public Vector output(Vector inputs) {
Vector in = inputs.append(bias);
this.inputs = in;
in = weights.multiply(in);
outputs = fcn.apply(in);
return outputs;
}
public Vector backProp(Vector error) {
derivs = fcn.applyDerivative(outputs);
errors = derivs.multiply(error);
Vector blame = weights.transpose().multiply(errors);
// take off the bias signal, the previous layer need not know
return blame.slice(blame.dim() - 1);
}
/* Combine TFs. */
public TransferFunction multiply(TransferFunction tf) {
Polynomial num_result = num.multiply(tf.getNumerator());
Polynomial den_result = den.multiply(tf.getDenominator());
return new TransferFunction(num_result, den_result);
}
