naivebayesinference.h

来自「gibbs」· C头文件 代码 · 共 82 行

#ifndef NAIVEBAYESINFERENCE_H
#define NAIVEBAYESINFERENCE_H

#include "InferenceMethod.h"
#include <list>
#include "VarSet.h"
#include "Distribution.h"
#include "AbstractionSet.h"
#include "JointAbstractionSet.h"

class NaiveBayesInference : public InferenceMethod
{
private:
    AbstractionSet* model;

    vector<GenDistribution*> marginals;
    JointAbstractionSet* jointModel;
    list<int> jointVars;

    void cleanUp() {

        // Free all memory given to us by the inference methods.
        vector<GenDistribution*>::iterator iter;
        for (iter = marginals.begin(); iter != marginals.end(); iter++) {
            delete (*iter);
        }
        marginals.clear();

        delete jointModel;
        jointModel = NULL;
    }

public:
    // Simple constructor
    NaiveBayesInference(AbstractionSet* m)
        : model(m), jointModel(NULL) { /* NOP */ }

    virtual ~NaiveBayesInference() {
        cleanUp();
    }

    virtual void runMarginalInference(const VarSet& evidence) {

        cleanUp();
        marginals = model->createMarginals(evidence);
    }

    virtual void runJointInference(const list<int>& queryVars,
            const VarSet& evidence) {

        cleanUp();
        jointModel = new JointAbstractionSet(model, evidence);
        jointVars = queryVars;
    }

    virtual double singleConditionalLogProb( const list<int>& queryVars,
            const VarSet& evidence, const VarSet& answer) const { 
        return (model->getLogLikelihood(answer) 
                 - model->getLogLikelihood(evidence));
    }

    virtual int getRange(int i) const { return model->getRange(i); }

    //virtual Distribution getMarginal(int i) const { return marginals[i]; };

    virtual double getMarginalProb(int i, double value) const {
        return marginals[i]->getProb(value);
    }

    virtual double getJointProb(const VarConfig& query) const {
        VarSet newquery(query.getNumVars());
        list<int>::const_iterator iter;
        for (iter = jointVars.begin(); iter != jointVars.end(); iter++) {
            newquery[*iter] = query[*iter];
        }

        return jointModel->getProb(newquery);
    }
};

#endif // ndef NAIVEBAYESINFERENCE_H