GaussianMixtureModels.h

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#ifndef GRT_GAUSSIAN_MIXTURE_MODELS_HEADER
#define GRT_GAUSSIAN_MIXTURE_MODELS_HEADER

#include "../../CoreModules/Clusterer.h"

GRT_BEGIN_NAMESPACE

class GRT_API GaussianMixtureModels : public Clusterer
{
public:
 GaussianMixtureModels(const UINT numClusters=10,const UINT minNumEpochs=5,const UINT maxNumEpochs=1000,const Float minChange=1.0e-5);
 
 GaussianMixtureModels(const GaussianMixtureModels &rhs);
 
 virtual ~GaussianMixtureModels();
 
 GaussianMixtureModels &operator=(const GaussianMixtureModels &rhs);
 
 virtual bool deepCopyFrom(const Clusterer *clusterer);
 
 virtual bool reset();
 
 virtual bool clear();
 
 virtual bool train_(MatrixFloat &trainingData);
 
 virtual bool train_(ClassificationData &trainingData);
 
 virtual bool train_(UnlabelledData &trainingData);
 
 
 virtual bool predict_(VectorDouble &inputVector);
 
 virtual bool saveModelToFile( std::fstream &file ) const;
 
 virtual bool loadModelFromFile( std::fstream &file );
 
 MatrixFloat getMu() const { if( trained ){ return mu; } return MatrixFloat(); }
 
 Vector< MatrixFloat > getSigma() const { if( trained ){ return sigma; } return Vector< MatrixFloat >(); }
 
 MatrixFloat getSigma(const UINT k) const{
 if( k < numClusters && trained ){
 return sigma[k];
 }
 return MatrixFloat();
 }
 
 //Tell the compiler we are using the base class train method to stop hidden virtual function warnings
 using MLBase::saveModelToFile;
 using MLBase::loadModelFromFile;
 
protected:
 bool estep( const MatrixFloat &data, VectorDouble &u, VectorDouble &v, Float &change );
 bool mstep( const MatrixFloat &data );
 bool computeInvAndDet();
 inline void SWAP(UINT &a,UINT &b);
 inline Float SQR(const Float v){ return v*v; }
 
 Float gauss(const VectorDouble &x,const UINT clusterIndex,const VectorDouble &det,const MatrixFloat &mu,const Vector< MatrixFloat > &invSigma){
 
 Float y = 0;
 Float sum = 0;
 UINT i,j = 0;
 const UINT N = (UINT)x.size();
 VectorDouble temp(N,0);
 
 //Compute the first part of the equation
 y = (1.0/pow(TWO_PI,N/2.0)) * (1.0/pow(det[clusterIndex],0.5));
 
 //Compute the later half
 for(i=0; i<N; i++){
 for(j=0; j<N; j++){
 temp[i] += (x[j]-mu[clusterIndex][j]) * invSigma[clusterIndex][j][i];
 }
 sum += (x[i]-mu[clusterIndex][i]) * temp[i];
 }
 
 return ( y*exp( -0.5*sum ) );
 }
 
 UINT numTrainingSamples; 
 Float loglike; 
 MatrixFloat mu; 
 MatrixFloat resp; 
 VectorDouble frac; 
 VectorDouble lndets; 
 VectorDouble det; 
 Vector< MatrixFloat > sigma;
 Vector< MatrixFloat > invSigma;
 
private:
 static RegisterClustererModule< GaussianMixtureModels > registerModule;
};
 
GRT_END_NAMESPACE

#endif //GRT_GAUSSIAN_MIXTURE_MODELS_HEADER