void TestDelNode()
{
BayesNet *net = SevenNodesModel();
net->DelNode("node2");
// all continuous nodes are observed
net->EditEvidence("node0^0.3");
net->EditEvidence("node1^0.2");
net->EditEvidence("node5^0.9");
net->EditEvidence("node4^True");
net->SetProperty("Inference", "jtree");
TokArr jpd3 = net->GetJPD("node3");
std::cout<< "jpd node3:\t"<<jpd3 << "\n";
TokArr jpd6 = net->GetJPD("node6");
std::cout<< "jpd node6:\t"<<jpd6 << "\n";
delete net;
std::cout << "TestDelArc is completed successfully" << std::endl;
}
void TestsPnlHigh::TestConditionalGaussianGetJPD()
{
printf("TestConditionalGaussianGetJPD\n");
BayesNet *net = SimpleCGM1();
net->SetPGaussian("Cont0", "1.5 -0.5", "1.0 0.3 0.3 2.0", TokArr(), TokArr());
net->SetPGaussian("Cont1", "0.0", "2.5", "1.0 3.0", "Tab0^State0");
net->SetPGaussian("Cont1", "-1.5", "0.75", "0.5 2.5", "Tab0^State1");
net->SetProperty("Inference", "naive");
net->EditEvidence("Tab0^State0");
net->GetJPD("Cont0");
net->GetJPD("Cont1");
net->GetJPD("Cont2");
net->ClearEvid();
Tok tok0("Cont0^Dim0^0.0");
Tok tok1("Cont0^Dim1^0.0");
TokIdNode *id0 = tok0.Node();
TokIdNode *id1 = tok1.Node();
bool is_int0 = id0->id[id0->id.size()-1].is_int;
bool is_int1 = id1->id[id1->id.size()-1].is_int;
int int_id0 = id0->id[id0->id.size()-1].int_id;
int int_id1 = id1->id[id1->id.size()-1].int_id;
TokIdNode *id = net->Net().Token().Node(Tok("Cont0"))->v_next;
TokIdNode *id2 = net->Net().Token().Node(Tok("Cont1"))->v_next;
TokIdNode *id3 = net->Net().Token().Node(Tok("Cont2"))->v_next;
TokIdNode *id4 = net->Net().Token().Node(Tok("Tab0"))->v_next;
//TokIdNode *a1 = id->v_next;
//TokIdNode *a2 = a1->h_next;
net->EditEvidence("Cont0^Dim0^0.0 Cont0^Dim1^1.0");
net->EditEvidence("Cont1^Dim0^0.0");
net->EditEvidence("Cont2^Dim0^0.0");
net->GetJPD("Tab0");
delete net;
};
void CrashTestJtreeInferenceSoftMax()
{
BayesNet *net = SimpleSoftMaxModel();
net->SetProperty("Inference", "jtree");
TokArr jpd5 = net->GetJPD("node5");
std::cout<< "jpd node5:\t"<<jpd5 << "\n";
delete net;
}
void TestJtreeInferenceSoftMax2()
{
BayesNet *net = SimpleSoftMaxModel();
// all discrete nodes are observed
net->EditEvidence("node5^True");
net->EditEvidence("node1^0.2");
net->SetProperty("Inference", "jtree");
TokArr jpd0 = net->GetJPD("node0");
std::cout<< "jpd node0:\t"<<jpd0 << "\n";
TokArr jpd2 = net->GetJPD("node2");
std::cout<< "jpd node2:\t"<<jpd2 << "\n";
delete net;
std::cout << "TestJtreeInferenceSoftMax2 is completed successfully" << std::endl;
}
void TestGibbsInferenceSoftMax()
{
BayesNet *net = SimpleSoftMaxModel();
// no observed nodes
net->SetProperty("Inference", "gibbs");
TokArr jpd0 = net->GetJPD("node0");
std::cout<< "jpd node0:\t"<<jpd0 << "\n";
TokArr jpd1 = net->GetJPD("node1");
std::cout<< "jpd node1:\t"<<jpd1 << "\n";
TokArr jpd2 = net->GetJPD("node2");
std::cout<< "jpd node2:\t"<<jpd2 << "\n";
TokArr jpd5 = net->GetJPD("node5");
std::cout<< "jpd node5:\t"<<jpd5 << "\n";
delete net;
std::cout << "TestGibbsInferenceSoftMax is completed successfully" << std::endl;
}
void TestJtreeInference2SevenNodesModel()
{
BayesNet *net = SevenNodesModel();
// all discrete nodes are observed
net->EditEvidence("node2^True");
net->EditEvidence("node3^False");
net->EditEvidence("node4^False");
net->EditEvidence("node6^True");
net->EditEvidence("node1^0.55");
net->SetProperty("Inference", "jtree");
TokArr jpd0 = net->GetJPD("node0");
std::cout<< "jpd node0:\t"<<jpd0 << "\n";
TokArr jpd5 = net->GetJPD("node1");
std::cout<< "jpd node5:\t"<<jpd5 << "\n";
delete net;
std::cout << "TestJtreeInference2SevenNodesModel is completed successfully" << std::endl;
}
void TestJtreeInferenceCondSoftMax1()
{
BayesNet *net = SimpleCondSoftMaxModel();
// all continuous nodes are observed
net->EditEvidence("node0^0.3");
net->EditEvidence("node1^0.2");
net->EditEvidence("node2^0.9");
net->SetProperty("Inference", "jtree");
TokArr jpd3 = net->GetJPD("node3");
std::cout<< "jpd node3:\t"<<jpd3 << "\n";
TokArr jpd5 = net->GetJPD("node5");
std::cout<< "jpd node5:\t"<<jpd5 << "\n";
TokArr jpd6 = net->GetJPD("node6");
std::cout<< "jpd node6:\t"<<jpd6 << "\n";
delete net;
std::cout << "TestJtreeInferenceCondSoftMax1 is completed successfully" << std::endl;
}
int main(int arg,char * argv[])
{
int a=1,b=2;
int c=a+b;
cout<<c<<endl;
//creating bayes net
//BayesNet net;
BayesNet net;
//adding node
net.AddNode("discrete^Cloudy","true false");
net.AddNode(discrete^"Sprinkler Rain WetGrass","true false");
//adding edges
net.AddArc("Cloudy","Sprinkler Rain");
net.AddArc("Sprinkler Rain","WetGrass");
//sopecfify the CPD
//cloudy
net.SetPTabular("Cloudy^true","0.6");
net.SetPTabular("Cloudy^false","0.4");
//spprinkler
net.SetPTabular("Sprinkler^true Sprinkler^false","0.1 0.9","Cloudy^true");
net.SetPTabular("Sprinkler^true Sprinkler^false","0.5 0.5","Cloudy^false");
//rain
net.SetPTabular("Rain^true Rain^false","0.8 0.2","Cloudy^true");
net.SetPTabular("Rain^true Rain^false","0.2 0.8","Cloudy^false");
//WetGrass
net.SetPTabular("WetGrass^true WetGrass^false","0.99 0.01","Sprinkler^true Rain^true");
net.SetPTabular("WetGrass^true WetGrass^false","0.9 0.1","Sprinkler^true Rain^false");
net.SetPTabular("WetGrass^true WetGrass^false","0.9 0.1","Sprinkler^false Rain^true");
net.SetPTabular("WetGrass^true WetGrass^false","0.0 1.0","Sprinkler^false Rain^false");
//get the cpd
TokArr PCloudy=net.GetPTabular("Cloudy");
String PCloudyStr=String(PCloudy);
float PCloudyTrueF=PCloudy[0].FltValue();
float PCloudyFalseF=PCloudy[1].FltValue();
cout<<endl<<"Cloudy"<<endl;
cout<<PCloudyStr<<endl;
cout<<PCloudyTrueF<<endl;
cout<<PCloudyFalseF<<endl;
/*
//adding evidence
//net.AddEvidToBuf("Rain^true WetGrass^true");
net.EditEvidence("Rain^true WetGrass^true");
net.CurEvidToBuf();
net.LearnParameters();
cout<<endl<<"evidence Rain^true WetGrass^true"<<endl;
//get the jpd
TokArr WetGrassMarg=net.GetJPD("WetGrass");
String WetGrassMargStr=String(WetGrassMarg);
cout<<endl<<"WetGrass JPD"<<endl<<WetGrassMargStr<<endl;
TokArr WetGrassAndSprinklerMarg=net.GetJPD("WetGrass Sprinkler Rain");
String WetGrassAndSprinklerMargStr=String(WetGrassAndSprinklerMarg);
cout<<endl<<"WetGrass and Sprinkler JPD"<<endl<<WetGrassAndSprinklerMargStr<<endl;
TokArr WetGrassMPE=net.GetMPE("WetGrass");
String WetGrassMPEStr=String(WetGrassMPE);
cout<<endl<<"WetGrass MPE"<<endl<<WetGrassMPEStr<<endl;
TokArr WetGrassAndSprinklerMPE=net.GetMPE("WetGrass Sprinkler Rain");
String WetGrassAndSprinklerMPEStr=String(WetGrassAndSprinklerMPE);
cout<<endl<<"WetGrass and Spinkler MPE"<<endl<<WetGrassAndSprinklerMPEStr<<endl;
//delete evidence
net.ClearEvid();
cout<<"ok"<<endl;*/
//net.AddEvidToBuf("Sprinkler^true WetGrass^true");
net.EditEvidence("Sprinkler^true WetGrass^true");
net.CurEvidToBuf();
net.LearnParameters();
cout<<endl<<"evidence Sprinkler^true WetGrass^true"<<endl;
//get jpd
TokArr WetGrassMarg=net.GetJPD("WetGrass");
String WetGrassMargStr=String(WetGrassMarg);
cout<<endl<<"WetGrass JPD"<<endl<<WetGrassMargStr<<endl;
TokArr WetGrassAndSprinklerMarg=net.GetJPD("WetGrass Sprinkler Rain");
String WetGrassAndSprinklerMargStr=String(WetGrassAndSprinklerMarg);
cout<<endl<<"WetGrass and Sprinkler JPD"<<endl<<WetGrassAndSprinklerMargStr<<endl;
TokArr WetGrassMPE=net.GetMPE("WetGrass");
String WetGrassMPEStr=String(WetGrassMPE);
cout<<endl<<"WetGrass MPE"<<endl<<WetGrassMPEStr<<endl;
TokArr WetGrassAndSprinklerMPE=net.GetMPE("WetGrass Sprinkler Rain Cloudy");
String WetGrassAndSprinklerMPEStr=String(WetGrassAndSprinklerMPE);
cout<<endl<<"WetGrass and Spinkler MPE"<<endl<<WetGrassAndSprinklerMPEStr<<endl;
cout<<endl<<"moonsea"<<endl;
return 0;
}
int main()
{
BayesNet net;
// adding nodes
net.AddNode("discrete^Cloudy", "true false");
net.AddNode("discrete^Sprinkler", "true false");
net.AddNode("discrete^Rain", "true false");
net.AddNode("discrete^WetGrass", "true false");
//adding edges
net.AddArc("Cloudy", "Sprinkler Rain");
net.AddArc("Sprinkler Rain", "WetGrass");
// specifying the conditional probabilities
net.SetPTabular("Cloudy^true Cloudy^false", "0.6 0.4");
net.SetPTabular("Sprinkler^true Sprinkler^false", "0.1 0.9", "Cloudy^true");
net.SetPTabular("Sprinkler^true Sprinkler^false", "0.5 0.5", "Cloudy^false");
net.SetPTabular("Rain^true Rain^false", "0.8 0.2", "Cloudy^true");
net.SetPTabular("Rain^true Rain^false", "0.2 0.8", "Cloudy^false");
//
net.SetPTabular("WetGrass^true WetGrass^false", "0.99 0.01", "Rain^true Sprinkler^true ");
net.SetPTabular("WetGrass^true WetGrass^false", "0.9 0.1", "Sprinkler^true Rain^false");
net.SetPTabular("WetGrass^true WetGrass^false", "0.9 0.1", "Sprinkler^false Rain^true");
net.SetPTabular("WetGrass^true WetGrass^false", "0.0 1.0", "Sprinkler^false Rain^false");
//To get the probability distribution of the node we must call the GetPTabular method:
TokArr PCloudy = net.GetPTabular("Cloudy");
// Now it is possible to represent this distribution as string or as float numbers:
String PCloudyStr = String(PCloudy);
float PCloudyTrueF = PCloudy[0].FltValue();
float PCloudyFalseF = PCloudy[1].FltValue();
cout << PCloudyStr << std::endl << PCloudyTrueF << "," << PCloudyFalseF << std::endl;
TokArr PSprinkler = net.GetPTabular("Sprinkler", "Cloudy^true");
String PSprinklerStr = String(PSprinkler);
float PSprinklerTrue = PSprinkler[0].FltValue();
float PSprinklerFalse = PSprinkler[1].FltValue();
cout << PSprinklerStr << std::endl << PSprinklerTrue << "," << PSprinklerFalse << std::endl;
// net.EditEvidence("Cloudy^false WetGrass^false");
// if the above line is un commented then after the net line the evidence buffer will have "Sprinkler^true Cloudy^true WetGrass^false"
net.EditEvidence("Sprinkler^true Cloudy^true");
TokArr PRain = net.GetJPD("Rain");
// Now it is possible to represent this distribution as string or as float numbers:
String PRainStr = String(PRain);
float PRainTrueF = PRain[0].FltValue();
float PRainFalseF = PRain[1].FltValue();
cout << PRainStr << std::endl << PRainTrueF << "," << PRainFalseF << std::endl;
TokArr PWetGrass = net.GetJPD("WetGrass");
String PWetGrassStr = String(PWetGrass);
float PWetGrassTrue = PWetGrass[0].FltValue();
float PWetGrassFalse = PWetGrass[1].FltValue();
cout << PWetGrassStr << std::endl << PWetGrassTrue << "," << PWetGrassFalse << std::endl;
return 0;
}