C++ TimeSeriesClassificationData::getClassTracker方法代码示例

bool ParticleClassifier::train_(TimeSeriesClassificationData &trainingData){
    
    clear();
    
    numClasses = trainingData.getNumClasses();
    numInputDimensions = trainingData.getNumDimensions();
    ranges = trainingData.getRanges();
    
    //Scale the training data if needed
    if( useScaling ){
        trainingData.scale(0, 1);
    }
    
    //Train the particle filter
    particleFilter.train( numParticles, trainingData, sensorNoise, transitionSigma, phaseSigma, velocitySigma );
    
    classLabels.resize(numClasses);
    classLikelihoods.resize(numClasses,0);
    classDistances.resize(numClasses,0);
    
    for(unsigned int i=0; i<numClasses; i++){
        classLabels[i] = trainingData.getClassTracker()[i].classLabel;
    }
    
    trained = true;
  
    return trained;
}

bool TimeSeriesClassificationData::merge(const TimeSeriesClassificationData &labelledData){

    if( labelledData.getNumDimensions() != numDimensions ){
        errorLog << "merge(TimeSeriesClassificationData &labelledData) - The number of dimensions in the labelledData (" << labelledData.getNumDimensions() << ") does not match the number of dimensions of this dataset (" << numDimensions << ")" << std::endl;
        return false;
    }

    //The dataset has changed so flag that any previous cross validation setup will now not work
    crossValidationSetup = false;
    crossValidationIndexs.clear();

    //Add the data from the labelledData to this instance
    for(UINT i=0; i<labelledData.getNumSamples(); i++){
        addSample(labelledData[i].getClassLabel(), labelledData[i].getData());
    }

    //Set the class names from the dataset
    Vector< ClassTracker > classTracker = labelledData.getClassTracker();
    for(UINT i=0; i<classTracker.size(); i++){
        setClassNameForCorrespondingClassLabel(classTracker[i].className, classTracker[i].classLabel);
    }

    return true;
}

bool HMM::train_continuous(TimeSeriesClassificationData &trainingData){
    
    clear();
    
    if( trainingData.getNumSamples() == 0 ){
        errorLog << "train_continuous(TimeSeriesClassificationData &trainingData) - There are no training samples to train the CHMM classifer!" << endl;
        return false;
    }
    
    //Reset the CHMM
    numInputDimensions = trainingData.getNumDimensions();
    numClasses = trainingData.getNumClasses();
    classLabels.resize( numClasses );
    for(UINT k=0; k<numClasses; k++){
        classLabels[k] = trainingData.getClassTracker()[k].classLabel;
    }
    
    //Scale the training data if needed
    ranges = trainingData.getRanges();
    if( useScaling ){
        trainingData.scale(0, 1);
    }
    
    //Setup the models, there will be 1 model for each training sample
    const UINT numTrainingSamples = trainingData.getNumSamples();
    continuousModels.resize( numTrainingSamples );
    
    //Train each of the models
    for(UINT k=0; k<numTrainingSamples; k++){
        
        //Init the model
        continuousModels[k].setDownsampleFactor( downsampleFactor );
        continuousModels[k].setModelType( modelType );
        continuousModels[k].setDelta( delta );
        continuousModels[k].setSigma( sigma );
        continuousModels[k].setAutoEstimateSigma( autoEstimateSigma );
        continuousModels[k].enableScaling( false ); //Scaling should always off for the models as we do any scaling in the CHMM
        
        //Train the model
        if( !continuousModels[k].train_( trainingData[k] ) ){
            errorLog << "train_continuous(TimeSeriesClassificationData &trainingData) - Failed to train CHMM for sample " << k << endl;
            return false;
        }
    }
    
    if( committeeSize > trainingData.getNumSamples() ){
        committeeSize = trainingData.getNumSamples();
        warningLog << "train_continuous(TimeSeriesClassificationData &trainingData) - The committeeSize is larger than the number of training sample. Setting committeeSize to number of training samples: " << trainingData.getNumSamples() << endl;
    }
    
    //Flag that the model has been trained
    trained = true;
    
    //Compute any null rejection thresholds if needed
    if( useNullRejection ){
        //Compute the rejection thresholds
        nullRejectionThresholds.resize(numClasses);
    }
    
    return true;
}

bool HMM::train_discrete(TimeSeriesClassificationData &trainingData){
    
    clear();
    
    if( trainingData.getNumSamples() == 0 ){
        errorLog << "train_discrete(TimeSeriesClassificationData &trainingData) - There are no training samples to train the HMM classifer!" << endl;
        return false;
    }
    
    if( trainingData.getNumDimensions() != 1 ){
        errorLog << "train_discrete(TimeSeriesClassificationData &trainingData) - The number of dimensions in the training data must be 1. If your training data is not 1 dimensional then you must quantize the training data using one of the GRT quantization algorithms" << endl;
        return false;
    }
    
    //Reset the HMM
    numInputDimensions = trainingData.getNumDimensions();
    numClasses = trainingData.getNumClasses();
    discreteModels.resize( numClasses );
    classLabels.resize( numClasses );
    
    //Init the models
    for(UINT k=0; k<numClasses; k++){
        discreteModels[k].resetModel(numStates,numSymbols,modelType,delta);
        discreteModels[k].setMaxNumEpochs( maxNumEpochs );
        discreteModels[k].setMinChange( minChange );
    }
    
    //Train each of the models
    for(UINT k=0; k<numClasses; k++){
        //Get the class ID of this gesture
        UINT classID = trainingData.getClassTracker()[k].classLabel;
        classLabels[k] = classID;
        
        //Convert this classes training data into a list of observation sequences
        TimeSeriesClassificationData classData = trainingData.getClassData( classID );
        vector< vector< UINT > > observationSequences;
        if( !convertDataToObservationSequence( classData, observationSequences ) ){
            return false;
        }
        
        //Train the model
        if( !discreteModels[k].train( observationSequences ) ){
            errorLog << "train_discrete(TimeSeriesClassificationData &trainingData) - Failed to train HMM for class " << classID << endl;
            return false;
        }
    }
    
    //Compute the rejection thresholds
    nullRejectionThresholds.resize(numClasses);
    
    for(UINT k=0; k<numClasses; k++){
        //Get the class ID of this gesture
        UINT classID = trainingData.getClassTracker()[k].classLabel;
        classLabels[k] = classID;
        
        //Convert this classes training data into a list of observation sequences
        TimeSeriesClassificationData classData = trainingData.getClassData( classID );
        vector< vector< UINT > > observationSequences;
        if( !convertDataToObservationSequence( classData, observationSequences ) ){
            return false;
        }
        
        //Test the model
        double loglikelihood = 0;
        double avgLoglikelihood = 0;
        for(UINT i=0; i<observationSequences.size(); i++){
            loglikelihood = discreteModels[k].predict( observationSequences[i] );
            avgLoglikelihood += fabs( loglikelihood );
        }
        nullRejectionThresholds[k] = -( avgLoglikelihood / double( observationSequences.size() ) );
    }
    
    //Flag that the model has been trained
    trained = true;
    
    return true;
    
}