1 changed files with 282 additions and 282 deletions
ovpCBoxAlgorithmXDAWNTrainer.cpp → XDAWN_Trainer/ovpCBoxAlgorithmXDAWNTrainer.cpp
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| @@ -1,282 +1,282 @@ | |||
| #include "ovpCBoxAlgorithmXDAWNTrainer.h" | |||
| #include "fs/Files.h" | |||
| #include <cstdio> | |||
| #include <iostream> | |||
| 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<size_t> erpSampleIndexes; | |||
| std::array<Eigen::MatrixXd, 2> X; // X[0] is session matrix, X[1] is averaged ERP | |||
| std::array<Eigen::MatrixXd, 2> C; // Covariance matrices | |||
| std::array<size_t, 2> 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<CBoxAlgorithmXDAWNTrainer>& 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<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>( | |||
| 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<Eigen::MatrixXd> eigenSolver(C[0].selfadjointView<Eigen::Lower>(), C[1].selfadjointView<Eigen::Lower>()); | |||
| 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<MatrixXdRowMajor> 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, "<OpenViBE-SettingsOverride>\n"); | |||
| fprintf(file, "\t<SettingValue>"); | |||
| for (size_t i = 0; i < eigenVectors.getBufferElementCount(); ++i) { fprintf(file, "%e ", eigenVectors.getBuffer()[i]); } | |||
| fprintf(file, "</SettingValue>\n"); | |||
| fprintf(file, "\t<SettingValue>%u</SettingValue>\n", (unsigned int)m_filterDim); | |||
| fprintf(file, "\t<SettingValue>%u</SettingValue>\n", (unsigned int)nChannel); | |||
| fprintf(file, "\t<SettingValue></SettingValue>\n"); | |||
| fprintf(file, "</OpenViBE-SettingsOverride>"); | |||
| } | |||
| 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 | |||
| #include "ovpCBoxAlgorithmXDAWNTrainer.h" | |||
| #include "fs/Files.h" | |||
| #include <cstdio> | |||
| #include <iostream> | |||
| 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<size_t> erpSampleIndexes; | |||
| std::array<Eigen::MatrixXd, 2> X; // X[0] is session matrix, X[1] is averaged ERP | |||
| std::array<Eigen::MatrixXd, 2> C; // Covariance matrices | |||
| std::array<size_t, 2> 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<CBoxAlgorithmXDAWNTrainer>& 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<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>( | |||
| 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<Eigen::MatrixXd> eigenSolver(C[0].selfadjointView<Eigen::Lower>(), C[1].selfadjointView<Eigen::Lower>()); | |||
| 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<MatrixXdRowMajor> 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, "<OpenViBE-SettingsOverride>\n"); | |||
| fprintf(file, "\t<SettingValue>"); | |||
| for (size_t i = 0; i < eigenVectors.getBufferElementCount(); ++i) { fprintf(file, "%e ", eigenVectors.getBuffer()[i]); } | |||
| fprintf(file, "</SettingValue>\n"); | |||
| fprintf(file, "\t<SettingValue>%u</SettingValue>\n", (unsigned int)m_filterDim); | |||
| fprintf(file, "\t<SettingValue>%u</SettingValue>\n", (unsigned int)nChannel); | |||
| fprintf(file, "\t<SettingValue></SettingValue>\n"); | |||
| fprintf(file, "</OpenViBE-SettingsOverride>"); | |||
| } | |||
| 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 | |||
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