Esempio n. 1
0
double 
Potential::getCondPotValueFor(INTDBLMAP& assignment)
{
	if(assignment.find(factorVariables.begin()->first)==assignment.end())
	{
		cerr <<"Fatal error! No variable assignment for " << factorVariables.begin()->first << endl;
		exit(-1);
	}
	double newmean=0;
	for(INTDBLMAP_ITER aIter=mbcondMean_Vect.begin();aIter!=mbcondMean_Vect.end();aIter++)
	{
		if(assignment.find(aIter->first)==assignment.end())
		{
			cout <<"Warning! No variable assignment for " << aIter->first << endl;
			continue;
		}
		double aval=assignment[aIter->first];
		newmean=newmean+(aval*aIter->second);
	}
	newmean=newmean+mbcondMean_Part;
	double normsq=2*PI*mbcondVar;
	double norm=sqrt(2*PI*mbcondVar);
	norm=sqrt(normsq);
	double x=assignment[factorVariables.begin()->first];
	double dev=(x-newmean)*(x-newmean);
	dev=dev/(2*mbcondVar);
	double eval=exp(-1.0*dev);
	double pval=eval/norm;
	return pval;
}
Esempio n. 2
0
double
PotentialManager::getPseudoLikelihood(SlimFactor* sFactor,VSET& varSet, bool train,int& status, Potential* aPotFunc)
{
	status=0;
	Variable* aVar=varSet[sFactor->fId];
	aPotFunc->setAssocVariable(aVar,Potential::FACTOR);
	for(INTINTMAP_ITER aIter=sFactor->mergedMB.begin();aIter!=sFactor->mergedMB.end();aIter++)
	{
		Variable* aVar=varSet[aIter->first];
		aPotFunc->setAssocVariable(aVar,Potential::MARKOV_BNKT);
	}
	aPotFunc->potZeroInit();
	populatePotential(aPotFunc,false);
	//This function creates a submatrix of the covariance matrix and inverts it
	aPotFunc->initMBCovMean();
	if(aPotFunc->getCondVariance()<0)
	{
		status=-1;
		return 0;
	}
	INTINTMAP* dataSet=NULL;
	if(train)
	{
		dataSet=&(evMgr->getTrainingSet());
	}
	else
	{
		dataSet=&(evMgr->getTestSet());
	}
	INTDBLMAP subData;
	double pll=0;
	int thresholded=0;
	for(INTINTMAP_ITER dIter=dataSet->begin();dIter!=dataSet->end();dIter++)
	{
		EMAP* evidMap=NULL;
		evidMap=evMgr->getEvidenceAt(dIter->first);
		Evidence* evid=(*evidMap)[sFactor->fId];
		double val=evid->getEvidVal();
		subData[sFactor->fId]=val;
		for(INTINTMAP_ITER vIter=sFactor->mergedMB.begin();vIter!=sFactor->mergedMB.end(); vIter++)
		{
			int vId=vIter->first;
			Evidence* evid=(*evidMap)[vIter->first];
			double val=evid->getEvidVal();
			subData[vId]=val;
		}
		double cll=aPotFunc->getCondPotValueFor(subData);

		if(cll<1e-50)
		{
			cll=1e-50;
			thresholded++;
		}
		pll=pll+log(cll);
	}
	subData.clear();
	return pll;
}
Esempio n. 3
0
int 
Potential::setCondWeight(INTDBLMAP& wt)
{
	for(INTDBLMAP_ITER aIter=wt.begin();aIter!=wt.end();aIter++)
	{
		double aval=aIter->second;
		mbcondMean_Vect[aIter->first]=aval;
	}
	return 0;
}
Esempio n. 4
0
int
PotentialManager::estimateAllMeanCov(bool random, INTDBLMAP& gMean, map<int,INTDBLMAP*>& gCovar,INTINTMAP& trainEvidSet)
{
	int evidCnt=trainEvidSet.size();
	//First get the mean and then the variance
	int dId=0;
	for(INTINTMAP_ITER eIter=trainEvidSet.begin();eIter!=trainEvidSet.end();eIter++)
	{
		EMAP* evidMap=NULL;
		if(random)
		{
			evidMap=evMgr->getRandomEvidenceAt(eIter->first);
		}
		else
		{
			evidMap=evMgr->getEvidenceAt(eIter->first);
		}
		for(EMAP_ITER vIter=evidMap->begin();vIter!=evidMap->end(); vIter++)
		{
			int vId=vIter->first;
			Evidence* evid=vIter->second;
			double val=evid->getEvidVal();
			if(gMean.find(vId)==gMean.end())
			{
				gMean[vId]=val;
			}
			else
			{
				gMean[vId]=gMean[vId]+val;
			}
		}
		dId++;	
	}
	//Now estimate the mean
	for(INTDBLMAP_ITER idIter=gMean.begin();idIter!=gMean.end();idIter++)
	{
		idIter->second=idIter->second/(double) evidCnt;
		INTDBLMAP* vcov=new INTDBLMAP;
		gCovar[idIter->first]=vcov;
	}
	return 0;
}
Esempio n. 5
0
double
Potential::predictSample(INTDBLMAP& jointConf, int vId)
{
	if(jointConf.find(factorVariables.begin()->first)==jointConf.end())
	{
		cerr <<"Fatal error! No variable assignment for " << factorVariables.begin()->first << endl;
		exit(-1);
	}
	double newmean=0;
	for(INTDBLMAP_ITER aIter=mbcondMean_Vect.begin();aIter!=mbcondMean_Vect.end();aIter++)
	{
		if(jointConf.find(aIter->first)==jointConf.end())
		{
			cerr <<"Fatal error! No variable assignment for " << aIter->first << endl;
			exit(-1);
		}
		double aval=jointConf[aIter->first];
		newmean=newmean+(aval*aIter->second);
	}
	newmean=newmean+mbcondMean_Part;
	return newmean;
}
Esempio n. 6
0
double
Potential::generateSample(INTDBLMAP& jointConf, int vId,gsl_rng* r,double gVar)
{
	if(jointConf.find(factorVariables.begin()->first)==jointConf.end())
	{
		cerr <<"Fatal error! No variable assignment for " << factorVariables.begin()->first << endl;
		exit(-1);
	}
	double newmean=0;
	for(INTDBLMAP_ITER aIter=mbcondMean_Vect.begin();aIter!=mbcondMean_Vect.end();aIter++)
	{
		if(jointConf.find(aIter->first)==jointConf.end())
		{
			cerr <<"Fatal error! No variable assignment for " << aIter->first << endl;
			exit(-1);
		}
		double aval=jointConf[aIter->first];
		newmean=newmean+(aval*aIter->second);
	}
	newmean=newmean+mbcondMean_Part;
	double x=gsl_ran_gaussian(r,sqrt(gVar));
	x=x+newmean;
	return x;
}
Esempio n. 7
0
//Get the joint prob value for a particular configuration
double 
Potential::getJointPotValueFor(INTDBLMAP& varConf)
{
	string aKey;
	double pVal=0;
	Matrix* valMat=new Matrix(varSet.size(),1);
	for(INTDBLMAP_ITER idIter=varConf.begin();idIter!=varConf.end();idIter++)
	{
		int i=vIDMatIndMap[idIter->first];
		valMat->setValue(idIter->second,i,0);
	}
	Matrix* meanDiff=valMat->subtractMatrix(mean);
	Matrix* diffT=meanDiff->transMatrix();
	Matrix* p1=diffT->multiplyMatrix(inverse);
	Matrix* p2=p1->multiplyMatrix(meanDiff);
	double prod=p2->getValue(0,0);
	pVal=exp(-0.5*prod);
	pVal=pVal/normFactor;
	delete meanDiff;
	delete diffT;
	delete p1;
	delete p2;
	return pVal;
}
Esempio n. 8
0
int
PotentialManager::estimateAllMeanCov(bool random, INTDBLMAP& gMean, map<int,INTDBLMAP*>& gCovar,INTINTMAP& trainEvidSet, const char* mFName, const char* sdFName,int leaveOutData)
{
	ofstream mFile;
	ofstream sdFile;

	if(!random)
	{
		if((mFName!=NULL) && (sdFName!=NULL))
		{
			mFile.open(mFName);
			sdFile.open(sdFName);
		}
	}

	int evidCnt=trainEvidSet.size();
	if(leaveOutData!=-1)
	{
		evidCnt=evidCnt-1;
	}
	//First get the mean and then the variance
	int dId=0;
	for(INTINTMAP_ITER eIter=trainEvidSet.begin();eIter!=trainEvidSet.end();eIter++)
	{
		if(dId==leaveOutData)
		{	
			dId++;
			continue;
		}
		EMAP* evidMap=NULL;
		if(random)
		{
			evidMap=evMgr->getRandomEvidenceAt(eIter->first);
		}
		else
		{
			evidMap=evMgr->getEvidenceAt(eIter->first);
		}
		for(EMAP_ITER vIter=evidMap->begin();vIter!=evidMap->end(); vIter++)
		{
			int vId=vIter->first;
			Evidence* evid=vIter->second;
			double val=evid->getEvidVal();
			if(gMean.find(vId)==gMean.end())
			{
				gMean[vId]=val;
			}
			else
			{
				gMean[vId]=gMean[vId]+val;
			}
		}
		dId++;	
	}
	//Now estimate the mean
	for(INTDBLMAP_ITER idIter=gMean.begin();idIter!=gMean.end();idIter++)
	{
		if(idIter->first==176)
		{
			//cout <<"Stop here: Variable " << idIter->first << " mean " << idIter->second << endl;
		}
		idIter->second=idIter->second/(double) evidCnt;
		if(!random)
		{
			if(mFile.good())
			{
				mFile<<idIter->first<<"\t" << idIter->second<< endl;
			}
		}
	}
	int covPair=0;
	//Now the variance
	for(INTINTMAP_ITER eIter=trainEvidSet.begin();eIter!=trainEvidSet.end();eIter++)
	{
		EMAP* evidMap=NULL;
		if(random)
		{
			evidMap=evMgr->getRandomEvidenceAt(eIter->first);
		}
		else
		{
			evidMap=evMgr->getEvidenceAt(eIter->first);
		}
		for(EMAP_ITER vIter=evidMap->begin();vIter!=evidMap->end(); vIter++)
		{
			int vId=vIter->first;
			Evidence* evid=vIter->second;
			double vval=evid->getEvidVal();
			double vmean=gMean[vId];
			INTDBLMAP* vcov=NULL;
			if(gCovar.find(vId)==gCovar.end())
			{
				vcov=new INTDBLMAP;
				gCovar[vId]=vcov;
			}
			else
			{
				vcov=gCovar[vId];
			}
			for(EMAP_ITER uIter=vIter;uIter!=evidMap->end();uIter++)
			{
				int uId=uIter->first;
				//Don't compute covariance of vId uId pairs that both are not in the restrictedNeighborSet, when
				//the restrictedNeighborSet is empty
			/*	if((!random) && (vId!=uId) && (restrictedNeighborSet.size()>0))
				{
					if((restrictedNeighborSet.find(vId)==restrictedNeighborSet.end()) && (restrictedNeighborSet.find(uId)==restrictedNeighborSet.end()))
					{
						continue;
					}
				}*/
				Evidence* evid1=uIter->second;
				double uval=evid1->getEvidVal();
				double umean=gMean[uId];
				double diffprod=(vval-vmean)*(uval-umean);
				INTDBLMAP* ucov=NULL;
				if(gCovar.find(uId)==gCovar.end())
				{
					ucov=new INTDBLMAP;
					gCovar[uId]=ucov;
				}
				else
				{
					ucov=gCovar[uId];
				}
				if(vcov->find(uId)==vcov->end())
				{
					covPair++;
					(*vcov)[uId]=diffprod;
				}
				else
				{
					(*vcov)[uId]=(*vcov)[uId]+diffprod;
				}
				if(uId!=vId)
				{
					if(ucov->find(vId)==ucov->end())
					{
						(*ucov)[vId]=diffprod;
					}
					else
					{
						(*ucov)[vId]=(*ucov)[vId]+diffprod;
					}
				}
			}
		}

	}
	cout <<"Total covariance pairs estimated " << covPair << endl;
	//Now estimate the variance
	for(map<int,INTDBLMAP*>::iterator idIter=gCovar.begin();idIter!=gCovar.end();idIter++)
	{
		INTDBLMAP* var=idIter->second;
		for(INTDBLMAP_ITER vIter=var->begin();vIter!=var->end();vIter++)
		{
			if(vIter->first==idIter->first)
			{
				//vIter->second=2*vIter->second/((double)(gCovar.size()-1));
				//vIter->second=2*vIter->second/((double)(evidCnt-1));
				//vIter->second=(0.001+vIter->second)/((double)(evidCnt-1));
				vIter->second=(vIter->second)/((double)(evidCnt-1));
				double variance=vIter->second;
				if(idIter->first==176)
				{
				//	cout <<"Stop here: Variable " << idIter->first << " variance " << idIter->second << endl;
				}
			}
			else
			{
				vIter->second=vIter->second/((double)(evidCnt-1));
				//vIter->second=vIter->second/((double)(gCovar.size()-1));
				//vIter->second=0;
			}
			if(!random)
			{
				if(sdFile.good())
				{
					sdFile<<idIter->first<<"\t" << vIter->first <<"\t" << vIter->second << endl;
				}
			}
		}
	}
	if(!random)
	{
		if(mFile.good())
		{
			mFile.close();
		}
		if(sdFile.good())
		{
			sdFile.close();
		}
	}	
	return 0;
}
Esempio n. 9
0
int 
EvidenceManager::populateEvidence(Evidence** evid,const char* evidStr)
{
	//first check for validity of evidStr
	if(strchr(evidStr,'=')==NULL)
	{
		return -1;
	}
	*evid=new Evidence;
	
	INTDBLMAP evidData;
	int currInd=0;
	int ttInd=0;
	int tokId=0;
	char tempTok[256];
	while(evidStr[currInd]!='\0')
	{
		if((evidStr[currInd]=='=') || 
		   (evidStr[currInd]==']') ||
		   (evidStr[currInd]==',')
		  )
		{
			tempTok[ttInd]='\0';
			ttInd=0;
			if(tokId==0)
			{
				//This is the variable
				int vId=atoi(tempTok);
				Variable* var=vMgr->getVariableAt(vId);
				var->initEvidence(evidData);
				(*evid)->assocVariable(vId);
			}
			else
			{
				char* pos=strchr(tempTok,'|');
				//Hard evidence
				if(pos==NULL)
				{
					int varVal=atoi(tempTok);
					if(evidData.find(varVal)==evidData.end())
					{
						cout <<"No value "<< varVal << " in the domain of  a variable" << endl;
						return -1;
					}
					evidData[varVal]=1.0;
					(*evid)->setType(Evidence::HARD);
				}
				else
				{
					*pos='\0';
					int varVal=atoi(tempTok);
					double varValProb=atof(pos+1);
					if(evidData.find(varVal)==evidData.end())
					{
						cout <<"No value "<< varVal << " in the domain of  a variable" << endl;
						return -1;
					}
					evidData[varVal]=varValProb;
					//Will be setting it multiple times but its ok for now.
					(*evid)->setType(Evidence::SOFT);
				}
			}
			tokId++;
		}
		else if(evidStr[currInd]!='[')
		{
			tempTok[ttInd]=evidStr[currInd];
			ttInd++;
		}
		currInd++;
	}
	(*evid)->setData(evidData);
	return 0;
}