MatrixFloat LDA::computeWithinClassScatterMatrix( ClassificationData &data ){
MatrixFloat sw(numInputDimensions,numInputDimensions);
sw.setAllValues( 0 );
for(UINT k=0; k<numClasses; k++){
//Compute the scatter matrix for class k
ClassificationData classData = data.getClassData( data.getClassTracker()[k].classLabel );
MatrixFloat scatterMatrix = classData.getCovarianceMatrix();
//Add this to the main scatter matrix
for(UINT m=0; m<numInputDimensions; m++){
for(UINT n=0; n<numInputDimensions; n++){
sw[m][n] += scatterMatrix[m][n];
}
}
}
return sw;
}
bool MinDist::train_(ClassificationData &labelledTrainingData){
//Clear any previous models
clear();
const unsigned int M = labelledTrainingData.getNumSamples();
const unsigned int N = labelledTrainingData.getNumDimensions();
const unsigned int K = labelledTrainingData.getNumClasses();
if( M == 0 ){
errorLog << "train_(ClassificationData &labelledTrainingData) - Training data has zero samples!" << endl;
return false;
}
if( M <= numClusters ){
errorLog << "train_(ClassificationData &labelledTrainingData) - There are not enough training samples for the number of clusters. Either reduce the number of clusters or increase the number of training samples!" << endl;
return false;
}
numInputDimensions = N;
numClasses = K;
models.resize(K);
classLabels.resize(K);
nullRejectionThresholds.resize(K);
ranges = labelledTrainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
labelledTrainingData.scale(0, 1);
}
//Train each of the models
for(UINT k=0; k<numClasses; k++){
//Get the class label for the kth class
UINT classLabel = labelledTrainingData.getClassTracker()[k].classLabel;
//Set the kth class label
classLabels[k] = classLabel;
//Get all the training data for this class
ClassificationData classData = labelledTrainingData.getClassData(classLabel);
MatrixDouble data(classData.getNumSamples(),N);
//Copy the training data into a matrix
for(UINT i=0; i<data.getNumRows(); i++){
for(UINT j=0; j<data.getNumCols(); j++){
data[i][j] = classData[i][j];
}
}
//Train the model for this class
models[k].setGamma( nullRejectionCoeff );
if( !models[k].train(classLabel,data,numClusters) ){
errorLog << "train_(ClassificationData &labelledTrainingData) - Failed to train model for class: " << classLabel;
errorLog << ". This is might be because this class does not have enough training samples! You should reduce the number of clusters or increase the number of training samples for this class." << endl;
models.clear();
return false;
}
//Set the null rejection threshold
nullRejectionThresholds[k] = models[k].getRejectionThreshold();
}
trained = true;
return true;
}
bool Softmax::train_(ClassificationData &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 << __GRT_LOG__ << " Training data has zero samples!" << std::endl;
return false;
}
numInputDimensions = N;
numOutputDimensions = K;
numClasses = K;
models.resize(K);
classLabels.resize(K);
ranges = trainingData.getRanges();
ClassificationData validationData;
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
if( useValidationSet ){
validationData = trainingData.split( 100-validationSetSize );
}
//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 << __GRT_LOG__ << " Failed to train model for class: " << classLabels[k] << std::endl;
return false;
}
}
//Flag that the models have been trained
trained = true;
converged = true;
//Compute the final training stats
trainingSetAccuracy = 0;
validationSetAccuracy = 0;
//If scaling was on, then the data will already be scaled, so turn it off temporially so we can test the model accuracy
bool scalingState = useScaling;
useScaling = false;
if( !computeAccuracy( trainingData, trainingSetAccuracy ) ){
trained = false;
converged = false;
errorLog << __GRT_LOG__ << " Failed to compute training set accuracy! Failed to fully train model!" << std::endl;
return false;
}
if( useValidationSet ){
if( !computeAccuracy( validationData, validationSetAccuracy ) ){
trained = false;
converged = false;
errorLog << __GRT_LOG__ << " Failed to compute validation set accuracy! Failed to fully train model!" << std::endl;
return false;
}
}
trainingLog << "Training set accuracy: " << trainingSetAccuracy << std::endl;
if( useValidationSet ){
trainingLog << "Validation set accuracy: " << validationSetAccuracy << std::endl;
}
//Reset the scaling state for future prediction
useScaling = scalingState;
return trained;
}
bool ANBC::train_(ClassificationData &labelledTrainingData){
//Clear any previous model
clear();
const unsigned int M = labelledTrainingData.getNumSamples();
const unsigned int N = labelledTrainingData.getNumDimensions();
const unsigned int K = labelledTrainingData.getNumClasses();
if( M == 0 ){
errorLog << "train_(ClassificationData &labelledTrainingData) - Training data has zero samples!" << endl;
return false;
}
if( weightsDataSet ){
if( weightsData.getNumDimensions() != N ){
errorLog << "train_(ClassificationData &labelledTrainingData) - The number of dimensions in the weights data (" << weightsData.getNumDimensions() << ") is not equal to the number of dimensions of the training data (" << N << ")" << endl;
return false;
}
}
numInputDimensions = N;
numClasses = K;
models.resize(K);
classLabels.resize(K);
ranges = labelledTrainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
labelledTrainingData.scale(0, 1);
}
//Train each of the models
for(UINT k=0; k<numClasses; k++){
//Get the class label for the kth class
UINT classLabel = labelledTrainingData.getClassTracker()[k].classLabel;
//Set the kth class label
classLabels[k] = classLabel;
//Get the weights for this class
VectorDouble weights(numInputDimensions);
if( weightsDataSet ){
bool weightsFound = false;
for(UINT i=0; i<weightsData.getNumSamples(); i++){
if( weightsData[i].getClassLabel() == classLabel ){
weights = weightsData[i].getSample();
weightsFound = true;
break;
}
}
if( !weightsFound ){
errorLog << "train_(ClassificationData &labelledTrainingData) - Failed to find the weights for class " << classLabel << endl;
return false;
}
}else{
//If the weights data has not been set then all the weights are 1
for(UINT j=0; j<numInputDimensions; j++) weights[j] = 1.0;
}
//Get all the training data for this class
ClassificationData classData = labelledTrainingData.getClassData(classLabel);
MatrixDouble data(classData.getNumSamples(),N);
//Copy the training data into a matrix
for(UINT i=0; i<data.getNumRows(); i++){
for(UINT j=0; j<data.getNumCols(); j++){
data[i][j] = classData[i][j];
}
}
//Train the model for this class
models[k].gamma = nullRejectionCoeff;
if( !models[k].train(classLabel,data,weights) ){
errorLog << "train_(ClassificationData &labelledTrainingData) - Failed to train model for class: " << classLabel << endl;
//Try and work out why the training failed
if( models[k].N == 0 ){
errorLog << "train_(ClassificationData &labelledTrainingData) - N == 0!" << endl;
models.clear();
return false;
}
for(UINT j=0; j<numInputDimensions; j++){
if( models[k].mu[j] == 0 ){
errorLog << "train_(ClassificationData &labelledTrainingData) - The mean of column " << j+1 << " is zero! Check the training data" << endl;
models.clear();
return false;
}
}
models.clear();
return false;
}
}
//Store the null rejection thresholds
nullRejectionThresholds.resize(numClasses);
for(UINT k=0; k<numClasses; k++) {
nullRejectionThresholds[k] = models[k].threshold;
}
//Flag that the models have been trained
trained = true;
return trained;
}
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;
}
bool KNN::train_(ClassificationData &trainingData){
//Clear any previous models
clear();
if( trainingData.getNumSamples() == 0 ){
errorLog << "train_(ClassificationData &trainingData) - Training data has zero samples!" << endl;
return false;
}
//Get the ranges of the data
ranges = trainingData.getRanges();
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
//Store the number of features, classes and the training data
this->numInputDimensions = trainingData.getNumDimensions();
this->numClasses = trainingData.getNumClasses();
//TODO: In the future need to build a kdtree from the training data to allow better realtime prediction
this->trainingData = trainingData;
//Set the class labels
classLabels.resize(numClasses);
for(UINT k=0; k<numClasses; k++){
classLabels[k] = trainingData.getClassTracker()[k].classLabel;
}
//If we do not need to search for the best K value, then call the sub training function and return the result
if( !searchForBestKValue ){
return train_(trainingData,K);
}
//If we have got this far then we are going to search for the best K value
UINT index = 0;
double bestAccuracy = 0;
vector< IndexedDouble > trainingAccuracyLog;
for(UINT k=minKSearchValue; k<=maxKSearchValue; k++){
//Randomly spilt the data and use 80% to train the algorithm and 20% to test it
ClassificationData trainingSet(trainingData);
ClassificationData testSet = trainingSet.partition(80,true);
if( !train_(trainingSet, k) ){
errorLog << "Failed to train model for a k value of " << k << endl;
}else{
//Compute the classification error
double accuracy = 0;
for(UINT i=0; i<testSet.getNumSamples(); i++){
VectorDouble sample = testSet[i].getSample();
if( !predict( sample , k) ){
errorLog << "Failed to predict label for test sample with a k value of " << k << endl;
return false;
}
if( testSet[i].getClassLabel() == predictedClassLabel ){
accuracy++;
}
}
accuracy = accuracy /double( testSet.getNumSamples() ) * 100.0;
trainingAccuracyLog.push_back( IndexedDouble(k,accuracy) );
trainingLog << "K:\t" << k << "\tAccuracy:\t" << accuracy << endl;
if( accuracy > bestAccuracy ){
bestAccuracy = accuracy;
}
index++;
}
}
if( bestAccuracy > 0 ){
//Sort the training log by value
std::sort(trainingAccuracyLog.begin(),trainingAccuracyLog.end(),IndexedDouble::sortIndexedDoubleByValueDescending);
//Copy the top matching values into a temporary buffer
vector< IndexedDouble > tempLog;
//Add the first value
tempLog.push_back( trainingAccuracyLog[0] );
//Keep adding values until the value changes
for(UINT i=1; i<trainingAccuracyLog.size(); i++){
if( trainingAccuracyLog[i].value == tempLog[0].value ){
tempLog.push_back( trainingAccuracyLog[i] );
}else break;
}
//Sort the temp values by index (the index is the K value so we want to get the minimum K value with the maximum accuracy)
std::sort(tempLog.begin(),tempLog.end(),IndexedDouble::sortIndexedDoubleByIndexAscending);
trainingLog << "Best K Value: " << tempLog[0].index << "\tAccuracy:\t" << tempLog[0].value << endl;
//Use the minimum index, this should give us the best accuracy with the minimum K value
//We now need to train the model again to make sure all the training metrics are computed correctly
return train_(trainingData,tempLog[0].index);
}
return false;
}
bool MinDist::train_(ClassificationData &trainingData){
//Clear any previous models
clear();
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumDimensions();
const unsigned int K = trainingData.getNumClasses();
if( M == 0 ){
errorLog << __GRT_LOG__ << " Training data has zero samples!" << std::endl;
return false;
}
if( M <= numClusters ){
errorLog << __GRT_LOG__ << " There are not enough training samples for the number of clusters. Either reduce the number of clusters or increase the number of training samples!" << std::endl;
return false;
}
numInputDimensions = N;
numOutputDimensions = K;
numClasses = K;
models.resize(K);
classLabels.resize(K);
nullRejectionThresholds.resize(K);
ranges = trainingData.getRanges();
ClassificationData validationData;
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
if( useValidationSet ){
validationData = trainingData.split( 100-validationSetSize );
}
//Train each of the models
for(UINT k=0; k<numClasses; k++){
trainingLog << "Training model for class: " << trainingData.getClassTracker()[k].classLabel << std::endl;
//Pass the logging state onto the kmeans algorithm
models[k].setTrainingLoggingEnabled( this->getTrainingLoggingEnabled() );
//Get the class label for the kth class
UINT classLabel = trainingData.getClassTracker()[k].classLabel;
//Set the kth class label
classLabels[k] = classLabel;
//Get all the training data for this class
ClassificationData classData = trainingData.getClassData(classLabel);
MatrixFloat data(classData.getNumSamples(),N);
//Copy the training data into a matrix
for(UINT i=0; i<data.getNumRows(); i++){
for(UINT j=0; j<data.getNumCols(); j++){
data[i][j] = classData[i][j];
}
}
//Train the model for this class
models[k].setGamma( nullRejectionCoeff );
if( !models[k].train(classLabel,data,numClusters,minChange,maxNumEpochs) ){
errorLog << __GRT_LOG__ << " Failed to train model for class: " << classLabel;
errorLog << ". This is might be because this class does not have enough training samples! You should reduce the number of clusters or increase the number of training samples for this class." << std::endl;
models.clear();
return false;
}
//Set the null rejection threshold
nullRejectionThresholds[k] = models[k].getRejectionThreshold();
}
//Flag that the models have been trained
trained = true;
converged = true;
//Compute the final training stats
trainingSetAccuracy = 0;
validationSetAccuracy = 0;
//If scaling was on, then the data will already be scaled, so turn it off temporially so we can test the model accuracy
bool scalingState = useScaling;
useScaling = false;
if( !computeAccuracy( trainingData, trainingSetAccuracy ) ){
trained = false;
converged = false;
errorLog << __GRT_LOG__ << " Failed to compute training set accuracy! Failed to fully train model!" << std::endl;
return false;
}
if( useValidationSet ){
if( !computeAccuracy( validationData, validationSetAccuracy ) ){
trained = false;
converged = false;
errorLog << __GRT_LOG__ << " Failed to compute validation set accuracy! Failed to fully train model!" << std::endl;
return false;
}
}
trainingLog << "Training set accuracy: " << trainingSetAccuracy << std::endl;
if( useValidationSet ){
trainingLog << "Validation set accuracy: " << validationSetAccuracy << std::endl;
}
//Reset the scaling state for future prediction
useScaling = scalingState;
return trained;
}
bool train( CommandLineParser &parser ){
string trainDatasetFilename = "";
string modelFilename = "";
unsigned int forestSize = 0;
unsigned int maxDepth = 0;
unsigned int minNodeSize = 0;
unsigned int numSplits = 0;
bool removeFeatures = false;
double bootstrapWeight = 0.0;
//Get the filename
if( !parser.get("filename",trainDatasetFilename) ){
errorLog << "Failed to parse filename from command line! You can set the filename using the -f." << endl;
printUsage();
return false;
}
//Get the model filename
parser.get("model-filename",modelFilename);
//Get the forest size
parser.get("forest-size",forestSize);
//Get the max depth
parser.get("max-depth",maxDepth);
//Get the min node size
parser.get("min-node-size",minNodeSize);
//Get the number of random splits
parser.get("num-splits",numSplits);
//Get the remove features
parser.get("remove-features",removeFeatures);
//Get the bootstrap weight
parser.get("bootstrap-weight",bootstrapWeight);
//Load some training data to train the classifier
ClassificationData trainingData;
infoLog << "- Loading Training Data..." << endl;
if( !trainingData.load( trainDatasetFilename ) ){
errorLog << "Failed to load training data!\n";
return false;
}
const unsigned int N = trainingData.getNumDimensions();
Vector< ClassTracker > tracker = trainingData.getClassTracker();
infoLog << "- Num training samples: " << trainingData.getNumSamples() << endl;
infoLog << "- Num dimensions: " << N << endl;
infoLog << "- Num classes: " << trainingData.getNumClasses() << endl;
infoLog << "- Class stats: " << endl;
for(unsigned int i=0; i<tracker.getSize(); i++){
infoLog << "- class " << tracker[i].classLabel << " number of samples: " << tracker[i].counter << endl;
}
//Create a new RandomForests instance
RandomForests forest;
//Set the decision tree node that will be used for each tree in the forest
string nodeType = "cluster-node"; //TODO: make this a command line option in the future
if( nodeType == "cluster-node" ){
forest.setDecisionTreeNode( DecisionTreeClusterNode() );
}
if( nodeType == "threshold-node" ){
forest.setTrainingMode( Tree::BEST_RANDOM_SPLIT );
forest.setDecisionTreeNode( DecisionTreeThresholdNode() );
}
//Set the number of trees in the forest
forest.setForestSize( forestSize );
//Set the maximum depth of the tree
forest.setMaxDepth( maxDepth );
//Set the minimum number of samples allowed per node
forest.setMinNumSamplesPerNode( minNodeSize );
//Set the number of random splits used per node
forest.setNumRandomSplits( numSplits );
//Set if selected features should be removed at each node
forest.setRemoveFeaturesAtEachSplit( removeFeatures );
//Set the bootstrap weight
forest.setBootstrappedDatasetWeight( bootstrapWeight );
//Add the classifier to a pipeline
GestureRecognitionPipeline pipeline;
pipeline.setClassifier( forest );
infoLog << "- Training model..." << endl;
//Train the classifier
if( !pipeline.train( trainingData ) ){
errorLog << "Failed to train classifier!" << endl;
return false;
}
infoLog << "- Model trained!" << endl;
infoLog << "- Training time: " << (pipeline.getTrainingTime() * 0.001) / 60.0 << " (minutes)" << endl;
infoLog << "- Saving model to: " << modelFilename << endl;
//Save the pipeline
if( !pipeline.save( modelFilename ) ){
warningLog << "Failed to save model to file: " << modelFilename << endl;
}
return true;
}
