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[])
{
TimeSeriesClassificationData trainingData; //This will store our training data
GestureRecognitionPipeline pipeline; //This is a wrapper for our classifier and any pre/post processing modules
string dirPath = "/home/vlad/AndroidStudioProjects/DataCapture/dataSetGenerator/build";
if (!trainingData.loadDatasetFromFile(dirPath + "/acc-training-set-segmented.data")) {
printf("Cannot open training segmented set\n");
return 0;
}
printf("Successfully opened training data set ...\n");
DTW dtw;
// LowPassFilter lpf(0.1, 1, 1);
// pipeline.setPreProcessingModule(lpf);
// DoubleMovingAverageFilter filter( 1000, 3 );
// pipeline.setPreProcessingModule(filter);
//dtw.enableNullRejection( true );
//Set the null rejection coefficient to 3, this controls the thresholds for the automatic null rejection
//You can increase this value if you find that your real-time gestures are not being recognized
//If you are getting too many false positives then you should decrease this value
//dtw.setNullRejectionCoeff( 5 );
dtw.enableTrimTrainingData(true, 0.1, 90);
// dtw.setOffsetTimeseriesUsingFirstSample(true);
pipeline.setClassifier( dtw );
UINT KFolds = 5;
/* Separate input dataset using KFold */
KfoldTimeSeriesData* kFoldTS = new KfoldTimeSeriesData(trainingData);
if( !kFoldTS->spiltDataIntoKFolds(KFolds) ) {
printf("BaseTGTestModel: Failed to spiltDataIntoKFolds!");
return 0;
}
UINT maxTrainigSetSize = trainingData.getNumSamples() * (KFolds - 1) / (KFolds * trainingData.getNumClasses());
// KFolds
ofstream myfile;
myfile.open ("example.txt");
Float acc = 0;
for (GRT::UINT k = 1 ; k < KFolds; k++) {
printf("Running tests for: %d fold", k);
// maxTrainigSetSize
// for (UINT trainingSetSize = 1; trainingSetSize <= maxTrainigSetSize; trainingSetSize ++) {
/* Set up training datasets for current fold */
TimeSeriesClassificationData trainingDataset = kFoldTS->getTrainingFoldData(k, maxTrainigSetSize);
/* Set up validation datasets for current fold */
TimeSeriesClassificationDataStream testDataset = kFoldTS->getTestFoldData(k);
/* Log test dataset size */
//printf("Data set size: training %d; testing %d",
// trainingDataset.getNumSamples(), testDataset.getNumSamples());
/* Run test for current fold */
pipeline.train(trainingDataset);
pipeline.test(testDataset);
myfile << pipeline.getTestAccuracy() << "\n";
// }
}
myfile.close();
printf("Accuracy = %f ; %d\n", acc, maxTrainigSetSize);
}
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;
}
int main (int argc, const char * argv[])
{
//We are going to use the Iris dataset, you can find more about the orginal dataset at: http://en.wikipedia.org/wiki/Iris_flower_data_set
//Create a new instance of LabelledClassificationData to hold the training data
LabelledClassificationData trainingData;
//Load the training dataset from a file, the file should be in the same directory as this program
if( !trainingData.loadDatasetFromFile("IrisData.txt") ){
cout << "Failed to load Iris data from file!\n";
return EXIT_FAILURE;
}
//Print some basic stats about the dataset we have loaded
trainingData.printStats();
//Partition the training dataset into a training dataset and test dataset
//We will use 60% of the data to train the algorithm and 40% of the data to test it
//The true parameter flags that we want to use stratified sampling, which means there
//should be an equal class distribution between the training and test datasets
LabelledClassificationData testData = trainingData.partition( 60, true );
//Setup the gesture recognition pipeline
GestureRecognitionPipeline pipeline;
//Add a KNN classification algorithm as the main classifier with a K value of 10
pipeline.setClassifier( KNN(10) );
//Train the KNN algorithm using the training dataset
if( !pipeline.train( trainingData ) ){
cout << "Failed to train the pipeline!\n";
return EXIT_FAILURE;
}
//Test the KNN model using the test dataset
if( !pipeline.test( testData ) ){
cout << "Failed to test the pipeline!\n";
return EXIT_FAILURE;
}
//Print some metrics about how successful the classification was
//Print the accuracy
cout << "The classification accuracy was: " << pipeline.getTestAccuracy() << "%\n" << endl;
//Print the precision for each class
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
UINT classLabel = pipeline.getClassLabels()[k];
double classPrecision = pipeline.getTestPrecision( classLabel );
cout << "The precision for class " << classLabel << " was " << classPrecision << endl;
}
cout << endl;
//Print the recall for each class
for(UINT k=0; k<pipeline.getNumClassesInModel(); k++){
UINT classLabel = pipeline.getClassLabels()[k];
double classRecall = pipeline.getTestRecall( classLabel );
cout << "The recall for class " << classLabel << " was " << classRecall << endl;
}
cout << endl;
//Print the confusion matrix
Matrix< double > confusionMatrix = pipeline.getTestConfusionMatrix();
cout << "Confusion Matrix: \n";
for(UINT i=0; i<confusionMatrix.getNumRows(); i++){
for(UINT j=0; j<confusionMatrix.getNumCols(); j++){
cout << confusionMatrix[i][j] << "\t";
}
cout << endl;
}
cout << endl;
return EXIT_SUCCESS;
}
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 );
}