//
// PRIVATE METHODS
//
private BayesNet createWetGrassNetwork()
{
BayesNetNode cloudy = new BayesNetNode("Cloudy");
BayesNetNode sprinkler = new BayesNetNode("Sprinkler");
BayesNetNode rain = new BayesNetNode("Rain");
BayesNetNode wetGrass = new BayesNetNode("WetGrass");
sprinkler.influencedBy(cloudy);
rain.influencedBy(cloudy);
wetGrass.influencedBy(rain, sprinkler);
cloudy.setProbability(true, 0.5);
sprinkler.setProbability(true, 0.10);
sprinkler.setProbability(false, 0.50);
rain.setProbability(true, 0.8);
rain.setProbability(false, 0.2);
wetGrass.setProbability(true, true, 0.99);
wetGrass.setProbability(true, false, 0.90);
wetGrass.setProbability(false, true, 0.90);
wetGrass.setProbability(false, false, 0.00);
BayesNet net = new BayesNet(cloudy);
return(net);
}
public double[] mcmcAsk(String X, Dictionary <String, bool> evidence,
int numberOfVariables, Randomizer r)
{
double[] retval = new double[2];
List <String> _nonEvidenceVariables = nonEvidenceVariables(evidence, X);
Dictionary <String, bool> evt = createRandomEvent(
_nonEvidenceVariables, evidence, r);
for (int j = 0; j < numberOfVariables; j++)
{
foreach (string variable in _nonEvidenceVariables)
{
BayesNetNode node = getNodeOf(variable);
List <BayesNetNode> blanket = markovBlanket(node);
Dictionary <string, bool> mb = createMBValues(blanket, evt);
// event.put(node.getVariable(), node.isTrueFor(
// r.getProbability(), mb));
evt[node.getVariable()] = truthValue(rejectionSample(node
.getVariable(), mb, 100, r), r);
bool queryValue = evt[X];
if (queryValue)
{
retval[0] += 1;
}
else
{
retval[1] += 1;
}
}
}
return(Util.normalize(retval));
}
//
// PRIVATE METHODS
//
private BayesNet createBurglaryNetwork()
{
BayesNetNode burglary = new BayesNetNode("Burglary");
BayesNetNode earthquake = new BayesNetNode("EarthQuake");
BayesNetNode alarm = new BayesNetNode("Alarm");
BayesNetNode johnCalls = new BayesNetNode("JohnCalls");
BayesNetNode maryCalls = new BayesNetNode("MaryCalls");
alarm.influencedBy(burglary, earthquake);
johnCalls.influencedBy(alarm);
maryCalls.influencedBy(alarm);
burglary.setProbability(true, 0.001);// TODO behaviour changes if
// root node
earthquake.setProbability(true, 0.002);
alarm.setProbability(true, true, 0.95);
alarm.setProbability(true, false, 0.94);
alarm.setProbability(false, true, 0.29);
alarm.setProbability(false, false, 0.001);
johnCalls.setProbability(true, 0.90);
johnCalls.setProbability(false, 0.05);
maryCalls.setProbability(true, 0.70);
maryCalls.setProbability(false, 0.01);
BayesNet net = new BayesNet(burglary, earthquake);
return net;
}
//
// PRIVATE METHODS
//
private BayesNet createBurglaryNetwork()
{
BayesNetNode burglary = new BayesNetNode("Burglary");
BayesNetNode earthquake = new BayesNetNode("EarthQuake");
BayesNetNode alarm = new BayesNetNode("Alarm");
BayesNetNode johnCalls = new BayesNetNode("JohnCalls");
BayesNetNode maryCalls = new BayesNetNode("MaryCalls");
alarm.influencedBy(burglary, earthquake);
johnCalls.influencedBy(alarm);
maryCalls.influencedBy(alarm);
burglary.setProbability(true, 0.001);
earthquake.setProbability(true, 0.002);
alarm.setProbability(true, true, 0.95);
alarm.setProbability(true, false, 0.94);
alarm.setProbability(false, true, 0.29);
alarm.setProbability(false, false, 0.001);
johnCalls.setProbability(true, 0.90);
johnCalls.setProbability(false, 0.05);
maryCalls.setProbability(true, 0.70);
maryCalls.setProbability(false, 0.01);
BayesNet net = new BayesNet(burglary, earthquake);
return(net);
}
public void setUp()
{
a = new BayesNetNode("A");
b = new BayesNetNode("B");
c = new BayesNetNode("C");
d = new BayesNetNode("D");
e = new BayesNetNode("E");
c.influencedBy(a, b);
d.influencedBy(c);
e.influencedBy(c);
}
public void setUp()
{
a = new BayesNetNode("A");
b = new BayesNetNode("B");
c = new BayesNetNode("C");
d = new BayesNetNode("D");
e = new BayesNetNode("E");
c.influencedBy(a, b);
d.influencedBy(c);
e.influencedBy(c);
}
public double probabilityOf(String Y, bool value,
Dictionary <String, bool> evidence)
{
BayesNetNode y = getNodeOf(Y);
if (y == null)
{
throw new ApplicationException("Unable to find a node with variable "
+ Y);
}
else
{
List <BayesNetNode> parentNodes = y.getParents();
if (parentNodes.Count == 0)
{// root nodes
Dictionary <String, bool> YTable = new Dictionary <String, bool>();
YTable.Add(Y, value);
double prob = y.probabilityOf(YTable);
return(prob);
}
else
{// non rootnodes
Dictionary <String, bool> parentValues = new Dictionary <String, bool>();
foreach (BayesNetNode parent in parentNodes)
{
parentValues.Add(parent.getVariable(), evidence[parent
.getVariable()]);
}
double prob = y.probabilityOf(parentValues);
if (value.Equals(true))
{
return(prob);
}
else
{
return(1.0 - prob);
}
}
}
}
private List <BayesNetNode> markovBlanket(BayesNetNode node,
List <BayesNetNode> soFar)
{
// parents
List <BayesNetNode> parents = node.getParents();
foreach (BayesNetNode parent in parents)
{
if (!soFar.Contains(parent))
{
soFar.Add(parent);
}
}
// children
List <BayesNetNode> children = node.getChildren();
foreach (BayesNetNode child in children)
{
if (!soFar.Contains(child))
{
soFar.Add(child);
List <BayesNetNode> childsParents = child.getParents();
foreach (BayesNetNode childsParent in childsParents)
{
;
if ((!soFar.Contains(childsParent)) &&
(!(childsParent.Equals(node))))
{
soFar.Add(childsParent);
}
} // childsParents
} // end contains child
} // end child
return(soFar);
}
public BayesNet(BayesNetNode root1, BayesNetNode root2, BayesNetNode root3) : this(root1, root2)
{
roots.Add(root3);
}
//
// PRIVATE METHODS
//
private BayesNet createWetGrassNetwork()
{
BayesNetNode cloudy = new BayesNetNode("Cloudy");
BayesNetNode sprinkler = new BayesNetNode("Sprinkler");
BayesNetNode rain = new BayesNetNode("Rain");
BayesNetNode wetGrass = new BayesNetNode("WetGrass");
sprinkler.influencedBy(cloudy);
rain.influencedBy(cloudy);
wetGrass.influencedBy(rain, sprinkler);
cloudy.setProbability(true, 0.5);
sprinkler.setProbability(true, 0.10);
sprinkler.setProbability(false, 0.50);
rain.setProbability(true, 0.8);
rain.setProbability(false, 0.2);
wetGrass.setProbability(true, true, 0.99);
wetGrass.setProbability(true, false, 0.90);
wetGrass.setProbability(false, true, 0.90);
wetGrass.setProbability(false, false, 0.00);
BayesNet net = new BayesNet(cloudy);
return net;
}
public BayesNet(BayesNetNode root1, BayesNetNode root2) : this(root1)
{
roots.Add(root2);
}
public BayesNet(BayesNetNode root1, BayesNetNode root2, BayesNetNode root3) : this(root1, root2)
{
roots.Add(root3);
}
private List <BayesNetNode> markovBlanket(BayesNetNode node)
{
return(markovBlanket(node, new List <BayesNetNode>()));
}
public BayesNet(BayesNetNode root)
{
roots.Add(root);
}
public BayesNet(BayesNetNode root1, BayesNetNode root2) : this(root1)
{
roots.Add(root2);
}
public BayesNet(BayesNetNode root)
{
roots.Add(root);
}
private List<BayesNetNode> markovBlanket(BayesNetNode node)
{
return markovBlanket(node, new List<BayesNetNode>());
}
private List<BayesNetNode> markovBlanket(BayesNetNode node,
List<BayesNetNode> soFar)
{
// parents
List<BayesNetNode> parents = node.getParents();
foreach (BayesNetNode parent in parents)
{
if (!soFar.Contains(parent))
{
soFar.Add(parent);
}
}
// children
List<BayesNetNode> children = node.getChildren();
foreach (BayesNetNode child in children)
{
if (!soFar.Contains(child))
{
soFar.Add(child);
List<BayesNetNode> childsParents = child.getParents();
foreach (BayesNetNode childsParent in childsParents)
{
;
if ((!soFar.Contains(childsParent))
&& (!(childsParent.Equals(node))))
{
soFar.Add(childsParent);
}
}// childsParents
}// end contains child
}// end child
return soFar;
}
