int main (int argc, const char * argv[])
{
//Create a new gesture recognition pipeline
GestureRecognitionPipeline pipeline;
//Add an ANBC module
pipeline.setClassifier( ANBC() );
//Add a ClassLabelFilter as a post processing module with a minCount of 5 and a buffer size of 10
pipeline.addPostProcessingModule( ClassLabelFilter(5,10) );
//Load some training data to train and test the classifier
ClassificationData trainingData;
ClassificationData testData;
if( !trainingData.loadDatasetFromFile("ClassLabelFilterTrainingData.txt") ){
cout << "Failed to load training data!\n";
return EXIT_FAILURE;
}
if( !testData.loadDatasetFromFile("ClassLabelFilterTestData.txt") ){
cout << "Failed to load training data!\n";
return EXIT_FAILURE;
}
//Train the classifier
if( !pipeline.train( trainingData ) ){
cout << "Failed to train classifier!\n";
return EXIT_FAILURE;
}
//Use the test dataset to demonstrate the output of the ClassLabelFilter
for(UINT i=0; i<testData.getNumSamples(); i++){
VectorDouble inputVector = testData[i].getSample();
if( !pipeline.predict( inputVector ) ){
cout << "Failed to perform prediction for test sampel: " << i <<"\n";
return EXIT_FAILURE;
}
//Get the predicted class label (this will be the processed class label)
UINT predictedClassLabel = pipeline.getPredictedClassLabel();
//Get the unprocessed class label (i.e. the direct output of the classifier)
UINT unprocessedClassLabel = pipeline.getUnProcessedPredictedClassLabel();
//Also print the results to the screen
cout << "Processed Class Label: \t" << predictedClassLabel << "\tUnprocessed Class Label: \t" << unprocessedClassLabel << endl;
}
return EXIT_SUCCESS;
}
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[])
{
//Load the example data
ClassificationData data;
if( !data.loadDatasetFromFile("WiiAccShakeData.txt") ){
cout << "ERROR: Failed to load data from file!\n";
return EXIT_FAILURE;
}
//The variables used to initialize the zero crossing counter feature extraction
UINT searchWindowSize = 20;
double deadZoneThreshold = 0.01;
UINT numDimensions = data.getNumDimensions();
UINT featureMode = ZeroCrossingCounter::INDEPENDANT_FEATURE_MODE; //This could also be ZeroCrossingCounter::COMBINED_FEATURE_MODE
//Create a new instance of the ZeroCrossingCounter feature extraction
ZeroCrossingCounter zeroCrossingCounter(searchWindowSize,deadZoneThreshold,numDimensions,featureMode);
//Loop over the accelerometer data, at each time sample (i) compute the features using the new sample and then write the results to a file
for(UINT i=0; i<data.getNumSamples(); i++){
//Compute the features using this new sample
zeroCrossingCounter.computeFeatures( data[i].getSample() );
//Write the data to the file
cout << "InputVector: ";
for(UINT j=0; j<data.getNumDimensions(); j++){
cout << data[i].getSample()[j] << "\t";
}
//Get the latest feature vector
VectorDouble featureVector = zeroCrossingCounter.getFeatureVector();
//Write the features to the file
cout << "FeatureVector: ";
for(UINT j=0; j<featureVector.size(); j++){
cout << featureVector[j];
if( j != featureVector.size()-1 ) cout << "\t";
}
cout << endl;
}
//Save the zero crossing counter settings to a file
zeroCrossingCounter.saveModelToFile("ZeroCrossingCounterSettings.txt");
//You can then load the settings again if you need them
zeroCrossingCounter.loadModelFromFile("ZeroCrossingCounterSettings.txt");
return EXIT_SUCCESS;
}
int main (int argc, const char * argv[])
{
GestureRecognitionPipeline pipeline;
ANBC anbc;
ClassificationData trainingData;
trainingData.loadDatasetFromFile("training-data.txt")
pipeline.setClassifier(anbc);
pipeline.train(trainingData);
VectorDouble inputVector(SAMPLE_DIMENSION) = getDataFromSensor();
pipeline.predict(inputVector);
UINT predictedClassLabel = pipeline.getPredictedClassLabel();
double maxLikelihood = pipeline.getMaximumLikelihood();
printf("predictedClassLabel : %d , MaximumLikelihood : %f \n", predictedClassLabel, maxLikelihood);
return EXIT_SUCCESS;
}
int main (int argc, const char * argv[])
{
//Create a new Softmax instance
Softmax softmax;
//Load some training data to train the classifier
ClassificationData trainingData;
if( !trainingData.loadDatasetFromFile("SoftmaxTrainingData.txt") ){
cout << "Failed to load training data!\n";
return EXIT_FAILURE;
}
//Use 20% of the training dataset to create a test dataset
ClassificationData testData = trainingData.partition( 80 );
//Train the classifier
if( !softmax.train( trainingData ) ){
cout << "Failed to train classifier!\n";
return EXIT_FAILURE;
}
//Save the Softmax model to a file
if( !softmax.saveModelToFile("SoftmaxModel.txt") ){
cout << "Failed to save the classifier model!\n";
return EXIT_FAILURE;
}
//Load the Softmax model from a file
if( !softmax.loadModelFromFile("SoftmaxModel.txt") ){
cout << "Failed to load the classifier model!\n";
return EXIT_FAILURE;
}
//Use the test dataset to test the softmax model
double accuracy = 0;
for(UINT i=0; i<testData.getNumSamples(); i++){
//Get the i'th test sample
UINT classLabel = testData[i].getClassLabel();
vector< double > inputVector = testData[i].getSample();
//Perform a prediction using the classifier
if( !softmax.predict( inputVector ) ){
cout << "Failed to perform prediction for test sample: " << i <<"\n";
return EXIT_FAILURE;
}
//Get the predicted class label
UINT predictedClassLabel = softmax.getPredictedClassLabel();
vector< double > classLikelihoods = softmax.getClassLikelihoods();
vector< double > classDistances = softmax.getClassDistances();
//Update the accuracy
if( classLabel == predictedClassLabel ) accuracy++;
cout << "TestSample: " << i << " ClassLabel: " << classLabel << " PredictedClassLabel: " << predictedClassLabel << endl;
}
cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
return EXIT_SUCCESS;
}
void metrics_separate_data(){
// Training and test data
ClassificationData trainingData;
ClassificationData testData;
string file_path = "../../../data/";
if( !trainingData.loadDatasetFromFile(file_path + "train/grt/12345.txt") ){
std::cout <<"Failed to load training data!\n";
}
ANBC anbc;
anbc.enableScaling(true);
anbc.enableNullRejection(true);
SVM svm(SVM::RBF_KERNEL);
svm.enableScaling(true);
svm.enableNullRejection(true);
MinDist minDist;
minDist.setNumClusters(4);
minDist.enableScaling(true);
minDist.enableNullRejection(true);
ofstream outputFileStream("accuracy-mindist.csv");
outputFileStream << "classLabel,nullRejectionCoeff,accuracy, \n";
for(int class_name = 1; class_name<=5; class_name++){
if( !testData.loadDatasetFromFile(file_path + "test/grt/" + to_string(class_name) + ".txt") ){
std::cout <<"Failed to load training data!\n";
}
for(double nullRejectionCoeff = 0; nullRejectionCoeff <= 10; nullRejectionCoeff=nullRejectionCoeff+0.2){
// anbc.setNullRejectionCoeff(nullRejectionCoeff);
// svm.setNullRejectionCoeff(nullRejectionCoeff);
minDist.setNullRejectionCoeff(nullRejectionCoeff);
GestureRecognitionPipeline pipeline;
// pipeline.setClassifier(anbc);
// pipeline.setClassifier(svm);
pipeline.setClassifier(minDist);
// Train the pipeline
if( !pipeline.train( trainingData ) ){
std::cout << "Failed to train classifier!\n";
}
// Evaluation
double accuracy = 0;
for(UINT i=0; i<testData.getNumSamples(); i++){
UINT actualClassLabel = testData[i].getClassLabel();
vector< double > inputVector = testData[i].getSample();
if( !pipeline.predict( inputVector )){
std::cout << "Failed to perform prediction for test sampel: " << i <<"\n";
}
UINT predictedClassLabel = pipeline.getPredictedClassLabel();
if( actualClassLabel == predictedClassLabel) accuracy++;
}
outputFileStream << class_name << ',' << nullRejectionCoeff << ',' << accuracy/double(testData.getNumSamples())*100.0 << '\n';
cout<< "Done" << endl;
}
}
//---------------------- Null Gesture Test -----------------//
int class_name = 0;
if( !testData.loadDatasetFromFile(file_path + "test/grt/" + to_string(class_name) + ".txt") ){
std::cout <<"Failed to load training data!\n";
}
for(double nullRejectionCoeff = 0; nullRejectionCoeff <= 10; nullRejectionCoeff=nullRejectionCoeff+0.2){
// anbc.setNullRejectionCoeff(nullRejectionCoeff);
// svm.setNullRejectionCoeff(nullRejectionCoeff);
minDist.setNullRejectionCoeff(nullRejectionCoeff);
GestureRecognitionPipeline pipeline;
// pipeline.setClassifier(anbc);
// pipeline.setClassifier(svm);
pipeline.setClassifier(minDist);
// Train the pipeline
if( !pipeline.train( trainingData ) ){
std::cout << "Failed to train classifier!\n";
}
// Evaluation
double accuracy = 0;
for(UINT i=0; i<testData.getNumSamples(); i++){
vector< double > inputVector = testData[i].getSample();
if( !pipeline.predict( inputVector )){
std::cout << "Failed to perform prediction for test sampel: " << i <<"\n";
}
UINT predictedClassLabel = pipeline.getPredictedClassLabel();
if(predictedClassLabel == 0 ) accuracy++;
}
outputFileStream << class_name << ',' << nullRejectionCoeff << ',' << accuracy/double(testData.getNumSamples())*100.0 << '\n';
cout<< "Done" << endl;
}
}
void metrics_subset_data(){
ANBC anbc;
anbc.enableScaling(true);
anbc.enableNullRejection(true);
MinDist minDist;
minDist.setNumClusters(4);
minDist.enableScaling(true);
minDist.enableNullRejection(true);
// ofstream opRecall("anbc-recall-nr-0-10.csv");
// opRecall <<"nrCoeff,class0,class1,class2,class3,class4,class5\n";
//
// ofstream opInstanceRes("anbc-prediction-nr-2.csv");
// opInstanceRes <<"actualClass,predictedClass,maximumLikelihood,lZ,lY,lZ,rZ,rY,rZ\n";
//
// ofstream opMetrics("anbc-precision-recall-fmeasure-nr-2.csv");
// opMetrics <<"class1,class2,class3,class4,class5\n";
//
// ofstream opConfusion("anbc-confusion-nr-2.csv");
// opConfusion <<"class0,class1,class2,class3,class4,class5\n";
ofstream opRecall("mindist-recall-nr-0-10.csv");
opRecall <<"nrCoeff,class0,class1,class2,class3,class4,class5\n";
ofstream opInstanceRes("mindist-prediction-nr-2.csv");
opInstanceRes <<"actualClass,predictedClass,maximumLikelihood,lZ,lY,lZ,rZ,rY,rZ\n";
ofstream opMetrics("mindist-precision-recall-fmeasure-nr-2.csv");
opMetrics <<"class1,class2,class3,class4,class5\n";
ofstream opConfusion("mindist-confusion-nr-2.csv");
opConfusion <<"class0,class1,class2,class3,class4,class5\n";
// Training and test data
ClassificationData trainingData;
ClassificationData testData;
ClassificationData nullGestureData;
string file_path = "../../../data/";
if( !trainingData.loadDatasetFromFile(file_path + "train/grt/hri-training-dataset.txt") ){
std::cout <<"Failed to load training data!\n";
}
if( !nullGestureData.loadDatasetFromFile(file_path + "test/grt/0.txt") ){
std::cout <<"Failed to load null gesture data!\n";
}
testData = trainingData.partition(90);
testData.sortClassLabels();
// testData.saveDatasetToFile("anbc-validation-subset.txt");
testData.saveDatasetToFile("mindist-validation-subset.txt");
for(double nullRejectionCoeff = 0; nullRejectionCoeff <= 10; nullRejectionCoeff=nullRejectionCoeff+0.2){
// anbc.setNullRejectionCoeff(nullRejectionCoeff);
// GestureRecognitionPipeline pipeline;
// pipeline.setClassifier(anbc);
minDist.setNullRejectionCoeff(nullRejectionCoeff);
GestureRecognitionPipeline pipeline;
pipeline.setClassifier(minDist);
pipeline.train(trainingData);
pipeline.test(testData);
TestResult testRes = pipeline.getTestResults();
opRecall << nullRejectionCoeff << ",";
//null rejection prediction
double accuracy = 0;
for(UINT i=0; i<nullGestureData.getNumSamples(); i++){
vector< double > inputVector = nullGestureData[i].getSample();
if( !pipeline.predict( inputVector )){
std::cout << "Failed to perform prediction for test sampel: " << i <<"\n";
}
UINT predictedClassLabel = pipeline.getPredictedClassLabel();
if(predictedClassLabel == 0 ) accuracy++;
}
opRecall << accuracy/double(nullGestureData.getNumSamples()) << ",";
// other classes prediction
for(int cl = 0; cl < testRes.recall.size(); cl++ ){
opRecall << testRes.recall[cl];
if(cl < testRes.recall.size() - 1){
opRecall << ",";
}
}
opRecall<< endl;
// Calculate instance prediction, precision, recall, fmeasure and confusion matrix for nullRejection 2.0
if(AreDoubleSame(nullRejectionCoeff, 2.0))
{
//instance prediction
for(UINT i=0; i<testData.getNumSamples(); i++){
UINT actualClassLabel = testData[i].getClassLabel();
vector< double > inputVector = testData[i].getSample();
if( !pipeline.predict( inputVector )){
std::cout << "Failed to perform prediction for test sampel: " << i <<"\n";
}
UINT predictedClassLabel = pipeline.getPredictedClassLabel();
double maximumLikelihood = pipeline.getMaximumLikelihood();
opInstanceRes << actualClassLabel << "," << predictedClassLabel << "," << maximumLikelihood << ","
<< inputVector[0] << "," << inputVector[1] << "," << inputVector[2] << "," << inputVector[3] << "," << inputVector[4] << "," << inputVector[5] << "\n";
}
//precision, recall, fmeasure
for(int cl = 0; cl < testRes.precision.size(); cl++ ){
opMetrics << testRes.precision[cl];
if(cl < testRes.precision.size() - 1){
opMetrics << ",";
}
}
opMetrics<< endl;
for(int cl = 0; cl < testRes.recall.size(); cl++ ){
opMetrics << testRes.recall[cl];
if(cl < testRes.recall.size() - 1){
opMetrics << ",";
}
}
opMetrics<< endl;
for(int cl = 0; cl < testRes.fMeasure.size(); cl++ ){
opMetrics << testRes.fMeasure[cl];
if(cl < testRes.fMeasure.size() - 1){
opMetrics << ",";
}
}
opMetrics<< endl;
//confusion matrix
MatrixDouble matrix = testRes.confusionMatrix;
for(UINT i=0; i<matrix.getNumRows(); i++){
for(UINT j=0; j<matrix.getNumCols(); j++){
opConfusion << matrix[i][j];
if(j < matrix.getNumCols() - 1){
opConfusion << ",";
}
}
opConfusion << endl;
}
opConfusion << endl;
}
}
cout << "Done\n";
}
void prediction_axis_data(){
// Training and test data
ClassificationData trainingData;
ClassificationData testData;
string file_path = "../../../data/";
string class_name = "5";
if( !trainingData.loadDatasetFromFile(file_path + "train/grt/" + class_name + ".txt") ){
std::cout <<"Failed to load training data!\n";
}
if( !testData.loadDatasetFromFile(file_path + "test/grt/" + class_name + ".txt") ){
std::cout <<"Failed to load training data!\n";
}
// Pipeline setup
ANBC anbc;
anbc.setNullRejectionCoeff(1);
anbc.enableScaling(true);
anbc.enableNullRejection(true);
GestureRecognitionPipeline pipeline;
pipeline.setClassifier(anbc);
// Train the pipeline
if( !pipeline.train( trainingData ) ){
std::cout << "Failed to train classifier!\n";
}
// File stream
ofstream outputFileStream(class_name + ".csv");
// Evaluation
double accuracy = 0;
outputFileStream << "actualClass,predictedClass,maximumLikelihood,lZ,lY,lZ,rZ,rY,rZ \n";
for(UINT i=0; i<testData.getNumSamples(); i++){
UINT actualClassLabel = testData[i].getClassLabel();
vector< double > inputVector = testData[i].getSample();
if( !pipeline.predict( inputVector )){
std::cout << "Failed to perform prediction for test sampel: " << i <<"\n";
}
UINT predictedClassLabel = pipeline.getPredictedClassLabel();
double maximumLikelihood = pipeline.getMaximumLikelihood();
outputFileStream << actualClassLabel << "," << predictedClassLabel << "," << maximumLikelihood << ","
<< inputVector[0] << "," << inputVector[1] << "," << inputVector[2] << "," << inputVector[3] << "," << inputVector[4] << "," << inputVector[5] << "\n";
if( actualClassLabel == predictedClassLabel) accuracy++;
}
std::cout << "Test Accuracy testHandsUp : " << accuracy/double(testData.getNumSamples())*100.0 << " %\n";
}
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[])
{
//Create a new KNN classifier with a K value of 10
KNN knn(10);
knn.setNullRejectionCoeff( 10 );
knn.enableScaling( true );
knn.enableNullRejection( true );
//Train the classifier with some training data
ClassificationData trainingData;
if( !trainingData.loadDatasetFromFile("KNNTrainingData.grt") ){
cout << "Failed to load training data!\n";
return EXIT_FAILURE;
}
//Use 20% of the training dataset to create a test dataset
ClassificationData testData = trainingData.partition( 80 );
//Train the classifier
bool trainSuccess = knn.train( trainingData );
if( !trainSuccess ){
cout << "Failed to train classifier!\n";
return EXIT_FAILURE;
}
//Save the knn model to a file
if( !knn.save("KNNModel.grt") ){
cout << "Failed to save the classifier model!\n";
return EXIT_FAILURE;
}
//Load the knn model from a file
if( !knn.load("KNNModel.grt") ){
cout << "Failed to load the classifier model!\n";
return EXIT_FAILURE;
}
//Use the test dataset to test the KNN model
double accuracy = 0;
for(UINT i=0; i<testData.getNumSamples(); i++){
//Get the i'th test sample
UINT classLabel = testData[i].getClassLabel();
vector< double > inputVector = testData[i].getSample();
//Perform a prediction using the classifier
bool predictSuccess = knn.predict( inputVector );
if( !predictSuccess ){
cout << "Failed to perform prediction for test sampel: " << i <<"\n";
return EXIT_FAILURE;
}
//Get the predicted class label
UINT predictedClassLabel = knn.getPredictedClassLabel();
vector< double > classLikelihoods = knn.getClassLikelihoods();
vector< double > classDistances = knn.getClassDistances();
//Update the accuracy
if( classLabel == predictedClassLabel ) accuracy++;
cout << "TestSample: " << i << " ClassLabel: " << classLabel << " PredictedClassLabel: " << predictedClassLabel << endl;
}
cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
return EXIT_SUCCESS;
}
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[])
{
//Create a new AdaBoost instance
AdaBoost adaBoost;
//Set the weak classifier you want to use
adaBoost.setWeakClassifier( DecisionStump() );
//Load some training data to train the classifier
ClassificationData trainingData;
if( !trainingData.loadDatasetFromFile("AdaBoostTrainingData.txt") ){
cout << "Failed to load training data!\n";
return EXIT_FAILURE;
}
//Use 20% of the training dataset to create a test dataset
ClassificationData testData = trainingData.partition( 80 );
//Train the classifier
if( !adaBoost.train( trainingData ) ){
cout << "Failed to train classifier!\n";
return EXIT_FAILURE;
}
//Save the model to a file
if( !adaBoost.saveModelToFile("AdaBoostModel.txt") ){
cout << "Failed to save the classifier model!\n";
return EXIT_FAILURE;
}
//Load the model from a file
if( !adaBoost.loadModelFromFile("AdaBoostModel.txt") ){
cout << "Failed to load the classifier model!\n";
return EXIT_FAILURE;
}
//Use the test dataset to test the AdaBoost model
double accuracy = 0;
for(UINT i=0; i<testData.getNumSamples(); i++){
//Get the i'th test sample
UINT classLabel = testData[i].getClassLabel();
vector< double > inputVector = testData[i].getSample();
//Perform a prediction using the classifier
if( !adaBoost.predict( inputVector ) ){
cout << "Failed to perform prediction for test sampel: " << i <<"\n";
return EXIT_FAILURE;
}
//Get the predicted class label
UINT predictedClassLabel = adaBoost.getPredictedClassLabel();
double maximumLikelhood = adaBoost.getMaximumLikelihood();
vector< double > classLikelihoods = adaBoost.getClassLikelihoods();
vector< double > classDistances = adaBoost.getClassDistances();
//Update the accuracy
if( classLabel == predictedClassLabel ) accuracy++;
cout << "TestSample: " << i << " ClassLabel: " << classLabel;
cout << " PredictedClassLabel: " << predictedClassLabel << " Likelihood: " << maximumLikelhood;
cout << endl;
}
cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << 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 ClassificationData to hold the training data
ClassificationData 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
ClassificationData 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;
}
