659 lines
16 KiB
C++
659 lines
16 KiB
C++
#include <cstdlib>
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#include <cassert>
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#include <iostream>
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#include <fstream>
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#include <sstream>
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#include <iomanip>
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#include "xmlParser/xmlParser.h"
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#include "BayesNet.h"
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BayesNet::BayesNet (const char* fileName)
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{
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map<string, Domain> domains;
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XMLNode xMainNode = XMLNode::openFileHelper (fileName, "BIF");
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// only the first network is parsed, others are ignored
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XMLNode xNode = xMainNode.getChildNode ("NETWORK");
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unsigned nVars = xNode.nChildNode ("VARIABLE");
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for (unsigned i = 0; i < nVars; i++) {
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XMLNode var = xNode.getChildNode ("VARIABLE", i);
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string type = var.getAttribute ("TYPE");
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if (type != "nature") {
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cerr << "error: only \"nature\" variables are supported" << endl;
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abort();
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}
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Domain domain;
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string varLabel = var.getChildNode("NAME").getText();
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unsigned dsize = var.nChildNode ("OUTCOME");
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for (unsigned j = 0; j < dsize; j++) {
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if (var.getChildNode("OUTCOME", j).getText() == 0) {
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stringstream ss;
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ss << j + 1;
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domain.push_back (ss.str());
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} else {
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domain.push_back (var.getChildNode("OUTCOME", j).getText());
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}
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}
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domains.insert (make_pair (varLabel, domain));
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}
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unsigned nDefs = xNode.nChildNode ("DEFINITION");
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if (nVars != nDefs) {
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cerr << "error: different number of variables and definitions" << endl;
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abort();
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}
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queue<unsigned> indexes;
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for (unsigned i = 0; i < nDefs; i++) {
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indexes.push (i);
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}
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while (!indexes.empty()) {
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unsigned index = indexes.front();
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indexes.pop();
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XMLNode def = xNode.getChildNode ("DEFINITION", index);
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string varLabel = def.getChildNode("FOR").getText();
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map<string, Domain>::const_iterator iter;
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iter = domains.find (varLabel);
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if (iter == domains.end()) {
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cerr << "error: unknow variable `" << varLabel << "'" << endl;
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abort();
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}
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bool processItLatter = false;
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BnNodeSet parents;
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unsigned nParams = iter->second.size();
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for (int j = 0; j < def.nChildNode ("GIVEN"); j++) {
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string parentLabel = def.getChildNode("GIVEN", j).getText();
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BayesNode* parentNode = getBayesNode (parentLabel);
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if (parentNode) {
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nParams *= parentNode->getDomainSize();
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parents.push_back (parentNode);
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}
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else {
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iter = domains.find (parentLabel);
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if (iter == domains.end()) {
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cerr << "error: unknow parent `" << parentLabel << "'" << endl;
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abort();
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} else {
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// this definition contains a parent that doesn't
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// have a corresponding bayesian node instance yet,
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// so process this definition latter
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indexes.push (index);
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processItLatter = true;
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break;
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}
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}
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}
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if (!processItLatter) {
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unsigned count = 0;
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ParamSet params (nParams);
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stringstream s (def.getChildNode("TABLE").getText());
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while (!s.eof() && count < nParams) {
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s >> params[count];
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count ++;
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}
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if (count != nParams) {
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cerr << "error: invalid number of parameters " ;
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cerr << "for variable `" << varLabel << "'" << endl;
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abort();
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}
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params = reorderParameters (params, iter->second.size());
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addNode (varLabel, iter->second, parents, params);
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}
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}
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setIndexes();
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}
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BayesNet::~BayesNet (void)
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{
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for (unsigned i = 0; i < nodes_.size(); i++) {
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delete nodes_[i];
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}
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}
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BayesNode*
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BayesNet::addNode (Vid vid)
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{
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indexMap_.insert (make_pair (vid, nodes_.size()));
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nodes_.push_back (new BayesNode (vid));
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return nodes_.back();
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}
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BayesNode*
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BayesNet::addNode (Vid vid,
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unsigned dsize,
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int evidence,
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BnNodeSet& parents,
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Distribution* dist)
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{
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indexMap_.insert (make_pair (vid, nodes_.size()));
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nodes_.push_back (new BayesNode (
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vid, dsize, evidence, parents, dist));
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return nodes_.back();
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}
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BayesNode*
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BayesNet::addNode (string label,
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Domain domain,
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BnNodeSet& parents,
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ParamSet& params)
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{
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indexMap_.insert (make_pair (nodes_.size(), nodes_.size()));
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Distribution* dist = new Distribution (params);
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BayesNode* node = new BayesNode (
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nodes_.size(), label, domain, parents, dist);
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dists_.push_back (dist);
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nodes_.push_back (node);
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return node;
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}
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BayesNode*
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BayesNet::getBayesNode (Vid vid) const
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{
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IndexMap::const_iterator it = indexMap_.find (vid);
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if (it == indexMap_.end()) {
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return 0;
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} else {
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return nodes_[it->second];
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}
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}
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BayesNode*
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BayesNet::getBayesNode (string label) const
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{
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BayesNode* node = 0;
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for (unsigned i = 0; i < nodes_.size(); i++) {
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if (nodes_[i]->getLabel() == label) {
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node = nodes_[i];
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break;
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}
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}
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return node;
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}
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Variable*
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BayesNet::getVariable (Vid vid) const
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{
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return getBayesNode (vid);
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}
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void
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BayesNet::addDistribution (Distribution* dist)
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{
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dists_.push_back (dist);
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}
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Distribution*
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BayesNet::getDistribution (unsigned distId) const
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{
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Distribution* dist = 0;
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for (unsigned i = 0; i < dists_.size(); i++) {
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if (dists_[i]->id == distId) {
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dist = dists_[i];
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break;
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}
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}
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return dist;
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}
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const BnNodeSet&
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BayesNet::getBayesNodes (void) const
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{
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return nodes_;
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}
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unsigned
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BayesNet::getNumberOfNodes (void) const
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{
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return nodes_.size();
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}
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BnNodeSet
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BayesNet::getRootNodes (void) const
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{
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BnNodeSet roots;
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for (unsigned i = 0; i < nodes_.size(); i++) {
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if (nodes_[i]->isRoot()) {
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roots.push_back (nodes_[i]);
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}
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}
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return roots;
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}
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BnNodeSet
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BayesNet::getLeafNodes (void) const
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{
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BnNodeSet leafs;
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for (unsigned i = 0; i < nodes_.size(); i++) {
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if (nodes_[i]->isLeaf()) {
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leafs.push_back (nodes_[i]);
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}
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}
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return leafs;
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}
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VarSet
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BayesNet::getVariables (void) const
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{
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VarSet vars;
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for (unsigned i = 0; i < nodes_.size(); i++) {
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vars.push_back (nodes_[i]);
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}
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return vars;
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}
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BayesNet*
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BayesNet::getMinimalRequesiteNetwork (Vid vid) const
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{
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return getMinimalRequesiteNetwork (VidSet() = {vid});
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}
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BayesNet*
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BayesNet::getMinimalRequesiteNetwork (const VidSet& queryVids) const
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{
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BnNodeSet queryVars;
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for (unsigned i = 0; i < queryVids.size(); i++) {
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assert (getBayesNode (queryVids[i]));
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queryVars.push_back (getBayesNode (queryVids[i]));
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}
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// cout << "query vars: " ;
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// for (unsigned i = 0; i < queryVars.size(); i++) {
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// cout << queryVars[i]->getLabel() << " " ;
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// }
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// cout << endl;
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vector<StateInfo*> states (nodes_.size(), 0);
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Scheduling scheduling;
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for (BnNodeSet::const_iterator it = queryVars.begin();
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it != queryVars.end(); it++) {
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scheduling.push (ScheduleInfo (*it, false, true));
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}
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while (!scheduling.empty()) {
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ScheduleInfo& sch = scheduling.front();
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StateInfo* state = states[sch.node->getIndex()];
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if (!state) {
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state = new StateInfo();
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states[sch.node->getIndex()] = state;
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} else {
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state->visited = true;
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}
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if (!sch.node->hasEvidence() && sch.visitedFromChild) {
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if (!state->markedOnTop) {
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state->markedOnTop = true;
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scheduleParents (sch.node, scheduling);
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}
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if (!state->markedOnBottom) {
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state->markedOnBottom = true;
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scheduleChilds (sch.node, scheduling);
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}
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}
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if (sch.visitedFromParent) {
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if (sch.node->hasEvidence() && !state->markedOnTop) {
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state->markedOnTop = true;
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scheduleParents (sch.node, scheduling);
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}
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if (!sch.node->hasEvidence() && !state->markedOnBottom) {
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state->markedOnBottom = true;
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scheduleChilds (sch.node, scheduling);
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}
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}
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scheduling.pop();
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}
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/*
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cout << "\t\ttop\tbottom" << endl;
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cout << "variable\t\tmarked\tmarked\tvisited\tobserved" << endl;
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cout << "----------------------------------------------------------" ;
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cout << endl;
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for (unsigned i = 0; i < states.size(); i++) {
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cout << nodes_[i]->getLabel() << ":\t\t" ;
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if (states[i]) {
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states[i]->markedOnTop ? cout << "yes\t" : cout << "no\t" ;
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states[i]->markedOnBottom ? cout << "yes\t" : cout << "no\t" ;
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states[i]->visited ? cout << "yes\t" : cout << "no\t" ;
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nodes_[i]->hasEvidence() ? cout << "yes" : cout << "no" ;
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cout << endl;
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} else {
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cout << "no\tno\tno\t" ;
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nodes_[i]->hasEvidence() ? cout << "yes" : cout << "no" ;
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cout << endl;
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}
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}
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cout << endl;
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*/
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BayesNet* bn = new BayesNet();
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constructGraph (bn, states);
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for (unsigned i = 0; i < nodes_.size(); i++) {
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delete states[i];
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}
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return bn;
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}
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void
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BayesNet::constructGraph (BayesNet* bn,
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const vector<StateInfo*>& states) const
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{
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for (unsigned i = 0; i < nodes_.size(); i++) {
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bool isRequired = false;
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if (states[i]) {
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isRequired = (nodes_[i]->hasEvidence() && states[i]->visited)
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||
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states[i]->markedOnTop;
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}
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if (isRequired) {
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BnNodeSet parents;
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if (states[i]->markedOnTop) {
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const BnNodeSet& ps = nodes_[i]->getParents();
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for (unsigned j = 0; j < ps.size(); j++) {
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BayesNode* parent = bn->getBayesNode (ps[j]->getVarId());
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if (!parent) {
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parent = bn->addNode (ps[j]->getVarId());
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}
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parents.push_back (parent);
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}
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}
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BayesNode* node = bn->getBayesNode (nodes_[i]->getVarId());
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if (node) {
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node->setData (nodes_[i]->getDomainSize(),
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nodes_[i]->getEvidence(), parents,
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nodes_[i]->getDistribution());
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} else {
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node = bn->addNode (nodes_[i]->getVarId(),
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nodes_[i]->getDomainSize(),
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nodes_[i]->getEvidence(), parents,
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nodes_[i]->getDistribution());
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}
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if (nodes_[i]->hasDomain()) {
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node->setDomain (nodes_[i]->getDomain());
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}
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if (nodes_[i]->hasLabel()) {
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node->setLabel (nodes_[i]->getLabel());
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}
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}
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}
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bn->setIndexes();
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}
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bool
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BayesNet::isSingleConnected (void) const
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{
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return !containsUndirectedCycle();
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}
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void
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BayesNet::setIndexes (void)
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{
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for (unsigned i = 0; i < nodes_.size(); i++) {
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nodes_[i]->setIndex (i);
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}
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}
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void
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BayesNet::freeDistributions (void)
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{
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for (unsigned i = 0; i < dists_.size(); i++) {
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delete dists_[i];
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}
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}
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void
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BayesNet::printGraphicalModel (void) const
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{
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for (unsigned i = 0; i < nodes_.size(); i++) {
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cout << *nodes_[i];
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}
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}
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void
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BayesNet::exportToDotFormat (const char* fileName,
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bool showNeighborless,
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CVidSet& highlightVids) const
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{
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ofstream out (fileName);
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if (!out.is_open()) {
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cerr << "error: cannot open file to write at " ;
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cerr << "BayesNet::exportToDotFile()" << endl;
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abort();
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}
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out << "digraph \"" << fileName << "\" {" << endl;
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for (unsigned i = 0; i < nodes_.size(); i++) {
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if (showNeighborless || nodes_[i]->hasNeighbors()) {
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out << '"' << nodes_[i]->getLabel() << '"' ;
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if (nodes_[i]->hasEvidence()) {
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out << " [style=filled, fillcolor=yellow]" << endl;
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} else {
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out << endl;
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}
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}
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}
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for (unsigned i = 0; i < highlightVids.size(); i++) {
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BayesNode* node = getBayesNode (highlightVids[i]);
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if (node) {
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out << '"' << node->getLabel() << '"' ;
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// out << " [shape=polygon, sides=6]" << endl;
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out << " [shape=box3d]" << endl;
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} else {
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cout << "error: invalid variable id: " << highlightVids[i] << endl;
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abort();
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}
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}
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for (unsigned i = 0; i < nodes_.size(); i++) {
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const BnNodeSet& childs = nodes_[i]->getChilds();
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for (unsigned j = 0; j < childs.size(); j++) {
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out << '"' << nodes_[i]->getLabel() << '"' << " -> " ;
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out << '"' << childs[j]->getLabel() << '"' << endl;
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}
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}
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out << "}" << endl;
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out.close();
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}
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void
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BayesNet::exportToBifFormat (const char* fileName) const
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{
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ofstream out (fileName);
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if(!out.is_open()) {
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cerr << "error: cannot open file to write at " ;
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cerr << "BayesNet::exportToBifFile()" << endl;
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abort();
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}
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out << "<?xml version=\"1.0\" encoding=\"US-ASCII\"?>" << endl;
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out << "<BIF VERSION=\"0.3\">" << endl;
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out << "<NETWORK>" << endl;
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out << "<NAME>" << fileName << "</NAME>" << endl << endl;
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for (unsigned i = 0; i < nodes_.size(); i++) {
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out << "<VARIABLE TYPE=\"nature\">" << endl;
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out << "\t<NAME>" << nodes_[i]->getLabel() << "</NAME>" << endl;
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const Domain& domain = nodes_[i]->getDomain();
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for (unsigned j = 0; j < domain.size(); j++) {
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out << "\t<OUTCOME>" << domain[j] << "</OUTCOME>" << endl;
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}
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out << "</VARIABLE>" << endl << endl;
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}
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for (unsigned i = 0; i < nodes_.size(); i++) {
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out << "<DEFINITION>" << endl;
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out << "\t<FOR>" << nodes_[i]->getLabel() << "</FOR>" << endl;
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const BnNodeSet& parents = nodes_[i]->getParents();
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for (unsigned j = 0; j < parents.size(); j++) {
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out << "\t<GIVEN>" << parents[j]->getLabel();
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out << "</GIVEN>" << endl;
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}
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ParamSet params = revertParameterReorder (nodes_[i]->getParameters(),
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nodes_[i]->getDomainSize());
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out << "\t<TABLE>" ;
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for (unsigned j = 0; j < params.size(); j++) {
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out << " " << params[j];
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}
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out << " </TABLE>" << endl;
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out << "</DEFINITION>" << endl << endl;
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}
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out << "</NETWORK>" << endl;
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out << "</BIF>" << endl << endl;
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out.close();
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}
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bool
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BayesNet::containsUndirectedCycle (void) const
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{
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vector<bool> visited (nodes_.size(), false);
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for (unsigned i = 0; i < nodes_.size(); i++) {
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int v = nodes_[i]->getIndex();
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if (!visited[v]) {
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if (containsUndirectedCycle (v, -1, visited)) {
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return true;
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}
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}
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}
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return false;
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}
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bool
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BayesNet::containsUndirectedCycle (int v,
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int p,
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|
vector<bool>& visited) const
|
|
{
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|
visited[v] = true;
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|
vector<int> adjacencies = getAdjacentNodes (v);
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|
for (unsigned i = 0; i < adjacencies.size(); i++) {
|
|
int w = adjacencies[i];
|
|
if (!visited[w]) {
|
|
if (containsUndirectedCycle (w, v, visited)) {
|
|
return true;
|
|
}
|
|
}
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|
else if (visited[w] && w != p) {
|
|
return true;
|
|
}
|
|
}
|
|
return false; // no cycle detected in this component
|
|
}
|
|
|
|
|
|
|
|
vector<int>
|
|
BayesNet::getAdjacentNodes (int v) const
|
|
{
|
|
vector<int> adjacencies;
|
|
const BnNodeSet& parents = nodes_[v]->getParents();
|
|
const BnNodeSet& childs = nodes_[v]->getChilds();
|
|
for (unsigned i = 0; i < parents.size(); i++) {
|
|
adjacencies.push_back (parents[i]->getIndex());
|
|
}
|
|
for (unsigned i = 0; i < childs.size(); i++) {
|
|
adjacencies.push_back (childs[i]->getIndex());
|
|
}
|
|
return adjacencies;
|
|
}
|
|
|
|
|
|
|
|
ParamSet
|
|
BayesNet::reorderParameters (CParamSet params,
|
|
unsigned domainSize) const
|
|
{
|
|
// the interchange format for bayesian networks keeps the probabilities
|
|
// in the following order:
|
|
// p(a1|b1,c1) p(a2|b1,c1) p(a1|b1,c2) p(a2|b1,c2) p(a1|b2,c1) p(a2|b2,c1)
|
|
// p(a1|b2,c2) p(a2|b2,c2).
|
|
//
|
|
// however, in clpbn we keep the probabilities in this order:
|
|
// p(a1|b1,c1) p(a1|b1,c2) p(a1|b2,c1) p(a1|b2,c2) p(a2|b1,c1) p(a2|b1,c2)
|
|
// p(a2|b2,c1) p(a2|b2,c2).
|
|
unsigned count = 0;
|
|
unsigned rowSize = params.size() / domainSize;
|
|
ParamSet reordered;
|
|
while (reordered.size() < params.size()) {
|
|
unsigned idx = count;
|
|
for (unsigned i = 0; i < rowSize; i++) {
|
|
reordered.push_back (params[idx]);
|
|
idx += domainSize;
|
|
}
|
|
count++;
|
|
}
|
|
return reordered;
|
|
}
|
|
|
|
|
|
|
|
ParamSet
|
|
BayesNet::revertParameterReorder (CParamSet params,
|
|
unsigned domainSize) const
|
|
{
|
|
unsigned count = 0;
|
|
unsigned rowSize = params.size() / domainSize;
|
|
ParamSet reordered;
|
|
while (reordered.size() < params.size()) {
|
|
unsigned idx = count;
|
|
for (unsigned i = 0; i < domainSize; i++) {
|
|
reordered.push_back (params[idx]);
|
|
idx += rowSize;
|
|
}
|
|
count ++;
|
|
}
|
|
return reordered;
|
|
}
|
|
|