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;
}
