C++ HMM::getClassDistances方法代码示例

//.........这里部分代码省略.........
            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;
}