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NNetwork.pde
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NNetwork.pde
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public class NNetwork{
private int[] carNeuralNetworkArchitecture = {3, 5, 4, 4};
public float[][][] weights = new float[3][][];
public NNetwork(){
weights = new float[3][][];
for(int i = 0; i < weights.length; i++){
weights[i] = new float[this.carNeuralNetworkArchitecture[i + 1]][this.carNeuralNetworkArchitecture[i]];
}
for(int i = 0; i < weights.length; i++){
for(int j = 0; j < weights[i].length; j++){
for(int k = 0; k < weights[i][j].length; k++){
weights[i][j][k] = (float)(Math.random() * 2 - 1);
}
}
}
}
public NNetwork(NNetwork nn){
weights = new float[3][][];
for(int i = 0; i < weights.length; i++){
weights[i] = new float[this.carNeuralNetworkArchitecture[i + 1]][this.carNeuralNetworkArchitecture[i]];
}
for(int i = 0; i < weights.length; i++){
for(int j = 0; j < weights[i].length; j++){
for(int k = 0; k < weights[i][j].length; k++){
this.weights[i][j][k] = nn.weights[i][j][k];
}
}
}
}
public float[][] feedForward(float[][] input){
float[][] l1 = matrixMult(weights[0], input);
float[][] l2 = matrixMult(weights[1], l1);
float[][] output = matrixMult(weights[2], l2);
return output;
}
private float[][] matrixMult(float[][] a, float[][] b){
float[][] c = new float[a.length][b[0].length];
for(int i = 0; i < a.length; i++){
for(int j = 0; j < b[0].length; j++){
for(int k = 0; k < a[0].length; k++){
c[i][j] = c[i][j] + a[i][k] * b[k][j];
}
}
}
return c;
}
}