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 TestIndependant2()
{
BayesianNetwork network = new BayesianNetwork();
BayesianEvent a = network.CreateEvent("a");
BayesianEvent b = network.CreateEvent("b");
BayesianEvent c = network.CreateEvent("c");
BayesianEvent d = network.CreateEvent("d");
network.CreateDependency(a, b, c);
network.CreateDependency(b, d);
network.CreateDependency(c, d);
network.FinalizeStructure();
Assert.IsFalse(network.IsCondIndependent(b, c));
Assert.IsFalse(network.IsCondIndependent(b, c, d));
Assert.IsTrue(network.IsCondIndependent(a, c, a));
Assert.IsFalse(network.IsCondIndependent(a, c, a, d));
}
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);
}
public void Validate(BayesianNetwork network)
{
Assert.AreEqual(3, network.CalculateParameterCount());
}
/// <inheritdoc/>
public Object Read(Stream istream)
{
BayesianNetwork result = new BayesianNetwork();
EncogReadHelper input = new EncogReadHelper(istream);
EncogFileSection section;
String queryType = "";
String queryStr = "";
String contentsStr = "";
while ((section = input.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BAYES-NETWORK")
&& section.SubSectionName.Equals("BAYES-PARAM"))
{
IDictionary<String, String> p = section.ParseParams();
queryType = p["queryType"];
queryStr = p["query"];
contentsStr = p["contents"];
}
if (section.SectionName.Equals("BAYES-NETWORK")
&& section.SubSectionName.Equals("BAYES-TABLE"))
{
result.Contents = contentsStr;
// first, define relationships (1st pass)
foreach (String line in section.Lines)
{
result.DefineRelationship(line);
}
result.FinalizeStructure();
// now define the probabilities (2nd pass)
foreach (String line in section.Lines)
{
result.DefineProbability(line);
}
}
if (section.SectionName.Equals("BAYES-NETWORK")
&& section.SubSectionName.Equals("BAYES-PROPERTIES"))
{
IDictionary<String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
}
// define query, if it exists
if (queryType.Length > 0)
{
IBayesianQuery query = null;
if (queryType.Equals("EnumerationQuery"))
{
query = new EnumerationQuery(result);
}
else
{
query = new SamplingQuery(result);
}
if (query != null && queryStr.Length > 0)
{
result.Query = query;
result.DefineClassificationStructure(queryStr);
}
}
return result;
}
/// <summary>
/// Create a bayesian network.
/// </summary>
/// <param name="architecture">The architecture to use.</param>
/// <param name="input">The input neuron count.</param>
/// <param name="output">The output neuron count.</param>
/// <returns>The new bayesian network.</returns>
public IMLMethod Create(String architecture, int input,
int output)
{
var method = new BayesianNetwork {Contents = architecture};
return method;
}
/// <inheritdoc/>
public Object Read(Stream istream)
{
BayesianNetwork result = new BayesianNetwork();
EncogReadHelper input = new EncogReadHelper(istream);
EncogFileSection section;
String queryType = "";
String queryStr = "";
String contentsStr = "";
while ((section = input.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BAYES-NETWORK") &&
section.SubSectionName.Equals("BAYES-PARAM"))
{
IDictionary <String, String> p = section.ParseParams();
queryType = p["queryType"];
queryStr = p["query"];
contentsStr = p["contents"];
}
if (section.SectionName.Equals("BAYES-NETWORK") &&
section.SubSectionName.Equals("BAYES-TABLE"))
{
result.Contents = contentsStr;
// first, define relationships (1st pass)
foreach (String line in section.Lines)
{
result.DefineRelationship(line);
}
result.FinalizeStructure();
// now define the probabilities (2nd pass)
foreach (String line in section.Lines)
{
result.DefineProbability(line);
}
}
if (section.SectionName.Equals("BAYES-NETWORK") &&
section.SubSectionName.Equals("BAYES-PROPERTIES"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
}
// define query, if it exists
if (queryType.Length > 0)
{
IBayesianQuery query = null;
if (queryType.Equals("EnumerationQuery"))
{
query = new EnumerationQuery(result);
}
else
{
query = new SamplingQuery(result);
}
if (query != null && queryStr.Length > 0)
{
result.Query = query;
result.DefineClassificationStructure(queryStr);
}
}
return(result);
}
/// <inheritdoc/>
public void Save(Stream os, Object obj)
{
EncogWriteHelper o = new EncogWriteHelper(os);
BayesianNetwork b = (BayesianNetwork)obj;
o.AddSection("BAYES-NETWORK");
o.AddSubSection("BAYES-PARAM");
String queryType = "";
String queryStr = b.ClassificationStructure;
if (b.Query != null)
{
queryType = b.Query.GetType().Name;
}
o.WriteProperty("queryType", queryType);
o.WriteProperty("query", queryStr);
o.WriteProperty("contents", b.Contents);
o.AddSubSection("BAYES-PROPERTIES");
o.AddProperties(b.Properties);
o.AddSubSection("BAYES-TABLE");
foreach (BayesianEvent e in b.Events)
{
foreach (TableLine line in e.Table.Lines)
{
if (line == null)
{
continue;
}
StringBuilder str = new StringBuilder();
str.Append("P(");
str.Append(BayesianEvent.FormatEventName(e, line.Result));
if (e.Parents.Count > 0)
{
str.Append("|");
}
int index = 0;
bool first = true;
foreach (BayesianEvent parentEvent in e.Parents)
{
if (!first)
{
str.Append(",");
}
first = false;
int arg = line.Arguments[index++];
if (parentEvent.IsBoolean)
{
if (arg == 0)
{
str.Append("+");
}
else
{
str.Append("-");
}
}
str.Append(parentEvent.Label);
if (!parentEvent.IsBoolean)
{
str.Append("=");
if (arg >= parentEvent.Choices.Count)
{
throw new BayesianError("Argument value " + arg + " is out of range for event " + parentEvent.ToString());
}
str.Append(parentEvent.GetChoice(arg));
}
}
str.Append(")=");
str.Append(line.Probability);
str.Append("\n");
o.Write(str.ToString());
}
}
o.Flush();
}
