MatrixFloat MatrixFloat::multiple(const MatrixFloat &b) const{
const unsigned int M = rows;
const unsigned int N = cols;
const unsigned int K = b.getNumRows();
const unsigned int L = b.getNumCols();
if( N != K ) {
errorLog << "multiple(MatrixFloat b) - The number of rows in b (" << K << ") does not match the number of columns in this matrix (" << N << ")" << std::endl;
return MatrixFloat();
}
MatrixFloat c(M,L);
Float **pb = b.getDataPointer();
Float **pc = c.getDataPointer();
unsigned int i,j,k = 0;
for(i=0; i<M; i++){
for(j=0; j<L; j++){
pc[i][j] = 0;
for(k=0; k<K; k++){
pc[i][j] += dataPtr[i*cols+k] * pb[k][j];
}
}
}
return c;
}
bool RBMQuantizer::train_(MatrixFloat &trainingData){
//Clear any previous model
clear();
if( trainingData.getNumRows() == 0 ){
errorLog << "train_(MatrixFloat &trainingData) - Failed to train quantizer, the training data is empty!" << std::endl;
return false;
}
//Train the RBM model
rbm.setNumHiddenUnits( numClusters );
rbm.setLearningRate( learningRate );
rbm.setMinNumEpochs( minNumEpochs );
rbm.setMaxNumEpochs( maxNumEpochs );
rbm.setMinChange( minChange );
if( !rbm.train_( trainingData ) ){
errorLog << "train_(MatrixFloat &trainingData) - Failed to train quantizer!" << std::endl;
return false;
}
//Flag that the feature vector is now initalized
initialized = true;
trained = true;
numInputDimensions = trainingData.getNumCols();
numOutputDimensions = 1; //This is always 1 for the quantizer
featureVector.resize(numOutputDimensions,0);
quantizationDistances.resize(numClusters,0);
return true;
}
bool MatrixFloat::subtract(const MatrixFloat &b){
if( b.getNumRows() != rows ){
errorLog << "subtract(const MatrixFloat &b) - Failed to add matrix! The rows do not match!" << std::endl;
errorLog << " rows: " << rows << " b rows: " << b.getNumRows() << std::endl;
return false;
}
if( b.getNumCols() != cols ){
errorLog << "subtract(const MatrixFloat &b) - Failed to add matrix! The rows do not match!" << std::endl;
errorLog << " cols: " << cols << " b cols: " << b.getNumCols() << std::endl;
return false;
}
unsigned int i;
//Using direct pointers really helps speed up the computation time
Float *pb = b.getData();
const unsigned int size = rows*cols;
for(i=0; i<size; i++){
dataPtr[i] -= pb[i];
}
return true;
}
bool BernoulliRBM::predict_(const MatrixFloat &inputData,MatrixFloat &outputData,const UINT rowIndex){
if( !trained ){
errorLog << "predict_(const MatrixFloat &inputData,MatrixFloat &outputData,const UINT rowIndex) - Failed to run prediction - the model has not been trained." << std::endl;
return false;
}
if( inputData.getNumCols() != numVisibleUnits ){
errorLog << "predict_(const MatrixFloat &inputData,MatrixFloat &outputData,const UINT rowIndex) -";
errorLog << " Failed to run prediction - the number of columns in the input matrix (" << inputData.getNumCols() << ")";
errorLog << " does not match the number of visible units (" << numVisibleUnits << ")." << std::endl;
return false;
}
if( outputData.getNumCols() != numHiddenUnits ){
errorLog << "predict_(const MatrixFloat &inputData,MatrixFloat &outputData,const UINT rowIndex) -";
errorLog << " Failed to run prediction - the number of columns in the output matrix (" << outputData.getNumCols() << ")";
errorLog << " does not match the number of hidden units (" << numHiddenUnits << ")." << std::endl;
return false;
}
//Propagate the data up through the RBM
Float x = 0.0;
for(UINT j=0; j<numHiddenUnits; j++){
x = 0;
for(UINT i=0; i<numVisibleUnits; i++) {
x += weightsMatrix[j][i] * inputData[rowIndex][i];
}
outputData[rowIndex][j] = grt_sigmoid( x + hiddenLayerBias[j] ); //This gives P( h_j = 1 | input )
}
return true;
}
bool KMeans::setClusters(const MatrixFloat &clusters){
clear();
numClusters = clusters.getNumRows();
numInputDimensions = clusters.getNumCols();
this->clusters = clusters;
return true;
}
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;
}
bool MatrixFloat::subtract(const MatrixFloat &a,const MatrixFloat &b){
const unsigned int M = a.getNumRows();
const unsigned int N = a.getNumCols();
if( M != b.getNumRows() ){
errorLog << "subtract(const MatrixFloat &a,const MatrixFloat &b) - Failed to add matrix! The rows do not match!";
errorLog << " a rows: " << M << " b rows: " << b.getNumRows() << std::endl;
return false;
}
if( N != b.getNumCols() ){
errorLog << "subtract(const MatrixFloat &a,const MatrixFloat &b) - Failed to add matrix! The columns do not match!";
errorLog << " a cols: " << N << " b cols: " << b.getNumCols() << std::endl;
return false;
}
resize( M, N );
UINT i,j;
//Using direct pointers really helps speed up the computation time
Float **pa = a.getDataPointer();
Float **pb = b.getDataPointer();
for(i=0; i<M; i++){
for(j=0; j<N; j++){
dataPtr[i*cols+j] = pa[i][j] - pb[i][j];
}
}
return true;
}
bool MatrixFloat::add(const MatrixFloat &a,const MatrixFloat &b){
const unsigned int M = a.getNumRows();
const unsigned int N = a.getNumCols();
if( M != b.getNumRows() ){
errorLog << "add(const MatrixFloat &a,const MatrixFloat &b) - Failed to add matrix! The rows do not match!";
errorLog << " a rows: " << M << " b rows: " << b.getNumRows() << std::endl;
return false;
}
if( N != b.getNumCols() ){
errorLog << "add(const MatrixFloat &a,const MatrixFloat &b) - Failed to add matrix! The columns do not match!";
errorLog << " a cols: " << N << " b cols: " << b.getNumCols() << std::endl;
return false;
}
resize( M, N );
UINT i;
//Using direct pointers really helps speed up the computation time
Float *pa = a.getData();
Float *pb = b.getData();
const unsigned int size = M*N;
for(i=0; i<size; i++){
dataPtr[i] = pa[i] + pb[i];
}
return true;
}
// Tests the MatrixFloat type
TEST(DynamicType, MatrixFloatTest) {
DynamicType type;
MatrixFloat a(3,1);
a[0][0] = 1.1; a[1][0] = 1.2; a[2][0] = 1.3;
EXPECT_TRUE( type.set( a ) );
MatrixFloat b = type.get< MatrixFloat >();
EXPECT_EQ( a.getSize(), b.getSize() );
EXPECT_EQ( a.getNumRows(), b.getNumRows() );
EXPECT_EQ( a.getNumCols(), b.getNumCols() );
for(unsigned int i=0; i<a.getNumRows(); i++){
for(unsigned int j=0; j<a.getNumCols(); j++){
EXPECT_EQ( a[i][j], b[i][j] );
}
}
}
bool ClassificationDataStream::addSample(const UINT classLabel,const MatrixFloat &sample){
if( numDimensions != sample.getNumCols() ){
errorLog << "addSample(const UINT classLabel, const MatrixFloat &sample) - the number of columns in the sample (" << sample.getNumCols() << ") does not match the number of dimensions of the dataset (" << numDimensions << ")" << std::endl;
return false;
}
bool searchForNewClass = true;
if( trackingClass ){
if( classLabel != lastClassID ){
//The class ID has changed so update the time series tracker
timeSeriesPositionTracker[ timeSeriesPositionTracker.size()-1 ].setEndIndex( totalNumSamples-1 );
}else searchForNewClass = false;
}
if( searchForNewClass ){
bool newClass = true;
//Search to see if this class has been found before
for(UINT k=0; k<classTracker.size(); k++){
if( classTracker[k].classLabel == classLabel ){
newClass = false;
classTracker[k].counter += sample.getNumRows();
}
}
if( newClass ){
ClassTracker newCounter(classLabel,1);
classTracker.push_back( newCounter );
}
//Set the timeSeriesPositionTracker start position
trackingClass = true;
lastClassID = classLabel;
TimeSeriesPositionTracker newTracker(totalNumSamples,0,classLabel);
timeSeriesPositionTracker.push_back( newTracker );
}
ClassificationSample labelledSample( numDimensions );
for(UINT i=0; i<sample.getNumRows(); i++){
data.push_back( labelledSample );
data.back().setClassLabel( classLabel );
for(UINT j=0; j<numDimensions; j++){
data.back()[j] = sample[i][j];
}
}
totalNumSamples += sample.getNumRows();
return true;
}
bool KMeansQuantizer::train_(MatrixFloat &trainingData){
//Clear any previous model
clear();
//Train the KMeans model
KMeans kmeans;
kmeans.setNumClusters(numClusters);
kmeans.setComputeTheta( true );
kmeans.setMinChange( minChange );
kmeans.setMinNumEpochs( minNumEpochs );
kmeans.setMaxNumEpochs( maxNumEpochs );
if( !kmeans.train_(trainingData) ){
errorLog << "train_(MatrixFloat &trainingData) - Failed to train quantizer!" << std::endl;
return false;
}
trained = true;
initialized = true;
numInputDimensions = trainingData.getNumCols();
numOutputDimensions = 1; //This is always 1 for the KMeansQuantizer
featureVector.resize(numOutputDimensions,0);
clusters = kmeans.getClusters();
quantizationDistances.resize(numClusters,0);
return true;
}
bool saveResults( const GestureRecognitionPipeline &pipeline, const string &filename ){
infoLog << "Saving results to file: " << filename << endl;
fstream file( filename.c_str(), fstream::out );
if( !file.is_open() ){
errorLog << "Failed to open results file: " << filename << endl;
return false;
}
file << pipeline.getTestAccuracy() << endl;
Vector< UINT > classLabels = pipeline.getClassLabels();
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
file << pipeline.getTestPrecision( classLabels[k] );
if( k+1 < pipeline.getNumClassesInModel() ) file << "\t";
else file << endl;
}
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
file << pipeline.getTestRecall( classLabels[k] );
if( k+1 < pipeline.getNumClassesInModel() ) file << "\t";
else file << endl;
}
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
file << pipeline.getTestFMeasure( classLabels[k] );
if( k+1 < pipeline.getNumClassesInModel() ) file << "\t";
else file << endl;
}
MatrixFloat confusionMatrix = pipeline.getTestConfusionMatrix();
for(UINT i=0; i<confusionMatrix.getNumRows(); i++){
for(UINT j=0; j<confusionMatrix.getNumCols(); j++){
file << confusionMatrix[i][j];
if( j+1 < confusionMatrix.getNumCols() ) file << "\t";
}file << endl;
}
file.close();
infoLog << "Results saved." << endl;
return true;
}
int main (int argc, const char * argv[])
{
//Create a new KMeans instance
KMeans kmeans;
kmeans.setComputeTheta( true );
kmeans.setMinChange( 1.0e-10 );
kmeans.setMinNumEpochs( 10 );
kmeans.setMaxNumEpochs( 10000 );
//There are a number of ways of training the KMeans algorithm, depending on what you need the KMeans for
//These are:
//- with labelled training data (in the ClassificationData format)
//- with unlablled training data (in the UnlabelledData format)
//- with unlabelled training data (in a simple MatrixDouble format)
//This example shows you how to train the algorithm with ClassificationData
//Load some training data to train the KMeans algorithm
ClassificationData trainingData;
if( !trainingData.load("LabelledClusterData.csv") ){
cout << "Failed to load training data!\n";
return EXIT_FAILURE;
}
//Train the KMeans algorithm - K will automatically be set to the number of classes in the training dataset
if( !kmeans.train( trainingData ) ){
cout << "Failed to train model!\n";
return EXIT_FAILURE;
}
//Get the K clusters from the KMeans instance and print them
cout << "\nClusters:\n";
MatrixFloat clusters = kmeans.getClusters();
for(unsigned int k=0; k<clusters.getNumRows(); k++){
for(unsigned int n=0; n<clusters.getNumCols(); n++){
cout << clusters[k][n] << "\t";
}cout << endl;
}
return EXIT_SUCCESS;
}
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;
}
bool PrincipalComponentAnalysis::computeFeatureVector(const MatrixFloat &data,UINT numPrincipalComponents,bool normData){
trained = false;
if( numPrincipalComponents > data.getNumCols() ){
errorLog << "computeFeatureVector(const MatrixFloat &data,UINT numPrincipalComponents,bool normData) - The number of principal components (";
errorLog << numPrincipalComponents << ") is greater than the number of columns in your data (" << data.getNumCols() << ")" << std::endl;
return false;
}
this->numPrincipalComponents = numPrincipalComponents;
this->normData = normData;
return computeFeatureVector_(data,MAX_NUM_PCS);
}
bool MatrixFloat::multiple(const MatrixFloat &a,const MatrixFloat &b,const bool aTranspose){
const unsigned int M = !aTranspose ? a.getNumRows() : a.getNumCols();
const unsigned int N = !aTranspose ? a.getNumCols() : a.getNumRows();
const unsigned int K = b.getNumRows();
const unsigned int L = b.getNumCols();
if( N != K ) {
errorLog << "multiple(const MatrixFloat &a,const MatrixFloat &b,const bool aTranspose) - The number of rows in a (" << K << ") does not match the number of columns in matrix b (" << N << ")" << std::endl;
return false;
}
if( !resize( M, L ) ){
errorLog << "multiple(const MatrixFloat &b,const MatrixFloat &c,const bool bTranspose) - Failed to resize matrix!" << std::endl;
return false;
}
unsigned int i, j, k = 0;
//Using direct pointers really helps speed up the computation time
Float **pa = a.getDataPointer();
Float **pb = b.getDataPointer();
if( aTranspose ){
for(j=0; j<L; j++){
for(i=0; i<M; i++){
dataPtr[i*cols+j] = 0;
for(k=0; k<K; k++){
dataPtr[i*cols+j] += pa[k][i] * pb[k][j];
}
}
}
}else{
for(j=0; j<L; j++){
for(i=0; i<M; i++){
dataPtr[i*cols+j] = 0;
for(k=0; k<K; k++){
dataPtr[i*cols+j] += pa[i][k] * pb[k][j];
}
}
}
}
return true;
}
bool PrincipalComponentAnalysis::setModel( const VectorFloat &mean, const MatrixFloat &eigenvectors ){
if( (UINT)mean.size() != eigenvectors.getNumCols() ){
return false;
}
trained = true;
numInputDimensions = eigenvectors.getNumCols();
numPrincipalComponents = eigenvectors.getNumRows();
this->mean = mean;
stdDev.clear();
componentWeights.clear();
eigenvalues.clear();
sortedEigenvalues.clear();
this->eigenvectors = eigenvectors;
//The eigenvectors are already sorted, so the sorted eigenvalues just holds the default index
for(UINT i=0; i<numPrincipalComponents; i++){
sortedEigenvalues.push_back( IndexedDouble(i,0.0) );
}
return true;
}
bool MatrixFloat::add(const MatrixFloat &b){
if( b.getNumRows() != rows ){
errorLog << "add(const MatrixFloat &b) - Failed to add matrix! The rows do not match!" << std::endl;
return false;
}
if( b.getNumCols() != cols ){
errorLog << "add(const MatrixFloat &b) - Failed to add matrix! The rows do not match!" << std::endl;
return false;
}
unsigned int i = 0;
//Using direct pointers really helps speed up the computation time
const Float *p_b = &(b[0][0]);
for(i=0; i<rows*cols; i++){
dataPtr[i] += p_b[i];
}
return true;
}
bool DecisionTreeClusterNode::computeLeafNodeWeights( MatrixFloat &weights ) const{
if( isLeafNode ){ //If we reach a leaf node, there is nothing to do
return true;
}
if( featureIndex >= weights.getNumCols() ){ //Feature index is out of bounds
warningLog << __GRT_LOG__ << " Feature index is greater than weights Vector size!" << std::endl;
return false;
}
if( leftChild ){ //Recursively compute the weights for the left child until we reach the node above a leaf node
if( leftChild->getIsLeafNode() ){
if( classProbabilities.getSize() != weights.getNumRows() ){
warningLog << __GRT_LOG__ << " The number of rows in the weights matrix does not match the class probabilities Vector size!" << std::endl;
return false;
}
for(UINT i=0; i<classProbabilities.getSize(); i++){
weights[ i ][ featureIndex ] += classProbabilities[ i ];
}
} leftChild->computeLeafNodeWeights( weights );
}
if( rightChild ){ //Recursively compute the weights for the right child until we reach the node above a leaf node
if( rightChild->getIsLeafNode() ){
if( classProbabilities.getSize() != weights.getNumRows() ){
warningLog << __GRT_LOG__ << " The number of rows in the weights matrix does not match the class probabilities Vector size!" << std::endl;
return false;
}
for(UINT i=0; i<classProbabilities.getSize(); i++){
weights[ i ][ featureIndex ] += classProbabilities[ i ];
}
} rightChild->computeLeafNodeWeights( weights );
}
return true;
}
bool KMeans::train_(MatrixFloat &data){
trained = false;
if( numClusters == 0 ){
errorLog << "train_(MatrixFloat &data) - Failed to train model. NumClusters is zero!" << std::endl;
return false;
}
if( data.getNumRows() == 0 || data.getNumCols() == 0 ){
errorLog << "train_(MatrixFloat &data) - The number of rows or columns in the data is zero!" << std::endl;
return false;
}
numTrainingSamples = data.getNumRows();
numInputDimensions = data.getNumCols();
clusters.resize(numClusters,numInputDimensions);
assign.resize(numTrainingSamples);
count.resize(numClusters);
//Randomly pick k data points as the starting clusters
Random random;
Vector< UINT > randIndexs(numTrainingSamples);
for(UINT i=0; i<numTrainingSamples; i++) randIndexs[i] = i;
std::random_shuffle(randIndexs.begin(), randIndexs.end());
//Copy the clusters
for(UINT k=0; k<numClusters; k++){
for(UINT j=0; j<numInputDimensions; j++){
clusters[k][j] = data[ randIndexs[k] ][j];
}
}
return trainModel( data );
}
bool EigenvalueDecomposition::decompose(const MatrixFloat &a){
n = a.getNumCols();
eigenvectors.resize(n,n);
realEigenvalues.resize(n);
complexEigenvalues.resize(n);
issymmetric = true;
for(int j = 0; (j < n) & issymmetric; j++) {
for(int i = 0; (i < n) & issymmetric; i++) {
issymmetric = (a[i][j] == a[j][i]);
}
}
if (issymmetric) {
for(int i = 0; i < n; i++) {
for(int j = 0; j < n; j++) {
eigenvectors[i][j] = a[i][j];
}
}
// Tridiagonalize.
tred2();
// Diagonalize.
tql2();
} else {
h.resize(n,n);
ort.resize(n);
for(int j = 0; j < n; j++) {
for(int i = 0; i < n; i++) {
h[i][j] = a[i][j];
}
}
// Reduce to Hessenberg form.
orthes();
// Reduce Hessenberg to real Schur form.
hqr2();
}
return true;
}
void ShaderD3D::setMatrix(const char* name, const MatrixFloat& mat) const
{
MatrixFloat matrix = mat.transpose();
std::string stringName(name);
if (stringName.compare("worldMatrix") == 0) {
m_Matrices.worldMatrix = matrix;
} else if (stringName.compare("normalWorldMatrix") == 0) {
m_Matrices.normalWorldMatrix = matrix;
} else if (stringName.compare("viewMatrix") == 0) {
m_Matrices.viewMatrix = matrix;
} else if (stringName.compare("projMatrix") == 0) {
m_Matrices.projMatrix = matrix;
}
mapConstantBufferMatrix();
}
bool TimeSeriesClassificationData::addSample(const UINT classLabel,const MatrixFloat &trainingSample){
if( trainingSample.getNumCols() != numDimensions ){
errorLog << "addSample(UINT classLabel, MatrixFloat trainingSample) - The dimensionality of the training sample (" << trainingSample.getNumCols() << ") does not match that of the dataset (" << numDimensions << ")" << std::endl;
return false;
}
//The class label must be greater than zero (as zero is used for the null rejection class label
if( classLabel == GRT_DEFAULT_NULL_CLASS_LABEL && !allowNullGestureClass ){
errorLog << "addSample(UINT classLabel, MatrixFloat sample) - the class label can not be 0!" << std::endl;
return false;
}
TimeSeriesClassificationSample newSample(classLabel,trainingSample);
data.push_back( newSample );
totalNumSamples++;
if( classTracker.size() == 0 ){
ClassTracker tracker(classLabel,1);
classTracker.push_back(tracker);
}else{
bool labelFound = false;
for(UINT i=0; i<classTracker.size(); i++){
if( classLabel == classTracker[i].classLabel ){
classTracker[i].counter++;
labelFound = true;
break;
}
}
if( !labelFound ){
ClassTracker tracker(classLabel,1);
classTracker.push_back(tracker);
}
}
return true;
}
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;
}
int main (int argc, const char * argv[])
{
//Create some input data for the PCA algorithm - this data comes from the Matlab PCA example
MatrixFloat data(13,4);
data[0][0] = 7; data[0][1] = 26; data[0][2] = 6; data[0][3] = 60;
data[1][0] = 1; data[1][1] = 29; data[1][2] = 15; data[1][3] = 52;
data[2][0] = 11; data[2][1] = 56; data[2][2] = 8; data[2][3] = 20;
data[3][0] = 11; data[3][1] = 31; data[3][2] = 8; data[3][3] = 47;
data[4][0] = 7; data[4][1] = 52; data[4][2] = 6; data[4][3] = 33;
data[5][0] = 11; data[5][1] = 55; data[5][2] = 9; data[5][3] = 22;
data[6][0] = 3; data[6][1] = 71; data[6][2] = 17; data[6][3] = 6;
data[7][0] = 1; data[7][1] = 31; data[7][2] = 22; data[7][3] = 44;
data[8][0] = 2; data[8][1] = 54; data[8][2] = 18; data[8][3] = 22;
data[9][0] = 21; data[9][1] = 47; data[9][2] = 4; data[9][3] = 26;
data[10][0] = 1; data[10][1] = 40; data[10][2] = 23; data[10][3] = 34;
data[11][0] = 11; data[11][1] = 66; data[11][2] = 9; data[11][3] = 12;
data[12][0] = 10; data[12][1] = 68; data[12][2] = 8; data[12][3] = 12;
//Print the input data
data.print("Input Data:");
//Create a new principal component analysis instance
PrincipalComponentAnalysis pca;
//Run pca on the input data, setting the maximum variance value to 95% of the variance
if( !pca.computeFeatureVector( data, 0.95 ) ){
cout << "ERROR: Failed to compute feature vector!\n";
return EXIT_FAILURE;
}
//Get the number of principal components
UINT numPrincipalComponents = pca.getNumPrincipalComponents();
cout << "Number of Principal Components: " << numPrincipalComponents << endl;
//Project the original data onto the principal subspace
MatrixFloat prjData;
if( !pca.project( data, prjData ) ){
cout << "ERROR: Failed to project data!\n";
return EXIT_FAILURE;
}
//Print out the pca info
pca.print("PCA Info:");
//Print the projected data
cout << "ProjectedData:\n";
for(UINT i=0; i<prjData.getNumRows(); i++){
for(UINT j=0; j<prjData.getNumCols(); j++){
cout << prjData[i][j] << "\t";
}cout << endl;
}
//Save the model to a file
if( !pca.save( "pca-model.grt" ) ){
cout << "ERROR: Failed to save model to file!\n";
return EXIT_FAILURE;
}
//Load the model from the file
if( !pca.load( "pca-model.grt" ) ){
cout << "ERROR: Failed to load model from file!\n";
return EXIT_FAILURE;
}
//Print out the pca info again to make sure it matches
pca.print("PCA Info:");
return EXIT_SUCCESS;
}
bool SelfOrganizingMap::train_( MatrixFloat &data ){
//Clear any previous models
clear();
const UINT M = data.getNumRows();
const UINT N = data.getNumCols();
numInputDimensions = N;
numOutputDimensions = numClusters*numClusters;
Random rand;
//Setup the neurons
neurons.resize( numClusters, numClusters );
if( neurons.getSize() != numClusters*numClusters ){
errorLog << "train_( MatrixFloat &data ) - Failed to resize neurons matrix, there might not be enough memory!" << std::endl;
return false;
}
//Init the neurons
for(UINT i=0; i<numClusters; i++){
for(UINT j=0; j<numClusters; j++){
neurons[i][j].init( N, 0.5, SOM_MIN_TARGET, SOM_MAX_TARGET );
}
}
//Scale the data if needed
ranges = data.getRanges();
if( useScaling ){
for(UINT i=0; i<M; i++){
for(UINT j=0; j<numInputDimensions; j++){
data[i][j] = scale(data[i][j],ranges[j].minValue,ranges[j].maxValue,SOM_MIN_TARGET,SOM_MAX_TARGET);
}
}
}
Float error = 0;
Float lastError = 0;
Float trainingSampleError = 0;
Float delta = 0;
Float minChange = 0;
Float weightUpdate = 0;
Float alpha = 1.0;
Float neuronDiff = 0;
Float neuronWeightFunction = 0;
Float gamma = 0;
UINT iter = 0;
bool keepTraining = true;
VectorFloat trainingSample;
Vector< UINT > randomTrainingOrder(M);
//In most cases, the training data is grouped into classes (100 samples for class 1, followed by 100 samples for class 2, etc.)
//This can cause a problem for stochastic gradient descent algorithm. To avoid this issue, we randomly shuffle the order of the
//training samples. This random order is then used at each epoch.
for(UINT i=0; i<M; i++){
randomTrainingOrder[i] = i;
}
std::random_shuffle(randomTrainingOrder.begin(), randomTrainingOrder.end());
//Enter the main training loop
while( keepTraining ){
//Update alpha based on the current iteration
alpha = Util::scale(iter,0,maxNumEpochs,alphaStart,alphaEnd);
//Run one epoch of training using the online best-matching-unit algorithm
error = 0;
for(UINT m=0; m<M; m++){
trainingSampleError = 0;
//Get the i'th random training sample
trainingSample = data.getRowVector( randomTrainingOrder[m] );
//Find the best matching unit
Float dist = 0;
Float bestDist = grt_numeric_limits< Float >::max();
UINT bestIndexRow = 0;
UINT bestIndexCol = 0;
for(UINT i=0; i<numClusters; i++){
for(UINT j=0; j<numClusters; j++){
dist = neurons[i][j].getSquaredWeightDistance( trainingSample );
if( dist < bestDist ){
bestDist = dist;
bestIndexRow = i;
bestIndexCol = j;
}
}
}
error += bestDist;
//Update the weights based on the distance to the winning neuron
//Neurons closer to the winning neuron will have their weights update more
const Float bir = bestIndexRow;
const Float bic = bestIndexCol;
for(UINT i=0; i<numClusters; i++){
for(UINT j=0; j<numClusters; j++){
//Update the weights for all the neurons, pulling them a little closer to the input example
neuronDiff = 0;
gamma = 2.0 * grt_sqr( numClusters * sigmaWeight );
neuronWeightFunction = exp( -grt_sqr(bir-i)/gamma ) * exp( -grt_sqr(bic-j)/gamma );
//std::cout << "best index: " << bestIndexRow << " " << bestIndexCol << " bestDist: " << bestDist << " pos: " << i << " " << j << " neuronWeightFunction: " << neuronWeightFunction << std::endl;
for(UINT n=0; n<N; n++){
neuronDiff = trainingSample[n] - neurons[i][j][n];
weightUpdate = neuronWeightFunction * alpha * neuronDiff;
neurons[i][j][n] += weightUpdate;
}
}
}
}
error = error / M;
trainingLog << "iter: " << iter << " average error: " << error << std::endl;
//Compute the error
delta = fabs( error-lastError );
lastError = error;
//Check to see if we should stop
if( delta <= minChange && false ){
converged = true;
keepTraining = false;
}
if( grt_isinf( error ) ){
errorLog << "train_(MatrixFloat &data) - Training failed! Error is NAN!" << std::endl;
return false;
}
if( ++iter >= maxNumEpochs ){
keepTraining = false;
}
trainingLog << "Epoch: " << iter << " Squared Error: " << error << " Delta: " << delta << " Alpha: " << alpha << std::endl;
}
numTrainingIterationsToConverge = iter;
trained = true;
return true;
}
bool TimeSeriesClassificationData::loadDatasetFromCSVFile(const std::string &filename){
numDimensions = 0;
datasetName = "NOT_SET";
infoText = "";
//Clear any previous data
clear();
//Parse the CSV file
FileParser parser;
if( !parser.parseCSVFile(filename,true) ){
errorLog << "loadDatasetFromCSVFile(const std::string &filename) - Failed to parse CSV file!" << std::endl;
return false;
}
if( !parser.getConsistentColumnSize() ){
errorLog << "loadDatasetFromCSVFile(const std::string &filename) - The CSV file does not have a consistent number of columns!" << std::endl;
return false;
}
if( parser.getColumnSize() <= 2 ){
errorLog << "loadDatasetFromCSVFile(const std::string &filename) - The CSV file does not have enough columns! It should contain at least three columns!" << std::endl;
return false;
}
//Set the number of dimensions
numDimensions = parser.getColumnSize()-2;
//Reserve the memory for the data
data.reserve( parser.getRowSize() );
UINT sampleCounter = 0;
UINT lastSampleCounter = 0;
UINT classLabel = 0;
UINT j = 0;
UINT n = 0;
VectorFloat sample(numDimensions);
MatrixFloat timeseries;
for(UINT i=0; i<parser.getRowSize(); i++){
sampleCounter = grt_from_str< UINT >( parser[i][0] );
//Check to see if a new timeseries has started, if so then add the previous time series as a sample and start recording the new time series
if( sampleCounter != lastSampleCounter && i != 0 ){
//Add the labelled sample to the dataset
if( !addSample(classLabel, timeseries) ){
warningLog << "loadDatasetFromCSVFile(const std::string &filename,const UINT classLabelColumnIndex) - Could not add sample " << i << " to the dataset!" << std::endl;
}
timeseries.clear();
}
lastSampleCounter = sampleCounter;
//Get the class label
classLabel = grt_from_str< UINT >( parser[i][1] );
//Get the sample data
j=0;
n=2;
while( j != numDimensions ){
sample[j++] = grt_from_str< Float >( parser[i][n] );
n++;
}
//Add the sample to the timeseries
timeseries.push_back( sample );
}
if ( timeseries.getSize() > 0 )
//Add the labelled sample to the dataset
if( !addSample(classLabel, timeseries) ){
warningLog << "loadDatasetFromCSVFile(const std::string &filename,const UINT classLabelColumnIndex) - Could not add sample " << parser.getRowSize()-1 << " to the dataset!" << std::endl;
}
return true;
}
bool ContinuousHiddenMarkovModel::predict_( MatrixFloat ×eries ){
if( !trained ){
errorLog << "predict_( MatrixFloat ×eries ) - The model is not trained!" << std::endl;
return false;
}
if( timeseries.getNumCols() != numInputDimensions ){
errorLog << "predict_( MatrixFloat ×eries ) - The matrix column size (" << timeseries.getNumCols() << ") does not match the number of input dimensions (" << numInputDimensions << ")" << std::endl;
return false;
}
unsigned int t,i,j,k,index = 0;
Float maxAlpha = 0;
Float norm = 0;
//Downsample the observation timeseries using the same downsample factor of the training data
const unsigned int timeseriesLength = (unsigned int)timeseries.getNumRows();
const unsigned int T = downsampleFactor < timeseriesLength ? (unsigned int)floor( timeseriesLength / Float(downsampleFactor) ) : timeseriesLength;
const unsigned int K = downsampleFactor < timeseriesLength ? downsampleFactor : 1; //K is used to average over multiple bins
MatrixFloat obs(T,numInputDimensions);
for(j=0; j<numInputDimensions; j++){
index = 0;
for(i=0; i<T; i++){
norm = 0;
obs[i][j] = 0;
for(k=0; k<K; k++){
if( index < timeseriesLength ){
obs[i][j] += timeseries[index++][j];
norm += 1;
}
}
if( norm > 1 )
obs[i][j] /= norm;
}
}
//Resize alpha, c, and the estimated states vector as needed
if( alpha.getNumRows() != T || alpha.getNumCols() != numStates ) alpha.resize(T,numStates);
if( (unsigned int)c.size() != T ) c.resize(T);
if( (unsigned int)estimatedStates.size() != T ) estimatedStates.resize(T);
////////////////// Run the forward algorithm ////////////////////////
//Step 1: Init at t=0
t = 0;
c[t] = 0;
maxAlpha = 0;
for(i=0; i<numStates; i++){
alpha[t][i] = pi[i]*gauss(b,obs,sigmaStates,i,t,numInputDimensions);
c[t] += alpha[t][i];
//Keep track of the best state at time t
if( alpha[t][i] > maxAlpha ){
maxAlpha = alpha[t][i];
estimatedStates[t] = i;
}
}
//Set the inital scaling coeff
c[t] = 1.0/c[t];
//Scale alpha
for(i=0; i<numStates; i++) alpha[t][i] *= c[t];
//Step 2: Induction
for(t=1; t<T; t++){
c[t] = 0.0;
maxAlpha = 0;
for(j=0; j<numStates; j++){
alpha[t][j] = 0.0;
for(i=0; i<numStates; i++){
alpha[t][j] += alpha[t-1][i] * a[i][j];
}
alpha[t][j] *= gauss(b,obs,sigmaStates,j,t,numInputDimensions);
c[t] += alpha[t][j];
//Keep track of the best state at time t
if( alpha[t][j] > maxAlpha ){
maxAlpha = alpha[t][j];
estimatedStates[t] = j;
}
}
//Set the scaling coeff
c[t] = 1.0/c[t];
//Scale Alpha
for(j=0; j<numStates; j++) alpha[t][j] *= c[t];
}
//Termination
loglikelihood = 0.0;
for(t=0; t<T; t++) loglikelihood += log( c[t] );
loglikelihood = -loglikelihood; //Store the negative log likelihood
//Set the phase as the last estimated state, this will give a phase between [0 1]
phase = (estimatedStates[T-1]+1.0)/Float(numStates);
return true;
}
bool GaussianMixtureModels::train_(MatrixFloat &data){
trained = false;
//Clear any previous training results
det.clear();
invSigma.clear();
numTrainingIterationsToConverge = 0;
if( data.getNumRows() == 0 ){
errorLog << "train_(MatrixFloat &data) - Training Failed! Training data is empty!" << std::endl;
return false;
}
//Resize the variables
numTrainingSamples = data.getNumRows();
numInputDimensions = data.getNumCols();
//Resize mu and resp
mu.resize(numClusters,numInputDimensions);
resp.resize(numTrainingSamples,numClusters);
//Resize sigma
sigma.resize(numClusters);
for(UINT k=0; k<numClusters; k++){
sigma[k].resize(numInputDimensions,numInputDimensions);
}
//Resize frac and lndets
frac.resize(numClusters);
lndets.resize(numClusters);
//Scale the data if needed
ranges = data.getRanges();
if( useScaling ){
for(UINT i=0; i<numTrainingSamples; i++){
for(UINT j=0; j<numInputDimensions; j++){
data[i][j] = scale(data[i][j],ranges[j].minValue,ranges[j].maxValue,0,1);
}
}
}
//Pick K random starting points for the inital guesses of Mu
Random random;
Vector< UINT > randomIndexs(numTrainingSamples);
for(UINT i=0; i<numTrainingSamples; i++) randomIndexs[i] = i;
for(UINT i=0; i<numClusters; i++){
SWAP(randomIndexs[ i ],randomIndexs[ random.getRandomNumberInt(0,numTrainingSamples) ]);
}
for(UINT k=0; k<numClusters; k++){
for(UINT n=0; n<numInputDimensions; n++){
mu[k][n] = data[ randomIndexs[k] ][n];
}
}
//Setup sigma and the uniform prior on P(k)
for(UINT k=0; k<numClusters; k++){
frac[k] = 1.0/Float(numClusters);
for(UINT i=0; i<numInputDimensions; i++){
for(UINT j=0; j<numInputDimensions; j++) sigma[k][i][j] = 0;
sigma[k][i][i] = 1.0e-2; //Set the diagonal to a small number
}
}
loglike = 0;
bool keepGoing = true;
Float change = 99.9e99;
UINT numIterationsNoChange = 0;
VectorFloat u(numInputDimensions);
VectorFloat v(numInputDimensions);
while( keepGoing ){
//Run the estep
if( estep( data, u, v, change ) ){
//Run the mstep
mstep( data );
//Check for convergance
if( fabs( change ) < minChange ){
if( ++numIterationsNoChange >= minNumEpochs ){
keepGoing = false;
}
}else numIterationsNoChange = 0;
if( ++numTrainingIterationsToConverge >= maxNumEpochs ) keepGoing = false;
}else{
errorLog << "train_(MatrixFloat &data) - Estep failed at iteration " << numTrainingIterationsToConverge << std::endl;
return false;
}
}
//Compute the inverse of sigma and the determinants for prediction
if( !computeInvAndDet() ){
det.clear();
invSigma.clear();
errorLog << "train_(MatrixFloat &data) - Failed to compute inverse and determinat!" << std::endl;
return false;
}
//Flag that the model was trained
trained = true;
//Setup the cluster labels
clusterLabels.resize(numClusters);
for(UINT i=0; i<numClusters; i++){
clusterLabels[i] = i+1;
}
clusterLikelihoods.resize(numClusters,0);
clusterDistances.resize(numClusters,0);
return true;
}
void rand_covariance(MatrixFloat& cov, float s, int rank) {
Matrix x(cov.width, rank);
x.rand(-s,s);
cov.covariance(x);
}
