bool train( CommandLineParser &parser ){
infoLog << "Training regression model..." << endl;
string trainDatasetFilename = "";
string modelFilename = "";
string defaultFilename = "linear-regression-model.grt";
bool removeFeatures = false;
bool defaultRemoveFeatures = false;
//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,defaultFilename);
//Load the training data to train the model
RegressionData trainingData;
infoLog << "- Loading Training Data..." << endl;
if( !trainingData.load( trainDatasetFilename ) ){
errorLog << "Failed to load training data!\n";
return false;
}
const unsigned int N = trainingData.getNumInputDimensions();
const unsigned int T = trainingData.getNumTargetDimensions();
infoLog << "- Num training samples: " << trainingData.getNumSamples() << endl;
infoLog << "- Num input dimensions: " << N << endl;
infoLog << "- Num target dimensions: " << T << endl;
//Create a new regression instance
LogisticRegression regression;
regression.setMaxNumEpochs( 500 );
regression.setMinChange( 1.0e-5 );
regression.setUseValidationSet( true );
regression.setValidationSetSize( 20 );
regression.setRandomiseTrainingOrder( true );
regression.enableScaling( true );
//Create a new pipeline that will hold the regression algorithm
GestureRecognitionPipeline pipeline;
//Add a multidimensional regression instance and set the regression algorithm to Linear Regression
pipeline.setRegressifier( MultidimensionalRegression( regression, true ) );
infoLog << "- Training model...\n";
//Train the classifier
if( !pipeline.train( trainingData ) ){
errorLog << "Failed to train model!" << endl;
return false;
}
infoLog << "- Model trained!" << endl;
infoLog << "- Saving model to: " << modelFilename << endl;
//Save the pipeline
if( pipeline.save( modelFilename ) ){
infoLog << "- Model saved." << endl;
}else warningLog << "Failed to save model to file: " << modelFilename << endl;
infoLog << "- TrainingTime: " << pipeline.getTrainingTime() << endl;
return true;
}
bool train( CommandLineParser &parser ){
infoLog << "Training regression model..." << endl;
string trainDatasetFilename = "";
string modelFilename = "";
float learningRate = 0;
float minChange = 0;
unsigned int maxEpoch = 0;
unsigned int batchSize = 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;
printHelp();
return false;
}
//Get the parameters from the parser
parser.get("model-filename",modelFilename);
parser.get( "learning-rate", learningRate );
parser.get( "min-change", minChange );
parser.get( "max-epoch", maxEpoch );
parser.get( "batch-size", batchSize );
infoLog << "settings: learning-rate: " << learningRate << " min-change: " << minChange << " max-epoch: " << maxEpoch << " batch-size: " << batchSize << endl;
//Load the training data to train the model
RegressionData trainingData;
//Try and parse the input and target dimensions
unsigned int numInputDimensions = 0;
unsigned int numTargetDimensions = 0;
if( parser.get("num-inputs",numInputDimensions) && parser.get("num-targets",numTargetDimensions) ){
infoLog << "num input dimensions: " << numInputDimensions << " num target dimensions: " << numTargetDimensions << endl;
trainingData.setInputAndTargetDimensions( numInputDimensions, numTargetDimensions );
}
if( (numInputDimensions == 0 || numTargetDimensions == 0) && Util::stringEndsWith( trainDatasetFilename, ".csv" ) ){
errorLog << "Failed to parse num input dimensions and num target dimensions from input arguments. You must supply the input and target dimensions if the data format is CSV!" << endl;
printHelp();
return false;
}
infoLog << "- Loading Training Data..." << endl;
if( !trainingData.load( trainDatasetFilename ) ){
errorLog << "Failed to load training data!\n";
return false;
}
const unsigned int N = trainingData.getNumInputDimensions();
const unsigned int T = trainingData.getNumTargetDimensions();
infoLog << "- Num training samples: " << trainingData.getNumSamples() << endl;
infoLog << "- Num input dimensions: " << N << endl;
infoLog << "- Num target dimensions: " << T << endl;
//Create a new regression instance
LogisticRegression regression;
regression.setMaxNumEpochs( maxEpoch );
regression.setMinChange( minChange );
regression.setUseValidationSet( true );
regression.setValidationSetSize( 20 );
regression.setRandomiseTrainingOrder( true );
regression.enableScaling( true );
//Create a new pipeline that will hold the regression algorithm
GestureRecognitionPipeline pipeline;
//Add a multidimensional regression instance and set the regression algorithm to Linear Regression
pipeline.setRegressifier( MultidimensionalRegression( regression, true ) );
infoLog << "- Training model...\n";
//Train the classifier
if( !pipeline.train( trainingData ) ){
errorLog << "Failed to train model!" << endl;
return false;
}
infoLog << "- Model trained!" << endl;
infoLog << "- Saving model to: " << modelFilename << endl;
//Save the pipeline
if( pipeline.save( modelFilename ) ){
infoLog << "- Model saved." << endl;
}else warningLog << "Failed to save model to file: " << modelFilename << endl;
infoLog << "- TrainingTime: " << pipeline.getTrainingTime() << endl;
return true;
}
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;
}
bool train( CommandLineParser &parser ){
infoLog << "Training regression model..." << endl;
string trainDatasetFilename = "";
string modelFilename = "";
//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;
printHelp();
return false;
}
//Get the model filename
parser.get("model-filename",modelFilename);
//Load the training data to train the model
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();
const unsigned int K = trainingData.getNumClasses();
infoLog << "- Num training samples: " << trainingData.getNumSamples() << endl;
infoLog << "- Num input dimensions: " << N << endl;
infoLog << "- Num classes: " << K << endl;
float learningRate = 0;
float minChange = 0;
unsigned int maxEpoch = 0;
unsigned int batchSize = 0;
parser.get( "learning-rate", learningRate );
parser.get( "min-change", minChange );
parser.get( "max-epoch", maxEpoch );
parser.get( "batch-size", batchSize );
infoLog << "Softmax settings: learning-rate: " << learningRate << " min-change: " << minChange << " max-epoch: " << maxEpoch << " batch-size: " << batchSize << endl;
//Create a new softmax instance
bool enableScaling = true;
Softmax classifier(enableScaling,learningRate,minChange,maxEpoch,batchSize);
//Create a new pipeline that will hold the classifier
GestureRecognitionPipeline pipeline;
//Add the classifier to the pipeline
pipeline << classifier;
infoLog << "- Training model...\n";
//Train the classifier
if( !pipeline.train( trainingData ) ){
errorLog << "Failed to train model!" << endl;
return false;
}
infoLog << "- Model trained!" << endl;
infoLog << "- Saving model to: " << modelFilename << endl;
//Save the pipeline
if( pipeline.save( modelFilename ) ){
infoLog << "- Model saved." << endl;
}else warningLog << "Failed to save model to file: " << modelFilename << endl;
infoLog << "- TrainingTime: " << pipeline.getTrainingTime() << endl;
string logFilename = "";
if( parser.get( "log-filename", logFilename ) && logFilename.length() > 0 ){
infoLog << "Writing training log to: " << logFilename << endl;
fstream logFile( logFilename.c_str(), fstream::out );
if( !logFile.is_open() ){
errorLog << "Failed to open training log file: " << logFilename << endl;
return false;
}
Vector< TrainingResult > trainingResults = pipeline.getTrainingResults();
for(UINT i=0; i<trainingResults.getSize(); i++){
logFile << trainingResults[i].getTrainingIteration() << "\t" << trainingResults[i].getAccuracy() << endl;
}
logFile.close();
}
return true;
}