Improved MFNN Implementation

Apparently, it is a good decision to have a structure or a function to encapsulate feeding the signal from a layer to the next together with calculating action-potentials and activation levels for each neuron in the next layer.

#include <vector>
#include <numeric>

/*
 * NOTE:
 *
 * std::inner_product could be implemented as follows
 *
 * Result result = 0;
 * for(; begin1 < end1; ++begin1, ++begin2)
 *   result += (*begin1) * (*begin2);
 * return result;
 */

// this function calculates the dot-product of two vectors
template < class InputIt1, class InputIt2, class Result = double >
inline Result dot(InputIt1 begin1, InputIt1 end1, InputIt2 begin2) {
  return std::inner_product(
    std::move(begin1), std::move(end1), std::move(begin2),
    static_cast<Result>(0) );
}

void feed_forward(
  const std::vector<int>    & input,
  const std::vector<double> & weights,
  const std::vector<double> & theta,
  std::vector<int>          & output
) {
  size_t passes = weights.size() / input.size();
  std::vector <double> fields(passes , 0);
  auto weights_begin = weights.begin();
  size_t i;

  for (i = 0; i < passes; ++i) {
    fields[i] = dot(input.begin(), input.end(), weights_begin);
    weights_begin += input.size();
  }

  for (i = 0; i < fields.size(); ++i)
    output[i] = static_cast<int>(fields[i] >= theta[i]);
}

std::vector<int> xor_mfnn(const std::vector<int> & input) {

  assert(input.size() == 2);

  std::vector<double> weights_hidden = { 0.15, 0.15, 0.3, 0.3 };
  std::vector<double> theta_hidden   = { 0.2, 0.2 };
  std::vector<int>    hidden_output(2, 0);

  std::vector<double> weights_output = { -0.3, 0.3 };
  std::vector<double> theta_output   = { 0.2 };
  std::vector<int>    network_output(1, 0);

  // feed-forward from input layer to hidden layer
  feed_forward(
    input, weights_hidden, theta_hidden, hidden_output);

  // feed-forward from hidden layer to output layer
  feed_forward(
    hidden_output, weights_output, theta_output, network_output);

  return network_output;
}