bool saveResults( const GestureRecognitionPipeline &pipeline, const string &filename ){
infoLog << "Saving results to file: " << filename << endl;
fstream file( filename.c_str(), fstream::out );
if( !file.is_open() ){
errorLog << "Failed to open results file: " << filename << endl;
return false;
}
file << pipeline.getTestAccuracy() << endl;
vector< UINT > classLabels = pipeline.getClassLabels();
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
file << pipeline.getTestPrecision( classLabels[k] );
if( k+1 < pipeline.getNumClassesInModel() ) file << "\t";
else file << endl;
}
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
file << pipeline.getTestRecall( classLabels[k] );
if( k+1 < pipeline.getNumClassesInModel() ) file << "\t";
else file << endl;
}
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
file << pipeline.getTestFMeasure( classLabels[k] );
if( k+1 < pipeline.getNumClassesInModel() ) file << "\t";
else file << endl;
}
MatrixDouble confusionMatrix = pipeline.getTestConfusionMatrix();
for(UINT i=0; i<confusionMatrix.getNumRows(); i++){
for(UINT j=0; j<confusionMatrix.getNumCols(); j++){
file << confusionMatrix[i][j];
if( j+1 < confusionMatrix.getNumCols() ) file << "\t";
}file << endl;
}
file.close();
infoLog << "Results saved." << endl;
return true;
}
int main (int argc, const char * argv[])
{
//Load some training data from a file
ClassificationData trainingData;
if( !trainingData.loadDatasetFromFile("HelloWorldTrainingData.grt") ){
cout << "ERROR: Failed to load training data from file\n";
return EXIT_FAILURE;
}
cout << "Data Loaded\n";
//Print out some stats about the training data
trainingData.printStats();
//Partition the training data into a training dataset and a test dataset. 80 means that 80%
//of the data will be used for the training data and 20% will be returned as the test dataset
ClassificationData testData = trainingData.partition(80);
//Create a new Gesture Recognition Pipeline using an Adaptive Naive Bayes Classifier
GestureRecognitionPipeline pipeline;
pipeline.setClassifier( ANBC() );
//Train the pipeline using the training data
if( !pipeline.train( trainingData ) ){
cout << "ERROR: Failed to train the pipeline!\n";
return EXIT_FAILURE;
}
//Save the pipeline to a file
if( !pipeline.savePipelineToFile( "HelloWorldPipeline.grt" ) ){
cout << "ERROR: Failed to save the pipeline!\n";
return EXIT_FAILURE;
}
//Load the pipeline from a file
if( !pipeline.loadPipelineFromFile( "HelloWorldPipeline.grt" ) ){
cout << "ERROR: Failed to load the pipeline!\n";
return EXIT_FAILURE;
}
//Test the pipeline using the test data
if( !pipeline.test( testData ) ){
cout << "ERROR: Failed to test the pipeline!\n";
return EXIT_FAILURE;
}
//Print some stats about the testing
cout << "Test Accuracy: " << pipeline.getTestAccuracy() << endl;
cout << "Precision: ";
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
UINT classLabel = pipeline.getClassLabels()[k];
cout << "\t" << pipeline.getTestPrecision(classLabel);
}cout << endl;
cout << "Recall: ";
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
UINT classLabel = pipeline.getClassLabels()[k];
cout << "\t" << pipeline.getTestRecall(classLabel);
}cout << endl;
cout << "FMeasure: ";
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
UINT classLabel = pipeline.getClassLabels()[k];
cout << "\t" << pipeline.getTestFMeasure(classLabel);
}cout << endl;
MatrixDouble confusionMatrix = pipeline.getTestConfusionMatrix();
cout << "ConfusionMatrix: \n";
for(UINT i=0; i<confusionMatrix.getNumRows(); i++){
for(UINT j=0; j<confusionMatrix.getNumCols(); j++){
cout << confusionMatrix[i][j] << "\t";
}cout << endl;
}
return EXIT_SUCCESS;
}