VectorDouble ThresholdDetection::update(const VectorDouble &x){
if( !initialized ){
errorLog << "update(const VectorDouble &x) - Not Initialized!" << endl;
return vector<double>();
}
if( x.size() != numInputDimensions ){
errorLog << "update(const VectorDouble &x)- The Number Of Input Dimensions (" << numInputDimensions << ") does not match the size of the input vector (" << x.size() << ")!" << endl;
return vector<double>();
}
if (inNoise) {
//Add the new data to the data buffer
dataBuffer.push_back( x );
}
//Only flag that the feature data is ready if the data is full
if( dataBuffer.getBufferFilled() ){
featureDataReady = true;
}else featureDataReady = false;
VectorDouble meanFeatures(numInputDimensions,0);
VectorDouble stdDevFeatures(numInputDimensions,0);
for(UINT n=0; n<numInputDimensions; n++){
for(UINT i=0; i<bufferLength; i++){
//Update the mean
meanFeatures[n] += dataBuffer[i][n];
}
meanFeatures[n] /= bufferLength;
//Update the std dev
for(UINT i=0; i<bufferLength; i++){
stdDevFeatures[n] += (dataBuffer[i][n]-meanFeatures[n]) * (dataBuffer[i][n]-meanFeatures[n]);
}
double norm = bufferLength>1 ? bufferLength-1 : 1;
stdDevFeatures[n] = sqrt( stdDevFeatures[n]/norm );
}
// TODO(damellis): do something intelligent when numInputDimensions > 1
for(UINT n=0; n<numInputDimensions; n++){
if( x[n] > meanFeatures[n] + alpha * stdDevFeatures[n] ){
inNoise = false;
}
if (x[n] < meanFeatures[n] + beta * stdDevFeatures[n] ){
inNoise = true;
}
}
//Update the features
UINT index = 0;
featureVector[index++] = inNoise ? 0.0 : 1.0;
for(UINT n=0; n<numInputDimensions; n++){
featureVector[index++] = x[n];
featureVector[index++] = meanFeatures[n];
featureVector[index++] = stdDevFeatures[n];
featureVector[index++] = meanFeatures[n] + alpha * stdDevFeatures[n];
featureVector[index++] = meanFeatures[n] + beta * stdDevFeatures[n];
}
return featureVector;
}
VectorDouble TimeDomainFeatures::update(const VectorDouble &x){
if( !initialized ){
errorLog << "update(const VectorDouble &x) - Not Initialized!" << endl;
return vector<double>();
}
if( x.size() != numInputDimensions ){
errorLog << "update(const VectorDouble &x)- The Number Of Input Dimensions (" << numInputDimensions << ") does not match the size of the input vector (" << x.size() << ")!" << endl;
return vector<double>();
}
//Add the new data to the data buffer
dataBuffer.push_back( x );
//Only flag that the feature data is ready if the data is full
if( dataBuffer.getBufferFilled() ){
featureDataReady = true;
}else featureDataReady = false;
MatrixDouble meanFeatures(numInputDimensions,numFrames);
MatrixDouble stdDevFeatures(numInputDimensions,numFrames);
MatrixDouble normFeatures(numInputDimensions,numFrames);
MatrixDouble rmsFeatures(numInputDimensions,numFrames);
MatrixDouble data(bufferLength,numInputDimensions);
if( offsetInput ){
for(UINT n=0; n<numInputDimensions; n++){
data[0][n] = dataBuffer[0][n];
for(UINT i=1; i<bufferLength; i++){
data[i][n] = dataBuffer[i][n]-dataBuffer[0][n];
}
}
}else{
for(UINT n=0; n<numInputDimensions; n++){
for(UINT i=0; i<bufferLength; i++){
data[i][n] = dataBuffer[i][n];
}
}
}
if( useMean || useStdDev ){ meanFeatures.setAllValues(0); stdDevFeatures.setAllValues(0); }
if( useEuclideanNorm ) normFeatures.setAllValues(0);
if( useRMS ) rmsFeatures.setAllValues(0);
UINT frameSize = bufferLength / numFrames;
UINT frame = 0;
UINT index = 0;
for(UINT n=0; n<numInputDimensions; n++){
frame = 0;
index = 0;
for(UINT i=0; i<bufferLength; i++){
//Update the mean
meanFeatures[n][frame] += data[i][n];
//Update the norm features
if( useEuclideanNorm )
normFeatures[n][frame] += data[i][n]*data[i][n];
//Update the rms features
if( useRMS )
rmsFeatures[n][frame] += data[i][n]*data[i][n];
if( ++index == frameSize ){
frame++;
index = 0;
}
}
//Update the mean
for(UINT j=0; j<numFrames; j++){
meanFeatures[n][j] /= frameSize;
}
//Update the std dev if needed
if( useStdDev ){
frame = 0;
index = 0;
for(UINT i=0; i<bufferLength; i++){
stdDevFeatures[n][frame] += (data[i][n]-meanFeatures[n][frame]) * (data[i][n]-meanFeatures[n][frame]);
if( ++index == frameSize ){
frame++;
index = 0;
}
}
double norm = frameSize>1 ? frameSize-1 : 1;
for(UINT j=0; j<numFrames; j++){
stdDevFeatures[n][j] = sqrt( stdDevFeatures[n][j]/norm );
}
}
//Update the euclidean norm if needed
if( useEuclideanNorm ){
for(UINT j=0; j<numFrames; j++){
normFeatures[n][j] = sqrt( normFeatures[n][j] );
}
}
//Update the rms if needed
if( useRMS ){
for(UINT j=0; j<numFrames; j++){
rmsFeatures[n][j] = sqrt( rmsFeatures[n][j] / frameSize );
}
}
}
//Update the features
index = 0;
frame = 0;
for(UINT n=0; n<numInputDimensions; n++){
for(UINT j=0; j<numFrames; j++){
if( useMean ){
featureVector[index++] = meanFeatures[n][j];
}
if( useStdDev ){
featureVector[index++] = stdDevFeatures[n][j];
}
if( useEuclideanNorm ){
featureVector[index++] = normFeatures[n][j];
}
if( useRMS ){
featureVector[index++] = rmsFeatures[n][j];
}
}
}
return featureVector;
}
