Function nodes example analysis in Python

# __author__ = 'Bayes Server'
# __version__= '0.1'

import jpype  # pip install jpype1    (version 1.2.1 or later)
import jpype.imports
from jpype.types import *

classpath = "lib/bayesserver-10.8.jar"  # TODO download the Bayes Server Java API, and adjust the path

# Launch the JVM
jpype.startJVM(classpath=[classpath])

# import the Java modules

from com.bayesserver import *
from com.bayesserver.inference import *

# In this example we programatically create and query the sample
# network 'Functions' included with the Bayes Server User Interface.

network = Network("Functions")
nodes = network.getNodes()

# add the variables/nodes

d1 = State('D1')
d2 = State('D2')
d = Variable('D', [d1, d2])
d.setExpressionAlias('d')
network.getNodes().add(Node(d))

c = Variable('C', VariableValueType.CONTINUOUS)
c.setExpressionAlias('c')
nodes.add(Node(c))

f1 = Variable('F1', VariableValueType.FUNCTION)
f1.setExpressionAlias('f1')
nodes.add(Node(f1))

f2 = Variable('F2', VariableValueType.FUNCTION)
network.getNodes().add(Node(f2))

f3 = Variable('F3', VariableValueType.FUNCTION)
nodes.add(Node(f3))

# add some directed links

links = network.getLinks()
links.add(Link(d.getNode(), c.getNode()))
links.add(Link(f1.getNode(), f2.getNode()))  # the expression in f2 uses the result from f1, so we need a link here

tableD = d.getNode().newDistribution().getTable()

tableD.set(0.6961106280502056, d1)
tableD.set(0.3038893719497943, d2)

d.getNode().setDistribution(tableD)

gaussianC = c.getNode().newDistribution()
gaussianC.setMean(c, 100.0, d1)
gaussianC.setVariance(c, 20.0, d1)
gaussianC.setMean(c, -50.0, d2)
gaussianC.setVariance(c, 45.0, d2)

c.getNode().setDistribution(gaussianC)

f1.setFunction(FunctionVariableExpression(
    '''
    var d1 = d.States.Get("D1", true);
    var probD1 = ctx.TableValue(d1);

    var meanC1 = ctx.Mean(c);

    return probD1 * meanC1;
    ''',
    ExpressionReturnType.DOUBLE))

f2.setFunction(FunctionVariableExpression(
    '''
    var f1Val = ctx.FunctionValue(f1);

    return f1Val * 2.0;
    ''',
    ExpressionReturnType.DOUBLE))

f3.setFunction(FunctionVariableExpression(
    '''
    return "PI = " + Math.PI;
    ''',
    ExpressionReturnType.STRING))


# The network is now fully specified

# Now we will query the nodes

factory = RelevanceTreeInferenceFactory()
inference = factory.createInferenceEngine(network)
queryOptions = factory.createQueryOptions()
queryOutput = factory.createQueryOutput()

# set any evidence, e.g.
# inference.Evidence.SetState(d1);  // set D = D1

queryD = Table(d)
inference.getQueryDistributions().add(queryD)

queryC = CLGaussian(c)
inference.getQueryDistributions().add(queryC)

queryF1 = QueryFunctionOutput(f1)
inference.getQueryFunctions().add(queryF1)

queryF2 = QueryFunctionOutput(f2)
inference.getQueryFunctions().add(queryF2)

queryF3 = QueryFunctionOutput(f3)
inference.getQueryFunctions().add(queryF3)

inference.query(queryOptions, queryOutput) # note that this can raise an exception (see help for details)

print(f"P(D|-) = {{ {queryD.get(d1)}, {queryD.get(d2)} }}.")
print(f"P(C|-) = {{ {queryC.getMean(c)}, {queryC.getVariance(c)} }}.")

print(f"F1 = {{ {queryF1.getValue()} }}.")
print(f"F2 = {{ {queryF2.getValue()} }}.")
print(f"F3 = {{ {queryF3.getValue()} }}.")

# expected output...

# P(D | -) = { 0.6961106280502056,0.3038893719497943 }.
# P(C | -) = { 54.41659420753085,4787.261219528207 }.
# F1 = { 37.87996957015748 }.
# F2 = { 75.75993914031496 }.
# F3 = { PI = 3.141592653589793 }.