public void Execute(IExampleInterface app)
{
// build the bayesian network structure
BayesianNetwork network = new BayesianNetwork();
BayesianEvent BlueTaxi = network.CreateEvent("blue_taxi");
BayesianEvent WitnessSawBlue = network.CreateEvent("saw_blue");
network.CreateDependency(BlueTaxi, WitnessSawBlue);
network.FinalizeStructure();
// build the truth tales
BlueTaxi.Table.AddLine(0.85, true);
WitnessSawBlue.Table.AddLine(0.80, true, true);
WitnessSawBlue.Table.AddLine(0.20, true, false);
// validate the network
network.Validate();
// display basic stats
Console.WriteLine(network.ToString());
Console.WriteLine("Parameter count: " + network.CalculateParameterCount());
EnumerationQuery query = new EnumerationQuery(network);
//SamplingQuery query = new SamplingQuery(network);
query.DefineEventType(WitnessSawBlue, EventType.Evidence);
query.DefineEventType(BlueTaxi, EventType.Outcome);
query.SetEventValue(WitnessSawBlue, false);
query.SetEventValue(BlueTaxi, false);
query.Execute();
Console.WriteLine(query.ToString());
}
public void TestSampling2()
{
BayesianNetwork network = new BayesianNetwork();
BayesianEvent a = network.CreateEvent("a");
BayesianEvent x1 = network.CreateEvent("x1");
BayesianEvent x2 = network.CreateEvent("x2");
BayesianEvent x3 = network.CreateEvent("x3");
network.CreateDependency(a, x1, x2, x3);
network.FinalizeStructure();
a.Table.AddLine(0.5, true); // P(A) = 0.5
x1.Table.AddLine(0.2, true, true); // p(x1|a) = 0.2
x1.Table.AddLine(0.6, true, false);// p(x1|~a) = 0.6
x2.Table.AddLine(0.2, true, true); // p(x2|a) = 0.2
x2.Table.AddLine(0.6, true, false);// p(x2|~a) = 0.6
x3.Table.AddLine(0.2, true, true); // p(x3|a) = 0.2
x3.Table.AddLine(0.6, true, false);// p(x3|~a) = 0.6
network.Validate();
SamplingQuery query = new SamplingQuery(network);
query.DefineEventType(x1, EventType.Evidence);
query.DefineEventType(x2, EventType.Evidence);
query.DefineEventType(x3, EventType.Evidence);
query.DefineEventType(a, EventType.Outcome);
query.SetEventValue(a, true);
query.SetEventValue(x1, true);
query.SetEventValue(x2, true);
query.SetEventValue(x3, false);
query.Execute();
TestPercent(query.Probability, 18);
}
public BayesianNetwork Create()
{
BayesianNetwork network = new BayesianNetwork();
BayesianEvent a = network.CreateEvent("a");
BayesianEvent b = network.CreateEvent("b");
network.CreateDependency(a, b);
network.FinalizeStructure();
a.Table.AddLine(0.5, true); // P(A) = 0.5
b.Table.AddLine(0.2, true, true); // p(b|a) = 0.2
b.Table.AddLine(0.8, true, false);// p(b|~a) = 0.8
network.Validate();
return network;
}
public void TestCount()
{
BayesianNetwork network = new BayesianNetwork();
BayesianEvent a = network.CreateEvent("a");
BayesianEvent b = network.CreateEvent("b");
BayesianEvent c = network.CreateEvent("c");
BayesianEvent d = network.CreateEvent("d");
BayesianEvent e = network.CreateEvent("e");
network.CreateDependency(a, b, d, e);
network.CreateDependency(c, d);
network.CreateDependency(b, e);
network.CreateDependency(d, e);
network.FinalizeStructure();
Assert.AreEqual(16, network.CalculateParameterCount());
}
public void TestIndependant()
{
BayesianNetwork network = new BayesianNetwork();
BayesianEvent a = network.CreateEvent("a");
BayesianEvent b = network.CreateEvent("b");
BayesianEvent c = network.CreateEvent("c");
BayesianEvent d = network.CreateEvent("d");
BayesianEvent e = network.CreateEvent("e");
network.CreateDependency(a, b, d, e);
network.CreateDependency(c, d);
network.CreateDependency(b, e);
network.CreateDependency(d, e);
network.FinalizeStructure();
Assert.IsFalse(network.IsCondIndependent(c, e, a));
Assert.IsFalse(network.IsCondIndependent(b, d, c, e));
Assert.IsFalse(network.IsCondIndependent(a, c, e));
Assert.IsTrue(network.IsCondIndependent(a, c, b));
}
public void TestSampling1()
{
BayesianNetwork network = new BayesianNetwork();
BayesianEvent a = network.CreateEvent("a");
BayesianEvent b = network.CreateEvent("b");
network.CreateDependency(a, b);
network.FinalizeStructure();
a.Table.AddLine(0.5, true); // P(A) = 0.5
b.Table.AddLine(0.2, true, true); // p(b|a) = 0.2
b.Table.AddLine(0.8, true, false);// p(b|~a) = 0.8
network.Validate();
SamplingQuery query = new SamplingQuery(network);
query.DefineEventType(a, EventType.Evidence);
query.DefineEventType(b, EventType.Outcome);
query.SetEventValue(b, true);
query.SetEventValue(a, true);
query.Execute();
TestPercent(query.Probability, 20);
}
public void TestK2Structure()
{
String[] labels = { "available", "not" };
IMLDataSet data = new BasicMLDataSet(DATA, null);
BayesianNetwork network = new BayesianNetwork();
BayesianEvent x1 = network.CreateEvent("x1", labels);
BayesianEvent x2 = network.CreateEvent("x2", labels);
BayesianEvent x3 = network.CreateEvent("x3", labels);
network.FinalizeStructure();
TrainBayesian train = new TrainBayesian(network, data, 10);
train.InitNetwork = BayesianInit.InitEmpty;
while (!train.TrainingDone)
{
train.Iteration();
}
train.Iteration();
Assert.IsTrue(x1.Parents.Count == 0);
Assert.IsTrue(x2.Parents.Count == 1);
Assert.IsTrue(x3.Parents.Count == 1);
Assert.IsTrue(x2.Parents.Contains(x1));
Assert.IsTrue(x3.Parents.Contains(x2));
Assert.AreEqual(0.714, network.GetEvent("x2").Table.FindLine(1, new int[] { 1 }).Probability, 0.001);
}