multiclasscontinuousdistribution.h

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*/

// -*- C++ -*-

#if !defined _CLUS_MULTICLASSCONTINUOUSDISTRIBUTION_H
#define _CLUS_MULTICLASSCONTINUOUSDISTRIBUTION_H

#include "distribution.h"
#include "multiclassdistribution.h"
#include "statfct.h"

using namespace TNT;

namespace CLUS
{

/** The class is a repository of continuous sistributions that each predict
    one of the class labels of a discrete variable.
*/
template< class T_Distribution >
class MulticlassContinuousDistribution : public MulticlassDistribution
{
protected:
    Vector<T_Distribution> distributions;

public:
    /** Constructor.

        @param NoClasses  number of classes
        @param D          a distribution that contains all the parameters
                          the distributions inside the class have.
    */
    MulticlassContinuousDistribution(int NoClasses, T_Distribution& D):
            MulticlassDistribution(NoClasses), distributions(NoClasses,D)
    { }

    /** Infer will use data to produce noClasses normalized probabilities
    into result. 
    */
    virtual void Infer(const double* cdata, const int* ddata, double* result)
    {
        double Coef=0.0;
        for (int i=0; i<noClasses; i++)
        {
            Coef+=distributions[i].Probability(cdata);
        }

        for (int i=0; i<noClasses; i++)
            result[i]=distributions[i].NormalizeProbability(Coef);

    }

    /** MultiplicativeInfer uses data to produce probabilities and
    multiplies these probabilities with the ones in result.
    */
    virtual void MultiplicativeInfer(const double* cdata, const int* ddata, double* result)
    {
        for (int i=0; i<noClasses; i++)
            result[i]*=distributions[i].Probability(cdata);
    }

    /** Prepares the distributions for learning. */
    virtual void StartLearning(void)
    {
        for (int i=0; i<noClasses; i++)
            distributions[i].InitializeStatistics();
    }

    virtual void LearnSample(const double* cdata, const int* ddata, int classLabel, double weightSample=1.0)
    {
        distributions[classLabel].UpdateStatistics(cdata, weightSample);
    }

    virtual void LearnSample(const double* cdata, const int* ddata,
                             double* classProbabilities, double weightSample=1.0)
    {
        for (int i=0; i<noClasses; i++)
            distributions[i].UpdateStatistics(cdata, weightSample*classProbabilities[i]);
    }


    virtual void StopLearning(void)
    {
        for (int i=0; i<noClasses; i++)
            distributions[i].UpdateParameters();
    }

    virtual double PValueStatisticalTest(void);

#ifdef CLUS_USE_XML

    virtual void PrintToXmlStream(ostream& output)
    {
        output << "<MulticlassContinuousDistribution";
        PrintAttribute(output, "typeDistribution", T_Distribution::TypeName());
        output << ">" << endl;

        PrintVectorOfElements(output, distributions);

        output << "</MulticlassContinuousDistribution>" << endl;
    }
#endif

    virtual bool IsClassLabelAbsent(int index)
    {
        return distributions[index].HasZeroWeight();
    }
};

}

#endif // _CLUS_MULTICLASSCONTINUOUSDISTRIBUTION_H

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