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[]){ //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.partition( 80 ); //Create a new HMM instance HMM hmm; //Set the HMM as a Continuous HMM hmm.setHMMType( HMM_CONTINUOUS ); //Set the downsample factor, a higher downsample factor will speed up the prediction time, but might reduce the classification accuracy hmm.setDownsampleFactor( 5 ); //Set the committee size, this sets the (top) number of models that will be used to make a prediction hmm.setCommitteeSize( 10 ); //Tell the hmm algorithm that we want it to estimate sigma from the training data hmm.setAutoEstimateSigma( true ); //Set the minimum value for sigma, you might need to adjust this based on the range of your data //If you set setAutoEstimateSigma to false, then all sigma values will use the value below hmm.setSigma( 20.0 ); //Set the HMM model type to LEFTRIGHT with a delta of 1, this means the HMM can only move from the left-most state to the right-most state //in steps of 1 hmm.setModelType( HMM_LEFTRIGHT ); hmm.setDelta( 1 ); //Train the HMM model if( !hmm.train( trainingData ) ){ 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; } //Compute the accuracy of the HMM models using the test data double numCorrect = 0; double numTests = 0; for(UINT i=0; i<testData.getNumSamples(); i++){ UINT classLabel = testData[i].getClassLabel(); hmm.predict( testData[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; }