ClassificationData.h

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#ifndef GRT_CLASSIFICATION_DATA_HEADER
#define GRT_CLASSIFICATION_DATA_HEADER

#include "VectorFloat.h"
#include "../Util/GRTCommon.h"
#include "../CoreModules/GRTBase.h"
#include "ClassificationSample.h"
#include "RegressionData.h"
#include "UnlabelledData.h"

GRT_BEGIN_NAMESPACE

class ClassificationData : public GRTBase{
public:
 
 ClassificationData(UINT numDimensions = 0,std::string datasetName = "NOT_SET",std::string infoText = "");

 ClassificationData(const ClassificationData &rhs);

 virtual ~ClassificationData();

 ClassificationData& operator=(const ClassificationData &rhs);

 inline ClassificationSample& operator[] (const UINT &i){
 return data[i];
 }

 inline const ClassificationSample& operator[] (const UINT &i) const{
 return data[i];
 }

 void clear();
 
 bool setNumDimensions(UINT numDimensions);
 
 bool setDatasetName(std::string datasetName);
 
 bool setInfoText(std::string infoText);

 bool setClassNameForCorrespondingClassLabel(std::string className,UINT classLabel);
 
 bool setAllowNullGestureClass(bool allowNullGestureClass);

 bool addSample(UINT classLabel,const VectorFloat &sample);
 
 bool removeSample( const UINT index );
 
 bool removeLastSample();
 
 bool reserve(const UINT N);
 
 bool addClass(const UINT classLabel,const std::string className = "NOT_SET");
 
 UINT removeClass(const UINT classLabel);

 UINT eraseAllSamplesWithClassLabel(const UINT classLabel);
 
 bool relabelAllSamplesWithClassLabel(const UINT oldClassLabel,const UINT newClassLabel);
 
 bool setExternalRanges(const Vector< MinMax > &externalRanges,const bool useExternalRanges = false);
 
 bool enableExternalRangeScaling(const bool useExternalRanges);

 bool scale(const Float minTarget,const Float maxTarget);
 
 bool scale(const Vector<MinMax> &ranges,const Float minTarget,const Float maxTarget);
 
 bool save(const std::string &filename) const;
 
 bool load(const std::string &filename);
 
 bool saveDatasetToFile(const std::string &filename) const;
 
 bool loadDatasetFromFile(const std::string &filename);
 
 bool saveDatasetToCSVFile(const std::string &filename) const;
 
 bool loadDatasetFromCSVFile(const std::string &filename,const UINT classLabelColumnIndex = 0);
 
 bool printStats() const;

 bool sortClassLabels();
 
 bool merge(const ClassificationData &data);
 
 ClassificationData partition(const UINT partitionPercentage,const bool useStratifiedSampling = false);
 
 bool spiltDataIntoKFolds(const UINT K,const bool useStratifiedSampling = false);
 
 ClassificationData getTrainingFoldData(const UINT foldIndex) const;
 
 ClassificationData getTestFoldData(const UINT foldIndex) const;
 
 ClassificationData getClassData(const UINT classLabel) const;
 
 ClassificationData getBootstrappedDataset(UINT numSamples=0, bool balanceDataset=false ) const;
 
 RegressionData reformatAsRegressionData() const;
 
 UnlabelledData reformatAsUnlabelledData() const;
 
 std::string getDatasetName() const{ return datasetName; }
 
 std::string getInfoText() const{ return infoText; }

 std::string getStatsAsString() const;
 
 UINT inline getNumDimensions() const{ return numDimensions; }
 
 UINT inline getNumSamples() const{ return totalNumSamples; }
 
 UINT inline getNumClasses() const{ return classTracker.getSize(); }
 
 UINT getMinimumClassLabel() const;
 
 UINT getMaximumClassLabel() const;
 
 UINT getClassLabelIndexValue(const UINT classLabel) const;
 
 std::string getClassNameForCorrespondingClassLabel(const UINT classLabel) const;
 
 Vector<MinMax> getRanges() const;

 Vector< UINT > getClassLabels() const;

 Vector< UINT > getNumSamplesPerClass() const;

 Vector< ClassTracker > getClassTracker() const{ return classTracker; }

 MatrixFloat getClassHistogramData(const UINT classLabel,const UINT numBins) const;

 Vector< MatrixFloat > getHistogramData(const UINT numBins) const;
 
 Vector< ClassificationSample > getClassificationData() const{ return data; }
 
 VectorFloat getClassProbabilities() const;
 
 VectorFloat getClassProbabilities( const Vector< UINT > &classLabels ) const;

 VectorFloat getMean() const;
 
 VectorFloat getStdDev() const;

 MatrixFloat getClassMean() const;
 
 MatrixFloat getClassStdDev() const;
 
 MatrixFloat getCovarianceMatrix() const;

 Vector< UINT > getClassDataIndexes(const UINT classLabel) const;

 MatrixDouble getDataAsMatrixDouble() const;

 MatrixFloat getDataAsMatrixFloat() const;
 
 static bool generateGaussDataset( const std::string filename, const UINT numSamples = 10000, const UINT numClasses = 10, const UINT numDimensions = 3, const Float range = 10, const Float sigma = 1 );

private:
 
 std::string datasetName; 
 std::string infoText; 
 UINT numDimensions; 
 UINT totalNumSamples; 
 UINT kFoldValue; 
 bool crossValidationSetup; 
 bool useExternalRanges; 
 bool allowNullGestureClass; 
 Vector< MinMax > externalRanges; 
 Vector< ClassTracker > classTracker; 
 Vector< ClassificationSample > data; 
 Vector< Vector< UINT > > crossValidationIndexs; 
};

GRT_END_NAMESPACE

#endif //GRT_CLASSIFICATION_DATA_HEADER