bool PrincipalComponentAnalysis::project(const MatrixFloat &data,MatrixFloat &prjData){
if( !trained ){
warningLog << "project(const MatrixFloat &data,MatrixFloat &prjData) - The PrincipalComponentAnalysis module has not been trained!" << std::endl;
return false;
}
if( data.getNumCols() != numInputDimensions ){
warningLog << "project(const MatrixFloat &data,MatrixFloat &prjData) - The number of columns in the input vector (" << data.getNumCols() << ") does not match the number of input dimensions (" << numInputDimensions << ")!" << std::endl;
return false;
}
MatrixFloat msData( data );
prjData.resize(data.getNumRows(),numPrincipalComponents);
if( normData ){
//Mean subtract the data
for(UINT i=0; i<data.getNumRows(); i++)
for(UINT j=0; j<numInputDimensions; j++)
msData[i][j] = (msData[i][j]-mean[j])/stdDev[j];
}else{
//Mean subtract the data
for(UINT i=0; i<data.getNumRows(); i++)
for(UINT j=0; j<numInputDimensions; j++)
msData[i][j] -= mean[j];
}
//Projected Data
for(UINT row=0; row<msData.getNumRows(); row++){//For each row in the final data
for(UINT i=0; i<numPrincipalComponents; i++){//For each PC
prjData[row][i]=0;
for(UINT j=0; j<data.getNumCols(); j++)//For each feature
prjData[row][i] += msData[row][j] * eigenvectors[j][sortedEigenvalues[i].index];
}
}
return true;
}
bool PrincipalComponentAnalysis::computeFeatureVector_(const MatrixDouble &data,const UINT analysisMode) {
trained = false;
const UINT M = data.getNumRows();
const UINT N = data.getNumCols();
this->numInputDimensions = N;
MatrixDouble msData( M, N );
//Compute the mean and standard deviation of the input data
mean = data.getMean();
stdDev = data.getStdDev();
if( normData ) {
//Normalize the data
for(UINT i=0; i<M; i++)
for(UINT j=0; j<N; j++)
msData[i][j] = (data[i][j]-mean[j]) / stdDev[j];
} else {
//Mean Subtract Data
for(UINT i=0; i<M; i++)
for(UINT j=0; j<N; j++)
msData[i][j] = data[i][j] - mean[j];
}
//Get the covariance matrix
MatrixDouble cov = msData.getCovarianceMatrix();
//Use Eigen Value Decomposition to find eigenvectors of the covariance matrix
EigenvalueDecomposition eig;
if( !eig.decompose( cov ) ) {
mean.clear();
stdDev.clear();
componentWeights.clear();
sortedEigenvalues.clear();
eigenvectors.clear();
errorLog << "computeFeatureVector(const MatrixDouble &data,UINT analysisMode) - Failed to decompose input matrix!" << endl;
return false;
}
//Get the eigenvectors and eigenvalues
eigenvectors = eig.getEigenvectors();
VectorDouble eigenvalues = eig.getRealEigenvalues();
//Any eigenvalues less than 0 are not worth anything so set to 0
for(UINT i=0; i<eigenvalues.size(); i++) {
if( eigenvalues[i] < 0 )
eigenvalues[i] = 0;
}
//Sort the eigenvalues and compute the component weights
double sum = 0;
UINT componentIndex = 0;
sortedEigenvalues.clear();
componentWeights.resize(N,0);
while( true ) {
double maxValue = 0;
UINT index = 0;
for(UINT i=0; i<eigenvalues.size(); i++) {
if( eigenvalues[i] > maxValue ) {
maxValue = eigenvalues[i];
index = i;
}
}
if( maxValue == 0 || componentIndex >= eigenvalues.size() ) {
break;
}
sortedEigenvalues.push_back( IndexedDouble(index,maxValue) );
componentWeights[ componentIndex++ ] = eigenvalues[ index ];
sum += eigenvalues[ index ];
eigenvalues[ index ] = 0; //Set the maxValue to zero so it won't be used again
}
double cumulativeVariance = 0;
switch( analysisMode ) {
case MAX_VARIANCE:
//Normalize the component weights and workout how many components we need to use to reach the maxVariance
numPrincipalComponents = 0;
for(UINT k=0; k<N; k++) {
componentWeights[k] /= sum;
cumulativeVariance += componentWeights[k];
if( cumulativeVariance >= maxVariance && numPrincipalComponents==0 ) {
numPrincipalComponents = k+1;
}
}
break;
case MAX_NUM_PCS:
//Normalize the component weights and compute the maxVariance
maxVariance = 0;
for(UINT k=0; k<N; k++) {
componentWeights[k] /= sum;
if( k < numPrincipalComponents ) {
maxVariance += componentWeights[k];
}
}
break;
default:
errorLog << "computeFeatureVector(const MatrixDouble &data,UINT analysisMode) - Unknown analysis mode!" << endl;
break;
}
//Flag that the features have been computed
trained = true;
return true;
}
