bool GaussianMixtureModels::train_(ClassificationData &trainingData){
MatrixFloat data = trainingData.getDataAsMatrixFloat();
return train_( data );
}
bool RBMQuantizer::train_(ClassificationData &trainingData){
MatrixFloat data = trainingData.getDataAsMatrixFloat();
return train_( data );
}
bool SelfOrganizingMap::train_(ClassificationData &trainingData){
MatrixFloat data = trainingData.getDataAsMatrixFloat();
return train_(data);
}
bool GMM::train_(ClassificationData &trainingData){
//Clear any old models
clear();
if( trainingData.getNumSamples() == 0 ){
errorLog << "train_(ClassificationData &trainingData) - Training data is empty!" << std::endl;
return false;
}
//Set the number of features and number of classes and resize the models buffer
numInputDimensions = trainingData.getNumDimensions();
numClasses = trainingData.getNumClasses();
models.resize(numClasses);
if( numInputDimensions >= 6 ){
warningLog << "train_(ClassificationData &trainingData) - The number of features in your training data is high (" << numInputDimensions << "). The GMMClassifier does not work well with high dimensional data, you might get better results from one of the other classifiers." << std::endl;
}
//Get the ranges of the training data if the training data is going to be scaled
ranges = trainingData.getRanges();
if( !trainingData.scale(GMM_MIN_SCALE_VALUE, GMM_MAX_SCALE_VALUE) ){
errorLog << "train_(ClassificationData &trainingData) - Failed to scale training data!" << std::endl;
return false;
}
//Fit a Mixture Model to each class (independently)
for(UINT k=0; k<numClasses; k++){
UINT classLabel = trainingData.getClassTracker()[k].classLabel;
ClassificationData classData = trainingData.getClassData( classLabel );
//Train the Mixture Model for this class
GaussianMixtureModels gaussianMixtureModel;
gaussianMixtureModel.setNumClusters( numMixtureModels );
gaussianMixtureModel.setMinChange( minChange );
gaussianMixtureModel.setMaxNumEpochs( maxIter );
if( !gaussianMixtureModel.train( classData.getDataAsMatrixFloat() ) ){
errorLog << "train_(ClassificationData &trainingData) - Failed to train Mixture Model for class " << classLabel << std::endl;
return false;
}
//Setup the model container
models[k].resize( numMixtureModels );
models[k].setClassLabel( classLabel );
//Store the mixture model in the container
for(UINT j=0; j<numMixtureModels; j++){
models[k][j].mu = gaussianMixtureModel.getMu().getRowVector(j);
models[k][j].sigma = gaussianMixtureModel.getSigma()[j];
//Compute the determinant and invSigma for the realtime prediction
LUDecomposition ludcmp( models[k][j].sigma );
if( !ludcmp.inverse( models[k][j].invSigma ) ){
models.clear();
errorLog << "train_(ClassificationData &trainingData) - Failed to invert Matrix for class " << classLabel << "!" << std::endl;
return false;
}
models[k][j].det = ludcmp.det();
}
//Compute the normalize factor
models[k].recomputeNormalizationFactor();
//Compute the rejection thresholds
Float mu = 0;
Float sigma = 0;
VectorFloat predictionResults(classData.getNumSamples(),0);
for(UINT i=0; i<classData.getNumSamples(); i++){
VectorFloat sample = classData[i].getSample();
predictionResults[i] = models[k].computeMixtureLikelihood( sample );
mu += predictionResults[i];
}
//Update mu
mu /= Float( classData.getNumSamples() );
//Calculate the standard deviation
for(UINT i=0; i<classData.getNumSamples(); i++)
sigma += grt_sqr( (predictionResults[i]-mu) );
sigma = grt_sqrt( sigma / (Float(classData.getNumSamples())-1.0) );
sigma = 0.2;
//Set the models training mu and sigma
models[k].setTrainingMuAndSigma(mu,sigma);
if( !models[k].recomputeNullRejectionThreshold(nullRejectionCoeff) && useNullRejection ){
warningLog << "train_(ClassificationData &trainingData) - Failed to recompute rejection threshold for class " << classLabel << " - the nullRjectionCoeff value is too high!" << std::endl;
}
//cout << "Training Mu: " << mu << " TrainingSigma: " << sigma << " RejectionThreshold: " << models[k].getNullRejectionThreshold() << std::endl;
//models[k].printModelValues();
}
//Reset the class labels
classLabels.resize(numClasses);
for(UINT k=0; k<numClasses; k++){
classLabels[k] = models[k].getClassLabel();
}
//Resize the rejection thresholds
nullRejectionThresholds.resize(numClasses);
for(UINT k=0; k<numClasses; k++){
nullRejectionThresholds[k] = models[k].getNullRejectionThreshold();
}
//Flag that the models have been trained
trained = true;
return true;
}
