QkoSad
76 lines
2.0 KiB
JavaScript
76 lines
2.0 KiB
JavaScript
class NeuralNetwork {
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constructor(neuronCounts) {
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this.levels = [];
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for (let i = 0; i < neuronCounts.length - 1; i++) {
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this.levels.push(
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new Level(neuronCounts[i], neuronCounts[i + 1])
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);
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}
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}
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static feedForward(givenInputs, network) {
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let outputs = Level.feedForward(givenInputs, network.levels[0]);
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for (let i = 1; i < network.levels.length; i++) {
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outputs = Level.feedForward(outputs, network.levels[i]);
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}
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return outputs;
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}
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static mutate(network, amount = 1) {
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network.levels.forEach((level) => {
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for (let i = 0; i < level.biases.length; i++) {
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level.biases[i] = lerp(
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level.biases[i],
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Math.random() * 2 - 1,
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amount
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);
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}
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for (let i = 0; i < level.weights.length; i++) {
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for (let j = 0; j < level.weights.length; j++) {
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level.weights[i][j] = lerp(
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level.weights[i][j],
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Math.random() * 2 - 1,
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amount
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);
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}
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}
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});
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}
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}
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class Level {
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constructor(inputCount, outputCount) {
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this.inputs = new Array(inputCount);
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this.outputs = new Array(outputCount);
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this.biases = new Array(outputCount);
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this.weights = [];
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for (let i = 0; i < inputCount; i++) {
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this.weights[i] = new Array(outputCount);
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}
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Level.#randomize(this);
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}
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static #randomize(level) {
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for (let i = 0; i < level.inputs.length; i++) {
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for (let j = 0; j < level.outputs.length; j++) {
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level.weights[i][j] = Math.random() * 2 - 1;
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}
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}
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for (let i = 0; i < level.biases.length; i++) {
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level.biases[i] = Math.random() * 2 - 1;
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}
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}
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static feedForward(givenInputs, level) {
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for (let i = 0; i < level.inputs.length; i++) {
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level.inputs[i] = givenInputs[i];
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}
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for (let i = 0; i < level.outputs.length; i++) {
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let sum = 0;
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for (let j = 0; j < level.inputs.length; j++) {
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sum += level.inputs[j] * level.weights[j][i];
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}
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if (sum > level.biases[i]) level.outputs[i] = 1;
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else level.outputs[i] = 0;
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}
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return level.outputs;
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}
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}
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