From 59332c2be6c7824e2d18e580aa464f5178599744 Mon Sep 17 00:00:00 2001 From: Tobias Baumann Date: Thu, 10 Feb 2022 18:02:29 +0000 Subject: [PATCH] =?UTF-8?q?Dateien=20hochladen=20nach=20=E2=80=9E=E2=80=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ovpCBoxAlgorithmXDAWNTrainer.cpp | 282 +++++++++++++++++++++++++++++++ 1 file changed, 282 insertions(+) create mode 100644 ovpCBoxAlgorithmXDAWNTrainer.cpp diff --git a/ovpCBoxAlgorithmXDAWNTrainer.cpp b/ovpCBoxAlgorithmXDAWNTrainer.cpp new file mode 100644 index 0000000..565ef4c --- /dev/null +++ b/ovpCBoxAlgorithmXDAWNTrainer.cpp @@ -0,0 +1,282 @@ +#include "ovpCBoxAlgorithmXDAWNTrainer.h" + +#include "fs/Files.h" + +#include +#include + +namespace OpenViBE { +namespace Plugins { +namespace SignalProcessing { + +CBoxAlgorithmXDAWNTrainer::CBoxAlgorithmXDAWNTrainer() {} + +bool CBoxAlgorithmXDAWNTrainer::initialize() +{ + m_trainStimulationID = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0); + m_filterFilename = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1); + + OV_ERROR_UNLESS_KRF(m_filterFilename.length() != 0, "The filter filename is empty.\n", Kernel::ErrorType::BadSetting); + + if (FS::Files::fileExists(m_filterFilename)) + { + FILE* file = FS::Files::open(m_filterFilename, "wt"); + + OV_ERROR_UNLESS_KRF(file != nullptr, "The filter file exists but cannot be used.\n", Kernel::ErrorType::BadFileRead); + + fclose(file); + } + + const int filterDimension = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 2); + + OV_ERROR_UNLESS_KRF(filterDimension > 0, "The dimension of the filter must be strictly positive.\n", Kernel::ErrorType::OutOfBound); + + m_filterDim = size_t(filterDimension); + + m_saveAsBoxConfig = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 3); + + + m_stimDecoder.initialize(*this, 0); + m_signalDecoder[0].initialize(*this, 1); + m_signalDecoder[1].initialize(*this, 2); + m_stimEncoder.initialize(*this, 0); + + return true; +} + +bool CBoxAlgorithmXDAWNTrainer::uninitialize() +{ + m_stimDecoder.uninitialize(); + m_signalDecoder[0].uninitialize(); + m_signalDecoder[1].uninitialize(); + m_stimEncoder.uninitialize(); + + return true; +} + +bool CBoxAlgorithmXDAWNTrainer::processInput(const size_t index) +{ + if (index == 0) { this->getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess(); } + + return true; +} + +bool CBoxAlgorithmXDAWNTrainer::process() +{ + Kernel::IBoxIO& dynamicBoxContext = this->getDynamicBoxContext(); + + bool train = false; + + for (size_t i = 0; i < dynamicBoxContext.getInputChunkCount(0); ++i) + { + m_stimEncoder.getInputStimulationSet()->clear(); + m_stimDecoder.decode(i); + + if (m_stimDecoder.isHeaderReceived()) { m_stimEncoder.encodeHeader(); } + if (m_stimDecoder.isBufferReceived()) + { + for (size_t j = 0; j < m_stimDecoder.getOutputStimulationSet()->getStimulationCount(); ++j) + { + const uint64_t stimulationId = m_stimDecoder.getOutputStimulationSet()->getStimulationIdentifier(j); + + if (stimulationId == m_trainStimulationID) + { + train = true; + + m_stimEncoder.getInputStimulationSet()->appendStimulation( + OVTK_StimulationId_TrainCompleted, m_stimDecoder.getOutputStimulationSet()->getStimulationDate(j), 0); + } + } + + m_stimEncoder.encodeBuffer(); + } + if (m_stimDecoder.isEndReceived()) { m_stimEncoder.encodeEnd(); } + + dynamicBoxContext.markOutputAsReadyToSend(0, dynamicBoxContext.getInputChunkStartTime(0, i), dynamicBoxContext.getInputChunkEndTime(0, i)); + } + + if (train) + { + std::vector erpSampleIndexes; + std::array X; // X[0] is session matrix, X[1] is averaged ERP + std::array C; // Covariance matrices + std::array n; + size_t nChannel = 0; + + this->getLogManager() << Kernel::LogLevel_Info << "Received train stimulation...\n"; + + // Decodes input signals + + for (size_t j = 0; j < 2; ++j) + { + n[j] = 0; + + for (size_t i = 0; i < dynamicBoxContext.getInputChunkCount(j + 1); ++i) + { + Toolkit::TSignalDecoder& decoder = m_signalDecoder[j]; + decoder.decode(i); + + CMatrix* matrix = decoder.getOutputMatrix(); + nChannel = matrix->getDimensionSize(0); + const size_t nSample = matrix->getDimensionSize(1); + const size_t sampling = size_t(decoder.getOutputSamplingRate()); + + if (decoder.isHeaderReceived()) + { + OV_ERROR_UNLESS_KRF(sampling > 0, "Input sampling frequency is equal to 0. Plugin can not process.\n", Kernel::ErrorType::OutOfBound); + OV_ERROR_UNLESS_KRF(nChannel > 0, "For condition " << j + 1 << " got no channel in signal stream.\n", Kernel::ErrorType::OutOfBound); + OV_ERROR_UNLESS_KRF(nSample > 0, "For condition " << j + 1 << " got no samples in signal stream.\n", Kernel::ErrorType::OutOfBound); + OV_ERROR_UNLESS_KRF(m_filterDim <= nChannel, "The filter dimension must not be superior than the channel count.\n", Kernel::ErrorType::OutOfBound); + + if (!n[0]) // Initialize signal buffer (X[0]) only when receiving input signal header. + { + X[j].resize(nChannel, (dynamicBoxContext.getInputChunkCount(j + 1) - 1) * nSample); + } + else // otherwise, only ERP averaging buffer (X[1]) is reset + { + X[j] = Eigen::MatrixXd::Zero(nChannel, nSample); + } + } + + if (decoder.isBufferReceived()) + { + Eigen::MatrixXd A = Eigen::Map>( + matrix->getBuffer(), nChannel, nSample); + + switch (j) + { + case 0: // Session + X[j].block(0, n[j] * A.cols(), A.rows(), A.cols()) = A; + break; + + case 1: // ERP + X[j] = X[j] + A; // Computes sumed ERP + + // $$$ Assumes continuous session signal starting at date 0 + { + size_t ERPSampleIndex = size_t(((dynamicBoxContext.getInputChunkStartTime(j + 1, i) >> 16) * sampling) >> 16); + erpSampleIndexes.push_back(ERPSampleIndex); + } + break; + + default: + break; + } + + n[j]++; + } + +#if 0 + if (decoder.isEndReceived()) + { + } +#endif + } + + OV_ERROR_UNLESS_KRF(n[j] != 0, "Did not have input signal for condition " << j + 1 << "\n", Kernel::ErrorType::BadValue); + + switch (j) + { + case 0: // Session + break; + + case 1: // ERP + X[j] = X[j] / double(n[j]); // Averages ERP + break; + + default: + break; + } + } + + // We need equal number of channels + OV_ERROR_UNLESS_KRF(X[0].rows() == X[1].rows(), + "Dimension mismatch, first input had " << size_t(X[0].rows()) << " channels while second input had " << size_t(X[1].rows()) << + " channels\n", + Kernel::ErrorType::BadValue); + + // Grabs usefull values + + const size_t sampleCountSession = X[0].cols(); + const size_t sampleCountERP = X[1].cols(); + + // Now we compute matrix D + + const Eigen::MatrixXd DI = Eigen::MatrixXd::Identity(sampleCountERP, sampleCountERP); + Eigen::MatrixXd D = Eigen::MatrixXd::Zero(sampleCountERP, sampleCountSession); + + for (size_t sampleIndex : erpSampleIndexes) { D.block(0, sampleIndex, sampleCountERP, sampleCountERP) += DI; } + + // Computes covariance matrices + + C[0] = X[0] * X[0].transpose(); + C[1] = /*Y * Y.transpose();*/ X[1] * /* D.transpose() * */ (D * D.transpose()).fullPivLu().inverse() /* * D */ * X[1].transpose(); + + // Solves generalized eigen decomposition + + const Eigen::GeneralizedSelfAdjointEigenSolver eigenSolver(C[0].selfadjointView(), C[1].selfadjointView()); + + if (eigenSolver.info() != Eigen::Success) + { + const enum Eigen::ComputationInfo error = eigenSolver.info(); + const char* errorMessage = "unknown"; + + switch (error) + { + case Eigen::NumericalIssue: errorMessage = "Numerical issue"; + break; + case Eigen::NoConvergence: errorMessage = "No convergence"; + break; + // case Eigen::InvalidInput: errorMessage="Invalid input"; break; // FIXME + default: break; + } + + OV_ERROR_KRF("Could not solve generalized eigen decomposition, got error[" << CString(errorMessage) << "]\n", + Kernel::ErrorType::BadProcessing); + } + + // Create a CMatrix mapper that can spool the filters to a file + + CMatrix eigenVectors; + eigenVectors.resize(m_filterDim, nChannel); + + Eigen::Map vectorsMapper(eigenVectors.getBuffer(), m_filterDim, nChannel); + + vectorsMapper.block(0, 0, m_filterDim, nChannel) = eigenSolver.eigenvectors().block(0, 0, nChannel, m_filterDim).transpose(); + + // Saves filters + + FILE* file = FS::Files::open(m_filterFilename.toASCIIString(), "wt"); + + OV_ERROR_UNLESS_KRF(file != nullptr, "Could not open file [" << m_filterFilename << "] for writing.\n", Kernel::ErrorType::BadFileWrite); + + if (m_saveAsBoxConfig) + { + fprintf(file, "\n"); + fprintf(file, "\t"); + + for (size_t i = 0; i < eigenVectors.getBufferElementCount(); ++i) { fprintf(file, "%e ", eigenVectors.getBuffer()[i]); } + + fprintf(file, "\n"); + fprintf(file, "\t%u\n", (unsigned int)m_filterDim); + fprintf(file, "\t%u\n", (unsigned int)nChannel); + fprintf(file, "\t\n"); + fprintf(file, ""); + } + else + { + OV_ERROR_UNLESS_KRF(Toolkit::Matrix::saveToTextFile(eigenVectors, m_filterFilename), + "Unable to save to [" << m_filterFilename << "]\n", Kernel::ErrorType::BadFileWrite); + } + + OV_WARNING_UNLESS_K(fclose(file) == 0, "Could not close file[" << m_filterFilename << "].\n"); + + this->getLogManager() << Kernel::LogLevel_Info << "Training finished and saved to [" << m_filterFilename << "]!\n"; + } + + return true; +} + +} // namespace SignalProcessing +} // namespace Plugins +} // namespace OpenViBE