public void train(double[,] inputs_array, double[,] targets_array)
{
//converts the array into a useable matrices object
Matrix_Math inputs = new Matrix_Math(inputs_array);
Matrix_Math targets = new Matrix_Math(targets_array);
//Multiplie the input-hidden weight matrix with the inputs to create the input of the hidden layer
Matrix_Math hidden_inputs = Matrix_Math.multiply(this.Winput_hidden, inputs);
//Sigmoids the input of the hidden layer to create the output of the hidden layer
Matrix_Math hidden_outputs = Matrix_Math.Map(hidden_inputs, sigmoid);
//Multiplie the hidden-output weight matrix with the output of the hidden layer to create the input of the final layer
Matrix_Math final_inputs = Matrix_Math.multiply(this.Whidden_output, hidden_outputs);
//Sigmoids the input of the hidden layer to create the output of the final layer
Matrix_Math final_outputs = Matrix_Math.Map(final_inputs, sigmoid);
//Calculates the error for the output layer - (target - actual)
Matrix_Math output_errors = Matrix_Math.Subtract(targets, final_outputs);
//Calculates the errors of the hidden layer by multiplying the transposed weights of the hidden - output layer by the errors of the output layer
Matrix_Math hidden_errors = Matrix_Math.multiply(Matrix_Math.Transpose(this.Whidden_output), output_errors);
//OOF, this multiples the learning rate by the dot product of the multipication of the output erros, the output of the final layer and (1 - the output of the final layer) and the transposed output of the hidden layer
this.Whidden_output.AddSelf(Matrix_Math.multiply(Matrix_Math.multiply(Matrix_Math.MultiplyHadamard(Matrix_Math.MultiplyHadamard(output_errors, final_outputs), Matrix_Math.Map(final_outputs, dsigmoid)), Matrix_Math.Transpose(hidden_outputs)), this.lr));
//OOF, this multiples the learning rate by the dot product of the multipication of the hidden erros, the output of the hiddedn layer and (1 - the output of the hidden layer) and the transposed output of the input layer
this.Winput_hidden.AddSelf(Matrix_Math.multiply(Matrix_Math.multiply(Matrix_Math.MultiplyHadamard(Matrix_Math.MultiplyHadamard(hidden_errors, hidden_outputs), Matrix_Math.Map(hidden_outputs, dsigmoid)), Matrix_Math.Transpose(inputs)), this.lr));
}
public Matrix_Math query(double[,] inputs_array)
{
//converts the array into a useable matrix object
Matrix_Math inputs = new Matrix_Math(inputs_array);
//Multiplie the input-hidden weight matrix with the inputs to create the input of the hidden layer
Matrix_Math hidden_inputs = Matrix_Math.multiply(this.Winput_hidden, inputs);
//Sigmoids the input of the hidden layer to create the output of the hidden layer
Matrix_Math hidden_outputs = Matrix_Math.Map(hidden_inputs, sigmoid);
//Multiplie the hidden-output weight matrix with the output of the hidden layer to create the input of the final layer
Matrix_Math final_inputs = Matrix_Math.multiply(this.Whidden_output, hidden_outputs);
//Sigmoids the input of the hidden layer to create the output of the final layer
Matrix_Math final_outputs = Matrix_Math.Map(final_inputs, sigmoid);
return(final_outputs);
}
