„XDAWN_Trainer/ovpCBoxAlgorithmXDAWNTrainer.cpp“ ändern
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#include "ovpCBoxAlgorithmXDAWNTrainer.h"
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#include "ovpCBoxAlgorithmXDAWNTrainer.h"
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#include "fs/Files.h"
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#include "fs/Files.h"
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#include <cstdio>
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#include <cstdio>
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#include <iostream>
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#include <iostream>
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namespace OpenViBE {
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namespace OpenViBE {
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namespace Plugins {
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namespace Plugins {
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namespace SignalProcessing {
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namespace SignalProcessing {
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CBoxAlgorithmXDAWNTrainer::CBoxAlgorithmXDAWNTrainer() {}
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CBoxAlgorithmXDAWNTrainer::CBoxAlgorithmXDAWNTrainer() {}
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bool CBoxAlgorithmXDAWNTrainer::initialize()
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bool CBoxAlgorithmXDAWNTrainer::initialize()
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{
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{
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m_trainStimulationID = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0);
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m_trainStimulationID = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 0);
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m_filterFilename = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1);
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m_filterFilename = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 1);
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OV_ERROR_UNLESS_KRF(m_filterFilename.length() != 0, "The filter filename is empty.\n", Kernel::ErrorType::BadSetting);
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OV_ERROR_UNLESS_KRF(m_filterFilename.length() != 0, "The filter filename is empty.\n", Kernel::ErrorType::BadSetting);
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if (FS::Files::fileExists(m_filterFilename))
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if (FS::Files::fileExists(m_filterFilename))
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{
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{
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FILE* file = FS::Files::open(m_filterFilename, "wt");
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FILE* file = FS::Files::open(m_filterFilename, "wt");
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OV_ERROR_UNLESS_KRF(file != nullptr, "The filter file exists but cannot be used.\n", Kernel::ErrorType::BadFileRead);
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OV_ERROR_UNLESS_KRF(file != nullptr, "The filter file exists but cannot be used.\n", Kernel::ErrorType::BadFileRead);
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fclose(file);
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fclose(file);
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}
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}
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const int filterDimension = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 2);
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const int filterDimension = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 2);
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OV_ERROR_UNLESS_KRF(filterDimension > 0, "The dimension of the filter must be strictly positive.\n", Kernel::ErrorType::OutOfBound);
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OV_ERROR_UNLESS_KRF(filterDimension > 0, "The dimension of the filter must be strictly positive.\n", Kernel::ErrorType::OutOfBound);
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m_filterDim = size_t(filterDimension);
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m_filterDim = size_t(filterDimension);
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m_saveAsBoxConfig = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 3);
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m_saveAsBoxConfig = FSettingValueAutoCast(*this->getBoxAlgorithmContext(), 3);
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m_stimDecoder.initialize(*this, 0);
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m_stimDecoder.initialize(*this, 0);
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m_signalDecoder[0].initialize(*this, 1);
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m_signalDecoder[0].initialize(*this, 1);
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m_signalDecoder[1].initialize(*this, 2);
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m_signalDecoder[1].initialize(*this, 2);
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m_stimEncoder.initialize(*this, 0);
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m_stimEncoder.initialize(*this, 0);
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return true;
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return true;
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}
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}
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bool CBoxAlgorithmXDAWNTrainer::uninitialize()
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bool CBoxAlgorithmXDAWNTrainer::uninitialize()
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{
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{
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m_stimDecoder.uninitialize();
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m_stimDecoder.uninitialize();
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m_signalDecoder[0].uninitialize();
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m_signalDecoder[0].uninitialize();
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m_signalDecoder[1].uninitialize();
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m_signalDecoder[1].uninitialize();
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m_stimEncoder.uninitialize();
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m_stimEncoder.uninitialize();
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return true;
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return true;
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}
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}
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bool CBoxAlgorithmXDAWNTrainer::processInput(const size_t index)
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bool CBoxAlgorithmXDAWNTrainer::processInput(const size_t index)
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{
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{
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if (index == 0) { this->getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess(); }
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if (index == 0) { this->getBoxAlgorithmContext()->markAlgorithmAsReadyToProcess(); }
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return true;
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return true;
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}
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}
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bool CBoxAlgorithmXDAWNTrainer::process()
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bool CBoxAlgorithmXDAWNTrainer::process()
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{
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{
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Kernel::IBoxIO& dynamicBoxContext = this->getDynamicBoxContext();
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Kernel::IBoxIO& dynamicBoxContext = this->getDynamicBoxContext();
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bool train = false;
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bool train = false;
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for (size_t i = 0; i < dynamicBoxContext.getInputChunkCount(0); ++i)
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for (size_t i = 0; i < dynamicBoxContext.getInputChunkCount(0); ++i)
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{
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{
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m_stimEncoder.getInputStimulationSet()->clear();
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m_stimEncoder.getInputStimulationSet()->clear();
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m_stimDecoder.decode(i);
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m_stimDecoder.decode(i);
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if (m_stimDecoder.isHeaderReceived()) { m_stimEncoder.encodeHeader(); }
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if (m_stimDecoder.isHeaderReceived()) { m_stimEncoder.encodeHeader(); }
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if (m_stimDecoder.isBufferReceived())
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if (m_stimDecoder.isBufferReceived())
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{
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{
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for (size_t j = 0; j < m_stimDecoder.getOutputStimulationSet()->getStimulationCount(); ++j)
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for (size_t j = 0; j < m_stimDecoder.getOutputStimulationSet()->getStimulationCount(); ++j)
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{
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{
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const uint64_t stimulationId = m_stimDecoder.getOutputStimulationSet()->getStimulationIdentifier(j);
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const uint64_t stimulationId = m_stimDecoder.getOutputStimulationSet()->getStimulationIdentifier(j);
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if (stimulationId == m_trainStimulationID)
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if (stimulationId == m_trainStimulationID)
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{
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{
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train = true;
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train = true;
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m_stimEncoder.getInputStimulationSet()->appendStimulation(
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m_stimEncoder.getInputStimulationSet()->appendStimulation(
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OVTK_StimulationId_TrainCompleted, m_stimDecoder.getOutputStimulationSet()->getStimulationDate(j), 0);
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OVTK_StimulationId_TrainCompleted, m_stimDecoder.getOutputStimulationSet()->getStimulationDate(j), 0);
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}
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}
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}
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}
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m_stimEncoder.encodeBuffer();
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m_stimEncoder.encodeBuffer();
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}
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}
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if (m_stimDecoder.isEndReceived()) { m_stimEncoder.encodeEnd(); }
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if (m_stimDecoder.isEndReceived()) { m_stimEncoder.encodeEnd(); }
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dynamicBoxContext.markOutputAsReadyToSend(0, dynamicBoxContext.getInputChunkStartTime(0, i), dynamicBoxContext.getInputChunkEndTime(0, i));
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dynamicBoxContext.markOutputAsReadyToSend(0, dynamicBoxContext.getInputChunkStartTime(0, i), dynamicBoxContext.getInputChunkEndTime(0, i));
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}
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}
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if (train)
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if (train)
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{
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{
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std::vector<size_t> erpSampleIndexes;
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std::vector<size_t> erpSampleIndexes;
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std::array<Eigen::MatrixXd, 2> X; // X[0] is session matrix, X[1] is averaged ERP
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std::array<Eigen::MatrixXd, 2> X; // X[0] is session matrix, X[1] is averaged ERP
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std::array<Eigen::MatrixXd, 2> C; // Covariance matrices
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std::array<Eigen::MatrixXd, 2> C; // Covariance matrices
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std::array<size_t, 2> n;
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std::array<size_t, 2> n;
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size_t nChannel = 0;
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size_t nChannel = 0;
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this->getLogManager() << Kernel::LogLevel_Info << "Received train stimulation...\n";
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this->getLogManager() << Kernel::LogLevel_Info << "Received train stimulation...\n";
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// Decodes input signals
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// Decodes input signals
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for (size_t j = 0; j < 2; ++j)
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for (size_t j = 0; j < 2; ++j)
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{
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{
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n[j] = 0;
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n[j] = 0;
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for (size_t i = 0; i < dynamicBoxContext.getInputChunkCount(j + 1); ++i)
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for (size_t i = 0; i < dynamicBoxContext.getInputChunkCount(j + 1); ++i)
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{
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{
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Toolkit::TSignalDecoder<CBoxAlgorithmXDAWNTrainer>& decoder = m_signalDecoder[j];
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Toolkit::TSignalDecoder<CBoxAlgorithmXDAWNTrainer>& decoder = m_signalDecoder[j];
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decoder.decode(i);
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decoder.decode(i);
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CMatrix* matrix = decoder.getOutputMatrix();
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CMatrix* matrix = decoder.getOutputMatrix();
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nChannel = matrix->getDimensionSize(0);
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nChannel = matrix->getDimensionSize(0);
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const size_t nSample = matrix->getDimensionSize(1);
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const size_t nSample = matrix->getDimensionSize(1);
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const size_t sampling = size_t(decoder.getOutputSamplingRate());
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const size_t sampling = size_t(decoder.getOutputSamplingRate());
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if (decoder.isHeaderReceived())
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if (decoder.isHeaderReceived())
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{
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{
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OV_ERROR_UNLESS_KRF(sampling > 0, "Input sampling frequency is equal to 0. Plugin can not process.\n", Kernel::ErrorType::OutOfBound);
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OV_ERROR_UNLESS_KRF(sampling > 0, "Input sampling frequency is equal to 0. Plugin can not process.\n", Kernel::ErrorType::OutOfBound);
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OV_ERROR_UNLESS_KRF(nChannel > 0, "For condition " << j + 1 << " got no channel in signal stream.\n", Kernel::ErrorType::OutOfBound);
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OV_ERROR_UNLESS_KRF(nChannel > 0, "For condition " << j + 1 << " got no channel in signal stream.\n", Kernel::ErrorType::OutOfBound);
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OV_ERROR_UNLESS_KRF(nSample > 0, "For condition " << j + 1 << " got no samples in signal stream.\n", Kernel::ErrorType::OutOfBound);
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OV_ERROR_UNLESS_KRF(nSample > 0, "For condition " << j + 1 << " got no samples in signal stream.\n", Kernel::ErrorType::OutOfBound);
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OV_ERROR_UNLESS_KRF(m_filterDim <= nChannel, "The filter dimension must not be superior than the channel count.\n", Kernel::ErrorType::OutOfBound);
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OV_ERROR_UNLESS_KRF(m_filterDim <= nChannel, "The filter dimension must not be superior than the channel count.\n", Kernel::ErrorType::OutOfBound);
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if (!n[0]) // Initialize signal buffer (X[0]) only when receiving input signal header.
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if (!n[0]) // Initialize signal buffer (X[0]) only when receiving input signal header.
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{
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{
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X[j].resize(nChannel, (dynamicBoxContext.getInputChunkCount(j + 1) - 1) * nSample);
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X[j].resize(nChannel, (dynamicBoxContext.getInputChunkCount(j + 1) - 1) * nSample);
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}
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}
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else // otherwise, only ERP averaging buffer (X[1]) is reset
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else // otherwise, only ERP averaging buffer (X[1]) is reset
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{
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{
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X[j] = Eigen::MatrixXd::Zero(nChannel, nSample);
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X[j] = Eigen::MatrixXd::Zero(nChannel, nSample);
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}
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}
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}
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}
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if (decoder.isBufferReceived())
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if (decoder.isBufferReceived())
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{
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{
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Eigen::MatrixXd A = Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>(
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Eigen::MatrixXd A = Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>(
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matrix->getBuffer(), nChannel, nSample);
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matrix->getBuffer(), nChannel, nSample);
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switch (j)
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switch (j)
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{
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{
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case 0: // Session
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case 0: // Session
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X[j].block(0, n[j] * A.cols(), A.rows(), A.cols()) = A;
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X[j].block(0, n[j] * A.cols(), A.rows(), A.cols()) = A;
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break;
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break;
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case 1: // ERP
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case 1: // ERP
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X[j] = X[j] + A; // Computes sumed ERP
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X[j] = X[j] + A; // Computes sumed ERP
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// $$$ Assumes continuous session signal starting at date 0
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// $$$ Assumes continuous session signal starting at date 0
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{
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{
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size_t ERPSampleIndex = size_t(((dynamicBoxContext.getInputChunkStartTime(j + 1, i) >> 16) * sampling) >> 16);
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size_t ERPSampleIndex = size_t(((dynamicBoxContext.getInputChunkStartTime(j + 1, i) >> 16) * sampling) >> 16);
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erpSampleIndexes.push_back(ERPSampleIndex);
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erpSampleIndexes.push_back(ERPSampleIndex);
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}
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}
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break;
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break;
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default:
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default:
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break;
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break;
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}
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}
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n[j]++;
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n[j]++;
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}
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}
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#if 0
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#if 0
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if (decoder.isEndReceived())
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if (decoder.isEndReceived())
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{
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{
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}
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}
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#endif
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#endif
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}
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}
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OV_ERROR_UNLESS_KRF(n[j] != 0, "Did not have input signal for condition " << j + 1 << "\n", Kernel::ErrorType::BadValue);
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OV_ERROR_UNLESS_KRF(n[j] != 0, "Did not have input signal for condition " << j + 1 << "\n", Kernel::ErrorType::BadValue);
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switch (j)
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switch (j)
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{
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{
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case 0: // Session
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case 0: // Session
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break;
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break;
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case 1: // ERP
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case 1: // ERP
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X[j] = X[j] / double(n[j]); // Averages ERP
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X[j] = X[j] / double(n[j]); // Averages ERP
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break;
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break;
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default:
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default:
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break;
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break;
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}
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}
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}
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}
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// We need equal number of channels
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// We need equal number of channels
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OV_ERROR_UNLESS_KRF(X[0].rows() == X[1].rows(),
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OV_ERROR_UNLESS_KRF(X[0].rows() == X[1].rows(),
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"Dimension mismatch, first input had " << size_t(X[0].rows()) << " channels while second input had " << size_t(X[1].rows()) <<
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"Dimension mismatch, first input had " << size_t(X[0].rows()) << " channels while second input had " << size_t(X[1].rows()) <<
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" channels\n",
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" channels\n",
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Kernel::ErrorType::BadValue);
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Kernel::ErrorType::BadValue);
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// Grabs usefull values
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// Grabs usefull values
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const size_t sampleCountSession = X[0].cols();
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const size_t sampleCountSession = X[0].cols();
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const size_t sampleCountERP = X[1].cols();
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const size_t sampleCountERP = X[1].cols();
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// Now we compute matrix D
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// Now we compute matrix D
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const Eigen::MatrixXd DI = Eigen::MatrixXd::Identity(sampleCountERP, sampleCountERP);
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const Eigen::MatrixXd DI = Eigen::MatrixXd::Identity(sampleCountERP, sampleCountERP);
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Eigen::MatrixXd D = Eigen::MatrixXd::Zero(sampleCountERP, sampleCountSession);
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Eigen::MatrixXd D = Eigen::MatrixXd::Zero(sampleCountERP, sampleCountSession);
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for (size_t sampleIndex : erpSampleIndexes) { D.block(0, sampleIndex, sampleCountERP, sampleCountERP) += DI; }
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for (size_t sampleIndex : erpSampleIndexes) { D.block(0, sampleIndex, sampleCountERP, sampleCountERP) += DI; }
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// Computes covariance matrices
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// Computes covariance matrices
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C[0] = X[0] * X[0].transpose();
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C[0] = X[0] * X[0].transpose();
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C[1] = /*Y * Y.transpose();*/ X[1] * /* D.transpose() * */ (D * D.transpose()).fullPivLu().inverse() /* * D */ * X[1].transpose();
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C[1] = /*Y * Y.transpose();*/ X[1] * /* D.transpose() * */ (D * D.transpose()).fullPivLu().inverse() /* * D */ * X[1].transpose();
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// Solves generalized eigen decomposition
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// Solves generalized eigen decomposition
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const Eigen::GeneralizedSelfAdjointEigenSolver<Eigen::MatrixXd> eigenSolver(C[0].selfadjointView<Eigen::Lower>(), C[1].selfadjointView<Eigen::Lower>());
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const Eigen::GeneralizedSelfAdjointEigenSolver<Eigen::MatrixXd> eigenSolver(C[0].selfadjointView<Eigen::Lower>(), C[1].selfadjointView<Eigen::Lower>());
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if (eigenSolver.info() != Eigen::Success)
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if (eigenSolver.info() != Eigen::Success)
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{
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{
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const enum Eigen::ComputationInfo error = eigenSolver.info();
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const enum Eigen::ComputationInfo error = eigenSolver.info();
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const char* errorMessage = "unknown";
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const char* errorMessage = "unknown";
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switch (error)
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switch (error)
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{
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{
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case Eigen::NumericalIssue: errorMessage = "Numerical issue";
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case Eigen::NumericalIssue: errorMessage = "Numerical issue";
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break;
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break;
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case Eigen::NoConvergence: errorMessage = "No convergence";
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case Eigen::NoConvergence: errorMessage = "No convergence";
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break;
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break;
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// case Eigen::InvalidInput: errorMessage="Invalid input"; break; // FIXME
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// case Eigen::InvalidInput: errorMessage="Invalid input"; break; // FIXME
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default: break;
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default: break;
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}
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}
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OV_ERROR_KRF("Could not solve generalized eigen decomposition, got error[" << CString(errorMessage) << "]\n",
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OV_ERROR_KRF("Could not solve generalized eigen decomposition, got error[" << CString(errorMessage) << "]\n",
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Kernel::ErrorType::BadProcessing);
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Kernel::ErrorType::BadProcessing);
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}
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}
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// Create a CMatrix mapper that can spool the filters to a file
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// Create a CMatrix mapper that can spool the filters to a file
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CMatrix eigenVectors;
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CMatrix eigenVectors;
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eigenVectors.resize(m_filterDim, nChannel);
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eigenVectors.resize(m_filterDim, nChannel);
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Eigen::Map<MatrixXdRowMajor> vectorsMapper(eigenVectors.getBuffer(), m_filterDim, nChannel);
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Eigen::Map<MatrixXdRowMajor> vectorsMapper(eigenVectors.getBuffer(), m_filterDim, nChannel);
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vectorsMapper.block(0, 0, m_filterDim, nChannel) = eigenSolver.eigenvectors().block(0, 0, nChannel, m_filterDim).transpose();
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vectorsMapper.block(0, 0, m_filterDim, nChannel) = eigenSolver.eigenvectors().block(0, 0, nChannel, m_filterDim).transpose();
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// Saves filters
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// Saves filters
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FILE* file = FS::Files::open(m_filterFilename.toASCIIString(), "wt");
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FILE* file = FS::Files::open(m_filterFilename.toASCIIString(), "wt");
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OV_ERROR_UNLESS_KRF(file != nullptr, "Could not open file [" << m_filterFilename << "] for writing.\n", Kernel::ErrorType::BadFileWrite);
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OV_ERROR_UNLESS_KRF(file != nullptr, "Could not open file [" << m_filterFilename << "] for writing.\n", Kernel::ErrorType::BadFileWrite);
|
||||||
|
|
||||||
if (m_saveAsBoxConfig)
|
if (m_saveAsBoxConfig)
|
||||||
{
|
{
|
||||||
fprintf(file, "<OpenViBE-SettingsOverride>\n");
|
fprintf(file, "<OpenViBE-SettingsOverride>\n");
|
||||||
fprintf(file, "\t<SettingValue>");
|
fprintf(file, "\t<SettingValue>");
|
||||||
|
|
||||||
for (size_t i = 0; i < eigenVectors.getBufferElementCount(); ++i) { fprintf(file, "%e ", eigenVectors.getBuffer()[i]); }
|
for (size_t i = 0; i < eigenVectors.getBufferElementCount(); ++i) { fprintf(file, "%e ", eigenVectors.getBuffer()[i]); }
|
||||||
|
|
||||||
fprintf(file, "</SettingValue>\n");
|
fprintf(file, "</SettingValue>\n");
|
||||||
fprintf(file, "\t<SettingValue>%u</SettingValue>\n", (unsigned int)m_filterDim);
|
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>%u</SettingValue>\n", (unsigned int)nChannel);
|
||||||
fprintf(file, "\t<SettingValue></SettingValue>\n");
|
fprintf(file, "\t<SettingValue></SettingValue>\n");
|
||||||
fprintf(file, "</OpenViBE-SettingsOverride>");
|
fprintf(file, "</OpenViBE-SettingsOverride>");
|
||||||
}
|
}
|
||||||
else
|
else
|
||||||
{
|
{
|
||||||
OV_ERROR_UNLESS_KRF(Toolkit::Matrix::saveToTextFile(eigenVectors, m_filterFilename),
|
OV_ERROR_UNLESS_KRF(Toolkit::Matrix::saveToTextFile(eigenVectors, m_filterFilename),
|
||||||
"Unable to save to [" << m_filterFilename << "]\n", Kernel::ErrorType::BadFileWrite);
|
"Unable to save to [" << m_filterFilename << "]\n", Kernel::ErrorType::BadFileWrite);
|
||||||
}
|
}
|
||||||
|
|
||||||
OV_WARNING_UNLESS_K(fclose(file) == 0, "Could not close file[" << m_filterFilename << "].\n");
|
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";
|
this->getLogManager() << Kernel::LogLevel_Info << "Training finished and saved to [" << m_filterFilename << "]!\n";
|
||||||
}
|
}
|
||||||
|
|
||||||
return true;
|
return true;
|
||||||
}
|
}
|
||||||
|
|
||||||
} // namespace SignalProcessing
|
} // namespace SignalProcessing
|
||||||
} // namespace Plugins
|
} // namespace Plugins
|
||||||
} // namespace OpenViBE
|
} // namespace OpenViBE
|
||||||
Loading…
x
Reference in New Issue
Block a user