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 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;
}
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 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 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 KMeans::setClusters(const MatrixFloat &clusters){
clear();
numClusters = clusters.getNumRows();
numInputDimensions = clusters.getNumCols();
this->clusters = clusters;
return true;
}
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 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 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 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;
}
// 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 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 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::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 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;
}
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 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;
}
int main (int argc, const char * argv[])
{
//Parse the data filename from the argument list, you should pass in the data path to the iris data set in the GRT data folder
if( argc != 2 ){
cout << "Error: failed to parse data filename from command line. You should run this example with one argument pointing to the data filename!\n";
return EXIT_FAILURE;
}
const string filename = argv[1];
//We are going to use the Iris dataset, you can find more about the orginal dataset at: http://en.wikipedia.org/wiki/Iris_flower_data_set
//Create a new instance of ClassificationData to hold the training data
ClassificationData trainingData;
//Load the training dataset from a file, the file should be in the same directory as this program
if( !trainingData.load( filename ) ){
cout << "Failed to load Iris data from file!\n";
return EXIT_FAILURE;
}
//Print some basic stats about the dataset we have loaded
trainingData.printStats();
//Partition the training dataset into a training dataset and test dataset
//We will use 60% of the data to train the algorithm and 40% of the data to test it
//The true parameter flags that we want to use stratified sampling, which means there
//should be an equal class distribution between the training and test datasets
ClassificationData testData = trainingData.split( 60, true );
//Setup the gesture recognition pipeline
GestureRecognitionPipeline pipeline;
//Add a KNN classification algorithm as the main classifier with a K value of 10
pipeline << KNN(10);
//Train the KNN algorithm using the training dataset
if( !pipeline.train( trainingData ) ){
cout << "Failed to train the pipeline!\n";
return EXIT_FAILURE;
}
//Test the KNN model using the test dataset
if( !pipeline.test( testData ) ){
cout << "Failed to test the pipeline!\n";
return EXIT_FAILURE;
}
//Print some metrics about how successful the classification was
//Print the accuracy
cout << "The classification accuracy was: " << pipeline.getTestAccuracy() << "%\n" << endl;
//Print the precision for each class
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
UINT classLabel = pipeline.getClassLabels()[k];
double classPrecision = pipeline.getTestPrecision( classLabel );
cout << "The precision for class " << classLabel << " was " << classPrecision << endl;
}
cout << endl;
//Print the recall for each class
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
UINT classLabel = pipeline.getClassLabels()[k];
double classRecall = pipeline.getTestRecall( classLabel );
cout << "The recall for class " << classLabel << " was " << classRecall << endl;
}
cout << endl;
//Print the confusion matrix
MatrixFloat confusionMatrix = pipeline.getTestConfusionMatrix();
cout << "Confusion Matrix: \n";
for(UINT i=0; i<confusionMatrix.getNumRows(); i++){
for(UINT j=0; j<confusionMatrix.getNumCols(); j++){
cout << confusionMatrix[i][j] << "\t";
}
cout << endl;
}
cout << endl;
return EXIT_SUCCESS;
}
bool BernoulliRBM::train_(MatrixFloat &data){
const UINT numTrainingSamples = data.getNumRows();
numInputDimensions = data.getNumCols();
numOutputDimensions = numHiddenUnits;
numVisibleUnits = numInputDimensions;
trainingLog << "NumInputDimensions: " << numInputDimensions << std::endl;
trainingLog << "NumOutputDimensions: " << numOutputDimensions << std::endl;
if( randomizeWeightsForTraining ){
//Init the weights matrix
weightsMatrix.resize(numHiddenUnits, numVisibleUnits);
Float a = 1.0 / numVisibleUnits;
for(UINT i=0; i<numHiddenUnits; i++) {
for(UINT j=0; j<numVisibleUnits; j++) {
weightsMatrix[i][j] = rand.getRandomNumberUniform(-a, a);
}
}
//Init the bias units
visibleLayerBias.resize( numVisibleUnits );
hiddenLayerBias.resize( numHiddenUnits );
std::fill(visibleLayerBias.begin(),visibleLayerBias.end(),0);
std::fill(hiddenLayerBias.begin(),hiddenLayerBias.end(),0);
}else{
if( weightsMatrix.getNumRows() != numHiddenUnits ){
errorLog << "train_(MatrixFloat &data) - Weights matrix row size does not match the number of hidden units!" << std::endl;
return false;
}
if( weightsMatrix.getNumCols() != numVisibleUnits ){
errorLog << "train_(MatrixFloat &data) - Weights matrix row size does not match the number of visible units!" << std::endl;
return false;
}
if( visibleLayerBias.size() != numVisibleUnits ){
errorLog << "train_(MatrixFloat &data) - Visible layer bias size does not match the number of visible units!" << std::endl;
return false;
}
if( hiddenLayerBias.size() != numHiddenUnits ){
errorLog << "train_(MatrixFloat &data) - Hidden layer bias size does not match the number of hidden units!" << std::endl;
return false;
}
}
//Flag the model has been trained encase the user wants to save the model during a training iteration using an observer
trained = true;
//Make sure the data is scaled between [0 1]
ranges = data.getRanges();
if( useScaling ){
for(UINT i=0; i<numTrainingSamples; i++){
for(UINT j=0; j<numInputDimensions; j++){
data[i][j] = grt_scale(data[i][j], ranges[j].minValue, ranges[j].maxValue, 0.0, 1.0);
}
}
}
const UINT numBatches = static_cast<UINT>( ceil( Float(numTrainingSamples)/batchSize ) );
//Setup the batch indexs
Vector< BatchIndexs > batchIndexs( numBatches );
UINT startIndex = 0;
for(UINT i=0; i<numBatches; i++){
batchIndexs[i].startIndex = startIndex;
batchIndexs[i].endIndex = startIndex + batchSize;
//Make sure the last batch end index is not larger than the number of training examples
if( batchIndexs[i].endIndex >= numTrainingSamples ){
batchIndexs[i].endIndex = numTrainingSamples;
}
//Get the batch size
batchIndexs[i].batchSize = batchIndexs[i].endIndex - batchIndexs[i].startIndex;
//Set the start index for the next batch
startIndex = batchIndexs[i].endIndex;
}
Timer timer;
UINT i,j,n,epoch,noChangeCounter = 0;
Float startTime = 0;
Float alpha = learningRate;
Float error = 0;
Float err = 0;
Float delta = 0;
Float lastError = 0;
Vector< UINT > indexList(numTrainingSamples);
TrainingResult trainingResult;
MatrixFloat wT( numVisibleUnits, numHiddenUnits ); //Stores a transposed copy of the weights vector
MatrixFloat vW( numHiddenUnits, numVisibleUnits ); //Stores the weight velocity updates
MatrixFloat tmpW( numHiddenUnits, numVisibleUnits ); //Stores the weight values that will be used to update the main weights matrix at each batch update
MatrixFloat v1( batchSize, numVisibleUnits ); //Stores the real batch data during a batch update
MatrixFloat v2( batchSize, numVisibleUnits ); //Stores the sampled batch data during a batch update
MatrixFloat h1( batchSize, numHiddenUnits ); //Stores the hidden states given v1 and the current weightsMatrix
MatrixFloat h2( batchSize, numHiddenUnits ); //Stores the sampled hidden states given v2 and the current weightsMatrix
MatrixFloat c1( numHiddenUnits, numVisibleUnits ); //Stores h1' * v1
MatrixFloat c2( numHiddenUnits, numVisibleUnits ); //Stores h2' * v2
MatrixFloat vDiff( batchSize, numVisibleUnits ); //Stores the difference between v1-v2
MatrixFloat hDiff( batchSize, numVisibleUnits ); //Stores the difference between h1-h2
MatrixFloat cDiff( numHiddenUnits, numVisibleUnits ); //Stores the difference between c1-c2
VectorFloat vDiffSum( numVisibleUnits ); //Stores the column sum of vDiff
VectorFloat hDiffSum( numHiddenUnits ); //Stores the column sum of hDiff
VectorFloat visibleLayerBiasVelocity( numVisibleUnits ); //Stores the velocity update of the visibleLayerBias
VectorFloat hiddenLayerBiasVelocity( numHiddenUnits ); //Stores the velocity update of the hiddenLayerBias
//Set all the velocity weights to zero
vW.setAllValues( 0 );
std::fill(visibleLayerBiasVelocity.begin(),visibleLayerBiasVelocity.end(),0);
std::fill(hiddenLayerBiasVelocity.begin(),hiddenLayerBiasVelocity.end(),0);
//Randomize the order that the training samples will be used in
for(UINT i=0; i<numTrainingSamples; i++) indexList[i] = i;
if( randomiseTrainingOrder ){
std::random_shuffle(indexList.begin(), indexList.end());
}
//Start the main training loop
timer.start();
for(epoch=0; epoch<maxNumEpochs; epoch++) {
startTime = timer.getMilliSeconds();
error = 0;
//Randomize the batch order
std::random_shuffle(batchIndexs.begin(),batchIndexs.end());
//Run each of the batch updates
for(UINT k=0; k<numBatches; k+=batchStepSize){
//Resize the data matrices, the matrices will only be resized if the rows cols are different
v1.resize( batchIndexs[k].batchSize, numVisibleUnits );
h1.resize( batchIndexs[k].batchSize, numHiddenUnits );
v2.resize( batchIndexs[k].batchSize, numVisibleUnits );
h2.resize( batchIndexs[k].batchSize, numHiddenUnits );
//Setup the data pointers, using data pointers saves a few ms on large matrix updates
Float **w_p = weightsMatrix.getDataPointer();
Float **wT_p = wT.getDataPointer();
Float **vW_p = vW.getDataPointer();
Float **data_p = data.getDataPointer();
Float **v1_p = v1.getDataPointer();
Float **v2_p = v2.getDataPointer();
Float **h1_p = h1.getDataPointer();
Float **h2_p = h2.getDataPointer();
Float *vlb_p = &visibleLayerBias[0];
Float *hlb_p = &hiddenLayerBias[0];
//Get the batch data
UINT index = 0;
for(i=batchIndexs[k].startIndex; i<batchIndexs[k].endIndex; i++){
for(j=0; j<numVisibleUnits; j++){
v1_p[index][j] = data_p[ indexList[i] ][j];
}
index++;
}
//Copy a transposed version of the weights matrix, this is used to compute h1 and h2
for(i=0; i<numHiddenUnits; i++)
for(j=0; j<numVisibleUnits; j++)
wT_p[j][i] = w_p[i][j];
//Compute h1
h1.multiple(v1, wT);
for(n=0; n<batchIndexs[k].batchSize; n++){
for(i=0; i<numHiddenUnits; i++){
h1_p[n][i] = sigmoidRandom( h1_p[n][i] + hlb_p[i] );
}
}
//Compute v2
v2.multiple(h1, weightsMatrix);
for(n=0; n<batchIndexs[k].batchSize; n++){
for(i=0; i<numVisibleUnits; i++){
v2_p[n][i] = sigmoidRandom( v2_p[n][i] + vlb_p[i] );
}
}
//Compute h2
h2.multiple(v2,wT);
for(n=0; n<batchIndexs[k].batchSize; n++){
for(i=0; i<numHiddenUnits; i++){
h2_p[n][i] = grt_sigmoid( h2_p[n][i] + hlb_p[i] );
}
}
//Compute c1, c2 and the difference between v1-v2
c1.multiple(h1,v1,true);
c2.multiple(h2,v2,true);
vDiff.subtract(v1, v2);
//Compute the sum of vdiff
for(j=0; j<numVisibleUnits; j++){
vDiffSum[j] = 0;
for(i=0; i<batchIndexs[k].batchSize; i++){
vDiffSum[j] += vDiff[i][j];
}
}
//Compute the difference between h1 and h2
hDiff.subtract(h1, h2);
for(j=0; j<numHiddenUnits; j++){
hDiffSum[j] = 0;
for(i=0; i<batchIndexs[k].batchSize; i++){
hDiffSum[j] += hDiff[i][j];
}
}
//Compute the difference between c1 and c2
cDiff.subtract(c1,c2);
//Update the weight velocities
for(i=0; i<numHiddenUnits; i++){
for(j=0; j<numVisibleUnits; j++){
vW_p[i][j] = ((momentum * vW_p[i][j]) + (alpha * cDiff[i][j])) / batchIndexs[k].batchSize;
}
}
for(i=0; i<numVisibleUnits; i++){
visibleLayerBiasVelocity[i] = ((momentum * visibleLayerBiasVelocity[i]) + (alpha * vDiffSum[i])) / batchIndexs[k].batchSize;
}
for(i=0; i<numHiddenUnits; i++){
hiddenLayerBiasVelocity[i] = ((momentum * hiddenLayerBiasVelocity[i]) + (alpha * hDiffSum[i])) / batchIndexs[k].batchSize;
}
//Update the weights
weightsMatrix.add( vW );
//Update the bias for the visible layer
for(i=0; i<numVisibleUnits; i++){
visibleLayerBias[i] += visibleLayerBiasVelocity[i];
}
//Update the bias for the visible layer
for(i=0; i<numHiddenUnits; i++){
hiddenLayerBias[i] += hiddenLayerBiasVelocity[i];
}
//Compute the reconstruction error
err = 0;
for(i=0; i<batchIndexs[k].batchSize; i++){
for(j=0; j<numVisibleUnits; j++){
err += SQR( v1[i][j] - v2[i][j] );
}
}
error += err / batchIndexs[k].batchSize;
}
error /= numBatches;
delta = lastError - error;
lastError = error;
trainingLog << "Epoch: " << epoch+1 << "/" << maxNumEpochs;
trainingLog << " Epoch time: " << (timer.getMilliSeconds()-startTime)/1000.0 << " seconds";
trainingLog << " Learning rate: " << alpha;
trainingLog << " Momentum: " << momentum;
trainingLog << " Average reconstruction error: " << error;
trainingLog << " Delta: " << delta << std::endl;
//Update the learning rate
alpha *= learningRateUpdate;
trainingResult.setClassificationResult(epoch, error, this);
trainingResults.push_back(trainingResult);
trainingResultsObserverManager.notifyObservers( trainingResult );
//Check for convergance
if( fabs(delta) < minChange ){
if( ++noChangeCounter >= minNumEpochs ){
trainingLog << "Stopping training. MinChange limit reached!" << std::endl;
break;
}
}else noChangeCounter = 0;
}
trainingLog << "Training complete after " << epoch << " epochs. Total training time: " << timer.getMilliSeconds()/1000.0 << " seconds" << std::endl;
trained = true;
return true;
}
bool SelfOrganizingMap::train_( MatrixFloat &data ){
//Clear any previous models
clear();
const UINT M = data.getNumRows();
const UINT N = data.getNumCols();
numInputDimensions = N;
numOutputDimensions = numClusters;
Random rand;
//Setup the neurons
neurons.resize( numClusters );
if( neurons.size() != numClusters ){
errorLog << "train_( MatrixFloat &data ) - Failed to resize neurons Vector, there might not be enough memory!" << std::endl;
return false;
}
for(UINT j=0; j<numClusters; j++){
//Init the neuron
neurons[j].init( N, 0.5 );
//Set the weights as a random training example
neurons[j].weights = data.getRowVector( rand.getRandomNumberInt(0, M) );
}
//Setup the network weights
switch( networkTypology ){
case RANDOM_NETWORK:
networkWeights.resize(numClusters, numClusters);
//Set the diagonal weights as 1 (as i==j)
for(UINT i=0; i<numClusters; i++){
networkWeights[i][i] = 1;
}
//Randomize the other weights
UINT indexA = 0;
UINT indexB = 0;
Float weight = 0;
for(UINT i=0; i<numClusters*numClusters; i++){
indexA = rand.getRandomNumberInt(0, numClusters);
indexB = rand.getRandomNumberInt(0, numClusters);
//Make sure the two random indexs are the same (as this is a diagonal and should be 1)
if( indexA != indexB ){
//Pick a random weight between these two neurons
weight = rand.getRandomNumberUniform(0,1);
//The weight betwen neurons a and b is the mirrored
networkWeights[indexA][indexB] = weight;
networkWeights[indexB][indexA] = weight;
}
}
break;
}
//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,0,1);
}
}
}
Float error = 0;
Float lastError = 0;
Float trainingSampleError = 0;
Float delta = 0;
Float minChange = 0;
Float weightUpdate = 0;
Float weightUpdateSum = 0;
Float alpha = 1.0;
Float neuronDiff = 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 i=0; i<M; i++){
trainingSampleError = 0;
//Get the i'th random training sample
trainingSample = data.getRowVector( randomTrainingOrder[i] );
//Find the best matching unit
Float dist = 0;
Float bestDist = grt_numeric_limits< Float >::max();
UINT bestIndex = 0;
for(UINT j=0; j<numClusters; j++){
dist = neurons[j].getSquaredWeightDistance( trainingSample );
if( dist < bestDist ){
bestDist = dist;
bestIndex = j;
}
}
//Update the weights based on the distance to the winning neuron
//Neurons closer to the winning neuron will have their weights update more
for(UINT j=0; j<numClusters; j++){
//Update the weights for the j'th neuron
weightUpdateSum = 0;
neuronDiff = 0;
for(UINT n=0; n<N; n++){
neuronDiff = trainingSample[n] - neurons[j][n];
weightUpdate = networkWeights[bestIndex][j] * alpha * neuronDiff;
neurons[j][n] += weightUpdate;
weightUpdateSum += neuronDiff;
}
trainingSampleError += grt_sqr( weightUpdateSum );
}
error += grt_sqrt( trainingSampleError / numClusters );
}
//Compute the error
delta = fabs( error-lastError );
lastError = error;
//Check to see if we should stop
if( delta <= minChange ){
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 PrincipalComponentAnalysis::computeFeatureVector_(const MatrixFloat &data,const UINT analysisMode){
trained = false;
const UINT M = data.getNumRows();
const UINT N = data.getNumCols();
this->numInputDimensions = N;
MatrixFloat 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
MatrixFloat 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 MatrixFloat &data,UINT analysisMode) - Failed to decompose input matrix!" << std::endl;
return false;
}
//Get the eigenvectors and eigenvalues
eigenvectors = eig.getEigenvectors();
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
Float sum = 0;
UINT componentIndex = 0;
sortedEigenvalues.clear();
componentWeights.resize(N,0);
while( true ){
Float 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
}
Float 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 MatrixFloat &data,UINT analysisMode) - Unknown analysis mode!" << std::endl;
break;
}
//Get the raw eigenvalues (encase the user asks for these later)
eigenvalues = eig.getRealEigenvalues();
//Flag that the features have been computed
trained = true;
return true;
}
int main (int argc, const char * argv[])
{
//Create a matrix for the test data
MatrixFloat data(4,2);
//Populate the test data
data[0][0] = 1;
data[0][1] = 2;
data[1][0] = 3;
data[1][1] = 4;
data[2][0] = 5;
data[2][1] = 6;
data[3][0] = 7;
data[3][1] = 8;
cout << "Data:\n";
for(UINT i=0; i<data.getNumRows(); i++) {
for(UINT j=0; j<data.getNumCols(); j++) {
cout << data[i][j] << "\t";
}
cout << endl;
}
//Create a new instance of the SVD class
SVD svd;
//Computes the singular value decomposition of the data matrix
if( !svd.solve(data) ) {
cout << "ERROR: Failed to solve SVD solution!\n";
return EXIT_FAILURE;
}
//Get the U, V, and W results (V is sometimes called S in other packages like Matlab)
MatrixFloat u = svd.getU();
MatrixFloat v = svd.getV();
VectorFloat w = svd.getW();
cout << "U:\n";
for(UINT i=0; i<u.getNumRows(); i++) {
for(UINT j=0; j<u.getNumCols(); j++) {
cout << u[i][j] << "\t";
}
cout << endl;
}
cout << "V:\n";
for(UINT i=0; i<v.getNumRows(); i++) {
for(UINT j=0; j<v.getNumCols(); j++) {
cout << v[i][j] << "\t";
}
cout << endl;
}
cout << "W:\n";
for(UINT i=0; i<w.getSize(); i++) {
cout << w[i] << "\t";
}
cout << endl;
return EXIT_SUCCESS;
}
bool HierarchicalClustering::train_(MatrixFloat &data){
trained = false;
clusters.clear();
distanceMatrix.clear();
if( data.getNumRows() == 0 || data.getNumCols() == 0 ){
return false;
}
//Set the rows and columns
M = data.getNumRows();
N = data.getNumCols();
//Build the distance matrix
distanceMatrix.resize(M,M);
//Build the distance matrix
for(UINT i=0; i<M; i++){
for(UINT j=0; j<M; j++){
if( i== j ) distanceMatrix[i][j] = grt_numeric_limits< Float >::max();
else{
distanceMatrix[i][j] = squaredEuclideanDistance(data[i], data[j]);
}
}
}
//Build the initial clusters, at the start each sample gets its own cluster
UINT uniqueClusterID = 0;
Vector< ClusterInfo > clusterData(M);
for(UINT i=0; i<M; i++){
clusterData[i].uniqueClusterID = uniqueClusterID++;
clusterData[i].addSampleToCluster(i);
}
trainingLog << "Starting clustering..." << std::endl;
//Create the first cluster level, each sample is it's own cluster
UINT level = 0;
ClusterLevel newLevel;
newLevel.level = level;
for(UINT i=0; i<M; i++){
newLevel.clusters.push_back( clusterData[i] );
}
clusters.push_back( newLevel );
//Move to level 1 and start the search
level++;
bool keepClustering = true;
while( keepClustering ){
//Find the closest two clusters within the cluster data
Float minDist = grt_numeric_limits< Float >::max();
Vector< Vector< UINT > > clusterPairs;
UINT K = (UINT)clusterData.size();
for(UINT i=0; i<K; i++){
for(UINT j=0; j<K; j++){
if( i != j ){
Float dist = computeClusterDistance( clusterData[i], clusterData[j] );
if( dist < minDist ){
minDist = dist;
Vector< UINT > clusterPair(2);
clusterPair[0] = i;
clusterPair[1] = j;
clusterPairs.clear();
clusterPairs.push_back( clusterPair );
}
}
}
}
if( minDist == grt_numeric_limits< Float >::max() ){
keepClustering = false;
warningLog << "train_(MatrixFloat &data) - Failed to find any cluster at level: " << level << std::endl;
return false;
}else{
//Merge the two closest clusters together and create a new level
ClusterLevel newLevel;
newLevel.level = level;
//Create the new cluster
ClusterInfo newCluster;
newCluster.uniqueClusterID = uniqueClusterID++;
const UINT numClusterPairs = clusterPairs.getSize();
for(UINT k=0; k<numClusterPairs; k++){
//Add all the samples in the first cluster to the new cluster
UINT numSamplesInClusterA = clusterData[ clusterPairs[k][0] ].getNumSamplesInCluster();
for(UINT i=0; i<numSamplesInClusterA; i++){
UINT index = clusterData[ clusterPairs[k][0] ][ i ];
newCluster.addSampleToCluster( index );
}
//Add all the samples in the second cluster to the new cluster
UINT numSamplesInClusterB = clusterData[ clusterPairs[k][1] ].getNumSamplesInCluster();
for(UINT i=0; i<numSamplesInClusterB; i++){
UINT index = clusterData[ clusterPairs[k][1] ][ i ];
newCluster.addSampleToCluster( index );
}
//Compute the cluster variance
newCluster.clusterVariance = computeClusterVariance( newCluster, data );
//Remove the two cluster pairs (so they will not be used in the next search
UINT idA = clusterData[ clusterPairs[k][0] ].getUniqueClusterID();
UINT idB = clusterData[ clusterPairs[k][1] ].getUniqueClusterID();
UINT numRemoved = 0;
Vector< ClusterInfo >::iterator iter = clusterData.begin();
while( iter != clusterData.end() ){
if( iter->getUniqueClusterID() == idA || iter->getUniqueClusterID() == idB ){
iter = clusterData.erase( iter );
if( ++numRemoved >= 2 ) break;
}else iter++;
}
}
//Add the merged cluster to the clusterData
clusterData.push_back( newCluster );
//Add the new level and cluster data to the main cluster buffer
newLevel.clusters.push_back( newCluster );
clusters.push_back( newLevel );
//Update the level
level++;
}
//Check to see if we should stop clustering
if( level >= M ){
keepClustering = false;
}
if( clusterData.size() == 0 ){
keepClustering = false;
}
trainingLog << "Cluster level: " << level << " Number of clusters: " << clusters.back().getNumClusters() << std::endl;
}
//Flag that the model is 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;
}