int main(void){
cout << "ClassificationData Test" << endl;
ClassificationData cdata;
// load data file that in Nick Gillian Format
if(cdata.loadDatasetFromFile("irisNG.txt")){
cout << "error loading csv file" << endl;
}
cdata.printStats();
cout << "convert dataset to csv" << endl;
//convert it to CSV. the first column indicate the class
cdata.saveDatasetToCSVFile("irisCSVFromNG.txt");
//obviously we can load the data from CSV that we generated
//note that class names are now lost
cdata.loadDatasetFromCSVFile("irisCSVFromNG.txt");
cdata.printStats();
//try to load a CSV file that includes strings
//cdata.loadDatasetFromCSVFile("irisCSV.txt", 4);
//commented out because we get error while loading
//load CSV file without strings but the classes are stored is the 5th column
cdata.loadDatasetFromCSVFile("irisCSVNoText.txt", 4);
cdata.printStats();
cdata.loadDatasetFromCSVFile("TestCSV.txt");
cdata.printStats();
return 0;
}
int main (int argc, const char * argv[])
{
//Create a new instance of the ClassificationData
ClassificationData trainingData;
//Set the dimensionality of the data (you need to do this before you can add any samples)
trainingData.setNumDimensions( 3 );
//You can also give the dataset a name (the name should have no spaces)
trainingData.setDatasetName("DummyData");
//You can also add some info text about the data
trainingData.setInfoText("This data contains some dummy data");
//Here you would grab some data from your sensor and label it with the corresponding gesture it belongs to
UINT gestureLabel = 1;
VectorDouble sample(3);
//For now we will just add some random data
Random random;
for(UINT i=0; i<100; i++){
sample[0] = random.getRandomNumberUniform(-1.0,1.0);
sample[1] = random.getRandomNumberUniform(-1.0,1.0);
sample[2] = random.getRandomNumberUniform(-1.0,1.0);
//Add the sample to the training data
trainingData.addSample( gestureLabel, sample );
}
//After recording your training data you can then save it to a file
if( !trainingData.saveDatasetToFile( "TrainingData.txt" ) ){
cout << "ERROR: Failed to save dataset to file!\n";
return EXIT_FAILURE;
}
//This can then be loaded later
if( !trainingData.loadDatasetFromFile( "TrainingData.txt" ) ){
cout << "ERROR: Failed to load dataset from file!\n";
return EXIT_FAILURE;
}
//You can also save and load the training data to a CSV file
//Each row will contain a sample, with the first column containing the class label and the remaining columns containing the data
if( !trainingData.saveDatasetToCSVFile( "TrainingData.csv" ) ){
cout << "ERROR: Failed to save dataset to csv file!\n";
return EXIT_FAILURE;
}
if( !trainingData.loadDatasetFromCSVFile( "TrainingData.csv" ) ){
cout << "ERROR: Failed to load dataset from csv file!\n";
return EXIT_FAILURE;
}
//This is how you can get some stats from the training data
string datasetName = trainingData.getDatasetName();
string infoText = trainingData.getInfoText();
UINT numSamples = trainingData.getNumSamples();
UINT numDimensions = trainingData.getNumDimensions();
UINT numClasses = trainingData.getNumClasses();
cout << "Dataset Name: " << datasetName << endl;
cout << "InfoText: " << infoText << endl;
cout << "NumberOfSamples: " << numSamples << endl;
cout << "NumberOfDimensions: " << numDimensions << endl;
cout << "NumberOfClasses: " << numClasses << endl;
//You can also get the minimum and maximum ranges of the data
vector< MinMax > ranges = trainingData.getRanges();
cout << "The ranges of the dataset are: \n";
for(UINT j=0; j<ranges.size(); j++){
cout << "Dimension: " << j << " Min: " << ranges[j].minValue << " Max: " << ranges[j].maxValue << endl;
}
//If you want to partition the dataset into a training dataset and a test dataset then you can use the partition function
//A value of 80 means that 80% of the original data will remain in the training dataset and 20% will be returned as the test dataset
ClassificationData testData = trainingData.partition( 80 );
//If you have multiple datasets that you want to merge together then use the merge function
if( !trainingData.merge( testData ) ){
cout << "ERROR: Failed to save merge datasets!\n";
return EXIT_FAILURE;
}
//If you want to run K-Fold cross validation using the dataset then you should first spilt the dataset into K-Folds
//A value of 10 splits the dataset into 10 folds and the true parameter signals that stratified sampling should be used
if( !trainingData.spiltDataIntoKFolds( 10, true ) ){
cout << "ERROR: Failed to spiltDataIntoKFolds!\n";
return EXIT_FAILURE;
}
//After you have called the spilt function you can then get the training and test sets for each fold
for(UINT foldIndex=0; foldIndex<10; foldIndex++){
ClassificationData foldTrainingData = trainingData.getTrainingFoldData( foldIndex );
ClassificationData foldTestingData = trainingData.getTestFoldData( foldIndex );
}
//If need you can clear any training data that you have recorded
trainingData.clear();
return EXIT_SUCCESS;
}
int main (int argc, const char * argv[])
{
//Load some training data from a file
ClassificationData trainingData;
//Generate a basic dataset with 1000 samples, 5 classes, and 3 dimensions
cout << "Generating dataset..." << endl;
generateDataset( "data.csv", 1000, 5, 3 );
cout << "Loading dataset..." << endl;
if( !trainingData.loadDatasetFromCSVFile( "data.csv" ) ){
cout << "ERROR: Failed to load training data from file\n";
return EXIT_FAILURE;
}
cout << "Data Loaded" << endl;
//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
cout << "Splitting data into training/test split..." << endl;
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
cout << "Training model..." << endl;
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
cout << "Testing model..." << endl;
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;
}
