bool FFT::update(const MatrixFloat &x){
if( !initialized ){
errorLog << "update(const MatrixFloat &x) - Not initialized!" << std::endl;
return false;
}
if( x.getNumCols() != numInputDimensions ){
errorLog << "update(const MatrixFloat &x) - The number of columns in the inputMatrix (" << x.getNumCols() << ") does not match that of the FeatureExtraction (" << numInputDimensions << ")!" << std::endl;
return false;
}
featureDataReady = false;
for(UINT k=0; k<x.getNumRows(); k++){
//Add the current input to the data buffers
dataBuffer.push_back( x.getRow(k) );
if( ++hopCounter == hopSize ){
hopCounter = 0;
//Compute the FFT for each dimension
for(UINT j=0; j<numInputDimensions; j++){
//Copy the input data for this dimension into the temp buffer
for(UINT i=0; i<dataBufferSize; i++){
tempBuffer[i] = dataBuffer[i][j];
}
//Compute the FFT
if( !fft[j].computeFFT( tempBuffer ) ){
errorLog << "update(const VectorFloat &x) - Failed to compute FFT!" << std::endl;
return false;
}
}
//Flag that the fft was computed during this update
featureDataReady = true;
//Copy the FFT data to the feature vector
UINT index = 0;
for(UINT j=0; j<numInputDimensions; j++){
if( computeMagnitude ){
Float *mag = fft[j].getMagnitudeDataPtr();
for(UINT i=0; i<fft[j].getFFTSize()/2; i++){
featureVector[index++] = *mag++;
}
}
if( computePhase ){
Float *phase = fft[j].getPhaseDataPtr();
for(UINT i=0; i<fft[j].getFFTSize()/2; i++){
featureVector[index++] = *phase++;
}
}
}
}
}
return true;
}
ClassificationData ClassificationDataStream::getClassificationData( const bool includeNullGestures ) const {
ClassificationData classificationData;
classificationData.setNumDimensions( getNumDimensions() );
classificationData.setAllowNullGestureClass( includeNullGestures );
bool addSample = false;
for(UINT i=0; i<timeSeriesPositionTracker.size(); i++){
addSample = includeNullGestures ? true : timeSeriesPositionTracker[i].getClassLabel() != GRT_DEFAULT_NULL_CLASS_LABEL;
if( addSample ){
MatrixFloat dataSegment = getTimeSeriesData( timeSeriesPositionTracker[i] );
for(UINT j=0; j<dataSegment.getNumRows(); j++){
classificationData.addSample(timeSeriesPositionTracker[i].getClassLabel(), dataSegment.getRow(j) );
}
}
}
return classificationData;
}