public void train(List <float> input_list, List <float> target_list)
{
NNMatrix inputs = NNMatrix.fromList(input_list);
NNMatrix hidden = NNMatrix.multiply(this.weights_ih, inputs);
hidden.add(this.bias_h);
// activate function
hidden.map(ActivateFunction.sigmoid);
NNMatrix outputs = NNMatrix.multiply(this.weights_ho, hidden);
outputs.add(this.bias_o);
outputs.map(ActivateFunction.sigmoid);
NNMatrix targets = NNMatrix.fromList(target_list);
// calculate the error
// ERROR = TARGETS(答え) - OUTPUTS(実際の出力)
NNMatrix output_errors = NNMatrix.subtract(targets, outputs);
//var gradients = outputs * (1-outputs);
// calculate gradient
NNMatrix gradients = NNMatrix.map(outputs, ActivateFunction.dsigmoid);
gradients.multiply(output_errors);
gradients.multiply(this.learning_rate);
// calculate deltas
NNMatrix hidden_T = NNMatrix.transpose(hidden);
NNMatrix weights_ho_deltas = NNMatrix.multiply(gradients, hidden_T);
// Adjust the weights by deltas
this.weights_ho.add(weights_ho_deltas);
// Adjust bias by its delta (which is just the gradients)
this.bias_o.add(gradients);
// calculate the hidden layer errors
NNMatrix who_t = NNMatrix.transpose(this.weights_ho);
NNMatrix hidden_errors = NNMatrix.multiply(who_t, output_errors);
// calculate hidden gradients
NNMatrix hidden_gradient = NNMatrix.map(hidden, ActivateFunction.dsigmoid);
hidden_gradient.multiply(hidden_errors);
hidden_gradient.multiply(this.learning_rate);
// calculate input->hidden deltas
NNMatrix input_T = NNMatrix.transpose(inputs);
NNMatrix weight_ih_deltas = NNMatrix.multiply(hidden_gradient, input_T);
this.weights_ih.add(weight_ih_deltas);
// Adjust bias by its delta (which is just the hiddent gradients)
this.bias_h.add(hidden_gradient);
}
public List <float> feedforward(List <float> input_list)
{
NNMatrix inputs = NNMatrix.fromList(input_list);
NNMatrix hidden = NNMatrix.multiply(this.weights_ih, inputs);
hidden.add(this.bias_h);
// activate function
hidden.map(ActivateFunction.sigmoid);
NNMatrix output = NNMatrix.multiply(this.weights_ho, hidden);
output.add(this.bias_o);
output.map(ActivateFunction.sigmoid);
return(output.toList());
}