by pressing Kudos button;\n3. Please vote for the questions themselves by pressing Kudos button;\n4. Please share your views on difficulty level of the questions, so that we have most precise evaluation.\n\nThank you!\n\n_________________\nIntern", null, "", null, "Joined: 27 Oct 2011\nPosts: 22\nRe: If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\n5\na + b = c ,\ntaking b to the other side, a = c-b ,\ntake a to the other side , 1 =$$\\frac{c-b}{a}$$\n\noption E.\n_________________\noblige me with Kudos.\n##### General Discussion\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 56366\nRe: If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\nSOLUTION\n\nIf a, b, and c are nonzero numbers and a + b = c, which of the following is equal to 1 ?\n\n(A) (a - b)/c\n(B) (a - c)/b\n(C) (b - c)/a\n(D) (b - a)/c\n(E) (c - b)/a\n\nFrom a + b = c:\n\na = c - b;\nb = c - a;\nc = a + b.\n\nOnly option E yields 1: (c - b)/a = a/a = 1.\n\n_________________\nManager", null, "", null, "Joined: 11 Jan 2014\nPosts: 87\nConcentration: Finance, Statistics\nGMAT Date: 03-04-2014\nGPA: 3.77\nWE: Analyst (Retail Banking)\nRe: If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\n1\nSince we are given that a+b=c, the answer we have to pick has to be in one of the following forms:\n\na= c-b\nb= c-a\nc= a+b\n\nThe only valid choice is (E), since it is dividing equal values.\nManager", null, "", null, "Joined: 20 Dec 2013\nPosts: 225\nLocation: India\nRe: If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\n1\nOption E.\n(c-b)/a=(a+b-b)/a=1\nGiven c=a+b\n\nPosted from my mobile device\nManager", null, "", null, "Status: GMATting\nJoined: 21 Mar 2011\nPosts: 105\nConcentration: Strategy, Technology\nGMAT 1: 590 Q45 V27", null, "Re: If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\n2\nI know it is not the quickest/best approach but I used number picking strategy: a=3; b=4; c=7;\n\nSub these values and you can clearly find out that answer is 1, especially if you are not so good at using variables.\nMath Expert", null, "V\nJoined: 02 Sep 2009\nPosts: 56366\nRe: If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\nSOLUTION\n\nIf a, b, and c are nonzero numbers and a + b = c, which of the following is equal to 1 ?\n\n(A) (a - b)/c\n(B) (a - c)/b\n(C) (b - c)/a\n(D) (b - a)/c\n(E) (c - b)/a\n\nFrom a + b = c:\n\na = c - b;\nb = c - a;\nc = a + b.\n\nOnly option E yields 1: (c - b)/a = a/a = 1.\n\n_________________\nSVP", null, "", null, "Status: The Best Or Nothing\nJoined: 27 Dec 2012\nPosts: 1787\nLocation: India\nConcentration: General Management, Technology\nWE: Information Technology (Computer Software)\nRe: If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\n2\nOne more method\n\nLooking at the equation a+b = c,\n\nwe can conclude that c > a & c > b\n\nOption A & Option D are not possible as a & b are on the same side of equation.\n\nOption B & Option C will be negative as c > a & c > b\n\n_________________\nKindly press \"+1 Kudos\" to appreciate", null, "Intern", null, "", null, "Joined: 24 Mar 2015\nPosts: 2\nRe: If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\nAnother way would be to just plug-in numbers for a,b and c.\nEMPOWERgmat Instructor", null, "V\nStatus: GMAT Assassin/Co-Founder\nAffiliations: EMPOWERgmat\nJoined: 19 Dec 2014\nPosts: 14620\nLocation: United States (CA)\nGMAT 1: 800 Q51 V49", null, "GRE 1: Q170 V170", null, "Re: If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\nHi All,\n\nThis question is perfect for TESTing VALUES.\n\nWe're told that A, B and C are non-zero numbers and that A + B = C. We're asked which of the following answers is equal to 1.\n\nIF....\nA = 2\nB = 3\nC = 5\n\nAnswer A: (2-3)/5 = -1/5 NOT a match\nAnswer B: (2-5)/3 = -3/3 NOT a match\nAnswer C: (3-5)/2 = -2/2 NOT a match\nAnswer D: (3-2)/5 = 1/5 NOT a match\nAnswer E: (5-3)/2 = 2/2 This IS a match\n\nGMAT assassins aren't born, they're made,\nRich\n_________________\n760+: Learn What GMAT Assassins Do to Score at the Highest Levels\nContact Rich at: Rich.C@empowergmat.com\n\n*****Select EMPOWERgmat Courses now include ALL 6 Official GMAC CATs!*****\n\n# Rich Cohen\n\nCo-Founder & GMAT Assassin", null, "Follow\nSpecial Offer: Save \\$75 + GMAT Club Tests Free\nOfficial GMAT Exam Packs + 70 Pt. Improvement Guarantee\nwww.empowergmat.com/\nManager", null, "", null, "Joined: 11 Oct 2013\nPosts: 103\nConcentration: Marketing, General Management\nGMAT 1: 600 Q41 V31", null, "If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\nWe know that a + b = c.\nSo, c > b > a\n\nAny option which has a - b, b - c, or a - c as numerator options will be incorrect --> such an option will deduce negative value.\nThis rules out option A, B and C.\n\nIt is also noteworthy that c cannot be in the denominator as it is the biggest of the three and the solution will be < 1.\nThis rules out option D.\n\nOnly option left is option E. You can either mark it, it test the option.\n\nAnother way would be to just solve the parent equation -\na + b = c\na = c - b\n1 = (c - b)/a --> Answer choice E!\n\nCheers!\n_________________\nIts not over..\nVP", null, "", null, "D\nJoined: 09 Mar 2016\nPosts: 1273\nRe: If a, b, and c are nonzero numbers and a + b = c, which of t  [#permalink]\n\n### Show Tags\n\nBunuel wrote:\nThe Official Guide For GMAT® Quantitative Review, 2ND Edition\n\nIf a, b, and c are nonzero numbers and a + b = c, which of the following is equal to 1 ?\n\n(A) (a - b)/c\n(B) (a - c)/b\n(C) (b - c)/a\n(D) (b - a)/c\n(E) (c - b)/a\n\nProblem Solving\nQuestion: 99\nCategory: Arithmetic Operations on rational numbers\nPage: 74\nDifficulty: 550\n\nGMAT Club is introducing a new project: The Official Guide For GMAT® Quantitative Review, 2ND Edition - Quantitative Questions Project\n\nEach week we'll be posting several questions from The Official Guide For GMAT® Quantitative Review, 2ND Edition and then after couple of days we'll provide Official Answer (OA) to them along with a slution.\n\nWe'll be glad if you participate in development of this project:\n2. Please vote for the best solutions by pressing Kudos button;\n3. Please vote for the questions themselves by pressing Kudos button;\n4. Please share your views on difficulty level of the questions, so that we have most precise evaluation.\n\nThank you!\n\nlet a, b, c be 1, 2, 3 respectively.\n\ntest the values.\n\nonly E yields 1", null, "", null, "Re: If a, b, and c are nonzero numbers and a + b = c, which of t   [#permalink] 05 Mar 2018, 09:38\nDisplay posts from previous: Sort by\n\n# If a, b, and c are nonzero numbers and a + b = c, which of t", null, "", null, "" ]
[ null, "https://cdn.gmatclub.com/cdn/files/forum/styles/gmatclub_light/theme/images/profile/close.png", null, "https://cdn.gmatclub.com/cdn/files/forum/styles/gmatclub_light/theme/images/profile/close.png", null, "https://gmatclub.com/forum/styles/gmatclub_light/theme/images/search/close.png", null, "https://cdn.gmatclub.com/cdn/files/forum/images/avatars/upload/avatar_73391.jpg", null, "https://cdn.gmatclub.com/cdn/files/forum/styles/gmatclub_light/theme/images/viewtopic/timer_play.png", null, "https://cdn.gmatclub.com/cdn/files/forum/styles/gmatclub_light/theme/images/viewtopic/timer_difficult_blue.png", null, "https://cdn.gmatclub.com/cdn/files/forum/styles/gmatclub_light/theme/images/viewtopic/timer_difficult_grey.png", null, "https://cdn.gmatclub.com/cdn/files/forum/styles/gmatclub_light/theme/images/viewtopic/timer_difficult_grey.png", null, "https://cdn.gmatclub.com/cdn/files/forum/styles/gmatclub_light/theme/images/viewtopic/timer_separator.png", null, "https://cdn.gmatclub.com/cdn/files/forum/styles/gmatclub_light/theme/images/viewtopic/timer_separator.png", null, "https://cdn.gmatclub.com/cdn/files/forum/styles/gmatclub_light/theme/images/viewtopic/timer_separator.png", null, "https://gmatclub.com/forum/images/got-banner.png", null, "https://cdn.gmatclub.com/cdn/files/forum/images/ranks/rank_phpbb_1.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/no_avatar.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/avatars/upload/avatar_73391.jpg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/ranks/rank_phpbb_3.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/avatars/upload/avatar_400823.jpg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/ranks/rank_phpbb_3.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/no_avatar.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/ranks/rank_phpbb_3.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/avatars/gallery/Futurama3/zapp_thinking.gif", null, "https://gmatclub.com/forum/images/unverified_score.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/avatars/upload/avatar_73391.jpg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/ranks/rank_phpbb_7.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/avatars/upload/avatar_310058.jpg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/smilies/icon_smile.gif", null, "https://cdn.gmatclub.com/cdn/files/forum/images/ranks/rank_phpbb_1.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/no_avatar.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/avatars/upload/avatar_466391.jpg", null, "https://gmatclub.com/forum/images/unverified_score.svg", null, "https://gmatclub.com/forum/images/unverified_score.svg", null, "https://gmatclub.com/static/images/signatures/arrow.png", null, "https://cdn.gmatclub.com/cdn/files/forum/images/ranks/rank_phpbb_3.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/avatars/upload/avatar_379520.jpg", null, "https://gmatclub.com/forum/images/unverified_score.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/ranks/rank_phpbb_6.svg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/avatars/upload/avatar_568100.jpg", null, "https://cdn.gmatclub.com/cdn/files/forum/images/smilies/1f603.png", null, "https://cdn.gmatclub.com/cdn/files/forum/styles/gmatclub_light/theme/images/viewtopic/posts_bot.png", null, "https://www.facebook.com/tr", null, "https://www.googleadservices.com/pagead/conversion/1071875456/", null ]
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https://help.scilab.org/docs/6.0.0/ja_JP/m2sci_close.html
[ "Scilab Home page | Wiki | Bug tracker | Forge | Mailing list archives | ATOMS | File exchange\nChange language to: English - Français - Português - Русский\n\nPlease note that the recommended version of Scilab is 6.1.1. This page might be outdated.\nSee the recommended documentation of this function\n\nScilabヘルプ >> Matlab to Scilab Conversion Tips > Matlab-Scilab equivalents > C > close (Matlab function)\n\n# close (Matlab function)\n\nDelete specified figure\n\n### Matlab/Scilab equivalent\n\n Matlab Scilab `close` `close - xdel - delete`\n\n### Particular cases\n\nclose\n\nIf current figure is a uicontrol one, Scilab and Matlab close are equivalent. But if current figure is a graphic window, Scilab equivalent for Matlab close is delete(gcf()).\n\nclose(h)\n\nIf h is a uicontrol figure, Scilab and Matlab close(h) are equivalent. But if h is a graphic window, Scilab equivalent for Matlab close(h) is delete(h).\n\nclose('all')\n\nScilab equivalent for Matlab close('all') is xdel(winsid()).\n\nclose(name)\n\nThere is no Scilab equivalent for Matlab close(name) however, mtlb_close can be an equivalent.\n\nclose('all','hidden')\n\nScilab equivalent for Matlab close('all','hidden') is xdel(winsid()) but Scilab kills all figures even if they are hidden." ]
[ null ]
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https://help.scilab.org/docs/5.5.0/en_US/xstringb.html
[ "Change language to:\nFrançais - 日本語 - Português - Русский\n\nSee the recommended documentation of this function\n\nScilab Help >> Graphics > text > xstringb\n\n# xstringb\n\ndraw strings into a box\n\n### Calling Sequence\n\nxstringb(x,y,str,w,h,[option])\n\n### Arguments\n\nx,y,w,h\n\nvector of 4 real scalars defining the box.\n\nstr\n\nmatrix of strings.\n\nStarting from Scilab 5.2, it is possible to write LaTeX or MathML expression.\n\noption\n\nstring.\n\n### Description\n\nxstringb draws the matrix of strings str centered inside the rectangle rect=[x,y,w,h] (lower-left point, width, height) in user coordinates.\n\nIf option is given with the value \"fill\", the character size is computed so as to fill as much as possible in the rectangle.\n\nEnter the command xstringb() to see a demo.\n\n### Examples\n\nstr=[\"Scilab\" \"is\";\"$\\sqrt{not}$\" \"elisaB\"];\nplot2d(0,0,[-1,1],\"010\",\" \",[0,0,1,1]);\n\nr=[0,0,1,0.5];\nxstringb(r(1),r(2),str,r(3),r(4),\"fill\");\nxrect(r(1),r(2)+r(4),r(3),r(4));\n\nr=[r(1),r(2)+r(4)+0.01,r(3),r(4)/2];\nxrect(r(1),r(2)+r(4),r(3),r(4))\nxstringb(r(1),r(2),str,r(3),r(4),\"fill\");", null, "" ]
[ null, "https://help.scilab.org/docs/5.5.0/en_US/xstringb_1.png", null ]
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https://edurev.in/course/quiz/attempt/18459_Mathematical-Logic--Advance-Level--1/147fa5c7-8293-44e4-be47-e383f154873f
[ "Courses\n\n# Mathematical Logic (Advance Level) - 1\n\n## 13 Questions MCQ Test Question Bank for GATE Computer Science Engineering | Mathematical Logic (Advance Level) - 1\n\nDescription\nThis mock test of Mathematical Logic (Advance Level) - 1 for GATE helps you for every GATE entrance exam. This contains 13 Multiple Choice Questions for GATE Mathematical Logic (Advance Level) - 1 (mcq) to study with solutions a complete question bank. The solved questions answers in this Mathematical Logic (Advance Level) - 1 quiz give you a good mix of easy questions and tough questions. GATE students definitely take this Mathematical Logic (Advance Level) - 1 exercise for a better result in the exam. You can find other Mathematical Logic (Advance Level) - 1 extra questions, long questions & short questions for GATE on EduRev as well by searching above.\nQUESTION: 1\n\n### Which one of the following is false? Read ∧ as AND, v as OR, ~ as NOT, → as one way implication and", null, "as two way implication.\n\nSolution:", null, "", null, "QUESTION: 2\n\n### What is the converse of the following assertion? I stay only if you go\n\nSolution:\n\nLet p : l stay\nq ; you go\nI stay only if you go", null, "Converse of", null, "Now convert the answers one-by-one into boolean form. Only option (a) i.e. “1 stay if you go” converts to", null, "QUESTION: 3\n\n### Consider two well-formed formulas in propositional logic:", null, "Which of the following statements is correct?\n\nSolution:", null, "So F1 is contingency. Hence, F1 is satisfiable but not valid.", null, "So F2 is tautology and therefore valid.\n\nQUESTION: 4\n\n“If X then Y unless Z” is represented by which of the following formulas in propostional logic? (\"_\") is negation, “∧” is conjunction, and \"→\" is implication)\n\nSolution:\n\nIf X then Y unless Z is represented by", null, "Now convert the answers one-by-one into boolean form only choice (a) converts to X'+ Y + Z as can be seen below:", null, "QUESTION: 5\n\nLet P, Q and, R be three atomic propositional assertions. Let X denote ( P v Q ) → R and Y denote (P → R) v (Q → R). Which one of the following is a tautology?\n\nSolution:", null, "", null, "QUESTION: 6\n\nWhich one of the following is the most appropriate logical formula to represent the statement:\n“Gold and silver ornaments are precious”\nThe following notations are used:\nG(x): x is a gold ornament\nS(x): x is a silver ornament\nP(x): x is precious\n\nSolution:\n\nThe correct translation of \"Gold and silver ornaments are precious ” is choice (d)", null, "which is read as “if an ornament is gold or silver, then it is precious”.\nNow since a given ornament cannot be both gold and silver at the same time.", null, "QUESTION: 7\n\nSuppose the predicate P(x, y, t) is used to represent the statement that person x can fool person y at time t. Which one of the statements below expresses best the meaning of the formula​", null, "Solution:", null, "≡  it is not true that (someone can fool all people at all time)\n≡  no one can fool everyone all the time\n\nQUESTION: 8\n\nWhat is the logical translation of the following statements?\n“None of my friends are perfect\"\n\nSolution:\n\nNone of my friends are perfect i.e., all of my friends are not perfect", null, "QUESTION: 9\n\nConsider the statement:\n\"Not all that glitters is gold\"\nPredicate glitters (x) is true if x glitters and predicate gold (x) is true if x is gold. Which one of the following logical formulae represents the above statement?\n\nSolution:", null, "There exist gold which is not glitter i.e. not all golds are glitters.", null, "Not all that glitters is gold i.e., there exist some which glitters and which is not gold.\n\nQUESTION: 10\n\nWhich one of the following propositional logic formulas is TRUE when exactly two of p, q, and r are TRUE?\n\nSolution:", null, "", null, "This is exactly the min-term form of a logical formula which is true when exactly two variables are true (only p, q true or only p, r true or only q, r true).\n\nQUESTION: 11\n\nWhich one of the following Boolean expressions is NOT a tautology?\n\nSolution:", null, "QUESTION: 12\n\nConsider the following statements:\nP : Good mobile phones are not cheap\nQ : Cheap mobile phones are not good\nL : P implies Q\nM : Q implies P\nN : P is equivalent to Q\nWhich one of the following about L, M and N is CORRECT?\n\nSolution:\n\ng : mobile is good\nc : mobile is cheap\nP : Good mobile .phones are not cheap", null, "Q : Cheap mobile phones are not good", null, "∴ Both P and Q are equivalent.", null, "Since both P and Q are equivalent, all three of L, M, N are true.\n\nQUESTION: 13\n\nConsider the following two statements:\nS1: If a candidate is known to be corrupt, then he will not be elected.\nS2: If a candidate is kind, he will be elected.\nWhich one of the following statements follows from S1 and S2 as per sound inference rules of logic?\n\nSolution:\n\nC : Person is corrupt\nK : Person is kind.\nE : Person is elected", null, "", null, "" ]
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http://www.ije.ir/article_88212.html
[ "# Performance of a Two-stages Gas Gun: Experimental, Analytical and Numerical Analysis\n\nAuthors\n\n1 Department of Mechanical Engineering, Bu-Ali Sina University, Hamedan, Iran\n\n2 Department of Mechanical Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran\n\nAbstract\n\nTwo stages gas guns are used for various purposes, particularly for mechanical characterization of materials at high rate of deformations. The performance of a two stages gas gun is studied in this work using the theory of the two-stage gas gun proposed by Rajesh, numerical simulation using combined Eulerian/ Lagrangian elements in Autodyna commercial code and experiment using a two stage gas gun developed by the authors of this study. Equations governing the motion of the piston and projectile are solved using Runge-Kutta method. The effects of parameters such as chamber pressure, pump tube pressure and piston mass on the performance of gun are explored. The results of numerical simulation and analytical methods are validated by experiment. Finally, a comparison between the analytical, numerical and experimental results shows that the theory proposed by Rajesh, yields reasonable predictions for the two stage gas gun performance in the first place, and Autodyn software, using combined Eulerian/ Lagrangian elements, gives accurate estimations for gas gun parameters, in the second place. A 3-D working diagram is provided for prediction of projectile velocity for any state of pump and chamber pressures which are the most important variables for a gas gun with a fixed geometry.\nTwo stages gas guns are used for various purposes, particularly for mechanical characterization of materials at high rate of deformations. The performance of a two stages gas gun is studied in this work using the theory of the two-stage gas gun proposed by Rajesh, numerical simulation using combined Eulerian/ Lagrangian elements in Autodyna commercial code and experiment using a two stage gas gun developed by the authors of this study. Equations governing the motion of the piston and projectile are solved using Runge-Kutta method. The effects of parameters such as chamber pressure, pump tube pressure and piston mass on the performance of gun are explored. The results of numerical simulation and analytical methods are validated by experiment. Finally, a comparison between the analytical, numerical and experimental results shows that the theory proposed by Rajesh, yields reasonable predictions for the two stage gas gun performance in the first place, and Autodyn software, using combined Eulerian/ Lagrangian elements, gives accurate estimations for gas gun parameters, in the second place. A 3-D working diagram is provided for prediction of projectile velocity for any state of pump and chamber pressures which are the most important variables for a gas gun with a fixed geometry.\n\nKeywords" ]
[ null ]
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https://webapps.stackexchange.com/tags/google-sheets/hot?filter=week
[ "# Tag Info\n\n1\n\nYou would just use and The only column containing a different variable is column B. So you could use =query(Sheet1!A:F, \"SELECT A, B, C, D, E, F where E contains 'Apple' and B contains 'had'\") Note: you do not need the parentheses around E contains 'Apple'\n\n1\n\nPlease use the following formula in your synced copied sheets. =QUERY(IMPORTRANGE(\"xxxxxx\",\"MasterDashboard!A2:F\"), \"select * where Col2= '\"&B1&\"' \") (where xxxxxx is your Master sheet ID and B1 is your reference cell in the synced sheet) Notes (for anyone using the formula) When using IMPORTRANGE one has to use Col1, Col2 etc To reference a ...\n\n1\n\nEDIT I just realized that you have cross-posted on both sites. I also realized that there is a much simpler case insensitive formula for your needs =COUNTIF(SPLIT(CONCATENATE(B1:B3), \" \"), \"*heRO*\") OR (if in cell A7 we have *HeRo*) =COUNTIF(SPLIT(CONCATENATE(B1:B3), \" \"), A7) If you want just the word Hero, remove the asterisks * around it. It also ...\n\n1\n\nYou could use this formula =ARRAYFORMULA(IF((W10:W13<>\"\")+(X10:X13<>\"\"),TODAY(),\"\")) and then give the desired date format to the results from the menu. (Please adjust ranges to your needs) Functions used: TODAY ArrayFormula IF\n\n1\n\nYou can simply copy and paste into the sheets, there is no need to use the Insert Menu you have described. Follow these steps to add an image to a cell in a Google Sheet: Copy the Image from your source (eg take a screenshot with snipping tool) Open the Sheet & Paste the Image from your clipboard Select the Cell you would like to add the Image too ...\n\n1\n\nThe following block \"doesn't work\" because it's inside of the first if block if(e.range.getA1Notation() === 'B5' && e.range.getSheet().getName()=='Temp1'){ if(e.value=='Base10') { e.range.getSheet().showRows(10,2); } try function onEdit(e){ if(e.range.getA1Notation() === 'B13' && e.range.getSheet().getName()=='Temp1'){ ...\n\nOnly top voted, non community-wiki answers of a minimum length are eligible" ]
[ null ]
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https://answers.opencv.org/question/68541/creating-a-sub-array-of-a-mat-throws-assertion-error/
[ "Creating a sub array of a Mat throws assertion error\n\nHey there,\n\nIn my program, I am looping through a 2D Mat, using a rectangle with width 5 and height 1, and checking float point values in this sub array. My code goes as follow:\n\nfor (int i = 0; i < dist.rows; ++i) {\nfor (int j = 2; j < dist.cols -2; ++j) {\nsearch_mask = dist(Rect(i, j - 2, 5, 1).clone();\nminMaxLoc(search_mask, NULL, NULL, NULL, &local_max);\nif (local_max.x == i) {\nskeleton.at<uchar>(i, j) = 255;\n}\n}\n}\n\nMy original picture has size 640*363. When the first loop reached the value i = 359, I get an assertion error:\n\nOpenCV Error: Assertion failed (0 <= roi.x && 0 <= roi.width && roi.x + roi.width <= m.cols && 0 <= roi.y && 0 <= roi.height && roi.y + roi.height <= m.rows) in cv:: Mat::Mat\n\nAs far as I can tell, the problem is, that when i = 359, roi.x = 359, roi. width = 5 and so roi.x + roi.width = 364, and m.cols = 363 and so 364 <= 363 is false.\n\nMy first question is, am I doing something wrong here?\n\nMy second question, why the hell would the number of rows matter when it comes to deciding if the number of columns in the sub array is not greater, than the number of columns in the original image.\n\nThank you for your help.\n\nAlso, sorry if I have left something important out.\n\nedit retag close merge delete\n\nSort by » oldest newest most voted", null, "If you want to extract a rectangular area from dist, the recommended way is\n\ndist.operator()(Rect(i,j-2,5,1))\n\nSo, your statement in the loop should read\n\nsearch_mask = dist.operator()(Rect(i, j - 2, 5, 1)).clone();\nmore\n\nThank you, but unfortunately this does not solve my problem.\n\n1\n\nRect should be defined with Rect(j-2,i,5,1). You are using row number in place of x.\n\nYou are right, now I just don't understand why they would mark the y coordinate with x and vice versa...\n\n2\n\nYou can establish a convention to use (c,r) for (column,row) in place of (x,y) to avoid confusion.\n\nWhat I don't understand is, that OpenCV has the (0,0) coordinate at the upper left corner, the first coordinate is for the row, the second is for the column, and the Rect function asks for an x coordinate first, then a y, so it should ask for the row first. Yet, it works only, when I provide the column first. Or am I missing out on something?\n\nIf you take your origin at the top left corner, then getting a (x,y) coordinate is exactly (cols,rows). Your x axis is always the horizontal one, and y the vertical one. Do you get it now?\n\nYou see, my problem is, that if you have the following code for example:\n\nfor (int r = 0; r < image.rows; ++r) {\nfor (int c = 0; c < image.cols; ++c) {\nimage.at<type>(r,c) = something here;\n}\n}\n\nThen you are accessing pixels using (row, column) not (column, row).\n\nUPDATE:\n\nI have just done some simple drawings, which resulted in exactly what you said, the origin is at the top left corner, and coordinates can be accessed using (column, row).\n\nConsidering this, you see, why it is confusing, in my code, you can access pixels using (row, column)\n\nStats\n\nAsked: 2015-08-13 09:51:30 -0500\n\nSeen: 207 times\n\nLast updated: Aug 13 '15" ]
[ null, "https://www.gravatar.com/avatar/d4ddf9dd040b42f80911e4a53010dfc7", null ]
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https://anylearnnepal.com/fortran-tutorial-problem-solving-1/
[ "# FORTRAN Tutorial Lesson 5 | Problem Solving 1\n\nWelcome to the first post for problem solving in FORTRAN.\n\nI strongly recommend watching my previous lessons.\n\nCheck my FORTRAN programming page for more tutorials.\n\nIf you are confident enough about the previous lessons, lets dive right to the problems.\n\n## Question 1:\n\nFind the mid-point of (2,4) and (4, -10).\n\nTo find the mid-point, we have formula:", null, "Lets see the steps written in comments first:\n\nprogram mid_point\nimplicit none\n! variable x1, x2, y1, y2 declaration. They are the input\n\n! variable x, y declaration. let that be output\n\n! writing formula for x\n\n! writing formula for y\n\n! printing out the value of x and y\n\nend program\n\nFirst try yourself to write a code taking hint from above.\n\nLook at the code below for any correction to your code.\n\nprogram mid_point\nimplicit none\nreal :: x1, x2, y1, y2\nreal :: x, y\nx1 = 2; y1 = 4; x2 = 4; y2 = -10\nx = (x1 + y1) / 2.0\ny = (x2 + y2) / 2.0\nprint*, \"The mid point is \", x, y\nend program\n\nThe output to the above code is:\n\nThe mid point is: 3.00000000 -3.00000000\n\nIf your answer is right, then your problem solving skill is good. Lets sharp them by another question.\n\n## Question 2\n\nFind the Gravitational Force exerted by the earth on 1 kg mass lying on the earth's surface.\nUse:\nearth mass =", null, "universal gravitational constant =", null, "We have formula:", null, "Our Formula has two quantity ‘m’ and ‘M’, which are recognized as the same in FORTRAN because FORTRAN is case insensitive. So lets change the formula to :", null, "First try to write yourself.\n\nCheck the below code for correction to your code.\n\nprogram force\nimplicit none\nreal, parameter :: G = 6.67E-11 ! for power of ten, write e OR E.....", null, "real :: m1, m2, R ! given value: m1 = mass of earth, m2 = mass of object, R = radius of earth\nreal :: F ! output variable F = force\n! assigning values\nm1 = 6*10**24\nm2 = 1\nR = 6400*1000\nF = (G*m1*m2) / R**2\nprint*, \"The force is\", F\nend program\n\nThe output is:\n\nThe force is 683.935547\n\nNow you are confident in problem solving using FORTRAN.\n\n## Examples of complex data type\n\nComplex number has two parts: real and imaginary part. In FORTRAN, we write complex number as (real, imaginary).\n\nWe can perform basic math operation in complex data type like in integer and real.\n\nBelow is code that shows the uses of complex numbers.\n\nprogram complex_math\nimplicit none\ncomplex :: a, b ! declaring variable a and b as complex data type\na = (3, 4) ! (3, 4) is 3 + 4i\nb = (1, -2) ! (1, -2) is 1 - 2i\nprint*, a+b, a-b, a*b, a/b\nend program\n\noutput:\n\n(4.00000000, 2.00000000) (2.00000000, 6.00000000) (11.00000000, -2.00000000) (-1.00000000, 2.00000000)\n\nFORTRAN performs such complications complex multiplication and vision easily, just like ordinary math.\n\n## Examples of character data type\n\nCharacter data means the text or string.\n\nEach letter or number or special character is a character. Combination of character becomes text.\n\nWhile declaring character data type, we have to provide another specifier len. Then len = \"value\"controls the length of the text. for eg.\n\nprogram character_example\nimplicit none\ncharacter(len=4) :: text\ntext = \"welcome\"\nprint*, text\nend program\n\noutput\n\nwelc ! because of len=4\nlen = 1 gives output w\nlen = 2 gives output we\nlen = 3 gives output wel\nlen = 4 gives output welc\nlen = 5 gives output welco\nlen = 6 gives output welcom\nlen = 7 gives output welcome\n\n\nSo while using len= \"value\", value should be greater than the length of text.\n\n## Example of logical data type\n\nLogical data type in FORTRAN has only two values. They are: .true. and .false.\n\nprogram logicalUse\nimplicit none\nlogical :: correct, wrong\ncorrect = .true.\nwrong = .false.\nprint*, correct\nprint*, wrong\nend program\n\nThe output is:\n\nT\nF\n\nLogical data type are not used for any calculations, rather it used to check conditional statements.\n\nNot Clear Enough? Watch the Video on problem solving 1." ]
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https://community.esri.com/t5/python-questions/hi-have-problem-with-creating-buffer-only-in/td-p/1025820
[ "# Hi. Have problem with Creating buffer only in specific direction. What's wrong?\n\n1233\n16\n02-11-2021 05:40 AM", null, "New Contributor II\n\nimport arcpy, math, gc\n\n# Workspace, overwrite\narcpy.env.workspace = r\"D:\\testScript\\My.gdb\"\narcpy.env.overwriteOutput = True\n\n# INPUTS\nobjects_input = \"objects.shp\" # must be polygons\nobjects = \"objects_lyr.shp\"\narcpy.MakeFeatureLayer_management(objects_input, objects)\n\n# OUTPUTS, most temporal\nresult = \"result.shp\"\nresult_erase = \"in_memory\" + \"\\\\\" + \"result_erase\"\npolygon = \"in_memory\" + \"\\\\\" + \"polygon\"\npolygon_dissolve = \"in_memory\" + \"\\\\\" + \"polygon_dissolve\"\n\narcpy.CreateFeatureclass_management(arcpy.env.workspace, result, \"POLYGON\")\n\n# Parameters\ndistance = 3 # distance for move in direction\ndirection = 90 # direction in degrees (90 is from north to south)\nindex = 0\n\n# Set UpdateCursor\ncur_objects = arcpy.da.UpdateCursor(objects, (\"FID\"))\nfor row_objects in cur_objects:\ntry:\nfid = row_objects\nsql = '\"FID\" = ' + str(index)\nindex += 1\n\n# Initialize lists\nlines_list = []\nlines_created = []\n\n# Select current feature\narcpy.SelectLayerByAttribute_management(objects, \"NEW_SELECTION\", sql)\nvertexes = \"in_memory\" + \"\\\\\" + \"vertexes\"\n\n# Convert object to vertexes\narcpy.FeatureVerticesToPoints_management(objects, vertexes, \"ALL\")\nindex_vertex = 0\n\n# Set SearchCursor for vertexes\ncur_vertexes = arcpy.da.SearchCursor(vertexes, (\"SHAPE@XY\"))\nfor row_vertexes in cur_vertexes:\nvertex_coords_x = row_vertexes\nvertex_coords_y = row_vertexes\n\n# Define points coordinates\npoint_move_x = vertex_coords_x - (distance) * math.cos(math.radians(direction))\npoint_move_y = vertex_coords_y - (distance) * math.cos(math.radians(90 - direction))\n\n# Make list of points\nnew_line = ([[vertex_coords_x, vertex_coords_y], [point_move_x, point_move_y]])\nlines_list.append(new_line)\n\n# From second cycle\nif index_vertex > 0:\nlines_vertexes = ([[vertex_coords_x, vertex_coords_y], start_line])\nlines_ends = ([[point_move_x, point_move_y], end_line])\nlines_list.append(lines_vertexes)\nlines_list.append(lines_ends)\nstart_line = [vertex_coords_x, vertex_coords_y]\nend_line = [point_move_x, point_move_y]\nindex_vertex = index_vertex + 1\n\n# Cycle that makes polylines from points\nfor lines_step in lines_list:\nlines_created.append(arcpy.Polyline(arcpy.Array([arcpy.Point(*sour) for sour in lines_step])))\n\narcpy.FeatureToPolygon_management(lines_created, polygon)\narcpy.AggregatePolygons_cartography(polygon, polygon_dissolve, 1)\n\n# Final editing\narcpy.Erase_analysis(polygon_dissolve, objects, result_erase)\narcpy.Append_management(result_erase, result, \"NO_TEST\")\narcpy.Delete_management(\"in_memory\")\narcpy.Delete_management(vertexes)\nstart_line = []\n\n# Clear selection, memory and deleting temps\narcpy.SelectLayerByAttribute_management(objects, \"CLEAR_SELECTION\")\nprint (\"Object number: \" + str(index - 1) + \" -- done.\")\ngc.collect()\n\n# Catch errors\nexcept Exception as e:\npass\nprint (\"Error:\")\nprint (e)\nprint (\"\\n\")\nindex += 1\n\n_______________________________________________________\n\nTraceback (most recent call last):\nFile \"D:/testScript/myDBpy.py\", line 10, in <module>\narcpy.MakeFeatureLayer_management(objects_input, objects)\nFile \"C:\\Program Files (x86)\\ArcGIS\\Desktop10.3\\ArcPy\\arcpy\\management.py\", line 6520, in MakeFeatureLayer\nraise e\nExecuteError: Failed to execute. Parameters are not valid.\nERROR 000732: Input Features: Dataset objects.shp does not exist or is not supported\nFailed to execute (MakeFeatureLayer).\n\nTags (1)\n16 Replies", null, "by", null, "MVP Frequent Contributor\n\nI've not read the entire script, also please consider formatting it to make it easier to read.\n\nI can however see a workspace envionment set as an FGDB, then referencing a shapefile called \"objects.shp\".\n\nYou wont have a shapefile in that workspace, so give the full path to the shapefile.", null, "New Contributor II\n\nimport arcpy, math, gc\n\n# Workspace, overwrite\narcpy.env.workspace = r\"D:\\testScript\\My.gdb\"\narcpy.env.overwriteOutput = True\n\n# INPUTS\nobjects_input = r\"D:\\testScript\\objects.shp\" # must be polygons\nobjects = \"objects_lyr.shp\"\narcpy.MakeFeatureLayer_management(objects_input, objects)\n\n# OUTPUTS, most temporal\nresult = \"result.shp\"\nresult_erase = \"in_memory\" + \"\\\\\" + \"result_erase\"\npolygon = \"in_memory\" + \"\\\\\" + \"polygon\"\npolygon_dissolve = \"in_memory\" + \"\\\\\" + \"polygon_dissolve\"\n\narcpy.CreateFeatureclass_management(arcpy.env.workspace, result, \"POLYGON\")\n\n# Parameters\ndistance = 9 # distance for move in direction\ndirection = 90 # direction in degrees (90 is from north to south)\nindex = 0\n\n# Set UpdateCursor\ncur_objects = arcpy.da.UpdateCursor(objects, (\"FID\"))\nfor row_objects in cur_objects:\ntry:\nfid = row_objects\nsql = '\"FID\" = ' + str(index)\nindex += 1\n\n# Initialize lists\nlines_list = []\nlines_created = []\n\n# Select current feature\narcpy.SelectLayerByAttribute_management(objects, \"NEW_SELECTION\", sql)\nvertexes = \"in_memory\" + \"\\\\\" + \"vertexes\"\n\n# Convert object to vertexes\narcpy.FeatureVerticesToPoints_management(objects, vertexes, \"ALL\")\nindex_vertex = 0\n\n# Set SearchCursor for vertexes\ncur_vertexes = arcpy.da.SearchCursor(vertexes, (\"SHAPE@XY\"))\nfor row_vertexes in cur_vertexes:\nvertex_coords_x = row_vertexes\nvertex_coords_y = row_vertexes\n\n# Define points coordinates\npoint_move_x = vertex_coords_x - (distance) * math.cos(math.radians(direction))\npoint_move_y = vertex_coords_y - (distance) * math.cos(math.radians(90 - direction))\n\n# Make list of points\nnew_line = ([[vertex_coords_x, vertex_coords_y], [point_move_x, point_move_y]])\nlines_list.append(new_line)\n\n# From second cycle\nif index_vertex > 0:\nlines_vertexes = ([[vertex_coords_x, vertex_coords_y], start_line])\nlines_ends = ([[point_move_x, point_move_y], end_line])\nlines_list.append(lines_vertexes)\nlines_list.append(lines_ends)\nstart_line = [vertex_coords_x, vertex_coords_y]\nend_line = [point_move_x, point_move_y]\nindex_vertex = index_vertex + 1\n\n# Cycle that makes polylines from points\nfor lines_step in lines_list:\nlines_created.append(arcpy.Polyline(arcpy.Array([arcpy.Point(*sour) for sour in lines_step])))\n\narcpy.FeatureToPolygon_management(lines_created, polygon)\narcpy.AggregatePolygons_cartography(polygon, polygon_dissolve, 1)\n\n# Final editing\narcpy.Erase_analysis(polygon_dissolve, objects, result_erase)\narcpy.Append_management(result_erase, result, \"NO_TEST\")\narcpy.Delete_management(\"in_memory\")\narcpy.Delete_management(vertexes)\nstart_line = []\n\n# Clear selection, memory and deleting temps\narcpy.SelectLayerByAttribute_management(objects, \"CLEAR_SELECTION\")\nprint (\"Object number: \" + str(index - 1) + \" -- done.\")\ngc.collect()\n\n# Catch errors\nexcept Exception as e:\npass\nprint (\"Error:\")\nprint (e)\nprint (\"\\n\")\nindex += 1", null, "by", null, "MVP Regular Contributor\n\nPlease mention any error messages or results that the script is currently producing.", null, "New Contributor II\n\nTraceback (most recent call last):\nFile \"D:/testScript/myDBpy2.py\", line 18, in <module>\narcpy.CreateFeatureclass_management(arcpy.env.workspace, result, \"POLYGON\")\nFile \"C:\\Program Files (x86)\\ArcGIS\\Desktop10.3\\ArcPy\\arcpy\\management.py\", line 1807, in CreateFeatureclass\nraise e\nExecuteError: ERROR 000354: The name contains invalid characters\nFailed to execute (CreateFeatureclass).", null, "by Anonymous User\nNot applicable\n\nYou also have an updateCursor that probably can be a search cursor since it doesn't look like it is ever updating the row.\n\nAnother tip that I see is that your objects is pointing to 'objects_lyr.shp'.  Since it is only a view (think of it as a temporary copy of the original data that you can do stuff to), you don't need the .shp at the end.  It can live and be referenced as 'objects_lyr' and you can also reference it through assigning the MakeFeatureLayer to a variable.   This doesn't effect the code at all, but more of a FYI.\n\n``````# INPUTS\nobjects = \"objects_lyr.shp\"\narcpy.MakeFeatureLayer_management(objects_input, objects)``````\n\nto\n\n``````# INPUTS\nobjects_input = \"objects.shp\" # must fix this per David's response\nobjectOutName = \"objects_lyr.shp\"\nobjLyr = arcpy.MakeFeatureLayer_management(objects_input, \"objects_lyr\")``````\n\nthen replace 'objects' with 'objLyr' where needed so its more readable and people know that it is referring to a Layer.", null, "by", null, "MVP Esteemed Contributor\n\nCode formatting ... the Community Version - GeoNet, The Esri Community would help with readability\n\n... sort of retired...", null, "New Contributor II\n\nThank You. I will try it!", null, "New Contributor II\n\nStill not workinng! May be cause  I'm verry junior in python coding for arcgis😔\n\nimport arcpy, math, gc\n\n# Workspace, overwrite\narcpy.env.workspace = r\"D:\\testScript\\My.gdb\"\narcpy.env.overwriteOutput = True\n\n# INPUTS\nobjects_input = r\"D:\\testScript\\objects.shp\" # must be polygons\nobjects = \"objects_lyr.shp\"\narcpy.MakeFeatureLayer_management(objects_input, objects)\n\n# OUTPUTS, most temporal\nresult = \"result.shp\"\nresult_erase = \"in_memory\" + \"\\\\\" + \"result_erase\"\npolygon = \"in_memory\" + \"\\\\\" + \"polygon\"\npolygon_dissolve = \"in_memory\" + \"\\\\\" + \"polygon_dissolve\"\n\narcpy.CreateFeatureclass_management(arcpy.env.workspace, result, \"POLYGON\")\n\n# Parameters\ndistance = 9 # distance for move in direction\ndirection = 90 # direction in degrees (90 is from north to south)\nindex = 0\n\n# Set UpdateCursor\ncur_objects = arcpy.da.UpdateCursor(objects, (\"FID\"))\nfor row_objects in cur_objects:\ntry:\nfid = row_objects\nsql = '\"FID\" = ' + str(index)\nindex += 1\n\n# Initialize lists\nlines_list = []\nlines_created = []\n\n# Select current feature\narcpy.SelectLayerByAttribute_management(objects, \"NEW_SELECTION\", sql)\nvertexes = \"in_memory\" + \"\\\\\" + \"vertexes\"\n\n# Convert object to vertexes\narcpy.FeatureVerticesToPoints_management(objects, vertexes, \"ALL\")\nindex_vertex = 0\n\n# Set SearchCursor for vertexes\ncur_vertexes = arcpy.da.SearchCursor(vertexes, (\"SHAPE@XY\"))\nfor row_vertexes in cur_vertexes:\nvertex_coords_x = row_vertexes\nvertex_coords_y = row_vertexes\n\n# Define points coordinates\npoint_move_x = vertex_coords_x - (distance) * math.cos(math.radians(direction))\npoint_move_y = vertex_coords_y - (distance) * math.cos(math.radians(90 - direction))\n\n# Make list of points\nnew_line = ([[vertex_coords_x, vertex_coords_y], [point_move_x, point_move_y]])\nlines_list.append(new_line)\n\n# From second cycle\nif index_vertex > 0:\nlines_vertexes = ([[vertex_coords_x, vertex_coords_y], start_line])\nlines_ends = ([[point_move_x, point_move_y], end_line])\nlines_list.append(lines_vertexes)\nlines_list.append(lines_ends)\nstart_line = [vertex_coords_x, vertex_coords_y]\nend_line = [point_move_x, point_move_y]\nindex_vertex = index_vertex + 1\n\n# Cycle that makes polylines from points\nfor lines_step in lines_list:\nlines_created.append(arcpy.Polyline(arcpy.Array([arcpy.Point(*sour) for sour in lines_step])))\n\narcpy.FeatureToPolygon_management(lines_created, polygon)\narcpy.AggregatePolygons_cartography(polygon, polygon_dissolve, 1)\n\n# Final editing\narcpy.Erase_analysis(polygon_dissolve, objects, result_erase)\narcpy.Append_management(result_erase, result, \"NO_TEST\")\narcpy.Delete_management(\"in_memory\")\narcpy.Delete_management(vertexes)\nstart_line = []\n\n# Clear selection, memory and deleting temps\narcpy.SelectLayerByAttribute_management(objects, \"CLEAR_SELECTION\")\nprint (\"Object number: \" + str(index - 1) + \" -- done.\")\ngc.collect()\n\n# Catch errors\nexcept Exception as e:\npass\nprint (\"Error:\")\nprint (e)\nprint (\"\\n\")\nindex += 1", null, "New Contributor II\n\nTraceback (most recent call last):\nFile \"D:/testScript/myDBpy2.py\", line 18, in <module>\narcpy.CreateFeatureclass_management(arcpy.env.workspace, result, \"POLYGON\")\nFile \"C:\\Program Files (x86)\\ArcGIS\\Desktop10.3\\ArcPy\\arcpy\\management.py\", line 1807, in CreateFeatureclass\nraise e\nExecuteError: ERROR 000354: The name contains invalid characters\nFailed to execute (CreateFeatureclass).", null, "" ]
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https://scholar.archive.org/work/thmv4xpchnhrpco27sdzbhqvzm
[ "### Numerical treatment of a Volterra integral equation with space maps\n\nMario Annunziato, Eleonora Messina\n2010 Applied Numerical Mathematics\nWe present numerical schemes for the Liouville Master Equation (LME) associated to a stochastic process that results from the action of a semi-Markov process on first order differential equations. The LME is a system of hyperbolic PDE with both local and non-local boundary conditions. We show that equation in differential and integral form and give a proof for the existence and uniqueness of the solution for the integral form. We use numerical schemes for solving the differential [1; 2] and\nmore » ... gral form . We study stability by Fourier analysis, and show some numerical experiments on cases of practical application that confirms the theoretical findings. The talk deals with the smoothing -interpolating (smoothing for a part of data and interpolating for the rest) problems in the abstract setting of a Hilbert space. Let X, Y be Hilbert spaces and assume that linear operators T : X → Y , A 1 : X → IR n and A 2 : X → IR m are continuous. We consider the conditional minimization problem ||T x|| −→ min x∈H Abstracts of MMA2010," ]
[ null ]
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https://avesis.metu.edu.tr/yayin/876cfb77-c555-4a72-9d7c-23d576dfa0e0/elements-of-prescribed-order-prescribed-traces-and-systems-of-rational-functions-over-finite-fields
[ "## Elements of prescribed order, prescribed traces and systems of rational functions over finite fields\n\nDESIGNS CODES AND CRYPTOGRAPHY, vol.34, no.1, pp.35-54, 2005 (Peer-Reviewed Journal)", null, "", null, "• Publication Type: Article / Article\n• Volume: 34 Issue: 1\n• Publication Date: 2005\n• Doi Number: 10.1007/s10623-003-4193-0\n• Journal Name: DESIGNS CODES AND CRYPTOGRAPHY\n• Journal Indexes: Science Citation Index Expanded, Scopus\n• Page Numbers: pp.35-54\n\n#### Abstract\n\nLet k greater than or equal to 1 and f(1),..., f(r) is an element of F-qk (x) be a system of rational functions forming a strongly linearly independent set over a finite field F-q. Let gamma(1),..., gamma(r) is an element of F-q be arbitrarily prescribed elements. We prove that for all sufficiently large extensions F-qkm, there is an element xi is an element of F-qkm of prescribed order such that Tr-Fqkm /Fq (f(i) (xi)) = gamma(i) for i = 1,..., r, where Tr-Fqkm/fq is the relative trace map from F-qkm onto F-q. We give some applications to BCH codes, finite field arithmetic and ordered orthogonal arrays. We also solve a question of Helleseth et al. (Hypercubic 4 and 5-designs from Double-Error-Correcting codes, Des. Codes. Cryptgr. 28(2003). pp. 265-282) completely." ]
[ null, "https://avesis.metu.edu.tr/Content/images/integrations/small/integrationtype_2.png", null, "https://avesis.metu.edu.tr/Content/images/integrations/small/integrationtype_1.png", null ]
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http://www.bazzocchi.com/wittgenstein/tractatus/eng/5_45.htm
[ "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "", null, "If there are logical primitive signs a correct logic must make clear their position relative to one another and justify their existence. The construction of logic out of its primitive signs must become clear. 5.451    If logic has primitive ideas these must be independent of one another. If a primitive idea is introduced it must be introduced in all contexts in which it occurs at all. One cannot therefore introduce it for one context and then again for another. For example, if denial is introduced, we must understand it in propositions of the form \"~p\", just as in propositions like \"~(p v q)\", \"(", null, "x).~fx\" and others. We may not first introduce it for one class of cases and then for another, for it would then remain doubtful whether its meaning in the two cases was the same, and there would be no reason to use the same way of symbolizing in the two cases. (In short, what Frege (\"Grundgesetze der Arithmetik\") has said about the introduction of signs by definitions holds, mutatis mutandis, for the introduction of primitive signs also.) 5.452    The introduction of a new expedient in the symbolism of logic must always be an event full of consequences. No new symbol may be introduced in logic in brackets or in the margin - with, so to speak, an entirely innocent face-. (Thus in the \"Principia Mathematica\" of Russell and Whitehead there occur definitions and primitive propositions in words. Why suddenly words here? This would need a justification. There was none, and can be none for the process is actually not allowed.) But if the introduction of a new expedient has proved necessary in one place, we must immediately ask: Where is this expedient always to be used? Its position in logic must be made clear. 5.453    All numbers in logic must be capable of justification. Or rather it must become plain that there are no numbers in logic. There are no pre-eminent numbers. 5.454 (1)  In logic there is no side by side, there can be no classification. In logic there cannot be a more general and a more special." ]
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https://numberworld.info/340442
[ "# Number 340442\n\n### Properties of number 340442\n\nCross Sum:\nFactorization:\n2 * 17 * 17 * 19 * 31\nDivisors:\nCount of divisors:\nSum of divisors:\nPrime number?\nNo\nFibonacci number?\nNo\nBell Number?\nNo\nCatalan Number?\nNo\nBase 2 (Binary):\nBase 3 (Ternary):\nBase 4 (Quaternary):\nBase 5 (Quintal):\nBase 8 (Octal):\n531da\nBase 32:\naceq\nsin(340442)\n0.16967619296525\ncos(340442)\n0.98549986785429\ntan(340442)\n0.17217272015944\nln(340442)\n12.738000052324\nlg(340442)\n5.5320431332076\nsqrt(340442)\n583.47407825884\nSquare(340442)\n\n### Number Look Up\n\nLook Up\n\n340442 which is pronounced (three hundred forty thousand four hundred forty-two) is a very amazing figure. The cross sum of 340442 is 17. If you factorisate 340442 you will get these result 2 * 17 * 17 * 19 * 31. The number 340442 has 24 divisors ( 1, 2, 17, 19, 31, 34, 38, 62, 289, 323, 527, 578, 589, 646, 1054, 1178, 5491, 8959, 10013, 10982, 17918, 20026, 170221, 340442 ) whith a sum of 589440. 340442 is not a prime number. The figure 340442 is not a fibonacci number. 340442 is not a Bell Number. The number 340442 is not a Catalan Number. The convertion of 340442 to base 2 (Binary) is 1010011000111011010. The convertion of 340442 to base 3 (Ternary) is 122021222222. The convertion of 340442 to base 4 (Quaternary) is 1103013122. The convertion of 340442 to base 5 (Quintal) is 41343232. The convertion of 340442 to base 8 (Octal) is 1230732. The convertion of 340442 to base 16 (Hexadecimal) is 531da. The convertion of 340442 to base 32 is aceq. The sine of the figure 340442 is 0.16967619296525. The cosine of the figure 340442 is 0.98549986785429. The tangent of the figure 340442 is 0.17217272015944. The square root of 340442 is 583.47407825884.\nIf you square 340442 you will get the following result 115900755364. The natural logarithm of 340442 is 12.738000052324 and the decimal logarithm is 5.5320431332076. You should now know that 340442 is very impressive figure!" ]
[ null ]
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https://math.stackexchange.com/questions/1906241/when-not-to-treat-dy-dx-as-a-fraction-in-single-variable-calculus/1906278
[ "# When not to treat dy/dx as a fraction in single-variable calculus?\n\nWhile I do know that $\\frac{dy}{dx}$ isn't a fraction and shouldn't be treated as such, in many situations, doing things like multiplying both sides by $dx$ and integrating, cancelling terms, doing things like $\\frac{dy}{dx} = \\frac{1}{\\frac{dx}{dy}}$ works out just fine.\n\nSo I wanted to know: Are there any particular cases (in single-variable calculus) we have to look out for, where treating $\\frac{dy}{dx}$ as a fraction gives incorrect answers, in particular, at an introductory level?\n\nNote: Please provide specific instances and examples where treating $\\frac{dy}{dx}$ as a fraction fails\n\n• Linking a related post here: Is $\\displaystyle\\frac{dy}{dx}$ not a ratio? – Frenzy Li Aug 28 '16 at 13:07\n• – Hans Lundmark Aug 28 '16 at 13:17\n• Huh. I've always found it helpful to always treat it like a fraction. :/ – user345895 Aug 28 '16 at 21:06\n• @HansLundmark At least the first one isn't, as this question is dealing specifically with single-variable calculus, and there the answers are all multivariable. – user345895 Aug 28 '16 at 21:10\n• @HansLundmark The reason I created this thread is because in all of the duplicates you've mentioned, none of the answers have shown an example where treating $dy/dx$ as a fraction in single variable calculus has led to incorrect answers or given circumstances where we should not treat it like a fraction, i.e, none of them have really answered the question. – xasthor Aug 29 '16 at 15:56\n\nIt is because of the extraordinary power of Leibniz's differential notation, which allows you to treat them as fractions while solving problems. The justification for this mechanical process is apparent from the following general result:\n\nLet $y=h(x)$ be any solution of the separated differential equation $A(y)\\dfrac{dy}{dx} = B(x)$... (i) such that $h'(x)$ is continuous on an open interval $I$, where $B(x)$ and $A(h(x))$ are assumed to be continuous on $I$. If $g$ is any primitive of $A$ (i.e. $g'=A$) on $I$, then $h$ satisfies the equation $g(y)=\\int {B(x)dx} + c$...(ii) for some constant $c$. Conversely, if $y$ satisfies (ii) then $y$ is a solution of (i).\n\nAlso, it would be advisable to say $\\dfrac{dy}{dx}=\\dfrac{1}{\\dfrac{dx}{dy}}$ only when the function $y(x)$ is invertible.\n\nSay you are asked to find the equation of normal to a curve $y(x)$ at a particular point $(x_1,y_1)$. In general you should write the slope of the equation as $-\\dfrac{1}{\\dfrac{dy}{dx}}\\big|_{(x_1,y_1)}$ instead of simply writing it as $-\\dfrac{dx}{dy}\\big|_{(x_1,y_1)}$ without checking for the invertibility of the function (which would be redundant here). However, the numerical calculations will remain the same in any case.\n\nEDIT.\n\nThe Leibniz notation ensures that no problem will arise if one treats the differentials as fractions because it beautifully works out in single-variable calculus. But explicitly stating them as 'fractions' in any exam/test could cost one the all important marks. One could be criticised in this case to be not formal enough in his/her approach.\n\nAlso have a look at this answer which explains the likely pitfalls of the fraction treatment.\n\n• I think you mixed up $h$ and $g$ in the yellow box. For example, you write \"then $h$ satisfies the equation [...]\" but there is no $h$ in that equation. – Vincent Aug 28 '16 at 17:07\n• @Vincent It means $h$ is a solution of that equation. – StubbornAtom Aug 28 '16 at 17:26\n• -1, because I don't think this answers the question. – Martin Argerami Aug 28 '16 at 19:36\n• @MartinArgerami nothing here answers the real question. – A---B Aug 28 '16 at 23:03\n• @MartinArgerami I guess it is clear now. – StubbornAtom Aug 29 '16 at 6:36\n\nIn calculus we have this relationship between differentials: $dy = f^{\\prime}(x) dx$ which could be written $dy = \\frac{dy}{dx} dx$. If you have $\\frac{dy}{dx} = \\sin x$, then it's legal to multiply both sides by $dx$. On the left you have $\\frac{dy}{dx} dx$. When you replace it with $dy$ using the above relationship, it looks just like you've cancelled the $dx$'s. Such a replacement is so much like division we can hardly tell the difference.\n\nHowever if you have an implicitly defined function $f(x,y) = 0$, the total differential is $f_x \\;dx + f_y \\;dy = 0$. \"Solving\" for $\\frac{dy}{dx}$ gives $$\\frac{dy}{dx} = -\\frac{f_x}{f_y} = -\\frac{\\partial f / \\partial x}{\\partial f /\\partial y}.$$ This is the correct formula for implicit differentiation, which we arrived at by treating $\\frac{dy}{dx}$ as a ration, but then look at the last fraction. If you simplify it, it makes the equation $$\\frac{dy}{dx} = -\\frac{dy}{dx}.$$ That pesky minus sign sneeks in because we reversed the roles of $x$ and $y$ between the two partial derivatives. Maddening.\n\n• I like the last example . . . I found it last year myself and showed it to my classmates . . . no one was able to understand what happened (they did not know partial differentitation) – Kartik Aug 28 '16 at 14:19\n• But the last fraction has partial derivatives, which are not only written differently, but also are something different. So to arrive at your final result, you would first have to replace $\\partial$ with $d$, and only then \"cancel\" $df$. So it's not a real counterexample (not to mention that the question explicitly asked for single-variable calculus). – celtschk Aug 28 '16 at 14:28\n• I second @celtschk's remark. Partial derivatives are too often used misleadingly in attempts to discredit the approach to $\\frac{dy}{dx}$ as a ratio. It is not a valid objection. – Mikhail Katz Aug 28 '16 at 16:24\n• This is a bit like worrying whether we hurt Pluto's feelings when we decided it wasn't a planet. I'm not trying to \"discredit\" anything. In this case, 1. Try to define the difference between $d$ and $\\partial$ so that it matters for this question. 2. No, you don't have to change the $\\partial$ to $d$ to make the $\\partial F$'s cancel. 3. I am assuming the OP is a student, in which case communication needs to precede rigor. – B. Goddard Aug 28 '16 at 17:51\n• This is not an example from single-variable calculus, as the question requests. Partial derivatives belong to multivariable calculus. – pregunton Aug 30 '16 at 15:16\n\nThere are places where it is \"obvious\" that we should not blindly apply the laws of arithmetic to $\\frac{dy}{dx}$ as if it were a ratio of real numbers $dy$ and $dx$. An example from another question is $$\\frac{dy}{dx}+\\frac{du}{dv} \\overset ?= \\frac{dy\\,dv+dx\\,du}{dx\\, dv},$$ where the left-hand side has a clear interpretation but the right-hand side does not.\n\nAs for any false equation that you might actually be tempted to write by treating $\\frac{dy}{dx}$ as a ratio, however, I have not seen any actual counterexamples in any of the several related questions and their answers (including the question already mentioned, this question, or this question).\n\nIn practice, the problem I see with treating $\\frac{dy}{dx}$ as if it were a ratio is not whether an equation is true or not, but how we know that it is true. For example, if you write $\\frac{dy}{dx} \\, \\frac{dx}{dt} = \\frac{dy}{dt}$ because it seems to you that the $dx$ terms cancel, without having first learned (or discovered) the chain rule and having recognized that it justifies this particular equation, then I would say you're just making an ill-educated guess about this equation rather than doing mathematics. (I'll grant that the equation is valid mathematics even if you don't remember that it's called the \"chain rule\". I think that particular detail is mainly important when teaching or when answering questions on calculus exams that are designed to test whether you were paying attention when that rule was introduced.)\n\nIn single-variable calculus, I am not aware of a single instance of getting incorrect results by treating $\\frac{dy}{dx}$ as a ratio. This is why in fact there are so few mistakes in Leibniz who did treat it as a ratio. However, there are certainly times when you should not treat it as a ratio. You shouldn't do that at the time of the exams in the course, because the instructor's reaction will surely be to take off points.\n\nThe big thing is that there is a thing called a \"differential\", and we can make things like $\\mathrm{d}y$ or $\\mathrm{d}f(t)$ mean one of those.\n\nWe can multiply differentials by functions (e.g. $x^2 \\mathrm{d}x$), and we can add differentials, and these operations will behave like you expect them to.\n\nDon't try to multiply two differentials, though: the right way to do that probably does not behave like you expect them to.\n\n$\\mathrm{d}$ satisfies the 'laws' of differentiation; e.g. $\\mathrm{d}f(t) = f'(t) \\mathrm{d}t$ and $\\mathrm{d}(xy) = x \\mathrm{d}y + y \\mathrm{d}x$.\n\nDon't try to differentiate a differential either; the usual way to do that again doesn't behave how you expect, and is probably unrelated to what you wanted to do anyways.\n\nAnyways, if you have an equation like $\\mathrm{d}y = 2x \\mathrm{d}x$ (e.g. by applying $\\mathrm{d}$ to the equation $y = x^2$) and $\\mathrm{d}x$ is \"nonzero\" in a suitable sense, then it makes sense to define $\\frac{\\mathrm{d}y}{\\mathrm{d}x}$ to mean the ratio between the differentials.\n\nSingle variable calculus is peculiar in that all of the variables and expressions you work with will have differentials that are multiples of one another. This isn't true in general; e.g. if $x$ and $y$ are independent, then $\\mathrm{d}x$ and $\\mathrm{d}y$ are not multiples of one another, and $\\frac{\\mathrm{d}y}{\\mathrm{d}x}$ is utter nonsense.\n\nDifferentials are still very useful in such a setting, though, although the \"usual\" approach tends to neglect them.\n\nThere is a notion called a \"partial derivative\", often given similar notation $\\frac{\\partial y}{\\partial x}$, but it really doesn't pay to treat it like a fraction, and there isn't really a corresponding notion of $\\partial x$." ]
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https://proxieslive.com/can-mathematica-factor-out-constrained-functions-when-it-simplifies/
[ "# Can Mathematica factor out constrained functions when it simplifies?\n\nFor example, distributions in the exponential family take a likelihood function of the form $$f(x)g(\\theta)e^{\\phi(\\theta)^{T}u(x)}$$. Given some ugly algebraic expression, can I tell mathematica to infer $$f(x),g(\\theta),\\phi(\\theta),u(x)$$ To say, show my ugly algebraic expression can be factored into that constrained form? I cannot find any documentation on this subject\n\nPosted on Categories cheapest proxies" ]
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https://community.khronos.org/t/normals/12429
[ "", null, "# Normals\n\nHi! there is one thing I don´t get. do I need to calculate the face/vertex normals every frame or can I calculate them at start of the program and put them in a lookup table ?\n\nIf the object you display does not change (i.e. it is a static mesg), its normals don’t change either… You can then calculate all normals once for all !\n\nRegards.\n\nEric\n\nso if I have a cube that alwas would be a cube I could calculate the normals at the start of my program", null, "but if I have a cube that will morph into a pyramid or somthing I have to recalculate the normals right ?", null, "Yep, you got it !\n\nRegards.\n\nEric\n\nall moving objects will also need to have their normals recalculated\n\nOriginally posted by Tim Stirling:\nall moving objects will also need to have their normals recalculated\n\nNot necessarily so…\n\nIf you move your objects with glTranslatex(…) then the normals do not have to be recomputed.\n\nIf you have an object whose geometry changes with time then you may need to recompute the normals. However, for non-changing geometries, a single calculation is all that is necessary.\n\nOriginally posted by Tim Stirling:\nall moving objects will also need to have their normals recalculated\n\nI know pleopard answered this, but I want to insist…\n\nNO, NO, and NO : YOU DON’T HAVE TO RECALCULATE YOUR NORMALS IF THE MESH IS STATIC !!!\n\nEven if you rotate/translate it, you don’t have to recalculate them… Actually, even if you scale it, you have nothing to do (except using glEnable(GL_NORMALIZE) !).\n\nYou have to recalculate the normals only if you change the mesh…\n\nRegards.\n\nEric" ]
[ null, "https://community.khronos.org/uploads/default/original/2X/b/b557c879c88ec7b8104d755db805309010d5e1c0.png", null, "http://www.opengl.org/discussion_boards/ubb/smile.gif", null, "http://www.opengl.org/discussion_boards/ubb/smile.gif", null ]
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https://www.rdocumentation.org/packages/fda.usc/versions/2.0.1/topics/fdata.methods
[ "# fdata.methods\n\n0th\n\nPercentile\n\n##### fdata S3 Group Generic Functions\n\nfdata Group generic methods defined for four specified groups of functions, Math, Ops, Summary and Complex.\n\norder.fdata and split.fdata: A wrapper for the order and split function for fdata object.\n\nKeywords\nmath, Descriptive\n##### Usage\n# S3 method for fdata\nMath(x, ...)# S3 method for fdata\nOps(e1, e2 = NULL)# S3 method for fdata\nSummary(..., na.rm = FALSE)# S3 method for fdata\nsplit(x, f, drop = FALSE, ...)order.fdata(y, fdataobj, na.last = TRUE, decreasing = FALSE)is.fdata(fdataobj)\n##### Arguments\nx\n\nAn fdata object containing values to be divided into groups or an list of fdata objects containing values to be combine by rows in a to be flatten one fdata object.\n\nFurther arguments passed to methods.\n\ne1, e2\n\nfdata class object\n\nna.rm\n\nlogical: should missing values be removed?\n\nf\n\na factor in the sense that as.factor(f) defines the grouping, or a list of such factors in which case their interaction is used for the grouping.\n\ndrop\n\nlogical indicating if levels that do not occur should be dropped (if f is a factor or a list).\n\ny\n\nA sequence of numeric, complex, character or logical vectors, all of the same length, or a classed R object.\n\nfdataobj\n\nfdata class object.\n\nna.last\n\nfor controlling the treatment of NAs. If TRUE, missing values in the data are put last; if FALSE, they are put first; if NA, they are removed; if \"keep\" they are kept with rank NA.\n\ndecreasing\n\nlogical Should the sort order be increasing or decreasing?.\n\n##### Details\n\nIn order.fdata the funcional data is ordered w.r.t the sample order of the values of vector.\n\nsplit.fdata divides the data in the fdata object x into the groups defined by f.\n\n##### Value\n\n• split.fdata: The value returned from split is a list of fdata objects containing the values for the groups. The components of the list are named by the levels of f (after converting to a factor, or if already a factor and drop = TRUE, dropping unused levels).\\\n\n• order.fdata: returns the functional data fdataobj w.r.t. a permutation which rearranges its first argument into ascending or descending order.\n\nSee Summary and Complex.\n\n##### Aliases\n• fdata.methods\n• Math.fdata\n• Ops.fdata\n• Summary.fdata\n• split.fdata\n• order.fdata\n• is.fdata\n##### Examples\n# NOT RUN {\ndata(tecator)\nabsor<-tecator\\$absorp.fdata\nabsor2<-fdata.deriv(absor,1)\nabsor<-absor2[1:5,1:4]\nabsor2<-absor2[1:5,1:4]\nsum(absor)\nround(absor,4)\nlog1<-log(absor)\n\nfdataobj<-fdata(MontrealTemp)\nfac<-factor(c(rep(1,len=17),rep(2,len=17)))\na1<-split(fdataobj,fac)\ndim(a1[]);dim(a1[])\n# }\n# NOT RUN {\n# }\n\nDocumentation reproduced from package fda.usc, version 2.0.1, License: GPL-2\n\n### Community examples\n\nLooks like there are no examples yet." ]
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