#ifndef BAYESNET_H#define BAYESNET_H#include <expat.h>#include <list>#include <vector>#include "VarSchema.h"#include "DecisionTree.h"#include "Range.h"class BayesNet{    vector<list<int> > parents;    vector<list<int> > children;    vector<DecisionTree*> decisionTrees;    int numVars;public:    BayesNet() : numVars(0) { /* NOP */ }    ~BayesNet() {        for (int i = 0; i < numVars; i++) {            delete decisionTrees[i];        }    }    // Construct a schema for caller    VarSchema getSchema() const {        VarSchema s(-1, numVars);        for (int i = 0; i < numVars; i++) {            s[i] = getRange(i);        }        return s;    }    int getNumVars() const { return numVars; }    int getRange(int var) const { return decisionTrees[var]->getRange(); }    void addVar(int numStates) {        children.push_back(list<int>());        parents.push_back(list<int>());        decisionTrees.push_back(new DecisionTree(numVars, numStates));        numVars++;    }    void addChild(int parent, int child) {        // DEBUG        if (parent >= numVars) {            cout << "ERROR: parent index out of range (" << parent                 << " >= " << numVars << ")\n";            return;        }        // DEBUG        if (child >= numVars) {            cout << "ERROR: child index out of range (" << child                 << " >= " << numVars << ")\n";            return;        }        children[parent].push_back(child);        parents[child].push_back(parent);        decisionTrees[child]->addInput(parent);    }    DecisionTree* getDecisionTree(int var) {        return decisionTrees[var];    }    const DecisionTree* getDecisionTree(int var) const {        return decisionTrees[var];    }    void setDecisionTree(int var, DecisionTree* dtree) {        // DEBUG        //cout << "Setting decision tree for " << var << endl;        delete decisionTrees[var];        decisionTrees[var] = dtree;    }    double getProb(int var, double* allVars) const {        // DEBUG        double p = decisionTrees[var]->getProb(allVars[var], allVars);        if (p == 0.0) {            cout << "ERROR: getProb returning 0.0!\n";        }        return p;    }    double getLogProb(int var, double* allVars) const {        double lp = decisionTrees[var]->getLogProb(allVars[var], allVars);        return lp;    }    // Get standard deviation of continuous variable, given    // values of its parents.    double getSD(int var, double* allVars) const {        const Leaf* l = decisionTrees[var]->getLeaf(allVars);        const BinGaussian* bg = (const BinGaussian*)l;        return bg->SD;    }    // Sample from the variable's parent    double sample(int var, double* allVars) const {        return decisionTrees[var]->sample(allVars);    }    // Sample from the variable's Markov blanket    vector<double> MBdist(int var, VarSet& allVars) const;    // Sample from the variable's Markov blanket    double MBsample(int var, VarSet& allVars) const;    // Generate a complete sample    void wholeSample(VarSet& allvars) const; private:    // Disable copy-constructor    BayesNet(const BayesNet& other);    friend class MarkovNet;};// Load a model from a file using C-style I/O.#include <stdio.h>void loadModel(FILE* in, BayesNet& model);istream& operator>>(istream& in, BayesNet& model);#endif // ndef BAYESNET_H