import org.ejml.simple.SimpleMatrix;

import java.util.Random;

/**
 * Goal: initialize layer without any neurons. Not yet implemented.
 * Layer initialized with 1 neuron.
 * Assumes that each new neuron is fully connected to every previous neuron (this will be changed in the future).
 */
public class BlankLayer extends Layer {
    SimpleMatrix weights;
    SimpleMatrix biases;

    public BlankLayer(int inputSize) {
        Random random = new Random();
        this.weights = new SimpleMatrix(inputSize, 1, true,
                random.doubles(inputSize, -1, 1).toArray());
        this.biases = new SimpleMatrix(1, 1, true,
                random.doubles(1, -1, 1).toArray());
    }

    @Override
    public SimpleMatrix forwardPropagation(SimpleMatrix inputs) {
        this.input = inputs;
        this.output = this.input.mult(this.weights).plus(this.biases);
        return this.output;
    }

    @Override
    public SimpleMatrix backwardPropagation(SimpleMatrix outputError, double learningRate) {
        SimpleMatrix inputError = outputError.mult(this.weights.transpose());
        SimpleMatrix weightsError = this.input.transpose().mult(outputError);

        this.weights = this.weights.plus(learningRate, weightsError);
        this.biases = this.biases.plus(learningRate, outputError);
        return inputError;
    }
}