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 Execute(IExampleInterface app)
{
// Create a Bayesian network
BayesianNetwork network = new BayesianNetwork();
// Create the Uber driver event
BayesianEvent UberDriver = network.CreateEvent("uber_driver");
// create the witness event
BayesianEvent WitnessSawUberDriver = network.CreateEvent("saw_uber_driver");
// Attach the two
network.CreateDependency(UberDriver, WitnessSawUberDriver);
network.FinalizeStructure();
// build the truth tables
UberDriver?.Table?.AddLine(0.85, true);
WitnessSawUberDriver?.Table?.AddLine(0.80, true, true);
WitnessSawUberDriver?.Table?.AddLine(0.20, true, false);
network.Validate();
Console.WriteLine(network.ToString());
Console.WriteLine($"Parameter count: {network.CalculateParameterCount()}");
EnumerationQuery query = new EnumerationQuery(network);
// The evidence is that someone saw the Uber driver hit the car
query.DefineEventType(WitnessSawUberDriver, EventType.Evidence);
// The result was the Uber driver did it
query.DefineEventType(UberDriver, EventType.Outcome);
query.SetEventValue(WitnessSawUberDriver, false);
query.SetEventValue(UberDriver, false);
query.Execute();
Console.WriteLine(query.ToString());
}
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());
}
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());
}
public void Execute(IExampleInterface app)
{
BayesianNetwork network = new BayesianNetwork();
BayesianEvent grass = network.CreateEvent("grass");
BayesianEvent sprinkler = network.CreateEvent("sprinkler");
BayesianEvent rain = network.CreateEvent("rain");
BayesianEvent DidRain = network.CreateEvent("did_rain");
BayesianEvent SprinklerOn = network.CreateEvent("sprinkler_on");
network.CreateDependency(rain, DidRain);
network.CreateDependency(sprinkler, SprinklerOn);
network.FinalizeStructure();
// build the truth tales
rain.Table.AddLine(0.35, true);
// 80% of the time they were right
DidRain.Table.AddLine(0.80, false, true);
// 20% of the time they were wrong
DidRain.Table.AddLine(0.20, true, true);
sprinkler.Table.AddLine(.65, true);
// 60% of the time they were right
SprinklerOn.Table.AddLine(.40, true, true);
// 40% of the time they were wrong
SprinklerOn.Table.AddLine(.60, false, true);
// 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(SprinklerOn, EventType.Evidence);
query.DefineEventType(sprinkler, EventType.Outcome);
query.DefineEventType(DidRain, EventType.Evidence);
query.DefineEventType(rain, EventType.Outcome);
query.Execute();
Console.WriteLine(query.ToString());
}
public void Validate(BayesianNetwork network)
{
Assert.AreEqual(3, network.CalculateParameterCount());
}
