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";
}
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);
}