bool TimeSeriesClassificationData::loadDatasetFromCSVFile(const string &filename){
numDimensions = 0;
datasetName = "NOT_SET";
infoText = "";
//Clear any previous data
clear();
//Parse the CSV file
FileParser parser;
if( !parser.parseCSVFile(filename,true) ){
errorLog << "loadDatasetFromCSVFile(const string &filename) - Failed to parse CSV file!" << endl;
return false;
}
if( !parser.getConsistentColumnSize() ){
errorLog << "loadDatasetFromCSVFile(const string &filename) - The CSV file does not have a consistent number of columns!" << endl;
return false;
}
if( parser.getColumnSize() <= 2 ){
errorLog << "loadDatasetFromCSVFile(const string &filename) - The CSV file does not have enough columns! It should contain at least three columns!" << endl;
return false;
}
//Set the number of dimensions
numDimensions = parser.getColumnSize()-2;
//Reserve the memory for the data
data.reserve( parser.getRowSize() );
UINT sampleCounter = 0;
UINT lastSampleCounter = 0;
UINT classLabel = 0;
UINT j = 0;
UINT n = 0;
VectorDouble sample(numDimensions);
MatrixDouble timeseries;
for(UINT i=0; i<parser.getRowSize(); i++){
sampleCounter = Util::stringToInt( parser[i][0] );
//Check to see if a new timeseries has started, if so then add the previous time series as a sample and start recording the new time series
if( sampleCounter != lastSampleCounter && i != 0 ){
//Add the labelled sample to the dataset
if( !addSample(classLabel, timeseries) ){
warningLog << "loadDatasetFromCSVFile(const string &filename,const UINT classLabelColumnIndex) - Could not add sample " << i << " to the dataset!" << endl;
}
timeseries.clear();
}
lastSampleCounter = sampleCounter;
//Get the class label
classLabel = Util::stringToInt( parser[i][1] );
//Get the sample data
j=0;
n=2;
while( j != numDimensions ){
sample[j++] = Util::stringToDouble( parser[i][n] );
n++;
}
//Add the sample to the timeseries
timeseries.push_back( sample );
}
if ( timeseries.getSize() > 0 )
//Add the labelled sample to the dataset
if( !addSample(classLabel, timeseries) ){
warningLog << "loadDatasetFromCSVFile(const string &filename,const UINT classLabelColumnIndex) - Could not add sample " << parser.getRowSize()-1 << " to the dataset!" << endl;
}
return true;
}
int main(int argc, const char * argv[]){
//Load the training data
TimeSeriesClassificationData trainingData;
if( !trainingData.loadDatasetFromFile("HMMTrainingData.grt") ){
cout << "ERROR: Failed to load training data!\n";
return false;
}
//Remove 20% of the training data to use as test data
TimeSeriesClassificationData testData = trainingData.partition( 80 );
//The input to the HMM must be a quantized discrete value
//We therefore use a KMeansQuantizer to covert the N-dimensional continuous data into 1-dimensional discrete data
const UINT NUM_SYMBOLS = 10;
KMeansQuantizer quantizer( NUM_SYMBOLS );
//Train the quantizer using the training data
if( !quantizer.train( trainingData ) ){
cout << "ERROR: Failed to train quantizer!\n";
return false;
}
//Quantize the training data
TimeSeriesClassificationData quantizedTrainingData( 1 );
for(UINT i=0; i<trainingData.getNumSamples(); i++){
UINT classLabel = trainingData[i].getClassLabel();
MatrixDouble quantizedSample;
for(UINT j=0; j<trainingData[i].getLength(); j++){
quantizer.quantize( trainingData[i].getData().getRowVector(j) );
quantizedSample.push_back( quantizer.getFeatureVector() );
}
if( !quantizedTrainingData.addSample(classLabel, quantizedSample) ){
cout << "ERROR: Failed to quantize training data!\n";
return false;
}
}
//Create a new HMM instance
HMM hmm;
//Set the number of states in each model
hmm.setNumStates( 4 );
//Set the number of symbols in each model, this must match the number of symbols in the quantizer
hmm.setNumSymbols( NUM_SYMBOLS );
//Set the HMM model type to LEFTRIGHT with a delta of 1
hmm.setModelType( HiddenMarkovModel::LEFTRIGHT );
hmm.setDelta( 1 );
//Set the training parameters
hmm.setMinImprovement( 1.0e-5 );
hmm.setMaxNumIterations( 100 );
hmm.setNumRandomTrainingIterations( 20 );
//Train the HMM model
if( !hmm.train( quantizedTrainingData ) ){
cout << "ERROR: Failed to train the HMM model!\n";
return false;
}
//Save the HMM model to a file
if( !hmm.save( "HMMModel.grt" ) ){
cout << "ERROR: Failed to save the model to a file!\n";
return false;
}
//Load the HMM model from a file
if( !hmm.load( "HMMModel.grt" ) ){
cout << "ERROR: Failed to load the model from a file!\n";
return false;
}
//Quantize the test data
TimeSeriesClassificationData quantizedTestData( 1 );
for(UINT i=0; i<testData.getNumSamples(); i++){
UINT classLabel = testData[i].getClassLabel();
MatrixDouble quantizedSample;
for(UINT j=0; j<testData[i].getLength(); j++){
quantizer.quantize( testData[i].getData().getRowVector(j) );
quantizedSample.push_back( quantizer.getFeatureVector() );
}
if( !quantizedTestData.addSample(classLabel, quantizedSample) ){
cout << "ERROR: Failed to quantize training data!\n";
return false;
}
}
//Compute the accuracy of the HMM models using the test data
double numCorrect = 0;
double numTests = 0;
for(UINT i=0; i<quantizedTestData.getNumSamples(); i++){
UINT classLabel = quantizedTestData[i].getClassLabel();
hmm.predict( quantizedTestData[i].getData() );
if( classLabel == hmm.getPredictedClassLabel() ) numCorrect++;
numTests++;
VectorDouble classLikelihoods = hmm.getClassLikelihoods();
VectorDouble classDistances = hmm.getClassDistances();
cout << "ClassLabel: " << classLabel;
cout << " PredictedClassLabel: " << hmm.getPredictedClassLabel();
cout << " MaxLikelihood: " << hmm.getMaximumLikelihood();
cout << " ClassLikelihoods: ";
for(UINT k=0; k<classLikelihoods.size(); k++){
cout << classLikelihoods[k] << "\t";
}
cout << "ClassDistances: ";
for(UINT k=0; k<classDistances.size(); k++){
cout << classDistances[k] << "\t";
}
cout << endl;
}
cout << "Test Accuracy: " << numCorrect/numTests*100.0 << endl;
return true;
}
int main (int argc, const char * argv[])
{
//Create a new instance of the TimeSeriesClassificationData
TimeSeriesClassificationData 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 timeseries data");
//Here you would record a time series, when you have finished recording the time series then add the training sample to the training data
UINT gestureLabel = 1;
MatrixDouble trainingSample;
//For now we will just add 10 x 20 random walk data timeseries
Random random;
for(UINT k=0; k<10; k++){//For the number of classes
gestureLabel = k+1;
//Get the init random walk position for this gesture
VectorDouble startPos( trainingData.getNumDimensions() );
for(UINT j=0; j<startPos.size(); j++){
startPos[j] = random.getRandomNumberUniform(-1.0,1.0);
}
//Generate the 20 time series
for(UINT x=0; x<20; x++){
//Clear any previous timeseries
trainingSample.clear();
//Generate the random walk
UINT randomWalkLength = random.getRandomNumberInt(90, 110);
VectorDouble sample = startPos;
for(UINT i=0; i<randomWalkLength; i++){
for(UINT j=0; j<startPos.size(); j++){
sample[j] += random.getRandomNumberUniform(-0.1,0.1);
}
//Add the sample to the training sample
trainingSample.push_back( sample );
}
//Add the training sample to the dataset
trainingData.addSample( gestureLabel, trainingSample );
}
}
//After recording your training data you can then save it to a file
if( !trainingData.saveDatasetToFile( "TrainingData.txt" ) ){
cout << "Failed to save dataset to file!\n";
return EXIT_FAILURE;
}
//This can then be loaded later
if( !trainingData.loadDatasetFromFile( "TrainingData.txt" ) ){
cout << "Failed to load dataset from 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
TimeSeriesClassificationData 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 << "Failed to 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 << "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++){
TimeSeriesClassificationData foldTrainingData = trainingData.getTrainingFoldData( foldIndex );
TimeSeriesClassificationData foldTestingData = trainingData.getTestFoldData( foldIndex );
}
//If need you can clear any training data that you have recorded
trainingData.clear();
return EXIT_SUCCESS;
}
int main() {
vector<string> gestures(0,"");
GetFilesInDirectory(gestures, "rawdata");
CreateDirectory("processed", NULL);
sort(gestures.begin(), gestures.end());
data = vector<vector<vector<double > > >(gestures.size(), vector<vector<double > >(0,vector<double>(0,0)));
for(size_t i = 0; i < gestures.size(); i++) {
ifstream fin(gestures[i]);
int n; fin >> n;
// cerr << gestures[i] << endl;
// cerr << n << endl;
data[i] = vector<vector<double> >(n, vector<double>(NUMPARAM, 0));
for(int j = 0; j < n; j++) {
for(int k = 0; k < NUMPARAM; k++) {
fin >> data[i][j][k];
}
}
fin.close();
}
//Create a new instance of the TimeSeriesClassificationDataStream
TimeSeriesClassificationData trainingData;
// ax, ay, az
trainingData.setNumDimensions(3);
trainingData.setDatasetName("processed\\GestureTrainingData.txt");
ofstream labelfile("processed\\GestureTrainingDataLabels.txt");
UINT currLabel = 1;
Random random;
map<string, int> gesturenames;
for(size_t overall = 0; overall < gestures.size(); overall++) {
string nam = gestures[overall].substr(8,gestures[overall].find_first_of('_')-8);
if(gesturenames.count(nam)) currLabel = gesturenames[nam];
else {
currLabel = gesturenames.size()+1;
gesturenames[nam] = currLabel;
labelfile << currLabel << " " << nam << endl;
}
MatrixDouble trainingSample;
VectorDouble currVec( trainingData.getNumDimensions() );
for(size_t k = 1; k < data[overall].size(); k++) {
for(UINT j=0; j<currVec.size(); j++){
currVec[j] = data[overall][k][j];
}
trainingSample.push_back(currVec);
}
trainingData.addSample(currLabel, trainingSample);
}
for(size_t i = 0; i < gestures.size(); i++) {
MatrixDouble trainingSample;
VectorDouble currVec(trainingData.getNumDimensions());
for(UINT j = 0; j < currVec.size(); j++) {
currVec[j] = random.getRandomNumberUniform(-1.0, 1.0);
}
for(size_t k = 0; k < 100; k++) {
trainingSample.push_back(currVec);
}
trainingData.addSample(0, trainingSample);
}
//After recording your training data you can then save it to a file
if( !trainingData.save( "processed\\TrainingData.grt" ) ){
cout << "ERROR: Failed to save dataset to file!\n";
return EXIT_FAILURE;
}
//This can then be loaded later
if( !trainingData.load( "processed\\TrainingData.grt" ) ){
cout << "ERROR: Failed to load dataset from 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;
}
DTW dtw;
if( !dtw.train( trainingData ) ){
cerr << "Failed to train classifier!\n";
exit(EXIT_FAILURE);
}
dtw.enableNullRejection(true);
dtw.setNullRejectionCoeff(4);
dtw.enableTrimTrainingData(true, 0.1, 90);
//Save the DTW model to a file
if( !dtw.saveModelToFile("processed\\DTWModel.txt") ){
cerr << "Failed to save the classifier model!\n";
exit(EXIT_FAILURE);
}
trainingData.clear();
return EXIT_SUCCESS;
}
int main (int argc, const char * argv[])
{
//Create an empty matrix double
MatrixDouble matrix;
//Resize the matrix
matrix.resize( 100, 2 );
//Set all the values in the matrix to zero
matrix.setAllValues( 0 );
//Loop over the data and set the values to random values
UINT counter = 0;
for(UINT i=0; i<matrix.getNumRows(); i++){
for(UINT j=0; j<matrix.getNumCols(); j++){
matrix[i][j] = counter++;
}
}
//Add a new row at the very end of the matrix
VectorDouble newRow(2);
newRow[0] = 1000;
newRow[1] = 2000;
matrix.push_back( newRow );
//Print the values
cout << "Matrix Data: \n";
for(UINT i=0; i<matrix.getNumRows(); i++){
for(UINT j=0; j<matrix.getNumCols(); j++){
cout << matrix[i][j] << "\t";
}
cout << endl;
}
cout << endl;
//Get the second row as a vector
VectorDouble rowVector = matrix.getRowVector( 1 );
cout << "Row Vector Data: \n";
for(UINT i=0; i<rowVector.size(); i++){
cout << rowVector[i] << "\t";
}
cout << endl;
//Get the second column as a vector
VectorDouble colVector = matrix.getColVector( 1 );
cout << "Column Vector Data: \n";
for(UINT i=0; i<colVector.size(); i++){
cout << colVector[i] << "\n";
}
cout << endl;
//Get the mean of each column
VectorDouble mean = matrix.getMean();
cout << "Mean: \n";
for(UINT i=0; i<mean.size(); i++){
cout << mean[i] << "\n";
}
cout << endl;
//Get the Standard Deviation of each column
VectorDouble stdDev = matrix.getStdDev();
cout << "StdDev: \n";
for(UINT i=0; i<stdDev.size(); i++){
cout << stdDev[i] << "\n";
}
cout << endl;
//Get the covariance matrix
MatrixDouble cov = matrix.getCovarianceMatrix();
cout << "Covariance Matrix: \n";
for(UINT i=0; i<cov.getNumRows(); i++){
for(UINT j=0; j<cov.getNumCols(); j++){
cout << cov[i][j] << "\t";
}
cout << endl;
}
cout << endl;
vector< MinMax > ranges = matrix.getRanges();
cout << "Ranges: \n";
for(UINT i=0; i<ranges.size(); i++){
cout << "i: " << i << "\tMinValue: " << ranges[i].minValue << "\tMaxValue:" << ranges[i].maxValue << "\n";
}
cout << endl;
//Save the matrix data to a csv file
matrix.save( "data.csv" );
//load the matrix data from a csv file
matrix.load( "data.csv" );
return EXIT_SUCCESS;
}
