std::string TimeSeriesClassificationData::getStatsAsString() const{
std::string stats;
stats += "DatasetName:\t" + datasetName + "\n";
stats += "DatasetInfo:\t" + infoText + "\n";
stats += "Number of Dimensions:\t" + Util::toString(numDimensions) + "\n";
stats += "Number of Samples:\t" + Util::toString(totalNumSamples) + "\n";
stats += "Number of Classes:\t" + Util::toString(getNumClasses()) + "\n";
stats += "ClassStats:\n";
for(UINT k=0; k<getNumClasses(); k++){
stats += "ClassLabel:\t" + Util::toString(classTracker[k].classLabel);
stats += "\tNumber of Samples:\t" + Util::toString( classTracker[k].counter );
stats +="\tClassName:\t" + classTracker[k].className + "\n";
}
Vector< MinMax > ranges = getRanges();
stats += "Dataset Ranges:\n";
for(UINT j=0; j<ranges.size(); j++){
stats += "[" + Util::toString( j+1 ) + "] Min:\t" + Util::toString( ranges[j].minValue ) + "\tMax: " + Util::toString( ranges[j].maxValue ) + "\n";
}
stats += "Timeseries Lengths:\n";
UINT M = (UINT)data.size();
for(UINT j=0; j<M; j++){
stats += "ClassLabel: " + Util::toString( data[j].getClassLabel() ) + " Length:\t" + Util::toString( data[j].getLength() ) + "\n";
}
return stats;
}
bool TimeSeriesClassificationDataStream::printStats() const {
cout << "DatasetName:\t" << datasetName << endl;
cout << "DatasetInfo:\t" << infoText << endl;
cout << "Number of Dimensions:\t" << numDimensions << endl;
cout << "Number of Samples:\t" << totalNumSamples << endl;
cout << "Number of Classes:\t" << getNumClasses() << endl;
cout << "ClassStats:\n";
for(UINT k=0; k<getNumClasses(); k++){
cout << "ClassLabel:\t" << classTracker[k].classLabel;
cout << "\tNumber of Samples:\t" << classTracker[k].counter;
cout << "\tClassName:\t" << classTracker[k].className << endl;
}
cout << "TimeSeriesMarkerStats:\n";
for(UINT i=0; i<timeSeriesPositionTracker.size(); i++){
cout << "ClassLabel: " << timeSeriesPositionTracker[i].getClassLabel();
cout << "\tStartIndex: " << timeSeriesPositionTracker[i].getStartIndex();
cout << "\tEndIndex: " << timeSeriesPositionTracker[i].getEndIndex();
cout << "\tLength: " << timeSeriesPositionTracker[i].getLength() << endl;
}
vector< MinMax > ranges = getRanges();
cout << "Dataset Ranges:\n";
for(UINT j=0; j<ranges.size(); j++){
cout << "[" << j+1 << "] Min:\t" << ranges[j].minValue << "\tMax: " << ranges[j].maxValue << endl;
}
return true;
}
bool LabelledTimeSeriesClassificationData::printStats() const {
cout << "DatasetName:\t" << datasetName << endl;
cout << "DatasetInfo:\t" << infoText << endl;
cout << "Number of Dimensions:\t" << numDimensions << endl;
cout << "Number of Samples:\t" << totalNumSamples << endl;
cout << "Number of Classes:\t" << getNumClasses() << endl;
cout << "ClassStats:\n";
for(UINT k=0; k<getNumClasses(); k++){
cout << "ClassLabel:\t" << classTracker[k].classLabel;
cout << "\tNumber of Samples:\t" << classTracker[k].counter;
cout << "\tClassName:\t" << classTracker[k].className << endl;
}
vector< MinMax > ranges = getRanges();
cout << "Dataset Ranges:\n";
for(UINT j=0; j<ranges.size(); j++){
cout << "[" << j+1 << "] Min:\t" << ranges[j].minValue << "\tMax: " << ranges[j].maxValue << endl;
}
cout << "Timeseries Lengths:\n";
UINT M = (UINT)data.size();
for(UINT j=0; j<M; j++){
cout << "ClassLabel: " << data[j].getClassLabel() << " Length:\t" << data[j].getLength() << endl;
}
return true;
}
Vector< UINT > ClassificationData::getClassLabels() const{
Vector< UINT > classLabels( getNumClasses(), 0 );
if( getNumClasses() == 0 ) return classLabels;
for(UINT i=0; i<getNumClasses(); i++){
classLabels[i] = classTracker[i].classLabel;
}
return classLabels;
}
Vector< UINT > ClassificationData::getNumSamplesPerClass() const{
Vector< UINT > classSampleCounts( getNumClasses(), 0 );
if( getNumSamples() == 0 ) return classSampleCounts;
for(UINT i=0; i<getNumClasses(); i++){
classSampleCounts[i] = classTracker[i].counter;
}
return classSampleCounts;
}
bool Project::renameClass(const std::string& currentName, const std::string& newName)
{
if (isLoaded())
{
ofLogVerbose("Project::renameClass") << "Renaming class...";
ofFile file(_sketchDir.getAbsolutePath() + "/" + currentName + "." + SKETCH_FILE_EXTENSION);
if (file.exists() && hasClasses())
{
unsigned int numClasses = getNumClasses();
for (unsigned int i = 0; i < numClasses; ++i)
{
if (_data["classes"][i]["name"] == currentName)
{
_data["classes"][i]["name"] = newName;
_data["classes"][i]["fileName"] = newName + "." + SKETCH_FILE_EXTENSION;
file.renameTo(_sketchDir.getAbsolutePath() + "/" + newName + "." + SKETCH_FILE_EXTENSION);
return true;
}
}
}
}
return false;
}
RegressionData ClassificationData::reformatAsRegressionData() const{
//Turns the classification into a regression data to enable regression algorithms like the MLP to be used as a classifier
//This sets the number of targets in the regression data equal to the number of classes in the classification data
//The output of each regression training sample will then be all 0's, except for the index matching the classLabel, which will be 1
//For this to work, the labelled classification data cannot have any samples with a classLabel of 0!
RegressionData regressionData;
if( totalNumSamples == 0 ){
return regressionData;
}
const UINT numInputDimensions = numDimensions;
const UINT numTargetDimensions = getNumClasses();
regressionData.setInputAndTargetDimensions(numInputDimensions, numTargetDimensions);
for(UINT i=0; i<totalNumSamples; i++){
VectorFloat targetVector(numTargetDimensions,0);
//Set the class index in the target Vector to 1 and all other values in the target Vector to 0
UINT classLabel = data[i].getClassLabel();
if( classLabel > 0 ){
targetVector[ classLabel-1 ] = 1;
}else{
regressionData.clear();
return regressionData;
}
regressionData.addSample(data[i].getSample(),targetVector);
}
return regressionData;
}
double ConfusionMatrix::averageClassAccuracy(bool includeVoid) const {
if (!normalized) {
ConfusionMatrix normalizedConfusionMatrix(*this);
normalizedConfusionMatrix.normalize();
assert(normalizedConfusionMatrix.isNormalized());
return normalizedConfusionMatrix.averageClassAccuracy(includeVoid);
}
utils::Average averageClassAccuracy;
for (unsigned int label = 0; label < getNumClasses(); label++) {
double classAccuracy = data(label, label);
assertProbability(classAccuracy);
bool ignore = false;
if (!includeVoid && !ignoredLabels.empty())
for (LabelType ID: ignoredLabels)
if (ID == label)
{
ignore = true;
break;
}
if (ignore)
continue;
else
averageClassAccuracy.addValue(classAccuracy);
}
return averageClassAccuracy.getAverage();
}
ClassificationData ClassificationData::getTestFoldData(const UINT foldIndex) const{
ClassificationData testData;
testData.setNumDimensions( numDimensions );
testData.setAllowNullGestureClass( allowNullGestureClass );
if( !crossValidationSetup ) return testData;
if( foldIndex >= kFoldValue ) return testData;
//Add the class labels to make sure they all exist
for(UINT k=0; k<getNumClasses(); k++){
testData.addClass( classTracker[k].classLabel, classTracker[k].className );
}
testData.reserve( crossValidationIndexs[ foldIndex ].getSize() );
//Add the data to the test fold
UINT index = 0;
for(UINT i=0; i<crossValidationIndexs[ foldIndex ].getSize(); i++){
index = crossValidationIndexs[ foldIndex ][i];
testData.addSample( data[ index ].getClassLabel(), data[ index ].getSample() );
}
//Sort the class labels
testData.sortClassLabels();
return testData;
}
bool Classifier::setNullRejectionThresholds(VectorDouble newRejectionThresholds){
if( newRejectionThresholds.size() == getNumClasses() ){
nullRejectionThresholds = newRejectionThresholds;
return true;
}
return false;
}
MatrixDouble RandomForests::getLeafNodeFeatureWeights( const bool normWeights ) const{
if( !trained ) return MatrixDouble();
MatrixDouble weights( getNumClasses(), numInputDimensions );
weights.setAllValues(0.0);
for(UINT i=0; i<forestSize; i++){
if( !forest[i]->computeLeafNodeWeights( weights ) ){
warningLog << "computeLeafNodeWeights( const bool normWeights ) - Failed to compute leaf node weights for tree: " << i << endl;
}
}
//Normalize the weights
if( normWeights ){
for(UINT j=0; j<weights.getNumCols(); j++){
double sum = 0.0;
for(UINT i=0; i<weights.getNumRows(); i++){
sum += weights[i][j];
}
if( sum != 0.0 ){
const double norm = 1.0 / sum;
for(UINT i=0; i<weights.getNumRows(); i++){
weights[i][j] *= norm;
}
}
}
}
return weights;
}
ClassificationData ClassificationData::getBootstrappedDataset(UINT numSamples) const{
Random rand;
ClassificationData newDataset;
newDataset.setNumDimensions( getNumDimensions() );
newDataset.setAllowNullGestureClass( allowNullGestureClass );
newDataset.setExternalRanges( externalRanges, useExternalRanges );
if( numSamples == 0 ) numSamples = totalNumSamples;
newDataset.reserve( numSamples );
//Add all the class labels to the new dataset to ensure the dataset has a list of all the labels
for(UINT k=0; k<getNumClasses(); k++){
newDataset.addClass( classTracker[k].classLabel );
}
//Randomly select the training samples to add to the new data set
UINT randomIndex;
for(UINT i=0; i<numSamples; i++){
randomIndex = rand.getRandomNumberInt(0, totalNumSamples);
newDataset.addSample(data[randomIndex].getClassLabel(), data[randomIndex].getSample());
}
//Sort the class labels so they are in order
newDataset.sortClassLabels();
return newDataset;
}
ClassificationData ClassificationData::getTrainingFoldData(const UINT foldIndex) const{
ClassificationData trainingData;
trainingData.setNumDimensions( numDimensions );
trainingData.setAllowNullGestureClass( allowNullGestureClass );
if( !crossValidationSetup ){
errorLog << "getTrainingFoldData(const UINT foldIndex) - Cross Validation has not been setup! You need to call the spiltDataIntoKFolds(UINT K,bool useStratifiedSampling) function first before calling this function!" << std::endl;
return trainingData;
}
if( foldIndex >= kFoldValue ) return trainingData;
//Add the class labels to make sure they all exist
for(UINT k=0; k<getNumClasses(); k++){
trainingData.addClass( classTracker[k].classLabel, classTracker[k].className );
}
//Add the data to the training set, this will consist of all the data that is NOT in the foldIndex
UINT index = 0;
for(UINT k=0; k<kFoldValue; k++){
if( k != foldIndex ){
for(UINT i=0; i<crossValidationIndexs[k].getSize(); i++){
index = crossValidationIndexs[k][i];
trainingData.addSample( data[ index ].getClassLabel(), data[ index ].getSample() );
}
}
}
//Sort the class labels
trainingData.sortClassLabels();
return trainingData;
}
ClassificationData ClassificationData::getBootstrappedDataset(UINT numSamples,bool balanceDataset) const{
Random rand;
ClassificationData newDataset;
newDataset.setNumDimensions( getNumDimensions() );
newDataset.setAllowNullGestureClass( allowNullGestureClass );
newDataset.setExternalRanges( externalRanges, useExternalRanges );
if( numSamples == 0 ) numSamples = totalNumSamples;
newDataset.reserve( numSamples );
const UINT K = getNumClasses();
//Add all the class labels to the new dataset to ensure the dataset has a list of all the labels
for(UINT k=0; k<K; k++){
newDataset.addClass( classTracker[k].classLabel );
}
if( balanceDataset ){
//Group the class indexs
Vector< Vector< UINT > > classIndexs( K );
for(UINT i=0; i<totalNumSamples; i++){
classIndexs[ getClassLabelIndexValue( data[i].getClassLabel() ) ].push_back( i );
}
//Get the class with the minimum number of examples
UINT numSamplesPerClass = (UINT)floor( numSamples / Float(K) );
//Randomly select the training samples from each class
UINT classIndex = 0;
UINT classCounter = 0;
UINT randomIndex = 0;
for(UINT i=0; i<numSamples; i++){
randomIndex = rand.getRandomNumberInt(0, (UINT)classIndexs[ classIndex ].size() );
randomIndex = classIndexs[ classIndex ][ randomIndex ];
newDataset.addSample(data[ randomIndex ].getClassLabel(), data[ randomIndex ].getSample());
if( classCounter++ >= numSamplesPerClass && classIndex+1 < K ){
classCounter = 0;
classIndex++;
}
}
}else{
//Randomly select the training samples to add to the new data set
UINT randomIndex;
for(UINT i=0; i<numSamples; i++){
randomIndex = rand.getRandomNumberInt(0, totalNumSamples);
newDataset.addSample( data[randomIndex].getClassLabel(), data[randomIndex].getSample() );
}
}
//Sort the class labels so they are in order
newDataset.sortClassLabels();
return newDataset;
}
Vector< MatrixFloat > ClassificationData::getHistogramData(UINT numBins) const{
const UINT K = getNumClasses();
Vector< MatrixFloat > histData(K);
for(UINT k=0; k<K; k++){
histData[k] = getClassHistogramData( classTracker[k].classLabel, numBins );
}
return histData;
}
void ConfusionMatrix::normalize() {
if (normalized) {
throw std::runtime_error("confusion matrix is already normalized");
}
cuv::ndarray<double, cuv::host_memory_space> sums(getNumClasses());
const unsigned int numClasses = getNumClasses();
for (unsigned int label = 0; label < numClasses; label++) {
sums(label) = 0.0;
for (unsigned int prediction = 0; prediction < numClasses; prediction++) {
double value = static_cast<double>(data(label, prediction));
sums(label) += value;
}
}
for (unsigned int label = 0; label < numClasses; label++) {
if (sums(label) == 0.0)
continue;
for (unsigned int prediction = 0; prediction < numClasses; prediction++) {
data(label, prediction) /= sums(label);
}
}
#ifndef NDEBUG
for (unsigned int label = 0; label < numClasses; label++) {
double sum = 0.0;
for (unsigned int prediction = 0; prediction < numClasses; prediction++) {
double v = static_cast<double>(data(label, prediction));
assert(v >= 0.0 && v <= 1.0);
sum += v;
}
assert(sum == 0.0 || abs(1.0 - sum) < 1e-6);
}
#endif
normalized = true;
}
MatrixFloat ClassificationData::getClassMean() const{
MatrixFloat mean(getNumClasses(),numDimensions);
VectorFloat counter(getNumClasses(),0);
mean.setAllValues( 0 );
for(UINT i=0; i<totalNumSamples; i++){
UINT classIndex = getClassLabelIndexValue( data[i].getClassLabel() );
for(UINT j=0; j<numDimensions; j++){
mean[classIndex][j] += data[i][j];
}
counter[ classIndex ]++;
}
for(UINT k=0; k<getNumClasses(); k++){
for(UINT j=0; j<numDimensions; j++){
mean[k][j] = counter[k] > 0 ? mean[k][j]/counter[k] : 0;
}
}
return mean;
}
void ConfusionMatrix::operator+=(const ConfusionMatrix& other) {
if (normalized) {
throw std::runtime_error("confusion matrix is already normalized");
}
if (other.getNumClasses() != getNumClasses()) {
std::ostringstream o;
o << "different number of classes in confusion matrix: " << this->getNumClasses() << " and "
<< other.getNumClasses();
throw std::runtime_error(o.str());
}
data += other.data;
}
string LabelledClassificationData::getStatsAsString() const{
string statsText;
statsText += "DatasetName:\t" + datasetName + "\n";
statsText += "DatasetInfo:\t" + infoText + "\n";
statsText += "Number of Dimensions:\t" + Util::toString( numDimensions ) + "\n";
statsText += "Number of Samples:\t" + Util::toString( totalNumSamples ) + "\n";
statsText += "Number of Classes:\t" + Util::toString( getNumClasses() ) + "\n";
statsText += "ClassStats:\n";
for(UINT k=0; k<getNumClasses(); k++){
statsText += "ClassLabel:\t" + Util::toString( classTracker[k].classLabel );
statsText += "\tNumber of Samples:\t" + Util::toString(classTracker[k].counter);
statsText += "\tClassName:\t" + classTracker[k].className + "\n";
}
vector< MinMax > ranges = getRanges();
statsText += "Dataset Ranges:\n";
for(UINT j=0; j<ranges.size(); j++){
statsText += "[" + Util::toString( j+1 ) + "] Min:\t" + Util::toString( ranges[j].minValue ) + "\tMax: " + Util::toString( ranges[j].maxValue ) + "\n";
}
return statsText;
}
MatrixFloat ClassificationData::getClassStdDev() const{
MatrixFloat mean = getClassMean();
MatrixFloat stdDev(getNumClasses(),numDimensions);
VectorFloat counter(getNumClasses(),0);
stdDev.setAllValues( 0 );
for(UINT i=0; i<totalNumSamples; i++){
UINT classIndex = getClassLabelIndexValue( data[i].getClassLabel() );
for(UINT j=0; j<numDimensions; j++){
stdDev[classIndex][j] += SQR(data[i][j]-mean[classIndex][j]);
}
counter[ classIndex ]++;
}
for(UINT k=0; k<getNumClasses(); k++){
for(UINT j=0; j<numDimensions; j++){
stdDev[k][j] = sqrt( stdDev[k][j] / Float(counter[k]-1) );
}
}
return stdDev;
}
bool Project::isClassName(const std::string& className) const
{
if (hasClasses())
{
unsigned int numClasses = getNumClasses();
for (unsigned int i = 0; i < numClasses; ++i)
{
if (_data["classes"][i]["name"] == className)
{
return true;
}
}
}
return false;
}
Json::Value Project::createClass(const std::string& className)
{
std::string fileContents = _classFileTemplate;
ofStringReplace(fileContents, "<classname>", className);
ofLogVerbose("Project::createClass") << "fileContents: "<< fileContents;
Json::Value classFile;
// TODO: Load extension from settings
classFile["fileName"] = className + "." + SKETCH_FILE_EXTENSION;
classFile["name"] = className;
classFile["fileContents"] = fileContents;
_data["classes"][getNumClasses()] = classFile;
// TODO: re-loading is a terribly slow way to delete. Come back and optimize.
// Simply need to remove the Json::Value class in _data["classes"]
_saveFile(classFile);
return classFile;
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void MultiEmmpmFilter::readFilterParameters(AbstractFilterParametersReader* reader, int index)
{
reader->openFilterGroup(this, index);
setInputDataArrayVector(reader->readDataArrayPathVector("InputDataArrayVector", getInputDataArrayVector()));
setNumClasses(reader->readValue("NumClasses", getNumClasses()));
setExchangeEnergy(reader->readValue("ExchangeEnergy", getExchangeEnergy()));
setHistogramLoops(reader->readValue("HistogramLoops", getHistogramLoops()));
setSegmentationLoops(reader->readValue("SegmentationLoops", getSegmentationLoops()));
setUseSimulatedAnnealing(reader->readValue("UseSimulatedAnnealing", getUseSimulatedAnnealing()));
setUseGradientPenalty(reader->readValue("UseGradientPenalty", getUseGradientPenalty()));
setGradientPenalty(reader->readValue("GradientPenalty", getGradientPenalty()));
setUseCurvaturePenalty(reader->readValue("UseCurvaturePenalty", getUseCurvaturePenalty()));
setCurvaturePenalty(reader->readValue("CurvaturePenalty", getCurvaturePenalty()));
setRMax(reader->readValue("RMax", getRMax()));
setEMLoopDelay(reader->readValue("EMLoopDelay", getEMLoopDelay()));
setOutputAttributeMatrixName(reader->readString("OutputAttributeMatrixName", getOutputAttributeMatrixName()));
setUsePreviousMuSigma(reader->readValue("UsePreviousMuSigma", getUsePreviousMuSigma()));
setOutputArrayPrefix(reader->readString("OutputArrayPrefix", getOutputArrayPrefix()));
reader->closeFilterGroup();
}
double ConfusionMatrix::averageClassAccuracy(bool includeVoid) const {
if (!normalized) {
ConfusionMatrix normalizedConfusionMatrix(*this);
normalizedConfusionMatrix.normalize();
assert(normalizedConfusionMatrix.isNormalized());
return normalizedConfusionMatrix.averageClassAccuracy(includeVoid);
}
utils::Average averageClassAccuracy;
for (unsigned int label = 0; label < getNumClasses(); label++) {
double classAccuracy = data(label, label);
assertProbability(classAccuracy);
if (includeVoid || label > 0) {
averageClassAccuracy.addValue(classAccuracy);
}
}
return averageClassAccuracy.getAverage();
}
VectorFloat ClassificationData::getClassProbabilities( const Vector< UINT > &classLabels ) const {
const UINT K = (UINT)classLabels.size();
const UINT N = getNumClasses();
Float sum = 0;
VectorFloat x(K,0);
for(UINT k=0; k<K; k++){
for(UINT n=0; n<N; n++){
if( classLabels[k] == classTracker[n].classLabel ){
x[k] = classTracker[n].counter;
sum += classTracker[n].counter;
break;
}
}
}
//Normalize the class probabilities
if( sum > 0 ){
for(UINT k=0; k<K; k++){
x[k] /= sum;
}
}
return x;
}
Vector< UINT > ClassificationData::getClassDataIndexes(UINT classLabel) const{
const UINT M = getNumSamples();
const UINT K = getNumClasses();
UINT N = 0;
//Get the number of samples in the class
for(UINT k=0; k<K; k++){
if( classTracker[k].classLabel == classLabel){
N = classTracker[k].counter;
break;
}
}
UINT index = 0;
Vector< UINT > classIndexes(N);
for(UINT i=0; i<M; i++){
if( data[i].getClassLabel() == classLabel ){
classIndexes[index++] = i;
}
}
return classIndexes;
}
bool TimeSeriesClassificationData::spiltDataIntoKFolds(const UINT K,const bool useStratifiedSampling){
crossValidationSetup = false;
crossValidationIndexs.clear();
//K can not be zero
if( K == 0 ){
errorLog << "spiltDataIntoKFolds(UINT K) - K can not be zero!" << std::endl;
return false;
}
//K can not be larger than the number of examples
if( K > totalNumSamples ){
errorLog << "spiltDataIntoKFolds(UINT K,bool useStratifiedSampling) - K can not be larger than the total number of samples in the dataset!" << std::endl;
return false;
}
//K can not be larger than the number of examples in a specific class if the stratified sampling option is true
if( useStratifiedSampling ){
for(UINT c=0; c<classTracker.size(); c++){
if( K > classTracker[c].counter ){
errorLog << "spiltDataIntoKFolds(UINT K,bool useStratifiedSampling) - K can not be larger than the number of samples in any given class!" << std::endl;
return false;
}
}
}
//Setup the dataset for k-fold cross validation
kFoldValue = K;
Vector< UINT > indexs( totalNumSamples );
//Work out how many samples are in each fold, the last fold might have more samples than the others
UINT numSamplesPerFold = (UINT) floor( totalNumSamples/Float(K) );
//Resize the cross validation indexs buffer
crossValidationIndexs.resize( K );
//Create the random partion indexs
Random random;
UINT randomIndex = 0;
if( useStratifiedSampling ){
//Break the data into seperate classes
Vector< Vector< UINT > > classData( getNumClasses() );
//Add the indexs to their respective classes
for(UINT i=0; i<totalNumSamples; i++){
classData[ getClassLabelIndexValue( data[i].getClassLabel() ) ].push_back( i );
}
//Randomize the order of the indexs in each of the class index buffers
for(UINT c=0; c<getNumClasses(); c++){
UINT numSamples = (UINT)classData[c].size();
for(UINT x=0; x<numSamples; x++){
//Pick a random index
randomIndex = random.getRandomNumberInt(0,numSamples);
//Swap the indexs
SWAP( classData[c][ x ] , classData[c][ randomIndex ] );
}
}
//Loop over each of the classes and add the data equally to each of the k folds until there is no data left
Vector< UINT >::iterator iter;
for(UINT c=0; c<getNumClasses(); c++){
iter = classData[ c ].begin();
UINT k = 0;
while( iter != classData[c].end() ){
crossValidationIndexs[ k ].push_back( *iter );
iter++;
k++;
k = k % K;
}
}
}else{
//Randomize the order of the data
for(UINT i=0; i<totalNumSamples; i++) indexs[i] = i;
for(UINT x=0; x<totalNumSamples; x++){
//Pick a random index
randomIndex = random.getRandomNumberInt(0,totalNumSamples);
//Swap the indexs
SWAP( indexs[ x ] , indexs[ randomIndex ] );
}
UINT counter = 0;
UINT foldIndex = 0;
for(UINT i=0; i<totalNumSamples; i++){
//Add the index to the current fold
crossValidationIndexs[ foldIndex ].push_back( indexs[i] );
//Move to the next fold if ready
if( ++counter == numSamplesPerFold && foldIndex < K-1 ){
foldIndex++;
counter = 0;
}
}
}
crossValidationSetup = true;
return true;
}
TimeSeriesClassificationData TimeSeriesClassificationData::split(const UINT trainingSizePercentage,const bool useStratifiedSampling){
//Partitions the dataset into a training dataset (which is kept by this instance of the TimeSeriesClassificationData) and
//a testing/validation dataset (which is return as a new instance of the TimeSeriesClassificationData). The trainingSizePercentage
//therefore sets the size of the data which remains in this instance and the remaining percentage of data is then added to
//the testing/validation dataset
//The dataset has changed so flag that any previous cross validation setup will now not work
crossValidationSetup = false;
crossValidationIndexs.clear();
TimeSeriesClassificationData trainingSet(numDimensions);
TimeSeriesClassificationData testSet(numDimensions);
trainingSet.setAllowNullGestureClass(allowNullGestureClass);
testSet.setAllowNullGestureClass(allowNullGestureClass);
Vector< UINT > indexs( totalNumSamples );
//Create the random partion indexs
Random random;
UINT randomIndex = 0;
if( useStratifiedSampling ){
//Break the data into seperate classes
Vector< Vector< UINT > > classData( getNumClasses() );
//Add the indexs to their respective classes
for(UINT i=0; i<totalNumSamples; i++){
classData[ getClassLabelIndexValue( data[i].getClassLabel() ) ].push_back( i );
}
//Randomize the order of the indexs in each of the class index buffers
for(UINT k=0; k<getNumClasses(); k++){
UINT numSamples = (UINT)classData[k].size();
for(UINT x=0; x<numSamples; x++){
//Pick a random index
randomIndex = random.getRandomNumberInt(0,numSamples);
//Swap the indexs
SWAP( classData[k][ x ] ,classData[k][ randomIndex ] );
}
}
//Loop over each class and add the data to the trainingSet and testSet
for(UINT k=0; k<getNumClasses(); k++){
UINT numTrainingExamples = (UINT) floor( Float(classData[k].size()) / 100.0 * Float(trainingSizePercentage) );
//Add the data to the training and test sets
for(UINT i=0; i<numTrainingExamples; i++){
trainingSet.addSample( data[ classData[k][i] ].getClassLabel(), data[ classData[k][i] ].getData() );
}
for(UINT i=numTrainingExamples; i<classData[k].size(); i++){
testSet.addSample( data[ classData[k][i] ].getClassLabel(), data[ classData[k][i] ].getData() );
}
}
//Overwrite the training data in this instance with the training data of the trainingSet
data = trainingSet.getClassificationData();
totalNumSamples = trainingSet.getNumSamples();
}else{
const UINT numTrainingExamples = (UINT) floor( Float(totalNumSamples) / 100.0 * Float(trainingSizePercentage) );
//Create the random partion indexs
Random random;
for(UINT i=0; i<totalNumSamples; i++) indexs[i] = i;
for(UINT x=0; x<totalNumSamples; x++){
//Pick a random index
randomIndex = random.getRandomNumberInt(0,totalNumSamples);
//Swap the indexs
SWAP( indexs[ x ] , indexs[ randomIndex ] );
}
//Add the data to the training and test sets
for(UINT i=0; i<numTrainingExamples; i++){
trainingSet.addSample( data[ indexs[i] ].getClassLabel(), data[ indexs[i] ].getData() );
}
for(UINT i=numTrainingExamples; i<totalNumSamples; i++){
testSet.addSample( data[ indexs[i] ].getClassLabel(), data[ indexs[i] ].getData() );
}
//Overwrite the training data in this instance with the training data of the trainingSet
data = trainingSet.getClassificationData();
totalNumSamples = trainingSet.getNumSamples();
}
return testSet;
}
LabelImage RandomForestImage::predict(const RGBDImage& image,
cuv::ndarray<float, cuv::host_memory_space>* probabilities, const bool onGPU, bool useDepthImages) const {
LabelImage prediction(image.getWidth(), image.getHeight());
const LabelType numClasses = getNumClasses();
if (treeData.size() != ensemble.size()) {
throw std::runtime_error((boost::format("tree data size: %d, ensemble size: %d. histograms normalized?")
% treeData.size() % ensemble.size()).str());
}
cuv::ndarray<float, cuv::host_memory_space> hostProbabilities(
cuv::extents[numClasses][image.getHeight()][image.getWidth()],
m_predictionAllocator);
if (onGPU) {
cuv::ndarray<float, cuv::dev_memory_space> deviceProbabilities(
cuv::extents[numClasses][image.getHeight()][image.getWidth()],
m_predictionAllocator);
cudaSafeCall(cudaMemset(deviceProbabilities.ptr(), 0, static_cast<size_t>(deviceProbabilities.size() * sizeof(float))));
{
utils::Profile profile("classifyImagesGPU");
for (const boost::shared_ptr<const TreeNodes>& data : treeData) {
classifyImage(treeData.size(), deviceProbabilities, image, numClasses, data, useDepthImages);
}
}
normalizeProbabilities(deviceProbabilities);
cuv::ndarray<LabelType, cuv::dev_memory_space> output(image.getHeight(), image.getWidth(),
m_predictionAllocator);
determineMaxProbabilities(deviceProbabilities, output);
hostProbabilities = deviceProbabilities;
cuv::ndarray<LabelType, cuv::host_memory_space> outputHost(image.getHeight(), image.getWidth(),
m_predictionAllocator);
outputHost = output;
{
utils::Profile profile("setLabels");
for (int y = 0; y < image.getHeight(); ++y) {
for (int x = 0; x < image.getWidth(); ++x) {
prediction.setLabel(x, y, static_cast<LabelType>(outputHost(y, x)));
}
}
}
} else {
utils::Profile profile("classifyImagesCPU");
tbb::parallel_for(tbb::blocked_range<size_t>(0, image.getHeight()),
[&](const tbb::blocked_range<size_t>& range) {
for(size_t y = range.begin(); y != range.end(); y++) {
for(int x=0; x < image.getWidth(); x++) {
for (LabelType label = 0; label < numClasses; label++) {
hostProbabilities(label, y, x) = 0.0f;
}
for (const auto& tree : ensemble) {
const auto& t = tree->getTree();
PixelInstance pixel(&image, 0, x, y);
const auto& hist = t->classifySoft(pixel);
assert(hist.size() == numClasses);
for(LabelType label = 0; label<hist.size(); label++) {
hostProbabilities(label, y, x) += hist[label];
}
}
double sum = 0.0f;
for (LabelType label = 0; label < numClasses; label++) {
sum += hostProbabilities(label, y, x);
}
float bestProb = -1.0f;
for (LabelType label = 0; label < numClasses; label++) {
hostProbabilities(label, y, x) /= sum;
float prob = hostProbabilities(label, y, x);
if (prob > bestProb) {
prediction.setLabel(x, y, label);
bestProb = prob;
}
}
}
}
});
}
if (probabilities) {
*probabilities = hostProbabilities;
}
return prediction;
}
LabelImage RandomForestImage::improveHistograms(const RGBDImage& image, const LabelImage& labelImage, const bool onGPU, bool useDepthImages) const {
LabelImage prediction(image.getWidth(), image.getHeight());
const LabelType numClasses = getNumClasses();
if (treeData.size() != ensemble.size()) {
throw std::runtime_error((boost::format("tree data size: %d, ensemble size: %d. histograms normalized?")
% treeData.size() % ensemble.size()).str());
}
cuv::ndarray<float, cuv::host_memory_space> hostProbabilities(
cuv::extents[numClasses][image.getHeight()][image.getWidth()],
m_predictionAllocator);
//These offsets should have been used instead of traversing to the leaf again
/* cuv::ndarray<unsigned int, cuv::dev_memory_space> nodeOffsets(
cuv::extents[image.getHeight()][image.getWidth()],
m_predictionAllocator);
*/
if (onGPU) {
cuv::ndarray<float, cuv::dev_memory_space> deviceProbabilities(
cuv::extents[numClasses][image.getHeight()][image.getWidth()],
m_predictionAllocator);
cudaSafeCall(cudaMemset(deviceProbabilities.ptr(), 0, static_cast<size_t>(deviceProbabilities.size() * sizeof(float))));
{
utils::Profile profile("classifyImagesGPU");
for (const boost::shared_ptr<const TreeNodes>& data : treeData) {
classifyImage(treeData.size(), deviceProbabilities, image, numClasses, data, useDepthImages);
bool found_tree = false;
//should be change to parallel for and add lock
for (size_t treeNr = 0; treeNr < ensemble.size(); treeNr++) {
if (data->getTreeId() == ensemble[treeNr]->getId()) {
found_tree =true;
const boost::shared_ptr<RandomTree<PixelInstance, ImageFeatureFunction> >& tree = ensemble[treeNr]->getTree();
//this should have been used and done before trying to classify the images, since it doesn't change
//std::vector<size_t> leafSet;
//tree->collectLeafNodes(leafSet);
for (int y = 0; y < image.getHeight(); y++)
for (int x = 0; x < image.getWidth(); x++) {
LabelType label = labelImage.getLabel(x,y);
if (!shouldIgnoreLabel(label)) {
PixelInstance pixel(&image, label, x, y);
//This should be changed. When classifying the image, the nodeoffsets should be returned and those used directly
//instead of traversing again to the leaves. As a test, can check if the nodeoffset is the same as the one returned
//by travertoleaf
tree->setAllPixelsHistogram(pixel);
}
}
}
if (found_tree)
break;
}
}
}
}
//should also add the CPU code!
return prediction;
}
