public double[,] Train(double[] inputArray, double[] targetsArray)
{
var inputs = Matrixs.FromArray(inputArray);
var targets = Matrixs.FromArray(targetsArray);
// Generating hidden outputs
var hidden = Matrixs.Multiply(weights_ih, inputs);
hidden.Add(bias_h);
// Activation function
hidden.Map(Sigmoid);
// Generating output's output
var outputs = Matrixs.Multiply(weights_ho, hidden);
outputs.Add(bias_o);
// Activation function
outputs.Map(Sigmoid);
// Convert array to matrix object
var outputErrors = Matrixs.Subtract(targets, outputs);
// Using stochastic gradient descent
// Calculate gradient
var gradients = Matrixs.Map(outputs, DSigmoid);
//outputs.Map(DSigmoid);
gradients.Multiply(outputErrors.data); // Element wise multuplication
gradients.Multiply(learningRate);
// Calculate deltas
var hidden_T = Matrixs.Transpose(hidden);
var weights_ho_deltas = Matrixs.Multiply(gradients, hidden_T);
// Adjust the weights by deltas
weights_ho.Add(weights_ho_deltas);
// Adjust the bias by its deltas
bias_o.Add(gradients);
// Calculate the error
// ERROR = TARGETS - OUTPUTS
// Calculate the hidden layer errors
var weights_ho_t = Matrixs.Transpose(weights_ho);
var hidden_errors = Matrixs.Multiply(weights_ho_t, outputErrors);
// Calculate the hidden gradient
var hidden_gradient = Matrixs.Map(hidden, DSigmoid);
hidden_gradient.Multiply(hidden_errors.data);
hidden_gradient.Multiply(learningRate);
// Calculate input->hidden deltas
var inputs_T = Matrixs.Transpose(inputs);
var weight_ih_deltas = Matrixs.Multiply(hidden_gradient, inputs_T);
// Adjust the weights by deltas
weights_ih.Add(weight_ih_deltas);
// Adjust the bias by its deltas
bias_h.Add(hidden_gradient);
/*
* Console.Write("Inputs: ");
* Matrixs.DisplayMatrix(inputArray);
* Console.Write("Targets: ");
* Matrixs.DisplayMatrix(targetsArray);
* Console.Write("Errors: ");
* Matrixs.DisplayMatrix(outputErrors);
* Console.WriteLine();
* Console.WriteLine();
*/
return(outputs.data);
}