bool combineModels( CommandLineParser &parser ){
infoLog << "Combining models..." << endl;
string directoryPath = "";
string modelFilename = "";
if( !parser.get("data-dir",directoryPath) ){
errorLog << "Failed to parse data-directory from command line! You can set the data-directory using the --data-dir option." << endl;
printUsage();
return false;
}
//Get the filename
if( !parser.get("model-filename",modelFilename) ){
errorLog << "Failed to parse filename from command line! You can set the model filename using the --model." << endl;
printUsage();
return false;
}
Vector< string > files;
infoLog << "- Parsing data directory: " << directoryPath << endl;
//Parse the directory to get all the csv files
if( !Util::parseDirectory( directoryPath, ".grt", files ) ){
errorLog << "Failed to parse data directory!" << endl;
return false;
}
RandomForests forest; //Used to validate the random forest type
GestureRecognitionPipeline *mainPipeline = NULL; // Points to the first valid pipeline that all the models will be merged to
Vector< GestureRecognitionPipeline* > pipelineBuffer; //Stores the pipeline for each file that is loaded
unsigned int inputVectorSize = 0; //Set to zero to mark we haven't loaded any models yet
const unsigned int numFiles = files.getSize();
bool mainPipelineSet = false;
bool combineModelsSuccessful = false;
pipelineBuffer.reserve( numFiles );
//Loop over the files, load them, and add valid random forest pipelines to the pipelineBuffer so they can be combined with the mainPipeline
for(unsigned int i=0; i<numFiles; i++){
infoLog << "- Loading model " << files[i] << ". File " << i+1 << " of " << numFiles << endl;
GestureRecognitionPipeline *pipeline = new GestureRecognitionPipeline;
if( pipeline->load( files[i] ) ){
infoLog << "- Pipeline loaded. Number of input dimensions: " << pipeline->getInputVectorDimensionsSize() << endl;
if( pipelineBuffer.size() == 0 ){
inputVectorSize = pipeline->getInputVectorDimensionsSize();
}
if( pipeline->getInputVectorDimensionsSize() != inputVectorSize ){
warningLog << "- Pipeline " << i+1 << " input vector size does not match the size of the first pipeline!" << endl;
}else{
Classifier *classifier = pipeline->getClassifier();
if( classifier ){
if( classifier->getClassifierType() == forest.getClassifierType() ){ //Validate the classifier is a random forest
if( !mainPipelineSet ){
mainPipelineSet = true;
mainPipeline = pipeline;
}else pipelineBuffer.push_back( pipeline );
}else{
warningLog << "- Pipeline " << i+1 << " does not contain a random forest classifer! Classifier type: " << classifier->getClassifierType() << endl;
}
}
}
}else{
warningLog << "- WARNING: Failed to load model from file: " << files[i] << endl;
}
}
if( mainPipelineSet ){
//Combine the random forest models with the main pipeline model
const unsigned int numPipelines = pipelineBuffer.getSize();
RandomForests *mainForest = mainPipeline->getClassifier< RandomForests >();
for(unsigned int i=0; i<numPipelines; i++){
infoLog << "- Combing model " << i+1 << " of " << numPipelines << " with main model..." << endl;
RandomForests *f = pipelineBuffer[i]->getClassifier< RandomForests >();
if( !mainForest->combineModels( *f ) ){
warningLog << "- WARNING: Failed to combine model " << i+1 << " with the main model!" << endl;
}
}
if( mainPipeline->getTrained() ){
infoLog << "- Saving combined pipeline to file..." << endl;
combineModelsSuccessful = mainPipeline->save( modelFilename );
}
}else{
errorLog << "Failed to combined models, no models were loaded!" << endl;
}
//Cleanup the pipeline buffer
for(unsigned int i=0; i<pipelineBuffer.getSize(); i++){
delete pipelineBuffer[i];
pipelineBuffer[i] = NULL;
}
return combineModelsSuccessful;
}
bool computeFeatureWeights( CommandLineParser &parser ){
infoLog << "Computing feature weights..." << endl;
string resultsFilename = "";
string modelFilename = "";
bool combineWeights = false;
//Get the model filename
if( !parser.get("model-filename",modelFilename) ){
errorLog << "Failed to parse filename from command line! You can set the model filename using the --model." << endl;
printUsage();
return false;
}
//Get the results filename
if( !parser.get("filename",resultsFilename) ){
errorLog << "Failed to parse results filename from command line! You can set the results filename using the -f." << endl;
printUsage();
return false;
}
//Get the results filename
parser.get("combine-weights",combineWeights);
//Load the model
GestureRecognitionPipeline pipeline;
if( !pipeline.load( modelFilename ) ){
errorLog << "Failed to load model from file: " << modelFilename << endl;
printUsage();
return false;
}
//Make sure the pipeline contains a random forest model and that it is trained
RandomForests *forest = pipeline.getClassifier< RandomForests >();
if( !forest ){
errorLog << "Model loaded, but the pipeline does not contain a RandomForests classifier!" << endl;
printUsage();
return false;
}
if( !forest->getTrained() ){
errorLog << "Model loaded, but the RandomForests classifier is not trained!" << endl;
printUsage();
return false;
}
//Compute the feature weights
if( combineWeights ){
VectorFloat weights = forest->getFeatureWeights();
if( weights.getSize() == 0 ){
errorLog << "Failed to compute feature weights!" << endl;
printUsage();
return false;
}
//Save the results to a file
fstream file;
file.open( resultsFilename.c_str(), fstream::out );
const unsigned int N = weights.getSize();
for(unsigned int i=0; i<N; i++){
file << weights[i] << endl;
}
file.close();
}else{
double norm = 0.0;
const unsigned int K = forest->getForestSize();
const unsigned int N = forest->getNumInputDimensions();
VectorFloat tmp( N, 0.0 );
MatrixDouble weights(K,N);
for(unsigned int i=0; i<K; i++){
DecisionTreeNode *tree = forest->getTree(i);
tree->computeFeatureWeights( tmp );
norm = 1.0 / Util::sum( tmp );
for(unsigned int j=0; j<N; j++){
tmp[j] *= norm;
weights[i][j] = tmp[j];
tmp[j] = 0;
}
}
//Save the results to a file
weights.save( resultsFilename );
}
return true;
}
bool test( CommandLineParser &parser ){
infoLog << "Testing model..." << endl;
string datasetFilename = "";
string modelFilename = "";
string resultsFilename = "";
//Get the model filename
if( !parser.get("model-filename",modelFilename) ){
errorLog << "Failed to parse model filename from command line! You can set the model filename using the -m." << endl;
printUsage();
return false;
}
//Get the filename
if( !parser.get("dataset-filename",datasetFilename) ){
errorLog << "Failed to parse dataset filename from command line! You can set the dataset filename using the -f." << endl;
printUsage();
return false;
}
//Get the model filename
parser.get("results-filename",resultsFilename,string("results.txt"));
//Load the pipeline from a file
GestureRecognitionPipeline pipeline;
infoLog << "- Loading model..." << endl;
if( !pipeline.load( modelFilename ) ){
errorLog << "Failed to load model from file: " << modelFilename << endl;
printUsage();
return false;
}
infoLog << "- Model loaded!" << endl;
//Load the data to test the classifier
ClassificationData data;
infoLog << "- Loading Training Data..." << endl;
if( !data.load( datasetFilename ) ){
errorLog << "Failed to load data!\n";
return false;
}
const unsigned int N = data.getNumDimensions();
infoLog << "- Num training samples: " << data.getNumSamples() << endl;
infoLog << "- Num dimensions: " << N << endl;
infoLog << "- Num classes: " << data.getNumClasses() << endl;
//Test the classifier
if( !pipeline.test( data ) ){
errorLog << "Failed to test pipeline!" << endl;
return false;
}
infoLog << "- Test complete in " << pipeline.getTestTime()/1000.0 << " seconds with and accuracy of: " << pipeline.getTestAccuracy() << endl;
return saveResults( pipeline, resultsFilename );
}