public void testGibbsAsk_compare()
{
// create two nodes: parent and child with an arc from parent to child
IRandomVariable rvParent = new RandVar("Parent", new BooleanDomain());
IRandomVariable rvChild = new RandVar("Child", new BooleanDomain());
FullCPTNode nodeParent = new FullCPTNode(rvParent, new double[] { 0.7, 0.3 });
new FullCPTNode(rvChild, new double[] { 0.8, 0.2, 0.2, 0.8 }, nodeParent);
// create net
BayesNet net = new BayesNet(nodeParent);
// query parent probability
IRandomVariable[] rvX = new IRandomVariable[] { rvParent };
// ...given child evidence (true)
AssignmentProposition[] propE = new AssignmentProposition[] { new AssignmentProposition(rvChild, true) };
// sample with LikelihoodWeighting
ICategoricalDistribution samplesLW = new LikelihoodWeighting().Ask(rvX, propE, net, 1000);
Assert.AreEqual(0.9, samplesLW.getValue(true), DELTA_THRESHOLD);
// sample with RejectionSampling
ICategoricalDistribution samplesRS = new RejectionSampling().Ask(rvX, propE, net, 1000);
Assert.AreEqual(0.9, samplesRS.getValue(true), DELTA_THRESHOLD);
// sample with GibbsAsk
ICategoricalDistribution samplesGibbs = new GibbsAsk().Ask(rvX, propE, net, 1000);
Assert.AreEqual(0.9, samplesGibbs.getValue(true), DELTA_THRESHOLD);
}
public static FiniteProbabilityModel getUmbrellaWorldModel()
{
// Prior belief state
FiniteNode rain_tm1 = new FullCPTNode(ExampleRV.RAIN_tm1_RV,
new double[] { 0.5, 0.5 });
// Transition Model
FiniteNode rain_t = new FullCPTNode(ExampleRV.RAIN_t_RV, new double[]
{
// R_t-1 = true, R_t = true
0.7,
// R_t-1 = true, R_t = false
0.3,
// R_t-1 = false, R_t = true
0.3,
// R_t-1 = false, R_t = false
0.7
}, rain_tm1);
// Sensor Model
FiniteNode umbrealla_t = new FullCPTNode(ExampleRV.UMBREALLA_t_RV,
new double[]
{
// R_t = true, U_t = true
0.9,
// R_t = true, U_t = false
0.1,
// R_t = false, U_t = true
0.2,
// R_t = false, U_t = false
0.8
}, rain_t);
return(new FiniteBayesModel(new BayesNet(rain_tm1)));
}
public static BayesianNetwork constructToothacheCavityCatchNetwork()
{
FiniteNode cavity = new FullCPTNode(ExampleRV.CAVITY_RV, new double[]
{
0.2, 0.8
});
FiniteNode toothache = new FullCPTNode(ExampleRV.TOOTHACHE_RV,
new double[]
{
// C=true, T=true
0.6,
// C=true, T=false
0.4,
// C=false, T=true
0.1,
// C=false, T=false
0.9
}, cavity);
FiniteNode catchN = new FullCPTNode(ExampleRV.CATCH_RV, new double[]
{
// C=true, Catch=true
0.9,
// C=true, Catch=false
0.1,
// C=false, Catch=true
0.2,
// C=false, Catch=false
0.8
}, cavity);
return new BayesNet(cavity);
}
public static BayesianNetwork constructToothacheCavityCatchNetwork()
{
FiniteNode cavity = new FullCPTNode(ExampleRV.CAVITY_RV, new double[]
{
0.2, 0.8
});
FiniteNode toothache = new FullCPTNode(ExampleRV.TOOTHACHE_RV,
new double[]
{
// C=true, T=true
0.6,
// C=true, T=false
0.4,
// C=false, T=true
0.1,
// C=false, T=false
0.9
}, cavity);
FiniteNode catchN = new FullCPTNode(ExampleRV.CATCH_RV, new double[]
{
// C=true, Catch=true
0.9,
// C=true, Catch=false
0.1,
// C=false, Catch=true
0.2,
// C=false, Catch=false
0.8
}, cavity);
return(new BayesNet(cavity));
}
public static BayesianNetwork constructBurglaryAlarmNetwork()
{
FiniteNode burglary = new FullCPTNode(ExampleRV.BURGLARY_RV,
new double[] { 0.001, 0.999 });
FiniteNode earthquake = new FullCPTNode(ExampleRV.EARTHQUAKE_RV,
new double[] { 0.002, 0.998 });
FiniteNode alarm = new FullCPTNode(ExampleRV.ALARM_RV, new double[]
{
// B=true, E=true, A=true
0.95,
// B=true, E=true, A=false
0.05,
// B=true, E=false, A=true
0.94,
// B=true, E=false, A=false
0.06,
// B=false, E=true, A=true
0.29,
// B=false, E=true, A=false
0.71,
// B=false, E=false, A=true
0.001,
// B=false, E=false, A=false
0.999
}, burglary, earthquake);
FiniteNode johnCalls = new FullCPTNode(ExampleRV.JOHN_CALLS_RV,
new double[]
{
// A=true, J=true
0.90,
// A=true, J=false
0.10,
// A=false, J=true
0.05,
// A=false, J=false
0.95
}, alarm);
FiniteNode maryCalls = new FullCPTNode(ExampleRV.MARY_CALLS_RV,
new double[]
{
// A=true, M=true
0.70,
// A=true, M=false
0.30,
// A=false, M=true
0.01,
// A=false, M=false
0.99
}, alarm);
return(new BayesNet(burglary, earthquake));
}
public static BayesianNetwork constructCloudySprinklerRainWetGrassNetwork()
{
FiniteNode cloudy = new FullCPTNode(ExampleRV.CLOUDY_RV, new double[]
{
0.5, 0.5
});
FiniteNode sprinkler = new FullCPTNode(ExampleRV.SPRINKLER_RV,
new double[]
{
// Cloudy=true, Sprinkler=true
0.1,
// Cloudy=true, Sprinkler=false
0.9,
// Cloudy=false, Sprinkler=true
0.5,
// Cloudy=false, Sprinkler=false
0.5
}, cloudy);
FiniteNode rain = new FullCPTNode(ExampleRV.RAIN_RV, new double[]
{
// Cloudy=true, Rain=true
0.8,
// Cloudy=true, Rain=false
0.2,
// Cloudy=false, Rain=true
0.2,
// Cloudy=false, Rain=false
0.8
}, cloudy);
FiniteNode wetGrass = new FullCPTNode(ExampleRV.WET_GRASS_RV,
new double[]
{
// Sprinkler=true, Rain=true, WetGrass=true
.99,
// Sprinkler=true, Rain=true, WetGrass=false
.01,
// Sprinkler=true, Rain=false, WetGrass=true
.9,
// Sprinkler=true, Rain=false, WetGrass=false
.1,
// Sprinkler=false, Rain=true, WetGrass=true
.9,
// Sprinkler=false, Rain=true, WetGrass=false
.1,
// Sprinkler=false, Rain=false, WetGrass=true
0.0,
// Sprinkler=false, Rain=false, WetGrass=false
1.0
}, sprinkler, rain);
return(new BayesNet(cloudy));
}
public static IBayesianNetwork construct2FairDiceNetwor()
{
IFiniteNode dice1 = new FullCPTNode(ExampleRV.DICE_1_RV, new double[] {
1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0,
1.0 / 6.0
});
IFiniteNode dice2 = new FullCPTNode(ExampleRV.DICE_2_RV, new double[] {
1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0,
1.0 / 6.0
});
return(new BayesNet(dice1, dice2));
}
public static BayesianNetwork construct2FairDiceNetwor()
{
FiniteNode dice1 = new FullCPTNode(ExampleRV.DICE_1_RV, new double[]
{
1.0/6.0, 1.0/6.0, 1.0/6.0, 1.0/6.0, 1.0/6.0,
1.0/6.0
});
FiniteNode dice2 = new FullCPTNode(ExampleRV.DICE_2_RV, new double[]
{
1.0/6.0, 1.0/6.0, 1.0/6.0, 1.0/6.0, 1.0/6.0,
1.0/6.0
});
return new BayesNet(dice1, dice2);
}
public static BayesianNetwork constructToothacheCavityCatchWeatherNetwork()
{
FiniteNode cavity = new FullCPTNode(ExampleRV.CAVITY_RV, new double[]
{
0.2, 0.8
});
FiniteNode toothache = new FullCPTNode(ExampleRV.TOOTHACHE_RV,
new double[]
{
// C=true, T=true
0.6,
// C=true, T=false
0.4,
// C=false, T=true
0.1,
// C=false, T=false
0.9
}, cavity);
FiniteNode catchN = new FullCPTNode(ExampleRV.CATCH_RV, new double[]
{
// C=true, Catch=true
0.9,
// C=true, Catch=false
0.1,
// C=false, Catch=true
0.2,
// C=false, Catch=false
0.8
}, cavity);
FiniteNode weather = new FullCPTNode(ExampleRV.WEATHER_RV,
new double[]
{
// sunny
0.6,
// rain
0.1,
// cloudy
0.29,
// snow
0.01
});
return new BayesNet(cavity, weather);
}
public static BayesianNetwork constructToothacheCavityCatchWeatherNetwork()
{
FiniteNode cavity = new FullCPTNode(ExampleRV.CAVITY_RV, new double[]
{
0.2, 0.8
});
FiniteNode toothache = new FullCPTNode(ExampleRV.TOOTHACHE_RV,
new double[]
{
// C=true, T=true
0.6,
// C=true, T=false
0.4,
// C=false, T=true
0.1,
// C=false, T=false
0.9
}, cavity);
FiniteNode catchN = new FullCPTNode(ExampleRV.CATCH_RV, new double[]
{
// C=true, Catch=true
0.9,
// C=true, Catch=false
0.1,
// C=false, Catch=true
0.2,
// C=false, Catch=false
0.8
}, cavity);
FiniteNode weather = new FullCPTNode(ExampleRV.WEATHER_RV,
new double[]
{
// sunny
0.6,
// rain
0.1,
// cloudy
0.29,
// snow
0.01
});
return(new BayesNet(cavity, weather));
}
/**
* Return a Dynamic Bayesian Network of the Umbrella World Network.
*
* @return a Dynamic Bayesian Network of the Umbrella World Network.
*/
public static DynamicBayesianNetwork getUmbrellaWorldNetwork()
{
FiniteNode prior_rain_tm1 = new FullCPTNode(ExampleRV.RAIN_tm1_RV,
new double[] { 0.5, 0.5 });
BayesNet priorNetwork = new BayesNet(prior_rain_tm1);
// Prior belief state
FiniteNode rain_tm1 = new FullCPTNode(ExampleRV.RAIN_tm1_RV,
new double[] { 0.5, 0.5 });
// Transition Model
FiniteNode rain_t = new FullCPTNode(ExampleRV.RAIN_t_RV, new double[]
{
// R_t-1 = true, R_t = true
0.7,
// R_t-1 = true, R_t = false
0.3,
// R_t-1 = false, R_t = true
0.3,
// R_t-1 = false, R_t = false
0.7
}, rain_tm1);
// Sensor Model
FiniteNode umbrealla_t = new FullCPTNode(ExampleRV.UMBREALLA_t_RV,
new double[]
{
// R_t = true, U_t = true
0.9,
// R_t = true, U_t = false
0.1,
// R_t = false, U_t = true
0.2,
// R_t = false, U_t = false
0.8
}, rain_t);
Map <RandomVariable, RandomVariable> X_0_to_X_1 = new HashMap <RandomVariable, RandomVariable>();
X_0_to_X_1.put(ExampleRV.RAIN_tm1_RV, ExampleRV.RAIN_t_RV);
Set <RandomVariable> E_1 = new HashSet <RandomVariable>();
E_1.add(ExampleRV.UMBREALLA_t_RV);
return(new DynamicBayesNet(priorNetwork, X_0_to_X_1, E_1, rain_tm1));
}
/**
* Return a Dynamic Bayesian Network of the Umbrella World Network.
*
* @return a Dynamic Bayesian Network of the Umbrella World Network.
*/
public static DynamicBayesianNetwork getUmbrellaWorldNetwork()
{
FiniteNode prior_rain_tm1 = new FullCPTNode(ExampleRV.RAIN_tm1_RV,
new double[] {0.5, 0.5});
BayesNet priorNetwork = new BayesNet(prior_rain_tm1);
// Prior belief state
FiniteNode rain_tm1 = new FullCPTNode(ExampleRV.RAIN_tm1_RV,
new double[] {0.5, 0.5});
// Transition Model
FiniteNode rain_t = new FullCPTNode(ExampleRV.RAIN_t_RV, new double[]
{
// R_t-1 = true, R_t = true
0.7,
// R_t-1 = true, R_t = false
0.3,
// R_t-1 = false, R_t = true
0.3,
// R_t-1 = false, R_t = false
0.7
}, rain_tm1);
// Sensor Model
FiniteNode umbrealla_t = new FullCPTNode(ExampleRV.UMBREALLA_t_RV,
new double[]
{
// R_t = true, U_t = true
0.9,
// R_t = true, U_t = false
0.1,
// R_t = false, U_t = true
0.2,
// R_t = false, U_t = false
0.8
}, rain_t);
Map<RandomVariable, RandomVariable> X_0_to_X_1 = new HashMap<RandomVariable, RandomVariable>();
X_0_to_X_1.put(ExampleRV.RAIN_tm1_RV, ExampleRV.RAIN_t_RV);
Set<RandomVariable> E_1 = new HashSet<RandomVariable>();
E_1.add(ExampleRV.UMBREALLA_t_RV);
return new DynamicBayesNet(priorNetwork, X_0_to_X_1, E_1, rain_tm1);
}
public static IBayesianNetwork constructMeningitisStiffNeckNetwork()
{
IFiniteNode meningitis = new FullCPTNode(ExampleRV.MENINGITIS_RV,
new double[] { 1.0 / 50000.0, 1.0 - (1.0 / 50000.0) });
IFiniteNode stiffneck = new FullCPTNode(ExampleRV.STIFF_NECK_RV,
new double[] {
// M=true, S=true
0.7,
// M=true, S=false
0.3,
// M=false, S=true
0.009986199723994478,
// M=false, S=false
0.9900138002760055
}, meningitis);
return(new BayesNet(meningitis));
}
/**
* Return a Dynamic Bayesian Network of the Umbrella World Network.
*
* @return a Dynamic Bayesian Network of the Umbrella World Network.
*/
public static IDynamicBayesianNetwork getUmbrellaWorldNetwork()
{
IFiniteNode prior_rain_tm1 = new FullCPTNode(ExampleRV.RAIN_tm1_RV, new double[] { 0.5, 0.5 });
BayesNet priorNetwork = new BayesNet(prior_rain_tm1);
// Prior belief state
IFiniteNode rain_tm1 = new FullCPTNode(ExampleRV.RAIN_tm1_RV, new double[] { 0.5, 0.5 });
// Transition Model
IFiniteNode rain_t = new FullCPTNode(ExampleRV.RAIN_t_RV, new double[] {
// R_t-1 = true, R_t = true
0.7,
// R_t-1 = true, R_t = false
0.3,
// R_t-1 = false, R_t = true
0.3,
// R_t-1 = false, R_t = false
0.7
}, rain_tm1);
// Sensor Model
IFiniteNode umbrealla_t = new FullCPTNode(ExampleRV.UMBREALLA_t_RV,
new double[] {
// R_t = true, U_t = true
0.9,
// R_t = true, U_t = false
0.1,
// R_t = false, U_t = true
0.2,
// R_t = false, U_t = false
0.8
}, rain_t);
IMap <IRandomVariable, IRandomVariable> X_0_to_X_1 = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, IRandomVariable>();
X_0_to_X_1.Put(ExampleRV.RAIN_tm1_RV, ExampleRV.RAIN_t_RV);
ISet <IRandomVariable> E_1 = CollectionFactory.CreateSet <IRandomVariable>();
E_1.Add(ExampleRV.UMBREALLA_t_RV);
return(new DynamicBayesNet(priorNetwork, X_0_to_X_1, E_1, rain_tm1));
}
public static BayesianNetwork constructCloudySprinklerRainWetGrassNetwork()
{
FiniteNode cloudy = new FullCPTNode(ExampleRV.CLOUDY_RV, new double[]
{
0.5, 0.5
});
FiniteNode sprinkler = new FullCPTNode(ExampleRV.SPRINKLER_RV,
new double[]
{
// Cloudy=true, Sprinkler=true
0.1,
// Cloudy=true, Sprinkler=false
0.9,
// Cloudy=false, Sprinkler=true
0.5,
// Cloudy=false, Sprinkler=false
0.5
}, cloudy);
FiniteNode rain = new FullCPTNode(ExampleRV.RAIN_RV, new double[]
{
// Cloudy=true, Rain=true
0.8,
// Cloudy=true, Rain=false
0.2,
// Cloudy=false, Rain=true
0.2,
// Cloudy=false, Rain=false
0.8
}, cloudy);
FiniteNode wetGrass = new FullCPTNode(ExampleRV.WET_GRASS_RV,
new double[]
{
// Sprinkler=true, Rain=true, WetGrass=true
.99,
// Sprinkler=true, Rain=true, WetGrass=false
.01,
// Sprinkler=true, Rain=false, WetGrass=true
.9,
// Sprinkler=true, Rain=false, WetGrass=false
.1,
// Sprinkler=false, Rain=true, WetGrass=true
.9,
// Sprinkler=false, Rain=true, WetGrass=false
.1,
// Sprinkler=false, Rain=false, WetGrass=true
0.0,
// Sprinkler=false, Rain=false, WetGrass=false
1.0
}, sprinkler, rain);
return new BayesNet(cloudy);
}
public static BayesianNetwork constructBurglaryAlarmNetwork()
{
FiniteNode burglary = new FullCPTNode(ExampleRV.BURGLARY_RV,
new double[] {0.001, 0.999});
FiniteNode earthquake = new FullCPTNode(ExampleRV.EARTHQUAKE_RV,
new double[] {0.002, 0.998});
FiniteNode alarm = new FullCPTNode(ExampleRV.ALARM_RV, new double[]
{
// B=true, E=true, A=true
0.95,
// B=true, E=true, A=false
0.05,
// B=true, E=false, A=true
0.94,
// B=true, E=false, A=false
0.06,
// B=false, E=true, A=true
0.29,
// B=false, E=true, A=false
0.71,
// B=false, E=false, A=true
0.001,
// B=false, E=false, A=false
0.999
}, burglary, earthquake);
FiniteNode johnCalls = new FullCPTNode(ExampleRV.JOHN_CALLS_RV,
new double[]
{
// A=true, J=true
0.90,
// A=true, J=false
0.10,
// A=false, J=true
0.05,
// A=false, J=false
0.95
}, alarm);
FiniteNode maryCalls = new FullCPTNode(ExampleRV.MARY_CALLS_RV,
new double[]
{
// A=true, M=true
0.70,
// A=true, M=false
0.30,
// A=false, M=true
0.01,
// A=false, M=false
0.99
}, alarm);
return new BayesNet(burglary, earthquake);
}
public static BayesianNetwork constructMeningitisStiffNeckNetwork()
{
FiniteNode meningitis = new FullCPTNode(ExampleRV.MENINGITIS_RV,
new double[] {1.0/50000.0, 1.0 - (1.0/50000.0)});
FiniteNode stiffneck = new FullCPTNode(ExampleRV.STIFF_NECK_RV,
new double[]
{
// M=true, S=true
0.7,
// M=true, S=false
0.3,
// M=false, S=true
0.009986199723994478,
// M=false, S=false
0.9900138002760055
}, meningitis);
return new BayesNet(meningitis);
}
