Example #1
0
  static typename std::enable_if<
      std::is_same<Border, FullConvolution>::value, void>::type
  Convolution(const arma::Mat<eT>& input,
              const arma::Mat<eT>& filter,
              arma::Mat<eT>& output)
  {
    // In case of the full convolution outputRows and outputCols doesn't
    // represent the true output size when the padLastDim parameter is set,
    // instead it's the working size.
    const size_t outputRows = input.n_rows + 2 * (filter.n_rows - 1);
    size_t outputCols = input.n_cols + 2 * (filter.n_cols - 1);

    if (padLastDim)
        outputCols++;

    // Pad filter and input to the working output shape.
    arma::Mat<eT> inputPadded = arma::zeros<arma::Mat<eT> >(outputRows,
        outputCols);
    inputPadded.submat(filter.n_rows - 1, filter.n_cols - 1,
          filter.n_rows - 1 + input.n_rows - 1,
          filter.n_cols - 1 + input.n_cols - 1) = input;

    arma::Mat<eT> filterPadded = filter;
    filterPadded.resize(outputRows, outputCols);

    // Perform FFT and IFFT
    output = arma::real(ifft2(arma::fft2(inputPadded) % arma::fft2(
        filterPadded)));

    // Extract the region of interest. We don't need to handle the padLastDim
    // parameter in a special way we just cut it out from the output matrix.
    output = output.submat(filter.n_rows - 1, filter.n_cols - 1,
        2 * (filter.n_rows - 1) + input.n_rows - 1,
        2 * (filter.n_cols - 1) + input.n_cols - 1);
  }
/**
 * Given a Reber string, return a Reber string with all reachable next symbols.
 *
 * @param transitions The Reber transistion matrix.
 * @param reber The Reber string used to generate all reachable next symbols.
 * @param nextReber All reachable next symbols.
 */
void GenerateNextReber(const arma::Mat<char>& transitions,
                       const std::string& reber, std::string& nextReber)
{
  size_t idx = 0;

  for (size_t grammer = 1; grammer < reber.length(); grammer++)
  {
    const int grammerIdx = arma::as_scalar(arma::find(
        transitions.row(idx) == reber[grammer], 1, "first"));

    idx = arma::as_scalar(transitions.submat(idx, grammerIdx + 2, idx,
        grammerIdx + 2)) - '0';
  }

  nextReber = arma::as_scalar(transitions.submat(idx, 0, idx, 0));
  nextReber += arma::as_scalar(transitions.submat(idx, 1, idx, 1));
}
/**
 * Generate a random Reber grammar.
 *
 * For more information, see the following thesis.
 *
 * @code
 * @misc{Gers2001,
 *   author = {Felix Gers},
 *   title = {Long Short-Term Memory in Recurrent Neural Networks},
 *   year = {2001}
 * }
 * @endcode
 *
 * @param transitions Reber grammar transition matrix.
 * @param reber The generated Reber grammar string.
 */
void GenerateReber(const arma::Mat<char>& transitions, std::string& reber)
{
  size_t idx = 0;
  reber = "B";

  do
  {
    const int grammerIdx = rand() % 2;
    reber += arma::as_scalar(transitions.submat(idx, grammerIdx, idx,
        grammerIdx));

    idx = arma::as_scalar(transitions.submat(idx, grammerIdx + 2, idx,
        grammerIdx + 2)) - '0';
  } while (idx != 0);

  reber =  "BPTVVE";
}
Example #4
0
  static typename std::enable_if<
      std::is_same<Border, ValidConvolution>::value, void>::type
  Convolution(const arma::Mat<eT>& input,
              const arma::Mat<eT>& filter,
              arma::Mat<eT>& output)
  {
    arma::Mat<eT> inputPadded = input;
    arma::Mat<eT> filterPadded = filter;

    if (padLastDim)
      inputPadded.resize(inputPadded.n_rows, inputPadded.n_cols + 1);

    // Pad filter and input to the output shape.
    filterPadded.resize(inputPadded.n_rows, inputPadded.n_cols);

    output = arma::real(ifft2(arma::fft2(inputPadded) % arma::fft2(
        filterPadded)));

    // Extract the region of interest. We don't need to handle the padLastDim in
    // a special way we just cut it out from the output matrix.
    output = output.submat(filter.n_rows - 1, filter.n_cols - 1,
        input.n_rows - 1, input.n_cols - 1);
  }