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;
	}
}