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#ifndef RANDOMFORESTCLASSIFIER_H
#define RANDOMFORESTCLASSIFIER_H
#include <itkDataObject.h>
#include <itkObjectFactory.h>
#include <itkSize.h>
#include <SNAPCommon.h>
#include <map>
template <class dataT, class labelT> class Histogram;
template <class dataT, class labelT> class AxisAlignedClassifier;
template <class HistT, class ClassT, class dataT> class DecisionForest;
/**
* This class encapsulates a Random Forest classifier
*/
class RandomForestClassifier : public itk::DataObject
{
public:
// Standard ITK stuff
irisITKObjectMacro(RandomForestClassifier, itk::DataObject)
// typedefs
typedef Histogram<GreyType, LabelType> RFHistogramType;
typedef AxisAlignedClassifier<GreyType, LabelType> RFAxisClassifierType;
typedef DecisionForest<RFHistogramType, RFAxisClassifierType, GreyType> RandomForestType;
typedef std::map<size_t, LabelType> MappingType;
typedef itk::Size<3> SizeType;
// A list of weights for each class - used to construct speed image
typedef std::vector<double> WeightArray;
// Reset the classifier
void Reset();
// Get the mapping from the class indices to labels
irisGetMacro(ClassToLabelMapping, const MappingType &)
// Get the random forest
irisGetMacro(Forest, RandomForestType *)
// Get the patch radius
irisGetMacro(PatchRadius, const SizeType &)
/** Whether coordinates of the voxels are used as features */
itkGetMacro(UseCoordinateFeatures, bool)
itkSetMacro(UseCoordinateFeatures, bool)
// Set the bias parameter (adjusts the mapping of FG probability to speed)
itkGetMacro(BiasParameter, double)
itkSetMacro(BiasParameter, double)
// Get a reference to the weight array
irisGetMacro(ClassWeights, const WeightArray &)
// Set the weight for a class
void SetClassWeight(size_t class_id, double weight);
// Test if the classifier is valid (has 2+ classes)
bool IsValidClassifier() const;
protected:
RandomForestClassifier();
~RandomForestClassifier();
// The actual decision forest
RandomForestType *m_Forest;
// Whether the labels are valid (?)
bool m_ValidLabel;
// Mapping of index to label (?)
MappingType m_ClassToLabelMapping;
// Weight of each class
WeightArray m_ClassWeights;
// The patch radius
SizeType m_PatchRadius;
// Whether coordinate features are used
bool m_UseCoordinateFeatures;
// Bias parameter
double m_BiasParameter;
// Let the engine handle our data
friend class RFClassificationEngine;
};
#endif // RANDOMFORESTCLASSIFIER_H
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