bool RandomForests::train(LabelledClassificationData trainingData){
//Clear any previous model
clear();
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumDimensions();
const unsigned int K = trainingData.getNumClasses();
if( M == 0 ){
errorLog << "train(LabelledClassificationData labelledTrainingData) - Training data has zero samples!" << endl;
return false;
}
numInputDimensions = N;
numClasses = K;
classLabels = trainingData.getClassLabels();
ranges = trainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
//Train the random forest
forestSize = 10;
Random random;
DecisionTree tree;
tree.enableScaling( false ); //We have already scaled the training data so we do not need to scale it again
tree.setTrainingMode( DecisionTree::BEST_RANDOM_SPLIT );
tree.setNumSplittingSteps( numRandomSplits );
tree.setMinNumSamplesPerNode( minNumSamplesPerNode );
tree.setMaxDepth( maxDepth );
for(UINT i=0; i<forestSize; i++){
LabelledClassificationData data = trainingData.getBootstrappedDataset();
if( !tree.train( data ) ){
errorLog << "train(LabelledClassificationData labelledTrainingData) - Failed to train tree at forest index: " << i << endl;
return false;
}
//Deep copy the tree into the forest
forest.push_back( tree.deepCopyTree() );
}
//Flag that the algorithm has been trained
trained = true;
return trained;
}
bool Softmax::train(LabelledClassificationData trainingData){
//Clear any previous model
clear();
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumDimensions();
const unsigned int K = trainingData.getNumClasses();
if( M == 0 ){
errorLog << "train(LabelledClassificationData labelledTrainingData) - Training data has zero samples!" << endl;
return false;
}
numFeatures = N;
numClasses = K;
models.resize(K);
classLabels.resize(K);
ranges = trainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
//Train a regression model for each class in the training data
for(UINT k=0; k<numClasses; k++){
//Set the class label
classLabels[k] = trainingData.getClassTracker()[k].classLabel;
//Train the model
if( !trainSoftmaxModel(classLabels[k],models[k],trainingData) ){
errorLog << "train(LabelledClassificationData labelledTrainingData) - Failed to train model for class: " << classLabels[k] << endl;
return false;
}
}
//Flag that the algorithm has been trained
trained = true;
return trained;
}
bool GMM::train(LabelledClassificationData trainingData){
//Clear any old models
models.clear();
trained = false;
numFeatures = 0;
numClasses = 0;
if( trainingData.getNumSamples() == 0 ){
errorLog << "train(LabelledClassificationData &trainingData) - Training data is empty!" << endl;
return false;
}
//Set the number of features and number of classes and resize the models buffer
numFeatures = trainingData.getNumDimensions();
numClasses = trainingData.getNumClasses();
models.resize(numClasses);
if( numFeatures >= 6 ){
warningLog << "train(LabelledClassificationData &trainingData) - The number of features in your training data is high (" << numFeatures << "). The GMMClassifier does not work well with high dimensional data, you might get better results from one of the other classifiers." << endl;
}
//Get the ranges of the training data if the training data is going to be scaled
if( useScaling ){
ranges = trainingData.getRanges();
}
//Fit a Mixture Model to each class (independently)
for(UINT k=0; k<numClasses; k++){
UINT classLabel = trainingData.getClassTracker()[k].classLabel;
LabelledClassificationData classData = trainingData.getClassData( classLabel );
//Scale the training data if needed
if( useScaling ){
if( !classData.scale(ranges,GMM_MIN_SCALE_VALUE, GMM_MAX_SCALE_VALUE) ){
errorLog << "train(LabelledClassificationData &trainingData) - Failed to scale training data!" << endl;
return false;
}
}
//Convert the labelled data to unlabelled data
UnlabelledClassificationData unlabelledData = classData.reformatAsUnlabelledClassificationData();
//Train the Mixture Model for this class
GaussianMixtureModels gaussianMixtureModel;
gaussianMixtureModel.setMinChange( minChange );
gaussianMixtureModel.setMaxIter( maxIter );
if( !gaussianMixtureModel.train(unlabelledData, numMixtureModels) ){
errorLog << "train(LabelledClassificationData &trainingData) - Failed to train Mixture Model for class " << classLabel << 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(LabelledClassificationData &trainingData) - Failed to invert Matrix for class " << classLabel << "!" << endl;
return false;
}
models[k][j].det = ludcmp.det();
}
//Compute the normalize factor
models[k].recomputeNormalizationFactor();
//Compute the rejection thresholds
double mu = 0;
double sigma = 0;
VectorDouble predictionResults(classData.getNumSamples(),0);
for(UINT i=0; i<classData.getNumSamples(); i++){
vector< double > sample = classData[i].getSample();
predictionResults[i] = models[k].computeMixtureLikelihood( sample );
mu += predictionResults[i];
}
//Update mu
mu /= double( classData.getNumSamples() );
//Calculate the standard deviation
for(UINT i=0; i<classData.getNumSamples(); i++)
sigma += SQR( (predictionResults[i]-mu) );
sigma = sqrt( sigma / (double(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(LabelledClassificationData &trainingData) - Failed to recompute rejection threshold for class " << classLabel << " - the nullRjectionCoeff value is too high!" << endl;
}
//cout << "Training Mu: " << mu << " TrainingSigma: " << sigma << " RejectionThreshold: " << models[k].getNullRejectionThreshold() << 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;
}