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new release from Tiago

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This commit is contained in:
Vítor Santos Costa
2011-12-12 15:29:51 +00:00
parent 03391e3feb
commit 33bf0bc0f5
33 changed files with 5943 additions and 1167 deletions
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405
packages/CLPBN/clpbn/bp/BayesNet.cpp
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@@ -4,111 +4,12 @@
#include <iostream>
#include <fstream>
#include <sstream>
#include <iomanip>
#include "xmlParser/xmlParser.h"
#include "BayesNet.h"
BayesNet::BayesNet (const char* fileName)
{
map<string, Domain> domains;
XMLNode xMainNode = XMLNode::openFileHelper (fileName, "BIF");
// only the first network is parsed, others are ignored
XMLNode xNode = xMainNode.getChildNode ("NETWORK");
unsigned nVars = xNode.nChildNode ("VARIABLE");
for (unsigned i = 0; i < nVars; i++) {
XMLNode var = xNode.getChildNode ("VARIABLE", i);
string type = var.getAttribute ("TYPE");
if (type != "nature") {
cerr << "error: only \"nature\" variables are supported" << endl;
abort();
}
Domain domain;
string varLabel = var.getChildNode("NAME").getText();
unsigned dsize = var.nChildNode ("OUTCOME");
for (unsigned j = 0; j < dsize; j++) {
if (var.getChildNode("OUTCOME", j).getText() == 0) {
stringstream ss;
ss << j + 1;
domain.push_back (ss.str());
} else {
domain.push_back (var.getChildNode("OUTCOME", j).getText());
}
}
domains.insert (make_pair (varLabel, domain));
}
unsigned nDefs = xNode.nChildNode ("DEFINITION");
if (nVars != nDefs) {
cerr << "error: different number of variables and definitions" << endl;
abort();
}
queue<unsigned> indexes;
for (unsigned i = 0; i < nDefs; i++) {
indexes.push (i);
}
while (!indexes.empty()) {
unsigned index = indexes.front();
indexes.pop();
XMLNode def = xNode.getChildNode ("DEFINITION", index);
string varLabel = def.getChildNode("FOR").getText();
map<string, Domain>::const_iterator iter;
iter = domains.find (varLabel);
if (iter == domains.end()) {
cerr << "error: unknow variable `" << varLabel << "'" << endl;
abort();
}
bool processItLatter = false;
BnNodeSet parents;
unsigned nParams = iter->second.size();
for (int j = 0; j < def.nChildNode ("GIVEN"); j++) {
string parentLabel = def.getChildNode("GIVEN", j).getText();
BayesNode* parentNode = getBayesNode (parentLabel);
if (parentNode) {
nParams *= parentNode->getDomainSize();
parents.push_back (parentNode);
}
else {
iter = domains.find (parentLabel);
if (iter == domains.end()) {
cerr << "error: unknow parent `" << parentLabel << "'" << endl;
abort();
} else {
// this definition contains a parent that doesn't
// have a corresponding bayesian node instance yet,
// so process this definition latter
indexes.push (index);
processItLatter = true;
break;
}
}
}
if (!processItLatter) {
unsigned count = 0;
ParamSet params (nParams);
stringstream s (def.getChildNode("TABLE").getText());
while (!s.eof() && count < nParams) {
s >> params[count];
count ++;
}
if (count != nParams) {
cerr << "error: invalid number of parameters " ;
cerr << "for variable `" << varLabel << "'" << endl;
abort();
}
params = reorderParameters (params, iter->second.size());
addNode (varLabel, iter->second, parents, params);
}
}
setIndexes();
}
BayesNet::~BayesNet (void)
{
@@ -119,26 +20,130 @@ BayesNet::~BayesNet (void)
BayesNode*
BayesNet::addNode (Vid vid)
void
BayesNet::readFromBifFormat (const char* fileName)
{
indexMap_.insert (make_pair (vid, nodes_.size()));
nodes_.push_back (new BayesNode (vid));
return nodes_.back();
XMLNode xMainNode = XMLNode::openFileHelper (fileName, "BIF");
// only the first network is parsed, others are ignored
XMLNode xNode = xMainNode.getChildNode ("NETWORK");
unsigned nVars = xNode.nChildNode ("VARIABLE");
for (unsigned i = 0; i < nVars; i++) {
XMLNode var = xNode.getChildNode ("VARIABLE", i);
if (string (var.getAttribute ("TYPE")) != "nature") {
cerr << "error: only \"nature\" variables are supported" << endl;
abort();
}
States states;
string label = var.getChildNode("NAME").getText();
unsigned nrStates = var.nChildNode ("OUTCOME");
for (unsigned j = 0; j < nrStates; j++) {
if (var.getChildNode("OUTCOME", j).getText() == 0) {
stringstream ss;
ss << j + 1;
states.push_back (ss.str());
} else {
states.push_back (var.getChildNode("OUTCOME", j).getText());
}
}
addNode (label, states);
}
unsigned nDefs = xNode.nChildNode ("DEFINITION");
if (nVars != nDefs) {
cerr << "error: different number of variables and definitions" << endl;
abort();
}
for (unsigned i = 0; i < nDefs; i++) {
XMLNode def = xNode.getChildNode ("DEFINITION", i);
string label = def.getChildNode("FOR").getText();
BayesNode* node = getBayesNode (label);
if (!node) {
cerr << "error: unknow variable `" << label << "'" << endl;
abort();
}
BnNodeSet parents;
unsigned nParams = node->nrStates();
for (int j = 0; j < def.nChildNode ("GIVEN"); j++) {
string parentLabel = def.getChildNode("GIVEN", j).getText();
BayesNode* parentNode = getBayesNode (parentLabel);
if (!parentNode) {
cerr << "error: unknow variable `" << parentLabel << "'" << endl;
abort();
}
nParams *= parentNode->nrStates();
parents.push_back (parentNode);
}
node->setParents (parents);
unsigned count = 0;
ParamSet params (nParams);
stringstream s (def.getChildNode("TABLE").getText());
while (!s.eof() && count < nParams) {
s >> params[count];
count ++;
}
if (count != nParams) {
cerr << "error: invalid number of parameters " ;
cerr << "for variable `" << label << "'" << endl;
abort();
}
params = reorderParameters (params, node->nrStates());
Distribution* dist = new Distribution (params);
node->setDistribution (dist);
addDistribution (dist);
}
setIndexes();
if (NSPACE == NumberSpace::LOGARITHM) {
distributionsToLogs();
}
}
void
BayesNet::addNode (BayesNode* n)
{
indexMap_.insert (make_pair (n->varId(), nodes_.size()));
nodes_.push_back (n);
}
BayesNode*
BayesNet::addNode (Vid vid,
BayesNet::addNode (string label, const States& states)
{
VarId vid = nodes_.size();
indexMap_.insert (make_pair (vid, nodes_.size()));
GraphicalModel::addVariableInformation (vid, label, states);
BayesNode* node = new BayesNode (VarNode (vid, states.size()));
nodes_.push_back (node);
return node;
}
BayesNode*
BayesNet::addNode (VarId vid,
unsigned dsize,
int evidence,
BnNodeSet& parents,
Distribution* dist)
{
indexMap_.insert (make_pair (vid, nodes_.size()));
nodes_.push_back (new BayesNode (
vid, dsize, evidence, parents, dist));
nodes_.push_back (new BayesNode (vid, dsize, evidence, parents, dist));
return nodes_.back();
}
BayesNode*
BayesNet::addNode (VarId vid,
unsigned dsize,
int evidence,
Distribution* dist)
{
indexMap_.insert (make_pair (vid, nodes_.size()));
nodes_.push_back (new BayesNode (vid, dsize, evidence, dist));
return nodes_.back();
}
@@ -146,14 +151,16 @@ BayesNet::addNode (Vid vid,
BayesNode*
BayesNet::addNode (string label,
Domain domain,
States states,
BnNodeSet& parents,
ParamSet& params)
{
indexMap_.insert (make_pair (nodes_.size(), nodes_.size()));
VarId vid = nodes_.size();
indexMap_.insert (make_pair (vid, nodes_.size()));
GraphicalModel::addVariableInformation (vid, label, states);
Distribution* dist = new Distribution (params);
BayesNode* node = new BayesNode (
nodes_.size(), label, domain, parents, dist);
vid, states.size(), NO_EVIDENCE, parents, dist);
dists_.push_back (dist);
nodes_.push_back (node);
return node;
@@ -162,7 +169,7 @@ BayesNet::addNode (string label,
BayesNode*
BayesNet::getBayesNode (Vid vid) const
BayesNet::getBayesNode (VarId vid) const
{
IndexMap::const_iterator it = indexMap_.find (vid);
if (it == indexMap_.end()) {
@@ -179,7 +186,7 @@ BayesNet::getBayesNode (string label) const
{
BayesNode* node = 0;
for (unsigned i = 0; i < nodes_.size(); i++) {
if (nodes_[i]->getLabel() == label) {
if (nodes_[i]->label() == label) {
node = nodes_[i];
break;
}
@@ -190,10 +197,25 @@ BayesNet::getBayesNode (string label) const
Variable*
BayesNet::getVariable (Vid vid) const
VarNode*
BayesNet::getVariableNode (VarId vid) const
{
return getBayesNode (vid);
BayesNode* node = getBayesNode (vid);
assert (node);
return node;
}
VarNodes
BayesNet::getVariableNodes (void) const
{
VarNodes vars;
for (unsigned i = 0; i < nodes_.size(); i++) {
vars.push_back (nodes_[i]);
}
return vars;
}
@@ -230,7 +252,7 @@ BayesNet::getBayesNodes (void) const
unsigned
BayesNet::getNumberOfNodes (void) const
BayesNet::nrNodes (void) const
{
return nodes_.size();
}
@@ -265,37 +287,25 @@ BayesNet::getLeafNodes (void) const
VarSet
BayesNet::getVariables (void) const
BayesNet*
BayesNet::getMinimalRequesiteNetwork (VarId vid) const
{
VarSet vars;
for (unsigned i = 0; i < nodes_.size(); i++) {
vars.push_back (nodes_[i]);
}
return vars;
return getMinimalRequesiteNetwork (VarIdSet() = {vid});
}
BayesNet*
BayesNet::getMinimalRequesiteNetwork (Vid vid) const
{
return getMinimalRequesiteNetwork (VidSet() = {vid});
}
BayesNet*
BayesNet::getMinimalRequesiteNetwork (const VidSet& queryVids) const
BayesNet::getMinimalRequesiteNetwork (const VarIdSet& queryVarIds) const
{
BnNodeSet queryVars;
for (unsigned i = 0; i < queryVids.size(); i++) {
assert (getBayesNode (queryVids[i]));
queryVars.push_back (getBayesNode (queryVids[i]));
for (unsigned i = 0; i < queryVarIds.size(); i++) {
assert (getBayesNode (queryVarIds[i]));
queryVars.push_back (getBayesNode (queryVarIds[i]));
}
// cout << "query vars: " ;
// for (unsigned i = 0; i < queryVars.size(); i++) {
// cout << queryVars[i]->getLabel() << " " ;
// cout << queryVars[i]->label() << " " ;
// }
// cout << endl;
@@ -344,7 +354,7 @@ BayesNet::getMinimalRequesiteNetwork (const VidSet& queryVids) const
cout << "----------------------------------------------------------" ;
cout << endl;
for (unsigned i = 0; i < states.size(); i++) {
cout << nodes_[i]->getLabel() << ":\t\t" ;
cout << nodes_[i]->label() << ":\t\t" ;
if (states[i]) {
states[i]->markedOnTop ? cout << "yes\t" : cout << "no\t" ;
states[i]->markedOnBottom ? cout << "yes\t" : cout << "no\t" ;
@@ -374,51 +384,46 @@ void
BayesNet::constructGraph (BayesNet* bn,
const vector<StateInfo*>& states) const
{
BnNodeSet mrnNodes;
vector<VarIdSet> parents;
for (unsigned i = 0; i < nodes_.size(); i++) {
bool isRequired = false;
if (states[i]) {
isRequired = (nodes_[i]->hasEvidence() && states[i]->visited)
||
||
states[i]->markedOnTop;
}
if (isRequired) {
BnNodeSet parents;
parents.push_back (VarIdSet());
if (states[i]->markedOnTop) {
const BnNodeSet& ps = nodes_[i]->getParents();
for (unsigned j = 0; j < ps.size(); j++) {
BayesNode* parent = bn->getBayesNode (ps[j]->getVarId());
if (!parent) {
parent = bn->addNode (ps[j]->getVarId());
}
parents.push_back (parent);
parents.back().push_back (ps[j]->varId());
}
}
BayesNode* node = bn->getBayesNode (nodes_[i]->getVarId());
if (node) {
node->setData (nodes_[i]->getDomainSize(),
nodes_[i]->getEvidence(), parents,
nodes_[i]->getDistribution());
} else {
node = bn->addNode (nodes_[i]->getVarId(),
nodes_[i]->getDomainSize(),
nodes_[i]->getEvidence(), parents,
nodes_[i]->getDistribution());
}
if (nodes_[i]->hasDomain()) {
node->setDomain (nodes_[i]->getDomain());
}
if (nodes_[i]->hasLabel()) {
node->setLabel (nodes_[i]->getLabel());
}
assert (bn->getBayesNode (nodes_[i]->varId()) == 0);
BayesNode* mrnNode = bn->addNode (nodes_[i]->varId(),
nodes_[i]->nrStates(),
nodes_[i]->getEvidence(),
nodes_[i]->getDistribution());
mrnNodes.push_back (mrnNode);
}
}
for (unsigned i = 0; i < mrnNodes.size(); i++) {
BnNodeSet ps;
for (unsigned j = 0; j < parents[i].size(); j++) {
assert (bn->getBayesNode (parents[i][j]) != 0);
ps.push_back (bn->getBayesNode (parents[i][j]));
}
mrnNodes[i]->setParents (ps);
}
bn->setIndexes();
}
bool
BayesNet::isSingleConnected (void) const
BayesNet::isPolyTree (void) const
{
return !containsUndirectedCycle();
}
@@ -435,6 +440,16 @@ BayesNet::setIndexes (void)
void
BayesNet::distributionsToLogs (void)
{
for (unsigned i = 0; i < dists_.size(); i++) {
Util::toLog (dists_[i]->params);
}
}
void
BayesNet::freeDistributions (void)
{
@@ -456,9 +471,9 @@ BayesNet::printGraphicalModel (void) const
void
BayesNet::exportToDotFormat (const char* fileName,
bool showNeighborless,
CVidSet& highlightVids) const
BayesNet::exportToGraphViz (const char* fileName,
bool showNeighborless,
const VarIdSet& highlightVarIds) const
{
ofstream out (fileName);
if (!out.is_open()) {
@@ -467,27 +482,32 @@ BayesNet::exportToDotFormat (const char* fileName,
abort();
}
out << "digraph \"" << fileName << "\" {" << endl;
out << "digraph {" << endl;
out << "ranksep=1" << endl;
for (unsigned i = 0; i < nodes_.size(); i++) {
if (showNeighborless || nodes_[i]->hasNeighbors()) {
out << '"' << nodes_[i]->getLabel() << '"' ;
out << nodes_[i]->varId() ;
if (nodes_[i]->hasEvidence()) {
out << " [style=filled, fillcolor=yellow]" << endl;
out << " [" ;
out << "label=\"" << nodes_[i]->label() << "\"," ;
out << "style=filled, fillcolor=yellow" ;
out << "]" ;
} else {
out << endl;
out << " [" ;
out << "label=\"" << nodes_[i]->label() << "\"" ;
out << "]" ;
}
out << endl;
}
}
for (unsigned i = 0; i < highlightVids.size(); i++) {
BayesNode* node = getBayesNode (highlightVids[i]);
for (unsigned i = 0; i < highlightVarIds.size(); i++) {
BayesNode* node = getBayesNode (highlightVarIds[i]);
if (node) {
out << '"' << node->getLabel() << '"' ;
// out << " [shape=polygon, sides=6]" << endl;
out << node->varId() ;
out << " [shape=box3d]" << endl;
} else {
cout << "error: invalid variable id: " << highlightVids[i] << endl;
cout << "error: invalid variable id: " << highlightVarIds[i] << endl;
abort();
}
}
@@ -495,8 +515,7 @@ BayesNet::exportToDotFormat (const char* fileName,
for (unsigned i = 0; i < nodes_.size(); i++) {
const BnNodeSet& childs = nodes_[i]->getChilds();
for (unsigned j = 0; j < childs.size(); j++) {
out << '"' << nodes_[i]->getLabel() << '"' << " -> " ;
out << '"' << childs[j]->getLabel() << '"' << endl;
out << nodes_[i]->varId() << " -> " << childs[j]->varId() << " [style=bold]" << endl ;
}
}
@@ -521,24 +540,24 @@ BayesNet::exportToBifFormat (const char* fileName) const
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 << "\t<NAME>" << nodes_[i]->label() << "</NAME>" << endl;
const States& states = nodes_[i]->states();
for (unsigned j = 0; j < states.size(); j++) {
out << "\t<OUTCOME>" << states[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;
out << "\t<FOR>" << nodes_[i]->label() << "</FOR>" << endl;
const BnNodeSet& parents = nodes_[i]->getParents();
for (unsigned j = 0; j < parents.size(); j++) {
out << "\t<GIVEN>" << parents[j]->getLabel();
out << "\t<GIVEN>" << parents[j]->label();
out << "</GIVEN>" << endl;
}
ParamSet params = revertParameterReorder (nodes_[i]->getParameters(),
nodes_[i]->getDomainSize());
nodes_[i]->nrStates());
out << "\t<TABLE>" ;
for (unsigned j = 0; j < params.size(); j++) {
out << " " << params[j];
@@ -571,9 +590,7 @@ BayesNet::containsUndirectedCycle (void) const
bool
BayesNet::containsUndirectedCycle (int v,
int p,
vector<bool>& visited) const
BayesNet::containsUndirectedCycle (int v, int p, vector<bool>& visited) const
{
visited[v] = true;
vector<int> adjacencies = getAdjacentNodes (v);
@@ -611,8 +628,7 @@ BayesNet::getAdjacentNodes (int v) const
ParamSet
BayesNet::reorderParameters (CParamSet params,
unsigned domainSize) const
BayesNet::reorderParameters (const ParamSet& params, unsigned dsize) const
{
// the interchange format for bayesian networks keeps the probabilities
// in the following order:
@@ -623,13 +639,13 @@ BayesNet::reorderParameters (CParamSet params,
// 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;
unsigned rowSize = params.size() / dsize;
ParamSet reordered;
while (reordered.size() < params.size()) {
unsigned idx = count;
for (unsigned i = 0; i < rowSize; i++) {
reordered.push_back (params[idx]);
idx += domainSize;
idx += dsize ;
}
count++;
}
@@ -639,15 +655,14 @@ BayesNet::reorderParameters (CParamSet params,
ParamSet
BayesNet::revertParameterReorder (CParamSet params,
unsigned domainSize) const
BayesNet::revertParameterReorder (const ParamSet& params, unsigned dsize) const
{
unsigned count = 0;
unsigned rowSize = params.size() / domainSize;
unsigned rowSize = params.size() / dsize;
ParamSet reordered;
while (reordered.size() < params.size()) {
unsigned idx = count;
for (unsigned i = 0; i < domainSize; i++) {
for (unsigned i = 0; i < dsize; i++) {
reordered.push_back (params[idx]);
idx += rowSize;
}