File size: 7,852 Bytes
b012e8c | 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 | /**
* NetworkToDisplay - Interface between neural network and 3D visualization
* Creates spheres and manages connections directly in the SceneManager.
*/
class NetworkToDisplay {
constructor(network, sceneManager, options = {}) {
this.network = network;
this.sceneManager = sceneManager;
const {
layerSpacing = 10,
neuronSpacing = 2,
neuronRadius = 0.5,
inputColor = '#ff6b6b',
hiddenColor = '#ffd93d',
outputColor = '#4ecdc4',
weightThreshold = 0.1,
lineWidth = 0.1,
opacity = 0.1
} = options;
this.layerSpacing = layerSpacing;
this.neuronSpacing = neuronSpacing;
this.neuronRadius = neuronRadius;
this.inputColor = inputColor;
this.hiddenColor = hiddenColor;
this.outputColor = outputColor;
this.weightThreshold = weightThreshold;
this.lineWidth = lineWidth;
this.opacity = opacity;
}
/**
* Create all neuron spheres and initial connections in the scene.
*/
initialGeneration() {
const layers = this.network.neuronCounts;
const numLayers = layers.length;
for (let layerIdx = 0; layerIdx < numLayers; layerIdx++) {
const neuronCount = layers[layerIdx];
const x = layerIdx * this.layerSpacing - (numLayers - 1) * this.layerSpacing / 2;
let color = this.hiddenColor;
if (layerIdx === 0) color = this.inputColor;
else if (layerIdx === numLayers - 1) color = this.outputColor;
if (layerIdx === 0) {
const gridSize = Math.sqrt(neuronCount);
console.assert(
Math.floor(gridSize) * Math.floor(gridSize) === neuronCount,
`Input layer neuron count ${neuronCount} is not a perfect square`
);
const intGridSize = Math.floor(gridSize);
const halfSize = (intGridSize - 1) * this.neuronSpacing / 2;
let sphereCount = 0;
for (let row = 0; row < intGridSize; row++) {
for (let col = 0; col < intGridSize; col++) {
const y = row * this.neuronSpacing - halfSize;
const z = col * this.neuronSpacing - halfSize;
this.sceneManager.sphereManager.addSphere(`L${layerIdx}_N${sphereCount}`, {
radius: this.neuronRadius,
color: color,
position: new THREE.Vector3(x, y, z),
opacity: 1
});
sphereCount++;
}
}
console.assert(
sphereCount === neuronCount,
`Generated ${sphereCount} spheres but expected ${neuronCount} neurons in input layer`
);
} else {
const verticalOffset = -(neuronCount - 1) * this.neuronSpacing / 2;
for (let neuronIdx = 0; neuronIdx < neuronCount; neuronIdx++) {
const y = verticalOffset + neuronIdx * this.neuronSpacing;
this.sceneManager.sphereManager.addSphere(`L${layerIdx}_N${neuronIdx}`, {
radius: this.neuronRadius,
color: color,
position: new THREE.Vector3(x, y, 0),
opacity: 1
});
}
}
}
this.updateConnections();
console.log('✓ Initial generation complete');
}
/**
* Update/create/delete connections in the scene based on current weights.
*/
updateConnections() {
const weightThreshold = this.weightThreshold;
for (let layerIdx = 0; layerIdx < this.network.weights.length; layerIdx++) {
const weightMatrix = this.network.weights[layerIdx];
const fromLayer = layerIdx;
const toLayer = layerIdx + 1;
const toNeuronCount = weightMatrix.length;
const fromNeuronCount = weightMatrix[0].length;
for (let toIdx = 0; toIdx < toNeuronCount; toIdx++) {
for (let fromIdx = 0; fromIdx < fromNeuronCount; fromIdx++) {
const weight = weightMatrix[toIdx][fromIdx];
const weightMagnitude = Math.abs(weight);
const connectionId = `W_L${layerIdx}_F${fromIdx}_T${toIdx}`;
const fromName = `L${fromLayer}_N${fromIdx}`;
const toName = `L${toLayer}_N${toIdx}`;
const existing = this.sceneManager.connectionManager.getConnection(connectionId);
if (weightMagnitude < weightThreshold) {
if (existing) {
this.sceneManager.connectionManager.removeConnection(connectionId);
}
continue;
}
const connectionColorThis = NetworkToDisplay.weightToColor(weight);
if (existing) {
this.sceneManager.connectionManager.updateConnectionColor(connectionId, connectionColorThis);
} else {
const fromSphere = this.sceneManager.sphereManager.getSphere(fromName);
const toSphere = this.sceneManager.sphereManager.getSphere(toName);
if (fromSphere && toSphere) {
this.sceneManager.connectionManager.addConnection(
connectionId,
fromSphere.position,
toSphere.position,
{
color: connectionColorThis,
lineWidth: this.lineWidth,
opacity: this.opacity,
fromSphere: fromName,
toSphere: toName,
weight: weight
}
);
}
}
}
}
}
}
/**
* Highlight a specific neuron activation
* @param {number} layerIdx - Layer index
* @param {number} neuronIdx - Neuron index
* @param {number} activation - Activation value (0-1)
*/
highlightNeuron(layerIdx, neuronIdx, activation) {
const sphereName = `L${layerIdx}_N${neuronIdx}`;
const sphere = this.sceneManager.sphereManager.getSphere(sphereName);
if (sphere) {
const scale = 1 + activation * 0.5;
sphere.scale.set(scale, scale, scale);
}
}
static weightToColor(weight) {
const positiveColor = { r: 0, g: 255, b: 0 }; // Green for positive weights
const neutralColor = { r: 255, g: 255, b: 255 }; // White for neutral weights
const negativeColor = { r: 255, g: 0, b: 0 }; // Red for negative weights
const magnitude = Math.min(1, Math.abs(weight) / 3); // Normalize weight magnitude
// Interpolate between neutral and sign-specific color
const target = weight >= 0 ? positiveColor : negativeColor;
const r = Math.round(neutralColor.r * (1 - magnitude) + target.r * magnitude);
const g = Math.round(neutralColor.g * (1 - magnitude) + target.g * magnitude);
const b = Math.round(neutralColor.b * (1 - magnitude) + target.b * magnitude);
return `#${((1 << 24) + (r << 16) + (g << 8) + b).toString(16).slice(1)}`;
}
}
// Expose globally for script-tag usage
window.NetworkToDisplay = NetworkToDisplay; |