self driving car

network.js

@@ -0,0 +1,75 @@
+class NeuralNetwork {
+	constructor(neuronCounts) {
+		this.levels = [];
+		for (let i = 0; i < neuronCounts.length - 1; i++) {
+			this.levels.push(
+				new Level(neuronCounts[i], neuronCounts[i + 1])
+			);
+		}
+	}
+	static feedForward(givenInputs, network) {
+		let outputs = Level.feedForward(givenInputs, network.levels[0]);
+		for (let i = 1; i < network.levels.length; i++) {
+			outputs = Level.feedForward(outputs, network.levels[i]);
+		}
+		return outputs;
+	}
+	static mutate(network, amount = 1) {
+		network.levels.forEach((level) => {
+			for (let i = 0; i < level.biases.length; i++) {
+				level.biases[i] = lerp(
+					level.biases[i],
+					Math.random() * 2 - 1,
+					amount
+				);
+			}
+			for (let i = 0; i < level.weights.length; i++) {
+				for (let j = 0; j < level.weights.length; j++) {
+					level.weights[i][j] = lerp(
+						level.weights[i][j],
+						Math.random() * 2 - 1,
+						amount
+					);
+				}
+			}
+		});
+	}
+}
+class Level {
+	constructor(inputCount, outputCount) {
+		this.inputs = new Array(inputCount);
+		this.outputs = new Array(outputCount);
+		this.biases = new Array(outputCount);
+
+		this.weights = [];
+		for (let i = 0; i < inputCount; i++) {
+			this.weights[i] = new Array(outputCount);
+		}
+		Level.#randomize(this);
+	}
+	static #randomize(level) {
+		for (let i = 0; i < level.inputs.length; i++) {
+			for (let j = 0; j < level.outputs.length; j++) {
+				level.weights[i][j] = Math.random() * 2 - 1;
+			}
+		}
+
+		for (let i = 0; i < level.biases.length; i++) {
+			level.biases[i] = Math.random() * 2 - 1;
+		}
+	}
+	static feedForward(givenInputs, level) {
+		for (let i = 0; i < level.inputs.length; i++) {
+			level.inputs[i] = givenInputs[i];
+		}
+		for (let i = 0; i < level.outputs.length; i++) {
+			let sum = 0;
+			for (let j = 0; j < level.inputs.length; j++) {
+				sum += level.inputs[j] * level.weights[j][i];
+			}
+			if (sum > level.biases[i]) level.outputs[i] = 1;
+			else level.outputs[i] = 0;
+		}
+		return level.outputs;
+	}
+}