File size: 54,272 Bytes
439a5cd 6e16701 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 |
<!DOCTYPE html>
<html lang="vi">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>MLP Interactive Visualization</title>
<script src="https://cdn.tailwindcss.com"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap" rel="stylesheet">
<style>
body { font-family: 'Inter', sans-serif; background-color: #f1f5f9; }
/* Custom class for active class selection buttons */
.button-active-blue {
background-color: #2563eb !important;
color: white !important;
border-color: #2563eb !important;
box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1);
}
.button-active-red {
background-color: #dc2626 !important;
color: white !important;
border-color: #dc2626 !important;
box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1);
}
/* Cursor styles for data input */
#plotCanvas { touch-action: none; }
#plotCanvas.cursor-class-0 { cursor: url('data:image/svg+xml;utf8,<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="%233b82f6" stroke="white" stroke-width="2"><circle cx="12" cy="12" r="8"/><path d="M12 2v4M12 18v4M22 12h-4M6 12H2"/></svg>') 12 12, crosshair; }
#plotCanvas.cursor-class-1 { cursor: url('data:image/svg+xml;utf8,<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="%23ef4444" stroke="white" stroke-width="2"><circle cx="12" cy="12" r="8"/><path d="M12 2v4M12 18v4M22 12h-4M6 12H2"/></svg>') 12 12, crosshair; }
/* Scrollbar styling */
#hiddenLayersConfigContainer::-webkit-scrollbar { width: 6px; }
#hiddenLayersConfigContainer::-webkit-scrollbar-track { background: #e2e8f0; border-radius: 8px; }
#hiddenLayersConfigContainer::-webkit-scrollbar-thumb { background: #94a3b8; border-radius: 8px; }
#hiddenLayersConfigContainer::-webkit-scrollbar-thumb:hover { background: #64748b; }
/* Network Visualization SVG styles */
.neuron {
stroke-width: 1.5;
transition: stroke-width 0.2s ease-in-out;
}
.neuron:hover {
stroke-width: 4;
}
.neuron.input { fill: #60a5fa; stroke: #2563eb; }
.neuron.hidden-0 { fill: #818cf8; stroke: #4f46e5; }
.neuron.hidden-1 { fill: #a78bfa; stroke: #7c3aed; }
.neuron.hidden-2 { fill: #c084fc; stroke: #9333ea; }
.neuron.hidden-3 { fill: #e879f9; stroke: #c026d3; }
.neuron.hidden-other { fill: #f472b6; stroke: #db2777; }
.neuron.output { fill: #f87171; stroke: #dc2626; }
.connection {
stroke: #cbd5e1;
stroke-width: 0.75;
transition: stroke-opacity 0.2s;
}
.layer-label {
font-size: 11px;
font-weight: 600;
fill: #475569;
text-anchor: middle;
}
.neuron-count-label {
font-size: 10px;
font-weight: 500;
fill: #64748b;
text-anchor: middle;
}
/* Utility icon styles */
.title-icon { margin-left: 0.6rem; font-size: 1.1rem; }
.action-icon { margin-right: 0.35rem; }
.status-icon { margin-right: 0.5rem; flex-shrink: 0; font-size: 1.1rem; }
.loading-icon { animation: spin 1.5s linear infinite; display: inline-block; }
@keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } }
/* Confusion Matrix styles */
.cm-cell {
display: flex;
align-items: center;
justify-content: center;
flex-direction: column;
line-height: 1.2;
padding: 0.5rem;
border-radius: 0.25rem;
transition: all 0.2s ease;
}
.cm-value { font-size: 1.125rem; font-weight: 800; }
.cm-label { font-size: 0.65rem; text-transform: uppercase; letter-spacing: 0.05em; font-weight: 600; }
</style>
</head>
<body class="text-slate-800">
<div class="container mx-auto p-2 sm:p-4 max-w-full">
<header class="mb-4 text-center">
<h1 class="text-3xl sm:text-4xl font-extrabold text-blue-600">MLP Interactive Visualization</h1>
<p class="text-md text-slate-600 mt-1">Build, train, and visualize a Multi-Layer Perceptron.</p>
</header>
<!-- Main layout with reduced gap -->
<div class="flex flex-col lg:flex-row gap-2.5">
<!-- Control Panel -->
<div class="lg:w-6/12 bg-white p-3.5 rounded-2xl shadow-xl border border-slate-200 space-y-5">
<!-- Data & Training Section -->
<div class="space-y-4">
<h2 class="text-xl font-bold text-slate-700 border-b border-slate-200 pb-2 flex items-center">
1. Data & Training <span class="title-icon">βοΈ</span>
</h2>
<div class="grid grid-cols-2 sm:grid-cols-4 gap-x-4 gap-y-3">
<div class="col-span-2 sm:col-span-4">
<label class="text-sm font-medium text-slate-700 block mb-1.5">Data Input Class:</label>
<div class="flex rounded-lg shadow-sm">
<button id="class0Button" class="flex-1 px-3 py-2 border border-slate-300 rounded-l-lg bg-white text-base font-semibold text-slate-700 hover:bg-slate-50 focus:z-10 focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500 transition-all duration-150" title="Select Class 0 for the next data points">Class 0 (Blue)</button>
<button id="class1Button" class="flex-1 px-3 py-2 border-t border-b border-r border-slate-300 rounded-r-lg bg-white text-base font-semibold text-slate-700 hover:bg-slate-50 focus:z-10 focus:outline-none focus:ring-2 focus:ring-red-500 focus:border-red-500 transition-all duration-150" title="Select Class 1 for the next data points">Class 1 (Red)</button>
</div>
</div>
<!-- Other controls... -->
<div>
<label for="datasetSelect" class="text-sm font-medium text-slate-700 block mb-1">Load Dataset:</label>
<select id="datasetSelect" class="mt-0 block w-full px-2 py-1.5 border border-slate-300 bg-white rounded-lg shadow-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500 text-sm" title="Choose a preset dataset.">
<option value="manual">Manual Input</option>
<option value="two_moons">Two Moons</option>
<option value="circles">Concentric Circles</option>
<option value="xor">XOR</option>
<option value="spiral">Spiral</option>
</select>
</div>
<div>
<label for="dataNoise" class="text-sm font-medium text-slate-700 block mb-1">Data Noise:</label>
<input type="number" id="dataNoise" value="0.05" min="0" max="0.5" step="0.01" class="mt-0 block w-full px-2 py-1.5 border border-slate-300 rounded-lg shadow-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500 text-sm" title="Add random noise to the data points.">
</div>
<div>
<label for="learningRate" class="text-sm font-medium text-slate-700 block mb-1">Learning Rate:</label>
<input type="number" id="learningRate" value="0.01" step="0.001" min="0.00001" class="mt-0 block w-full px-2 py-1.5 border border-slate-300 rounded-lg shadow-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500 text-sm" title="How fast the model learns.">
</div>
<div>
<label for="epochs" class="text-sm font-medium text-slate-700 block mb-1">Epochs:</label>
<input type="number" id="epochs" value="150" step="10" min="1" class="mt-0 block w-full px-2 py-1.5 border border-slate-300 rounded-lg shadow-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500 text-sm" title="Number of training iterations.">
</div>
<div>
<label for="optimizerSelect" class="text-sm font-medium text-slate-700 block mb-1">Optimizer:</label>
<select id="optimizerSelect" class="mt-0 block w-full px-2 py-1.5 border border-slate-300 bg-white rounded-lg shadow-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500 text-sm" title="Algorithm to update weights.">
<option value="adam">Adam</option>
<option value="sgd">SGD</option>
<option value="rmsprop">RMSprop</option>
</select>
</div>
<div>
<label for="batchSize" class="text-sm font-medium text-slate-700 block mb-1">Batch Size:</label>
<input type="number" id="batchSize" value="16" min="0" step="4" class="mt-0 block w-full px-2 py-1.5 border border-slate-300 rounded-lg shadow-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500 text-sm" title="Samples per weight update. 0=Full.">
</div>
<div>
<label for="regularizationTypeSelect" class="text-sm font-medium text-slate-700 block mb-1">Regularization:</label>
<select id="regularizationTypeSelect" class="mt-0 block w-full px-2 py-1.5 border border-slate-300 bg-white rounded-lg shadow-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500 text-sm" title="Technique to prevent overfitting.">
<option value="none">None</option>
<option value="l1">L1</option>
<option value="l2">L2</option>
</select>
</div>
<div id="regularizationRateContainer" class="hidden">
<label for="regularizationRateInput" class="text-sm font-medium text-slate-700 block mb-1">Reg. Rate (Ξ»):</label>
<input type="number" id="regularizationRateInput" value="0.001" step="0.0001" min="0" class="mt-0 block w-full px-2 py-1.5 border border-slate-300 rounded-lg shadow-sm focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-blue-500 text-sm" title="Strength of the L1/L2 penalty.">
</div>
</div>
</div>
<!-- MLP Architecture Section -->
<div class="space-y-2">
<h2 class="text-xl font-bold text-slate-700 border-b border-slate-200 pb-2 flex items-center">
2. MLP Architecture <span class="title-icon">π§ </span>
</h2>
<div id="networkVisualization" class="bg-slate-50 rounded-lg p-2 min-h-[120px] border border-slate-200"></div>
<h3 class="text-sm font-medium text-slate-700 pt-2">Hidden Layers:</h3>
<div id="hiddenLayersConfigContainer" class="grid grid-cols-1 md:grid-cols-2 gap-2 max-h-48 overflow-y-auto pr-1.5"></div>
<button id="addHiddenLayerButton" class="mt-1 w-full flex justify-center items-center py-2 px-3 border-2 border-dashed border-blue-400 rounded-lg shadow-sm text-sm font-semibold text-blue-600 bg-blue-50 hover:bg-blue-100 hover:border-blue-500 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500 transition-all duration-150" title="Add a new hidden layer.">
<span class="action-icon text-base">β</span> Add Layer
</button>
</div>
<!-- Actions Section -->
<div class="space-y-2">
<h2 class="text-xl font-bold text-slate-700 border-b border-slate-200 pb-2 flex items-center">
3. Actions <span class="title-icon">β‘οΈ</span>
</h2>
<div class="grid grid-cols-2 sm:grid-cols-4 gap-2.5">
<button id="trainButton" class="col-span-2 flex justify-center items-center py-2.5 px-3 border border-transparent rounded-lg shadow-md text-base font-bold text-white bg-green-600 hover:bg-green-700 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-green-500 transition-all duration-150 disabled:opacity-50 disabled:cursor-not-allowed disabled:bg-green-400" title="Start or retrain the model.">
<span class="action-icon">βΆοΈ</span> <span id="trainButtonText">Train</span>
</button>
<button id="stopButton" class="hidden col-span-2 flex justify-center items-center py-2.5 px-3 border border-transparent rounded-lg shadow-md text-base font-bold text-white bg-red-600 hover:bg-red-700 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-red-500 transition-all" title="Stop the current training process.">
<span class="action-icon">βΉοΈ</span> <span>Stop</span>
</button>
<button id="resetWeightsButton" class="flex justify-center items-center py-2.5 px-3 border border-slate-300 rounded-lg shadow-sm text-base font-semibold text-slate-700 bg-white hover:bg-slate-50 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-indigo-500 transition-all duration-150 disabled:opacity-50 disabled:cursor-not-allowed" title="Re-initialize the model's weights.">
<span class="action-icon">π</span> <span>Reset</span>
</button>
<button id="clearButton" class="flex justify-center items-center py-2.5 px-3 border border-slate-300 rounded-lg shadow-sm text-base font-semibold text-slate-700 bg-white hover:bg-slate-50 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-indigo-500 transition-all duration-150 disabled:opacity-50 disabled:cursor-not-allowed" title="Delete all data and reset settings.">
<span class="action-icon">ποΈ</span> <span>Clear All</span>
</button>
</div>
</div>
</div>
<!-- Visualization Area with reduced gap -->
<div class="lg:w-6/12 flex flex-col gap-2.5">
<div class="bg-white p-2 rounded-2xl shadow-xl border border-slate-200">
<canvas id="plotCanvas" class="border border-slate-200 rounded-xl w-full"></canvas>
</div>
<div class="bg-white p-3.5 rounded-2xl shadow-xl border border-slate-200 flex flex-col flex-grow">
<h2 class="text-xl font-bold text-slate-700 mb-2 border-b border-slate-200 pb-2 flex items-center">
Training Status <span class="title-icon">π</span>
</h2>
<div id="trainingParamsDisplay" class="text-xs mb-2 space-y-1"></div>
<div id="statusMessage" class="text-sm text-slate-600 p-2 bg-slate-50 rounded-lg min-h-[44px] mb-3 whitespace-pre-line leading-snug flex items-center justify-center border border-slate-200"></div>
<div class="flex-grow flex flex-col sm:flex-row gap-4">
<div class="w-full sm:w-2/3 relative min-h-[150px]">
<canvas id="trainingPlotCanvas" class="w-full h-full"></canvas>
</div>
<div id="confusionMatrixContainer" class="w-full sm:w-1/3">
<!-- Confusion Matrix will be rendered here -->
</div>
</div>
</div>
</div>
</div>
</div>
<script>
// --- App Namespace ---
// Encapsulate the entire application in a single object to avoid polluting the global namespace.
// This improves organization and prevents potential conflicts with other scripts.
const App = {
// --- STATE & CONFIG ---
state: {
model: null,
dataPoints: [],
currentClass: 0,
hiddenLayerConfigs: [],
trainingHistory: { loss: [], acc: [] },
isTraining: false,
},
ui: {}, // To hold DOM element references
config: {
pointRadius: 4.5,
classColors: {
0: { point: 'rgba(59, 130, 246, 1)', boundary: 'rgba(59, 130, 246, 0.3)' },
1: { point: 'rgba(239, 68, 68, 1)', boundary: 'rgba(239, 68, 68, 0.3)' }
},
statusIcons: {
info: 'βΉοΈ', success: 'β
', warning: 'β οΈ', error: 'β', loading: 'β³'
},
},
// --- INITIALIZATION ---
async init() {
this.cacheUIElements();
this.registerEventListeners();
await tf.ready();
try {
await tf.setBackend('cpu');
console.log("TensorFlow.js backend set to CPU.");
} catch (e) { console.warn("Could not set TF.js backend to CPU.", e); }
this.methods.resizePlotCanvas();
this.initializeApplicationState();
},
cacheUIElements() {
const ids = [
'class0Button', 'class1Button', 'datasetSelect', 'dataNoise', 'batchSize',
'hiddenLayersConfigContainer', 'addHiddenLayerButton', 'networkVisualization',
'optimizerSelect', 'learningRate', 'epochs', 'regularizationTypeSelect',
'regularizationRateContainer', 'regularizationRateInput', 'trainButton',
'stopButton', 'resetWeightsButton', 'clearButton', 'statusMessage',
'plotCanvas', 'trainingPlotCanvas', 'trainingParamsDisplay', 'trainButtonText',
'confusionMatrixContainer'
];
ids.forEach(id => { this.ui[id] = document.getElementById(id); });
this.ui.canvas = this.ui.plotCanvas;
this.ui.ctx = this.ui.canvas.getContext('2d');
},
registerEventListeners() {
window.addEventListener('resize', () => {
this.methods.resizePlotCanvas();
this.methods.drawTrainingPlot();
this.methods.drawNetworkVisualization();
});
this.ui.canvas.addEventListener('click', (e) => this.methods.handleCanvasClick(e));
this.ui.class0Button.addEventListener('click', () => this.methods.setActiveClass(0));
this.ui.class1Button.addEventListener('click', () => this.methods.setActiveClass(1));
this.ui.addHiddenLayerButton.addEventListener('click', () => this.methods.addHiddenLayerUI());
this.ui.trainButton.addEventListener('click', () => this.methods.trainAndVisualize());
this.ui.stopButton.addEventListener('click', () => this.methods.stopTraining());
this.ui.resetWeightsButton.addEventListener('click', () => this.methods.resetModelWeights());
this.ui.clearButton.addEventListener('click', () => this.methods.clearAllDataAndReseed());
this.ui.datasetSelect.addEventListener('change', () => this.methods.loadSelectedDataset());
this.ui.dataNoise.addEventListener('input', () => { if (this.ui.datasetSelect.value !== 'manual') this.methods.loadSelectedDataset(); });
this.ui.regularizationTypeSelect.addEventListener('change', () => this.methods.toggleRegularizationRateInput());
},
initializeApplicationState() {
this.methods.setActiveClass(0);
this.methods.addHiddenLayerUI(8, 'relu'); // Default architecture
this.methods.addHiddenLayerUI(4, 'relu');
this.methods.toggleRegularizationRateInput();
this.methods.drawAll();
this.methods.drawTrainingPlot();
this.methods.updateTrainingParamsDisplay();
this.methods.updateButtonStates();
this.methods.renderConfusionMatrix(null);
this.methods.updateStatus('Ready. Click canvas to add points or load a dataset.', 'info');
},
// --- METHODS (Logic & Handlers) ---
methods: {
// --- UI & State Management ---
resizePlotCanvas() {
const dpr = window.devicePixelRatio || 1;
const rect = App.ui.canvas.parentElement.getBoundingClientRect();
if (rect.width === 0) return;
App.ui.canvas.width = rect.width * dpr;
const newHeight = Math.min(rect.width * 0.85, Math.max(300, window.innerHeight * 0.5));
App.ui.canvas.height = newHeight * dpr;
App.ui.ctx.scale(dpr, dpr);
App.ui.canvas.style.width = `${rect.width}px`;
App.ui.canvas.style.height = `${newHeight}px`;
App.ui.canvasWidth = rect.width;
App.ui.canvasHeight = newHeight;
this.drawAll();
},
setActiveClass(classNum) {
App.state.currentClass = classNum;
App.ui.class0Button.classList.toggle('button-active-blue', classNum === 0);
App.ui.class1Button.classList.toggle('button-active-red', classNum === 1);
App.ui.plotCanvas.className = App.ui.plotCanvas.className.replace(/cursor-class-\d/, '');
App.ui.plotCanvas.classList.add(`cursor-class-${classNum}`);
},
toggleRegularizationRateInput() {
App.ui.regularizationRateContainer.classList.toggle('hidden', App.ui.regularizationTypeSelect.value === 'none');
},
updateStatus(message, type = 'info') {
const icon = App.config.statusIcons[type] || '';
const loadingClass = type === 'loading' ? 'loading-icon' : '';
const iconHtml = `<span class="status-icon ${loadingClass}">${icon}</span>`;
App.ui.statusMessage.innerHTML = `${iconHtml}<span>${message}</span>`;
const typeToColor = {
info: 'text-slate-600 bg-slate-50 border-slate-200',
success: 'text-green-700 bg-green-50 border-green-200 font-semibold',
warning: 'text-amber-700 bg-amber-50 border-amber-200 font-semibold',
error: 'text-red-700 bg-red-50 border-red-200 font-semibold',
loading: 'text-blue-700 bg-blue-50 border-blue-200'
};
App.ui.statusMessage.className = `text-sm p-2 rounded-lg min-h-[44px] whitespace-pre-line leading-snug flex items-center justify-center border ${typeToColor[type] || ''}`;
},
setTrainingState(training) {
App.state.isTraining = training;
App.ui.trainButton.classList.toggle('hidden', training);
App.ui.stopButton.classList.toggle('hidden', !training);
this.updateButtonStates();
},
updateButtonStates() {
const hasData = App.state.dataPoints.length > 0;
const hasModel = App.state.model != null;
App.ui.trainButton.disabled = App.state.isTraining || !hasData;
App.ui.clearButton.disabled = App.state.isTraining;
App.ui.resetWeightsButton.disabled = App.state.isTraining || !hasModel;
if (hasModel && App.state.trainingHistory.loss.length > 0 && !App.state.isTraining) {
App.ui.trainButtonText.textContent = 'Retrain';
} else {
App.ui.trainButtonText.textContent = 'Train';
}
},
// --- Architecture UI ---
addHiddenLayerUI(defaultNeurons = 8, defaultActivation = 'relu') {
if (App.state.hiddenLayerConfigs.length >= 8) {
this.updateStatus("Max 8 hidden layers reached.", 'warning');
return;
}
const layerIndex = App.state.hiddenLayerConfigs.length;
App.state.hiddenLayerConfigs.push({ neurons: parseInt(defaultNeurons), activation: defaultActivation });
const layerDiv = document.createElement('div');
layerDiv.className = 'layer-config-item bg-slate-50 border border-slate-200 p-2 rounded-lg flex items-center justify-between gap-2 hover:bg-slate-100 transition-colors';
layerDiv.dataset.index = layerIndex;
layerDiv.innerHTML = `
<div class="flex-grow flex items-center gap-x-3">
<div class="flex items-center">
<label for="neurons_${layerIndex}" class="text-sm font-medium text-slate-600 mr-2 whitespace-nowrap">L${layerIndex + 1} Units:</label>
<input type="number" id="neurons_${layerIndex}" value="${defaultNeurons}" min="1" max="64" step="1" class="w-16 px-2 py-1 border border-slate-300 rounded-md shadow-sm text-sm focus:ring-blue-500 focus:border-blue-500">
</div>
<div class="flex items-center">
<label for="activation_${layerIndex}" class="text-sm font-medium text-slate-600 mr-2">Act:</label>
<select id="activation_${layerIndex}" class="flex-1 min-w-[90px] px-2 py-1 border bg-white border-slate-300 rounded-md shadow-sm text-sm focus:ring-blue-500 focus:border-blue-500">
${['relu', 'sigmoid', 'tanh', 'leakyRelu'].map(act => `<option value="${act}" ${act === defaultActivation ? 'selected' : ''}>${act.charAt(0).toUpperCase() + act.slice(1)}</option>`).join('')}
</select>
</div>
</div>
<button title="Remove layer" class="remove-btn flex-shrink-0 p-1.5 text-red-500 hover:text-red-700 rounded-full hover:bg-red-100 focus:outline-none focus:ring-2 focus:ring-red-500 focus:ring-offset-1 transition-all duration-150">
<span class="text-base font-bold">β</span>
</button>
`;
App.ui.hiddenLayersConfigContainer.appendChild(layerDiv);
layerDiv.querySelector(`#neurons_${layerIndex}`).onchange = (e) => {
const value = parseInt(e.target.value);
App.state.hiddenLayerConfigs[layerIndex].neurons = !isNaN(value) && value > 0 ? value : 1;
e.target.value = App.state.hiddenLayerConfigs[layerIndex].neurons;
this.drawNetworkVisualization();
};
layerDiv.querySelector(`#activation_${layerIndex}`).onchange = (e) => {
App.state.hiddenLayerConfigs[layerIndex].activation = e.target.value;
this.drawNetworkVisualization();
};
layerDiv.querySelector('.remove-btn').onclick = () => {
App.state.hiddenLayerConfigs.splice(layerIndex, 1);
this.redrawLayerConfigs();
};
this.drawNetworkVisualization();
},
redrawLayerConfigs() {
const configs = [...App.state.hiddenLayerConfigs];
App.ui.hiddenLayersConfigContainer.innerHTML = '';
App.state.hiddenLayerConfigs = [];
configs.forEach(config => this.addHiddenLayerUI(config.neurons, config.activation));
},
// --- Data Handling ---
handleCanvasClick(event) {
if (!App.ui.canvas) return;
const rect = App.ui.canvas.getBoundingClientRect();
const x = event.clientX - rect.left;
const y = event.clientY - rect.top;
App.state.dataPoints.push({ x, y, normX: x / App.ui.canvasWidth, normY: y / App.ui.canvasHeight, label: App.state.currentClass });
this.drawPoints();
this.updateStatus(`Added Class ${App.state.currentClass} point. Total: ${App.state.dataPoints.length}.`, 'info');
App.ui.datasetSelect.value = "manual";
this.updateButtonStates();
},
loadSelectedDataset() {
if (App.state.model) { App.state.model.dispose(); App.state.model = null; }
tf.disposeVariables();
App.state.dataPoints = [];
const datasetName = App.ui.datasetSelect.value;
if (datasetName === 'manual') {
this.drawAll();
this.updateButtonStates();
return;
}
const noise = parseFloat(App.ui.dataNoise.value) || 0;
const nSamples = 150;
const generators = {
two_moons: (n, noise) => {
const n_per_moon = Math.floor(n / 2);
const radius = 0.3;
for (let i = 0; i < n_per_moon; i++) {
const angle = (i / n_per_moon) * Math.PI;
// First moon, shifted left and up
this.addDataPoint(
0.5 + radius * Math.cos(angle) - 0.125,
0.5 + radius * Math.sin(angle) + 0.1,
0, noise
);
// Second moon, shifted right and down
this.addDataPoint(
0.5 + radius * Math.cos(angle + Math.PI) + 0.125,
0.5 + radius * Math.sin(angle + Math.PI) - 0.1,
1, noise
);
}
},
circles: (n, noise) => { for (let i=0; i<n; i++) { const r=Math.random(), a=Math.random()*2*Math.PI, l=r<0.5?0:1, rs=l===0?0.2:0.4; this.addDataPoint(0.5+rs*Math.cos(a), 0.5+rs*Math.sin(a), l, noise); } },
xor: (n, noise) => { const q=Math.floor(n/4), s=0.3; for (let i=0; i<q; i++) { this.addDataPoint(0.5-s, 0.5-s, 0, noise*2); this.addDataPoint(0.5+s, 0.5-s, 1, noise*2); this.addDataPoint(0.5-s, 0.5+s, 1, noise*2); this.addDataPoint(0.5+s, 0.5+s, 0, noise*2); } },
spiral: (n, noise) => { const pts=Math.floor(n/2); for (let i=0; i<pts; i++) { const a=i/20*Math.PI, r=0.05+i/pts*0.4; this.addDataPoint(0.5+r*Math.cos(a), 0.5+r*Math.sin(a), 0, noise*0.5); this.addDataPoint(0.5+r*Math.cos(a+Math.PI), 0.5+r*Math.sin(a+Math.PI), 1, noise*0.5); } }
};
generators[datasetName](nSamples, noise);
App.state.dataPoints.forEach(p => { p.x = p.normX * App.ui.canvasWidth; p.y = p.normY * App.ui.canvasHeight; });
this.drawAll();
this.updateStatus(`Loaded '${datasetName}' dataset. Noise: ${noise}.`, 'info');
App.state.trainingHistory = { loss: [], acc: [] };
this.drawTrainingPlot();
this.renderConfusionMatrix(null);
this.updateButtonStates();
this.updateTrainingParamsDisplay();
},
addDataPoint(normX, normY, label, noise) {
App.state.dataPoints.push({
x: 0, y: 0,
normX: normX + (Math.random() - 0.5) * noise,
normY: normY + (Math.random() - 0.5) * noise,
label: label
});
},
// --- Drawing & Visualization ---
drawAll(boundaryGrid = null) {
if (!App.ui.ctx || !App.ui.canvasWidth || !App.ui.canvasHeight) return;
App.ui.ctx.clearRect(0, 0, App.ui.canvasWidth, App.ui.canvasHeight);
if (boundaryGrid) {
const { grid, resolution } = boundaryGrid;
for (let i = 0; i < grid.length; i++) {
for (let j = 0; j < grid[i].length; j++) {
App.ui.ctx.fillStyle = App.config.classColors[grid[i][j]].boundary;
App.ui.ctx.fillRect(j * resolution, i * resolution, resolution, resolution);
}
}
}
App.state.dataPoints.forEach(point => {
App.ui.ctx.beginPath();
App.ui.ctx.arc(point.x, point.y, App.config.pointRadius, 0, 2 * Math.PI);
App.ui.ctx.fillStyle = App.config.classColors[point.label].point;
App.ui.ctx.fill();
App.ui.ctx.strokeStyle = 'rgba(255,255,255,0.7)';
App.ui.ctx.lineWidth = 1.5;
App.ui.ctx.stroke();
});
},
drawPoints() { this.drawAll(); },
drawNetworkVisualization() {
const container = App.ui.networkVisualization;
container.innerHTML = '';
const allLayers = [
{ type: 'input', neurons: 2, activation: 'Input' },
...App.state.hiddenLayerConfigs.map((cfg, i) => ({ type: `hidden-${i % 5}`, neurons: cfg.neurons, activation: cfg.activation })),
{ type: 'output', neurons: 1, activation: 'Sigmoid' }
];
const svg = document.createElementNS("http://www.w3.org/2000/svg", "svg");
const rect = container.getBoundingClientRect();
if(rect.width === 0 || rect.height === 0) return;
svg.setAttribute('viewBox', `0 0 ${rect.width} ${rect.height}`);
const margin = { top: 25, right: 15, bottom: 20, left: 15 };
const width = rect.width - margin.left - margin.right;
const height = rect.height - margin.top - margin.bottom;
const layerSpacing = allLayers.length > 1 ? width / (allLayers.length - 1) : width;
// Connections
for (let i = 0; i < allLayers.length - 1; i++) {
const x1 = margin.left + i * layerSpacing;
const x2 = margin.left + (i + 1) * layerSpacing;
const maxNeurons = 8;
const currentNeurons = Math.min(allLayers[i].neurons, maxNeurons);
const nextNeurons = Math.min(allLayers[i+1].neurons, maxNeurons);
for (let j = 0; j < currentNeurons; j++) {
const y1 = margin.top + height * ((j + 0.5) / currentNeurons);
for (let k = 0; k < nextNeurons; k++) {
const y2 = margin.top + height * ((k + 0.5) / nextNeurons);
const line = document.createElementNS("http://www.w3.org/2000/svg", "line");
line.setAttribute('x1', x1); line.setAttribute('y1', y1);
line.setAttribute('x2', x2); line.setAttribute('y2', y2);
line.setAttribute('class', 'connection');
svg.appendChild(line);
}
}
}
// Neurons and Labels
allLayers.forEach((layer, i) => {
const x = margin.left + i * layerSpacing;
const maxNeurons = 8;
const displayNeurons = Math.min(layer.neurons, maxNeurons);
const neuronRadius = Math.max(3, Math.min(7, height / (displayNeurons * 2.5)));
for (let j = 0; j < displayNeurons; j++) {
const y = margin.top + height * ((j + 0.5) / displayNeurons);
const circle = document.createElementNS("http://www.w3.org/2000/svg", "circle");
circle.setAttribute('cx', x); circle.setAttribute('cy', y);
circle.setAttribute('r', neuronRadius);
circle.setAttribute('class', `neuron ${layer.type}`);
svg.appendChild(circle);
}
const labelText = layer.activation.charAt(0).toUpperCase() + layer.activation.slice(1);
const textLabel = document.createElementNS("http://www.w3.org/2000/svg", "text");
textLabel.setAttribute('x', x); textLabel.setAttribute('y', margin.top - 8);
textLabel.setAttribute('class', 'layer-label');
textLabel.textContent = labelText;
svg.appendChild(textLabel);
const countLabel = document.createElementNS("http://www.w3.org/2000/svg", "text");
countLabel.setAttribute('x', x); countLabel.setAttribute('y', margin.top + height + 15);
countLabel.setAttribute('class', 'neuron-count-label');
countLabel.textContent = `${layer.neurons} N`;
svg.appendChild(countLabel);
});
container.appendChild(svg);
},
drawTrainingPlot() {
const canvas = App.ui.trainingPlotCanvas;
const ctx = canvas.getContext('2d');
const dpr = window.devicePixelRatio || 1;
const rect = canvas.getBoundingClientRect();
if (rect.width === 0 || rect.height === 0) return;
canvas.width = rect.width * dpr;
canvas.height = rect.height * dpr;
ctx.scale(dpr, dpr);
const { width, height } = rect;
const padding = {top: 20, right: 15, bottom: 20, left: 30};
ctx.fillStyle = '#f8fafc';
ctx.fillRect(0,0,width,height);
if (App.state.trainingHistory.loss.length === 0) {
ctx.fillStyle = '#64748b';
ctx.textAlign = 'center';
ctx.font = '12px Inter';
ctx.fillText('Training history will be plotted here.', width / 2, height / 2);
return;
}
ctx.beginPath();
ctx.strokeStyle = '#e2e8f0';
ctx.lineWidth = 1;
for(let i = 0; i <= 4; i++){
const y = padding.top + i * (height - padding.top - padding.bottom) / 4;
ctx.moveTo(padding.left, y);
ctx.lineTo(width-padding.right, y);
}
ctx.stroke();
ctx.font = '10px Inter';
ctx.fillStyle = '#475569';
ctx.textAlign = 'right';
for(let i = 0; i <= 4; i++){
ctx.fillText((1 - i/4).toFixed(1), padding.left - 6, padding.top + 3 + i * (height - padding.top - padding.bottom) / 4);
}
const plotData = (data, color) => {
ctx.beginPath(); ctx.strokeStyle = color; ctx.lineWidth = 2; ctx.lineJoin = 'round'; ctx.lineCap = 'round';
data.forEach((val, i) => {
const x = padding.left + (i / (Math.max(1, data.length -1))) * (width - padding.left - padding.right);
const y = (height - padding.bottom) - Math.min(Math.max(val,0.0),1.0) * (height - padding.top - padding.bottom);
if (i === 0) ctx.moveTo(x, y); else ctx.lineTo(x, y);
});
ctx.stroke();
};
plotData(App.state.trainingHistory.loss, 'rgba(239, 68, 68, 0.9)');
plotData(App.state.trainingHistory.acc, 'rgba(37, 99, 235, 0.9)');
ctx.textAlign = 'left';
ctx.font = '600 11px Inter';
ctx.fillStyle = 'rgba(239, 68, 68, 1)'; ctx.fillRect(padding.left + 5, 5, 10, 3);
ctx.fillStyle = '#374151'; ctx.fillText('Loss', padding.left + 20, 10);
ctx.fillStyle = 'rgba(37, 99, 235, 1)'; ctx.fillRect(padding.left + 75, 5, 10, 3);
ctx.fillStyle = '#374151'; ctx.fillText('Accuracy', padding.left + 90, 10);
},
updateTrainingParamsDisplay() {
const container = App.ui.trainingParamsDisplay;
if (!App.state.model) {
container.innerHTML = `<div class="text-slate-500">Train a model to see parameters.</div>`;
return;
}
const lr = parseFloat(App.ui.learningRate.value);
const regType = App.ui.regularizationTypeSelect.value;
const regRate = parseFloat(App.ui.regularizationRateInput.value) || 0;
let regDesc = regType !== 'none' ? `${regType.toUpperCase()}(Ξ»=${regRate})` : 'None';
let hiddenDesc = App.state.hiddenLayerConfigs.map(l => l.neurons).join(' β ');
container.innerHTML = `
<div class="flex flex-wrap gap-x-4 gap-y-1">
<span><span class="font-semibold text-slate-500">Opt:</span> <span class="font-medium text-slate-800">${App.ui.optimizerSelect.value.toUpperCase()}</span></span>
<span><span class="font-semibold text-slate-500">LR:</span> <span class="font-medium text-slate-800">${lr.toExponential(1)}</span></span>
<span><span class="font-semibold text-slate-500">Batch:</span> <span class="font-medium text-slate-800">${parseInt(App.ui.batchSize.value) === 0 ? 'Full' : App.ui.batchSize.value}</span></span>
<span><span class="font-semibold text-slate-500">Reg:</span> <span class="font-medium text-slate-800">${regDesc}</span></span>
</div>
<div><span class="font-semibold text-slate-500">Layers:</span> <span class="font-medium text-slate-800">2 β ${hiddenDesc || '...'} β 1</span></div>
`;
},
renderConfusionMatrix(matrix) {
const container = App.ui.confusionMatrixContainer;
if (!matrix) {
container.innerHTML = `<div class="flex items-center justify-center h-full text-sm text-center text-slate-400 bg-slate-50 rounded-lg p-2 border border-slate-200">Confusion matrix appears here after training.</div>`;
return;
}
container.innerHTML = `
<div class="h-full flex flex-col">
<h4 class="text-sm font-semibold text-center text-slate-600 mb-1.5">Confusion Matrix</h4>
<div class="grid grid-cols-2 grid-rows-2 gap-1.5 flex-grow">
<div class="cm-cell bg-blue-100 text-blue-800" title="True Negative"><span class="cm-value">${matrix.tn}</span><span class="cm-label">TN</span></div>
<div class="cm-cell bg-red-100 text-red-800" title="False Positive"><span class="cm-value">${matrix.fp}</span><span class="cm-label">FP</span></div>
<div class="cm-cell bg-red-100 text-red-800" title="False Negative"><span class="cm-value">${matrix.fn}</span><span class="cm-label">FN</span></div>
<div class="cm-cell bg-green-100 text-green-800" title="True Positive"><span class="cm-value">${matrix.tp}</span><span class="cm-label">TP</span></div>
</div>
</div>
`;
},
// --- TENSORFLOW.JS & ML ---
getSafeNumericInput(element, defaultValue, isInteger = true) {
let value = isInteger ? parseInt(element.value, 10) : parseFloat(element.value);
if (isNaN(value)) {
value = defaultValue;
element.value = defaultValue;
}
return value;
},
buildModel() {
if (App.state.model) { App.state.model.dispose(); App.state.model = null; }
tf.disposeVariables();
const learningRate = this.getSafeNumericInput(App.ui.learningRate, 0.01, false);
const regType = App.ui.regularizationTypeSelect.value;
const regRate = this.getSafeNumericInput(App.ui.regularizationRateInput, 0, false);
const kernelRegularizer = (regType !== 'none' && regRate > 0)
? tf.regularizers[regType]({[regType]: regRate})
: null;
App.state.model = tf.sequential();
const inputShape = [2];
// Add hidden layers
App.state.hiddenLayerConfigs.forEach((layerConfig, index) => {
App.state.model.add(tf.layers.dense({
units: layerConfig.neurons,
inputShape: index === 0 ? inputShape : undefined,
activation: layerConfig.activation,
kernelRegularizer
}));
});
// Add output layer
App.state.model.add(tf.layers.dense({
units: 1,
activation: 'sigmoid',
inputShape: App.state.hiddenLayerConfigs.length === 0 ? inputShape : undefined,
}));
const optimizerInstance = tf.train[App.ui.optimizerSelect.value](learningRate);
App.state.model.compile({ optimizer: optimizerInstance, loss: 'binaryCrossentropy', metrics: ['accuracy'] });
App.state.model.stopTraining = false;
},
async trainAndVisualize() {
if (App.state.isTraining) return;
const uniqueLabels = new Set(App.state.dataPoints.map(p => p.label));
if (App.state.dataPoints.length < 4 || uniqueLabels.size < 2) {
this.updateStatus('Requires at least 4 points and data from both classes.', 'error');
return;
}
this.setTrainingState(true);
this.updateStatus('Starting training...', 'loading');
this.renderConfusionMatrix(null);
await tf.nextFrame();
this.buildModel();
this.updateTrainingParamsDisplay();
const epochs = this.getSafeNumericInput(App.ui.epochs, 150, true);
let batchSize = this.getSafeNumericInput(App.ui.batchSize, 16, true);
if (batchSize === 0) batchSize = App.state.dataPoints.length;
const [xs, ys] = tf.tidy(() => {
const normalized = App.state.dataPoints.map(p => [p.normX, p.normY]);
const labels = App.state.dataPoints.map(p => p.label);
return [tf.tensor2d(normalized), tf.tensor2d(labels, [labels.length, 1])];
});
App.state.trainingHistory = { loss: [], acc: [] };
this.drawTrainingPlot();
try {
await App.state.model.fit(xs, ys, {
epochs, batchSize,
callbacks: {
onEpochEnd: async (epoch, logs) => {
if (App.state.model.stopTraining) { App.state.model.stop(); return; }
this.updateStatus(`Epoch ${epoch + 1}/${epochs} - Loss: ${logs.loss.toFixed(4)}, Acc: ${logs.acc.toFixed(4)}`, 'loading');
App.state.trainingHistory.loss.push(logs.loss);
App.state.trainingHistory.acc.push(logs.acc);
this.drawTrainingPlot();
if ((epoch + 1) % Math.max(1, Math.floor(epochs / 25)) === 0) {
const boundaryGrid = await this.generateBoundaryGrid();
this.drawAll(boundaryGrid);
}
await tf.nextFrame();
},
onTrainEnd: async () => {
const finalAcc = App.state.trainingHistory.acc.slice(-1)[0] || 0;
if (!App.state.model.stopTraining) {
this.updateStatus(`Training complete! Final Accuracy: <b>${(finalAcc*100).toFixed(2)}%</b>`, 'success');
}
const boundaryGrid = await this.generateBoundaryGrid();
this.drawAll(boundaryGrid);
const confusionMatrix = await this.calculateConfusionMatrix(xs, ys);
this.renderConfusionMatrix(confusionMatrix);
this.setTrainingState(false);
}
}
});
} catch (error) {
console.error("Training error:", error);
this.updateStatus(`Training error: ${error.message}`, 'error');
this.setTrainingState(false);
} finally {
xs.dispose();
ys.dispose();
}
},
async calculateConfusionMatrix(xs, ys) {
if (!App.state.model) return null;
return tf.tidy(() => {
const predictions = App.state.model.predict(xs).round();
const tp = ys.mul(predictions).sum().dataSync()[0];
const tn = ys.sub(1).mul(-1).mul(predictions.sub(1).mul(-1)).sum().dataSync()[0];
const fp = predictions.sub(ys).relu().sum().dataSync()[0];
const fn = ys.sub(predictions).relu().sum().dataSync()[0];
return { tp, tn, fp, fn };
});
},
async generateBoundaryGrid() {
if (!App.state.model || !App.ui.canvasWidth || !App.ui.canvasHeight) return null;
const resolution = Math.max(5, Math.floor(App.ui.canvasWidth / 80));
const numCols = Math.floor(App.ui.canvasWidth / resolution);
const numRows = Math.floor(App.ui.canvasHeight / resolution);
if (numCols <= 0 || numRows <= 0) return null;
const boundaryData = tf.tidy(() => {
const gridPoints = [];
for (let i = 0; i < numRows; i++) {
for (let j = 0; j < numCols; j++) {
gridPoints.push([(j * resolution) / App.ui.canvasWidth, (i * resolution) / App.ui.canvasHeight]);
}
}
const predsTensor = App.state.model.predict(tf.tensor2d(gridPoints));
return predsTensor.dataSync(); // Use dataSync inside tidy
});
const grid = []; let k = 0;
for (let i = 0; i < numRows; i++) {
const row = [];
for (let j = 0; j < numCols; j++) row.push(boundaryData[k++] > 0.5 ? 1 : 0);
grid.push(row);
}
return { grid, resolution };
},
stopTraining() {
if (App.state.model) {
App.state.model.stopTraining = true;
this.updateStatus("Training stopped by user.", 'warning');
}
},
resetModelWeights() {
if (App.state.isTraining) return;
this.updateStatus("Model weights reset. Ready to train again.", 'info');
this.buildModel();
this.drawAll();
App.state.trainingHistory = { loss: [], acc: [] };
this.drawTrainingPlot();
this.renderConfusionMatrix(null);
this.updateButtonStates();
},
clearAllDataAndReseed() {
if (App.state.isTraining) return;
App.state.dataPoints = [];
if(App.state.model) { App.state.model.dispose(); App.state.model = null; }
tf.disposeVariables();
App.state.hiddenLayerConfigs = [];
App.ui.hiddenLayersConfigContainer.innerHTML = '';
App.initializeApplicationState();
this.updateStatus('Cleared all data and reset configuration.', 'info');
}
}
};
// --- Entry Point ---
document.addEventListener('DOMContentLoaded', () => App.init());
</script>
</body>
</html>
|