/// <summary>
/// Define the relationships.
/// </summary>
/// <param name="network">The network.</param>
public void DefineRelationships(BayesianNetwork network)
{
// define event relations, if they are not there already
ParsedEvent childParsed = ChildEvent;
BayesianEvent childEvent = network.RequireEvent(childParsed.Label);
foreach (ParsedEvent e in this.givenEvents)
{
BayesianEvent parentEvent = network.RequireEvent(e.Label);
network.CreateDependency(parentEvent, childEvent);
}
}
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);
}
static void Main(string[] args)
{
/*Przykład 4.2.3 Teoria Bayesa - Zakłady Produkcyjne Etap Manualny
* Lesson 1 - Machine Learning in C# for Amazon ML Group created by me :-)
* PS. Kill me I use polish :-)
*/
BayesianNetwork network = new BayesianNetwork();
BayesianEvent WadliwyElement = network.CreateEvent("wadliwy_element");
BayesianEvent A1 = network.CreateEvent("wadliwy_element_zaklad_A1");
BayesianEvent A2 = network.CreateEvent("wadliwy_element_zaklad_A2");
BayesianEvent A3 = network.CreateEvent("wadliwy_element_zaklad_A3");
BayesianEvent A4 = network.CreateEvent("wadliwy_element_zaklad_A4");
network.CreateDependency(WadliwyElement, A1, A2, A3, A4);
network.FinalizeStructure();
WadliwyElement?.Table?.AddLine(0.1083, true);
A1?.Table.AddLine(0.069, true, true);
A1?.Table.AddLine(1 - 0.069, true, false);
A2?.Table.AddLine(0.277, true, true);
A2?.Table.AddLine(1 - 0.277, true, false);
A3?.Table.AddLine(0.007, true, true);
A3?.Table.AddLine(1 - 0.007, true, false);
A4?.Table.AddLine(0.646, true, true);
A4?.Table.AddLine(1 - 0.646, true, false);
network.Validate();
Console.WriteLine(network.ToString() + "\n");
Console.WriteLine($"Liczba parametrów: {network.CalculateParameterCount()}");
EnumerationQuery query = new EnumerationQuery(network);
query.DefineEventType(WadliwyElement, EventType.Evidence);
query.DefineEventType(A1, EventType.Outcome);
query.DefineEventType(A2, EventType.Evidence);
query.DefineEventType(A3, EventType.Evidence);
query.DefineEventType(A4, EventType.Evidence);
query.SetEventValue(WadliwyElement, false);
query.SetEventValue(A1, false);
query.SetEventValue(A2, false);
query.SetEventValue(A3, false);
query.SetEventValue(A4, false);
query.Execute();
Console.WriteLine(query.ToString());
}
/// <summary>
/// Init to Naive Bayes.
/// </summary>
private void InitNaiveBayes()
{
// clear out anything from before
_network.RemoveAllRelations();
// locate the classification target event
BayesianEvent classificationTarget = _network
.ClassificationTargetEvent;
// now link everything to this event
foreach (BayesianEvent e in _network.Events)
{
if (e != classificationTarget)
{
_network.CreateDependency(classificationTarget, e);
}
}
_network.FinalizeStructure();
}
public void TestEnumeration1()
{
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();
EnumerationQuery query = new EnumerationQuery(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);
}
/// <summary>
/// Define the relationships.
/// </summary>
/// <param name="network">The network.</param>
public void DefineRelationships(BayesianNetwork network)
{
// define event relations, if they are not there already
ParsedEvent childParsed = ChildEvent;
BayesianEvent childEvent = network.RequireEvent(childParsed.Label);
foreach (ParsedEvent e in this.givenEvents)
{
BayesianEvent parentEvent = network.RequireEvent(e.Label);
network.CreateDependency(parentEvent, childEvent);
}
}
