#ifndef MARKOVNET_H#define MARKOVNET_H/* Definition for the MarkovNet class, representing a pairwise Markov  * random field.  Includes an implementation of belief propagation. * * Daniel Lowd <lowd@cs.washington.edu> */#include "Potential.h"#include "Distribution.h"#include "VarSet.h"#include "BayesNet.h"#include <list>class Edge;class Node{public:    int index;    list<Edge*> edges;    int fixedValue;    Distribution marginal;    Distribution phi;    Node() { /* NOP */ }};class Edge{public:    Node* n1;    Node* n2;    Distribution message1to2;    Distribution message2to1;    Potential psi;    Edge() { /* NOP */ }    Distribution& msgTo(Node* n) {        return (n == n1) ? message2to1 : message1to2;    }    Distribution& msgFrom(Node* n) {        return (n == n2) ? message2to1 : message1to2;    }    Node* otherNode(Node* n) {        return (n == n1) ? n2 : n1;    }    void sendMsg(Node* n, Distribution& message) {        if (n == n1) {            message1to2 = message * psi;            message1to2.normalize();        } else {            message2to1 = psi * message;            message2to1.normalize();        }    }};class MarkovNet{    // HACK: maybe this ought to be a vector?    Node* nodes;    int numNodes;    list<Edge> edges;public:    // Convert a Bayesian network to a pairwise Markov random field    MarkovNet(const BayesNet& bn);    ~MarkovNet() { delete [] nodes; }    void fixNodeValue(int nodeIndex, int value)    {        nodes[nodeIndex].fixedValue = value;        for (int i = 0; i < nodes[nodeIndex].marginal.dim(); i++) {            nodes[nodeIndex].marginal[i] = ((i == value) ? 1 : 0);        }    }    void resetNode(int nodeIndex)    { nodes[nodeIndex].fixedValue = -1; }    void resetAllNodes()    {        for (int i = 0; i < numNodes; i++) {            resetNode(i);        }    }    void resetAllMessages()    {        for (int i = 0; i < numNodes; i++) {            // Reset all messages in one direction to uniform            list<Edge*>::iterator e;            for (e = nodes[i].edges.begin(); e != nodes[i].edges.end(); e++) {                (*e)->message1to2 = Distribution((*e)->n2->marginal.dim());                (*e)->message2to1 = Distribution((*e)->n2->marginal.dim());            }        }    }    Distribution getMarginal(int nodeIndex) const    { return nodes[nodeIndex].marginal; }    double getLikelihood(VarSet query, VarSet evidence,            double threshold, double damping);    void runBP(double convergenceThreshold, double dampingFactor);private:    double BPiter(double dampingFactor);    void addEdge(Node* n1, Node* n2, Potential& psi);};#endif // ndef MARKOVNET_H