int main (int argc, const char * argv[])
{
//Create a new DTW instance, using the default parameters
DTW dtw;
//Load some training data to train the classifier - the DTW uses TimeSeriesClassificationData
TimeSeriesClassificationData trainingData;
if( !trainingData.load("DTWTrainingData.grt") ){
cout << "Failed to load training data!\n";
return EXIT_FAILURE;
}
//Use 20% of the training dataset to create a test dataset
TimeSeriesClassificationData testData = trainingData.split( 80 );
//Trim the training data for any sections of non-movement at the start or end of the recordings
dtw.enableTrimTrainingData(true,0.1,90);
//Train the classifier
if( !dtw.train( trainingData ) ){
cout << "Failed to train classifier!\n";
return EXIT_FAILURE;
}
//Save the DTW model to a file
if( !dtw.save("DTWModel.grt") ){
cout << "Failed to save the classifier model!\n";
return EXIT_FAILURE;
}
//Load the DTW model from a file
if( !dtw.load("DTWModel.grt") ){
cout << "Failed to load the classifier model!\n";
return EXIT_FAILURE;
}
//Use the test dataset to test the DTW model
double accuracy = 0;
for(UINT i=0; i<testData.getNumSamples(); i++){
//Get the i'th test sample - this is a timeseries
UINT classLabel = testData[i].getClassLabel();
MatrixDouble timeseries = testData[i].getData();
//Perform a prediction using the classifier
if( !dtw.predict( timeseries ) ){
cout << "Failed to perform prediction for test sampel: " << i <<"\n";
return EXIT_FAILURE;
}
//Get the predicted class label
UINT predictedClassLabel = dtw.getPredictedClassLabel();
double maximumLikelihood = dtw.getMaximumLikelihood();
VectorDouble classLikelihoods = dtw.getClassLikelihoods();
VectorDouble classDistances = dtw.getClassDistances();
//Update the accuracy
if( classLabel == predictedClassLabel ) accuracy++;
cout << "TestSample: " << i << "\tClassLabel: " << classLabel << "\tPredictedClassLabel: " << predictedClassLabel << "\tMaximumLikelihood: " << maximumLikelihood << endl;
}
cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
return EXIT_SUCCESS;
}
int main(int argc, const char * argv[]){
//Load the training data
TimeSeriesClassificationData trainingData;
if( !trainingData.load("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.split( 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().getRow(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 HMM as a Discrete HMM
hmm.setHMMType( HMM_DISCRETE );
//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( HMM_LEFTRIGHT );
hmm.setDelta( 1 );
//Set the training parameters
hmm.setMinChange( 1.0e-5 );
hmm.setMaxNumEpochs( 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().getRow(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++;
VectorFloat classLikelihoods = hmm.getClassLikelihoods();
VectorFloat 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;
}
