793 lines
19 KiB
C++
793 lines
19 KiB
C++
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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 <cassert>
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#include <cstdlib>
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#include <map>
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#include "xmlParser/xmlParser.h"
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#include "BayesNet.h"
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BayesNet::BayesNet (void)
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{
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}
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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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int nVars = xNode.nChildNode ("VARIABLE");
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for (int 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 label = var.getChildNode("NAME").getText();
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int domainSize = var.nChildNode ("OUTCOME");
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for (int j = 0; j < domainSize; 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 (label, domain));
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}
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int nDefs = xNode.nChildNode ("DEFINITION");
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if (nVars != nDefs) {
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cerr << "error: different number of variables and definitions";
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cerr << endl;
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}
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queue<int> indexes;
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for (int 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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int index = indexes.front();
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indexes.pop();
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XMLNode def = xNode.getChildNode ("DEFINITION", index);
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string label = def.getChildNode("FOR").getText();
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map<string, Domain>::const_iterator iter;
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iter = domains.find (label);
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if (iter == domains.end()) {
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cerr << "error: unknow variable `" << label << "'" << endl;
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abort();
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}
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bool processItLatter = false;
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NodeSet parents;
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int 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 = getNode (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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int 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 `" << label << "'" << 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 (label, 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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Statistics::writeStats();
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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 (unsigned varId)
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{
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indexMap_.insert (make_pair (varId, nodes_.size()));
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nodes_.push_back (new BayesNode (varId));
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return nodes_.back();
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}
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BayesNode*
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BayesNet::addNode (unsigned varId,
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unsigned dsize,
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int evidence,
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NodeSet& parents,
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Distribution* dist)
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{
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indexMap_.insert (make_pair (varId, nodes_.size()));
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nodes_.push_back (new BayesNode (
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varId, 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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NodeSet& 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::getNode (unsigned varId) const
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{
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IndexMap::const_iterator it = indexMap_.find(varId);
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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::getNode (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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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 NodeSet&
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BayesNet::getNodes (void) const
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{
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return nodes_;
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}
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int
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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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NodeSet
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BayesNet::getRootNodes (void) const
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{
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NodeSet 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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NodeSet
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BayesNet::getLeafNodes (void) const
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{
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NodeSet 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::pruneNetwork (BayesNode* queryNode) const
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{
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NodeSet queryNodes;
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queryNodes.push_back (queryNode);
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return pruneNetwork (queryNodes);
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}
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BayesNet*
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BayesNet::pruneNetwork (const NodeSet& interestedVars) const
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{
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/*
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cout << "interested vars: " ;
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for (unsigned i = 0; i < interestedVars.size(); i++) {
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cout << interestedVars[i]->getLabel() << " " ;
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}
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cout << endl;
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*/
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vector<StateInfo*> states (nodes_.size(), 0);
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Scheduling scheduling;
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for (NodeSet::const_iterator it = interestedVars.begin();
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it != interestedVars.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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states[i]->markedOnTop;
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}
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if (isRequired) {
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NodeSet parents;
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if (states[i]->markedOnTop) {
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const NodeSet& ps = nodes_[i]->getParents();
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for (unsigned j = 0; j < ps.size(); j++) {
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BayesNode* parent = bn->getNode (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->getNode (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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|
|
}
|
||
|
|
bn->setIndexes();
|
||
|
|
}
|
||
|
|
|
||
|
|
/*
|
||
|
|
void
|
||
|
|
BayesNet::constructGraph (BayesNet* bn,
|
||
|
|
const vector<StateInfo*>& states) const
|
||
|
|
{
|
||
|
|
for (unsigned i = 0; i < nodes_.size(); i++) {
|
||
|
|
if (states[i]) {
|
||
|
|
if (nodes_[i]->hasEvidence() && states[i]->visited) {
|
||
|
|
NodeSet parents;
|
||
|
|
if (states[i]->markedOnTop) {
|
||
|
|
const NodeSet& ps = nodes_[i]->getParents();
|
||
|
|
for (unsigned j = 0; j < ps.size(); j++) {
|
||
|
|
BayesNode* parent = bn->getNode (ps[j]->getVarId());
|
||
|
|
if (parent == 0) {
|
||
|
|
parent = bn->addNode (ps[j]->getVarId());
|
||
|
|
}
|
||
|
|
parents.push_back (parent);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
BayesNode* n = bn->getNode (nodes_[i]->getVarId());
|
||
|
|
if (n) {
|
||
|
|
n->setData (nodes_[i]->getDomainSize(),
|
||
|
|
nodes_[i]->getEvidence(), parents,
|
||
|
|
nodes_[i]->getDistribution());
|
||
|
|
} else {
|
||
|
|
bn->addNode (nodes_[i]->getVarId(),
|
||
|
|
nodes_[i]->getDomainSize(),
|
||
|
|
nodes_[i]->getEvidence(), parents,
|
||
|
|
nodes_[i]->getDistribution());
|
||
|
|
}
|
||
|
|
|
||
|
|
} else if (states[i]->markedOnTop) {
|
||
|
|
NodeSet parents;
|
||
|
|
const NodeSet& ps = nodes_[i]->getParents();
|
||
|
|
for (unsigned j = 0; j < ps.size(); j++) {
|
||
|
|
BayesNode* parent = bn->getNode (ps[j]->getVarId());
|
||
|
|
if (parent == 0) {
|
||
|
|
parent = bn->addNode (ps[j]->getVarId());
|
||
|
|
}
|
||
|
|
parents.push_back (parent);
|
||
|
|
}
|
||
|
|
|
||
|
|
BayesNode* n = bn->getNode (nodes_[i]->getVarId());
|
||
|
|
if (n) {
|
||
|
|
n->setData (nodes_[i]->getDomainSize(),
|
||
|
|
nodes_[i]->getEvidence(), parents,
|
||
|
|
nodes_[i]->getDistribution());
|
||
|
|
} else {
|
||
|
|
bn->addNode (nodes_[i]->getVarId(),
|
||
|
|
nodes_[i]->getDomainSize(),
|
||
|
|
nodes_[i]->getEvidence(), parents,
|
||
|
|
nodes_[i]->getDistribution());
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}*/
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
bool
|
||
|
|
BayesNet::isSingleConnected (void) const
|
||
|
|
{
|
||
|
|
return !containsUndirectedCycle();
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
vector<DomainConf>
|
||
|
|
BayesNet::getDomainConfigurationsOf (const NodeSet& nodes)
|
||
|
|
{
|
||
|
|
int nConfs = 1;
|
||
|
|
for (unsigned i = 0; i < nodes.size(); i++) {
|
||
|
|
nConfs *= nodes[i]->getDomainSize();
|
||
|
|
}
|
||
|
|
|
||
|
|
vector<DomainConf> confs (nConfs);
|
||
|
|
for (int i = 0; i < nConfs; i++) {
|
||
|
|
confs[i].resize (nodes.size());
|
||
|
|
}
|
||
|
|
|
||
|
|
int nReps = 1;
|
||
|
|
for (int i = nodes.size() - 1; i >= 0; i--) {
|
||
|
|
int index = 0;
|
||
|
|
while (index < nConfs) {
|
||
|
|
for (int j = 0; j < nodes[i]->getDomainSize(); j++) {
|
||
|
|
for (int r = 0; r < nReps; r++) {
|
||
|
|
confs[index][i] = j;
|
||
|
|
index++;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
nReps *= nodes[i]->getDomainSize();
|
||
|
|
}
|
||
|
|
|
||
|
|
return confs;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
vector<string>
|
||
|
|
BayesNet::getInstantiations (const NodeSet& parents_)
|
||
|
|
{
|
||
|
|
int nParents = parents_.size();
|
||
|
|
int rowSize = 1;
|
||
|
|
for (unsigned i = 0; i < parents_.size(); i++) {
|
||
|
|
rowSize *= parents_[i]->getDomainSize();
|
||
|
|
}
|
||
|
|
int nReps = 1;
|
||
|
|
vector<string> headers (rowSize);
|
||
|
|
for (int i = nParents - 1; i >= 0; i--) {
|
||
|
|
Domain domain = parents_[i]->getDomain();
|
||
|
|
int index = 0;
|
||
|
|
while (index < rowSize) {
|
||
|
|
for (int j = 0; j < parents_[i]->getDomainSize(); j++) {
|
||
|
|
for (int r = 0; r < nReps; r++) {
|
||
|
|
if (headers[index] != "") {
|
||
|
|
headers[index] = domain[j] + "," + headers[index];
|
||
|
|
} else {
|
||
|
|
headers[index] = domain[j];
|
||
|
|
}
|
||
|
|
index++;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
nReps *= parents_[i]->getDomainSize();
|
||
|
|
}
|
||
|
|
return headers;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
void
|
||
|
|
BayesNet::setIndexes (void)
|
||
|
|
{
|
||
|
|
for (unsigned i = 0; i < nodes_.size(); i++) {
|
||
|
|
nodes_[i]->setIndex (i);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
void
|
||
|
|
BayesNet::freeDistributions (void)
|
||
|
|
{
|
||
|
|
for (unsigned i = 0; i < dists_.size(); i++) {
|
||
|
|
delete dists_[i];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
void
|
||
|
|
BayesNet::printNetwork (void) const
|
||
|
|
{
|
||
|
|
for (unsigned i = 0; i < nodes_.size(); i++) {
|
||
|
|
cout << *nodes_[i];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
void
|
||
|
|
BayesNet::printNetworkToFile (const char* fileName) const
|
||
|
|
{
|
||
|
|
string s = "../../" ;
|
||
|
|
s += fileName;
|
||
|
|
ofstream out (s.c_str());
|
||
|
|
if (!out.is_open()) {
|
||
|
|
cerr << "error: cannot open file to write at " ;
|
||
|
|
cerr << "BayesNet::printToFile()" << endl;
|
||
|
|
abort();
|
||
|
|
}
|
||
|
|
for (unsigned i = 0; i < nodes_.size(); i++) {
|
||
|
|
out << *nodes_[i];
|
||
|
|
}
|
||
|
|
out.close();
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
void
|
||
|
|
BayesNet::exportToDotFile (const char* fileName,
|
||
|
|
bool showNeighborless,
|
||
|
|
const NodeSet& highlightNodes) const
|
||
|
|
{
|
||
|
|
string s = "../../" ;
|
||
|
|
s+= fileName;
|
||
|
|
ofstream out (s.c_str());
|
||
|
|
if (!out.is_open()) {
|
||
|
|
cerr << "error: cannot open file to write at " ;
|
||
|
|
cerr << "BayesNet::exportToDotFile()" << endl;
|
||
|
|
abort();
|
||
|
|
}
|
||
|
|
|
||
|
|
out << "digraph \"" << fileName << "\" {" << endl;
|
||
|
|
for (unsigned i = 0; i < nodes_.size(); i++) {
|
||
|
|
const NodeSet& childs = nodes_[i]->getChilds();
|
||
|
|
for (unsigned j = 0; j < childs.size(); j++) {
|
||
|
|
out << '"' << nodes_[i]->getLabel() << '"' << " -> " ;
|
||
|
|
out << '"' << childs[j]->getLabel() << '"' << endl;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
for (unsigned i = 0; i < nodes_.size(); i++) {
|
||
|
|
if (showNeighborless || nodes_[i]->hasNeighbors()) {
|
||
|
|
out << '"' << nodes_[i]->getLabel() << '"' ;
|
||
|
|
if (nodes_[i]->hasEvidence()) {
|
||
|
|
out << " [style=filled, fillcolor=yellow]" << endl;
|
||
|
|
} else {
|
||
|
|
out << endl;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
for (unsigned i = 0; i < highlightNodes.size(); i++) {
|
||
|
|
out << '"' << highlightNodes[i]->getLabel() << '"' ;
|
||
|
|
out << " [shape=box]" << endl;
|
||
|
|
}
|
||
|
|
|
||
|
|
out << "}" << endl;
|
||
|
|
out.close();
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
void
|
||
|
|
BayesNet::exportToBifFile (const char* fileName) const
|
||
|
|
{
|
||
|
|
string s = "../../" ;
|
||
|
|
s += fileName;
|
||
|
|
ofstream out (s.c_str());
|
||
|
|
if(!out.is_open()) {
|
||
|
|
cerr << "error: cannot open file to write at " ;
|
||
|
|
cerr << "BayesNet::exportToBifFile()" << endl;
|
||
|
|
abort();
|
||
|
|
}
|
||
|
|
out << "<?xml version=\"1.0\" encoding=\"US-ASCII\"?>" << endl;
|
||
|
|
out << "<BIF VERSION=\"0.3\">" << endl;
|
||
|
|
out << "<NETWORK>" << endl;
|
||
|
|
out << "<NAME>" << fileName << "</NAME>" << endl << endl;
|
||
|
|
for (unsigned i = 0; i < nodes_.size(); i++) {
|
||
|
|
out << "<VARIABLE TYPE=\"nature\">" << endl;
|
||
|
|
out << "\t<NAME>" << nodes_[i]->getLabel() << "</NAME>" << endl;
|
||
|
|
const Domain& domain = nodes_[i]->getDomain();
|
||
|
|
for (unsigned j = 0; j < domain.size(); j++) {
|
||
|
|
out << "\t<OUTCOME>" << domain[j] << "</OUTCOME>" << endl;
|
||
|
|
}
|
||
|
|
out << "</VARIABLE>" << endl << endl;
|
||
|
|
}
|
||
|
|
|
||
|
|
for (unsigned i = 0; i < nodes_.size(); i++) {
|
||
|
|
out << "<DEFINITION>" << endl;
|
||
|
|
out << "\t<FOR>" << nodes_[i]->getLabel() << "</FOR>" << endl;
|
||
|
|
const NodeSet& parents = nodes_[i]->getParents();
|
||
|
|
for (unsigned j = 0; j < parents.size(); j++) {
|
||
|
|
out << "\t<GIVEN>" << parents[j]->getLabel();
|
||
|
|
out << "</GIVEN>" << endl;
|
||
|
|
}
|
||
|
|
ParamSet params = revertParameterReorder (nodes_[i]->getParameters(),
|
||
|
|
nodes_[i]->getDomainSize());
|
||
|
|
out << "\t<TABLE>" ;
|
||
|
|
for (unsigned j = 0; j < params.size(); j++) {
|
||
|
|
out << " " << params[j];
|
||
|
|
}
|
||
|
|
out << " </TABLE>" << endl;
|
||
|
|
out << "</DEFINITION>" << endl << endl;
|
||
|
|
}
|
||
|
|
out << "</NETWORK>" << endl;
|
||
|
|
out << "</BIF>" << endl << endl;
|
||
|
|
out.close();
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
bool
|
||
|
|
BayesNet::containsUndirectedCycle (void) const
|
||
|
|
{
|
||
|
|
vector<bool> visited (nodes_.size(), false);
|
||
|
|
for (unsigned i = 0; i < nodes_.size(); i++) {
|
||
|
|
int v = nodes_[i]->getIndex();
|
||
|
|
if (!visited[v]) {
|
||
|
|
if (containsUndirectedCycle (v, -1, visited)) {
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return false;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
bool
|
||
|
|
BayesNet::containsUndirectedCycle (int v,
|
||
|
|
int p,
|
||
|
|
vector<bool>& visited) const
|
||
|
|
{
|
||
|
|
visited[v] = true;
|
||
|
|
vector<int> adjacencies = getAdjacentNodes (v);
|
||
|
|
for (unsigned i = 0; i < adjacencies.size(); i++) {
|
||
|
|
int w = adjacencies[i];
|
||
|
|
if (!visited[w]) {
|
||
|
|
if (containsUndirectedCycle (w, v, visited)) {
|
||
|
|
return true;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
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 NodeSet& parents = nodes_[v]->getParents();
|
||
|
|
const NodeSet& 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 (const ParamSet& params,
|
||
|
|
int 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 (const ParamSet& params,
|
||
|
|
int domainSize) const
|
||
|
|
{
|
||
|
|
unsigned count = 0;
|
||
|
|
unsigned rowSize = params.size() / domainSize;
|
||
|
|
ParamSet reordered;
|
||
|
|
while (reordered.size() < params.size()) {
|
||
|
|
unsigned idx = count;
|
||
|
|
for (int i = 0; i < domainSize; i++) {
|
||
|
|
reordered.push_back (params[idx]);
|
||
|
|
idx += rowSize;
|
||
|
|
}
|
||
|
|
count ++;
|
||
|
|
}
|
||
|
|
return reordered;
|
||
|
|
}
|
||
|
|
|