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; }
bool GaussianMixtureModels::train(const MatrixDouble &data,const UINT K){ modelTrained = false; failed = false; //Clear any previous training results det.clear(); invSigma.clear(); if( data.getNumRows() == 0 ){ errorLog << "train(const MatrixDouble &trainingData,const unsigned int K) - Training Failed! Training data is empty!" << endl; return false; } //Resize the variables M = data.getNumRows(); N = data.getNumCols(); this->K = K; //Resize mu and resp mu.resize(K,N); resp.resize(M,K); //Resize sigma sigma.resize(K); for(UINT k=0; k<K; k++){ sigma[k].resize(N,N); } //Resize frac and lndets frac.resize(K); lndets.resize(K); //Pick K random starting points for the inital guesses of Mu Random random; vector< UINT > randomIndexs(M); for(UINT i=0; i<M; i++) randomIndexs[i] = i; for(UINT i=0; i<M; i++){ SWAP(randomIndexs[ random.getRandomNumberInt(0,M) ],randomIndexs[ random.getRandomNumberInt(0,M) ]); } for(UINT k=0; k<K; k++){ for(UINT n=0; n<N; n++){ mu[k][n] = data[ randomIndexs[k] ][n]; } } //Setup sigma and the uniform prior on P(k) for(UINT k=0; k<K; k++){ frac[k] = 1.0/double(K); for(UINT i=0; i<N; i++){ for(UINT j=0; j<N; j++) sigma[k][i][j] = 0; sigma[k][i][i] = 1.0e-10; //Set the diagonal to a small number } } loglike = 0; UINT iterCounter = 0; bool keepGoing = true; double change = 99.9e99; while( keepGoing ){ change = estep( data ); mstep( data ); if( fabs( change ) < minChange ) keepGoing = false; if( ++iterCounter >= maxIter ) keepGoing = false; if( failed ) keepGoing = false; } if( failed ){ errorLog << "train(UnlabelledClassificationData &trainingData,unsigned int K) - Training failed!" << endl; return modelTrained; } //Compute the inverse of sigma and the determinants for prediction if( !computeInvAndDet() ){ det.clear(); invSigma.clear(); errorLog << "train(UnlabelledClassificationData &trainingData,unsigned int K) - Failed to compute inverse and determinat!" << endl; return false; } //Flag that the model was trained modelTrained = true; return true; }