//------------- public functions -----------//
/*
* processing functions
*/
public float[] process(float[] input)
{
//apply function y = f(Wx)
//store local copy of the input and create space for bias
ToolsCollection.CopyRBA(input, d_input);
d_input[d_input_size - 1] = 1.0f; //put in bias
//calculate and store activation
ToolsCollection.SetValue(d_activation, 0.0f);
for (int o = 0; o < d_output_size; o++)
{
for (int i = 0; i < d_input_size; i++)
{
d_activation[o] += d_weights[o, i] * d_input[i];
}
}
//calculate and store output
for (int o = 0; o < d_output_size; o++)
{
d_output[o] = d_output_function.Compute((float)d_activation[o]); //TODO double?
}
//and return the output to be processed by later layers
return(d_output);
}
public float[] back_propagate(float[] out_error)
{
//only use after forward signal has been processed
//store local copy of the output error
d_output_error = ToolsCollection.Select(out_error, 0, d_output_size); //will remove bias of next layer
//calculate and store activation error
for (int o = 0; o < d_output_size; o++)
{
activation_error[o] = d_output_function_derivative.Compute((float)d_activation[o]) * d_output_error[o];
}
//calculate and store the input error
ToolsCollection.SetValue(input_error, 0);
for (int i = 0; i < d_input_size; i++)
{
for (int o = 0; o < d_output_size; o++)
{
input_error[i] += activation_error[o] * d_weights[o, i];
}
}
//and return signal to be processed by previous layers
return(input_error);
}