C++ LabelledClassificationData::reformatAsUnlabelledClassificationData方法代码示例

bool GMM::train(LabelledClassificationData trainingData){
    
    //Clear any old models
    models.clear();
    trained = false;
    numFeatures = 0;
    numClasses = 0;
    
    if( trainingData.getNumSamples() == 0 ){
        errorLog << "train(LabelledClassificationData &trainingData) - Training data is empty!" << endl;
        return false;
    }
    
    //Set the number of features and number of classes and resize the models buffer
    numFeatures = trainingData.getNumDimensions();
    numClasses = trainingData.getNumClasses();
    models.resize(numClasses);
    
    if( numFeatures >= 6 ){
        warningLog << "train(LabelledClassificationData &trainingData) - The number of features in your training data is high (" << numFeatures << ").  The GMMClassifier does not work well with high dimensional data, you might get better results from one of the other classifiers." << endl;
    }
    
    //Get the ranges of the training data if the training data is going to be scaled
    if( useScaling ){
        ranges = trainingData.getRanges();
    }

    //Fit a Mixture Model to each class (independently)
    for(UINT k=0; k<numClasses; k++){
        UINT classLabel = trainingData.getClassTracker()[k].classLabel;
        LabelledClassificationData classData = trainingData.getClassData( classLabel );
        
        //Scale the training data if needed
        if( useScaling ){
            if( !classData.scale(ranges,GMM_MIN_SCALE_VALUE, GMM_MAX_SCALE_VALUE) ){
                errorLog << "train(LabelledClassificationData &trainingData) - Failed to scale training data!" << endl;
                return false;

            }
        }
        
        //Convert the labelled data to unlabelled data
        UnlabelledClassificationData unlabelledData = classData.reformatAsUnlabelledClassificationData();
        
        //Train the Mixture Model for this class
        GaussianMixtureModels gaussianMixtureModel;
        gaussianMixtureModel.setMinChange( minChange );
        gaussianMixtureModel.setMaxIter( maxIter );
        if( !gaussianMixtureModel.train(unlabelledData, numMixtureModels) ){
            errorLog << "train(LabelledClassificationData &trainingData) - Failed to train Mixture Model for class " << classLabel << endl;
            return false;
        }
        
        //Setup the model container
        models[k].resize( numMixtureModels );
        models[k].setClassLabel( classLabel );
        
        //Store the mixture model in the container
        for(UINT j=0; j<numMixtureModels; j++){
            models[k][j].mu = gaussianMixtureModel.getMu().getRowVector(j);
            models[k][j].sigma = gaussianMixtureModel.getSigma()[j];
            
            //Compute the determinant and invSigma for the realtime prediction
            LUDecomposition ludcmp(models[k][j].sigma);
            if( !ludcmp.inverse( models[k][j].invSigma ) ){
                models.clear();
                errorLog << "train(LabelledClassificationData &trainingData) - Failed to invert Matrix for class " << classLabel << "!" << endl;
                return false;
            }
            models[k][j].det = ludcmp.det();
        }
        
        //Compute the normalize factor
        models[k].recomputeNormalizationFactor();
        
        //Compute the rejection thresholds
        double mu = 0;
        double sigma = 0;
        VectorDouble predictionResults(classData.getNumSamples(),0);
        for(UINT i=0; i<classData.getNumSamples(); i++){
            vector< double > sample = classData[i].getSample();
            predictionResults[i] = models[k].computeMixtureLikelihood( sample );
            mu += predictionResults[i];
        }
        
        //Update mu
        mu /= double( classData.getNumSamples() );
        
        //Calculate the standard deviation
        for(UINT i=0; i<classData.getNumSamples(); i++) 
            sigma += SQR( (predictionResults[i]-mu) );
        sigma = sqrt( sigma / (double(classData.getNumSamples())-1.0) );
        sigma = 0.2;
        
        //Set the models training mu and sigma 
        models[k].setTrainingMuAndSigma(mu,sigma);
        
        if( !models[k].recomputeNullRejectionThreshold(nullRejectionCoeff) && useNullRejection ){
            warningLog << "train(LabelledClassificationData &trainingData) - Failed to recompute rejection threshold for class " << classLabel << " - the nullRjectionCoeff value is too high!" << endl;
        }
//.........这里部分代码省略.........