C++ WeakClassifier::predict方法代码示例

bool AdaBoost::train_(ClassificationData &trainingData){
    
    //Clear any previous model
    clear();
    
    if( trainingData.getNumSamples() <= 1 ){
        errorLog << "train_(ClassificationData &trainingData) - There are not enough training samples to train a model! Number of samples: " << trainingData.getNumSamples()  << endl;
        return false;
    }
    
    numInputDimensions = trainingData.getNumDimensions();
    numClasses = trainingData.getNumClasses();
    const UINT M = trainingData.getNumSamples();
    const UINT POSITIVE_LABEL = WEAK_CLASSIFIER_POSITIVE_CLASS_LABEL;
    const UINT NEGATIVE_LABEL = WEAK_CLASSIFIER_NEGATIVE_CLASS_LABEL;
    double alpha = 0;
    const double beta = 0.001;
    double epsilon = 0;
    TrainingResult trainingResult;
    
    const UINT K = (UINT)weakClassifiers.size();
    if( K == 0 ){
        errorLog << "train_(ClassificationData &trainingData) - No weakClassifiers have been set. You need to set at least one weak classifier first." << endl;
        return false;
    }

    classLabels.resize(numClasses);
    models.resize(numClasses);
    ranges = trainingData.getRanges();

    //Scale the training data if needed
    if( useScaling ){
        trainingData.scale(ranges,0,1);
    }
    
    //Create the weights vector
    VectorDouble weights(M);
    
    //Create the error matrix
    MatrixDouble errorMatrix(K,M);
    
    for(UINT classIter=0; classIter<numClasses; classIter++){
        
        //Get the class label for the current class
        classLabels[classIter] = trainingData.getClassLabels()[classIter];
        
        //Set the class label of the current model
        models[ classIter ].setClassLabel( classLabels[classIter] );
        
        //Setup the labels for this class, POSITIVE_LABEL == 1, NEGATIVE_LABEL == 2
        ClassificationData classData;
        classData.setNumDimensions(trainingData.getNumDimensions());
        for(UINT i=0; i<M; i++){
            UINT label = trainingData[i].getClassLabel()==classLabels[classIter] ? POSITIVE_LABEL : NEGATIVE_LABEL;
            VectorDouble trainingSample = trainingData[i].getSample();
            classData.addSample(label,trainingSample);
        }
        
        //Setup the initial training sample weights
        std::fill(weights.begin(),weights.end(),1.0/M);
        
        //Run the boosting loop
        bool keepBoosting = true;
        UINT t = 0;
        
        while( keepBoosting ){
            
            //Pick the classifier from the family of classifiers that minimizes the total error
            UINT bestClassifierIndex = 0;
            double minError = numeric_limits<double>::max();
            for(UINT k=0; k<K; k++){
                //Get the k'th possible classifier
                WeakClassifier *weakLearner = weakClassifiers[k];
                
                //Train the current classifier
                if( !weakLearner->train(classData,weights) ){
                    errorLog << "Failed to train weakLearner!" << endl;
                    return false;
                }
                
                //Compute the weighted error for this clasifier
                double e = 0;
                double positiveLabel = weakLearner->getPositiveClassLabel();
                double numCorrect = 0;
                double numIncorrect = 0;
                for(UINT i=0; i<M; i++){
                    //Only penalize errors
                    double prediction = weakLearner->predict( classData[i].getSample() );
                    
                    if( (prediction == positiveLabel && classData[i].getClassLabel() != POSITIVE_LABEL) ||        //False positive
                        (prediction != positiveLabel && classData[i].getClassLabel() == POSITIVE_LABEL) ){       //False negative
                        e += weights[i]; //Increase the error proportional to the weight of the example
                        errorMatrix[k][i] = 1; //Flag that there was an error
                        numIncorrect++;
                    }else{
                        errorMatrix[k][i] = 0; //Flag that there was no error
                        numCorrect++;
                    }
                }
                
//.........这里部分代码省略.........