Exception with FactoredFrontier and VMP

[rcabanasdepaz]

When I use FactoredFrontier with VMP, I get the following exception. This is not an issue with importance Sampling.

Exception in thread "main" java.lang.IllegalStateException: NaN KL
	at eu.amidst.core.exponentialfamily.EF_Normal.kl(EF_Normal.java:355)
	at eu.amidst.core.inference.messagepassing.VMP.computeELBO(VMP.java:165)
	at eu.amidst.core.inference.messagepassing.VMP.lambda$computeLogProbabilityOfEvidence$90(VMP.java:144)
	at java.util.stream.ReferencePipeline$6$1.accept(ReferencePipeline.java:244)
	at java.util.stream.ReferencePipeline$2$1.accept(ReferencePipeline.java:175)
	at java.util.ArrayList$ArrayListSpliterator.forEachRemaining(ArrayList.java:1374)
	at java.util.stream.AbstractPipeline.copyInto(AbstractPipeline.java:481)
	at java.util.stream.AbstractPipeline.wrapAndCopyInto(AbstractPipeline.java:471)
	at java.util.stream.ReduceOps$ReduceOp.evaluateSequential(ReduceOps.java:708)
	at java.util.stream.AbstractPipeline.evaluate(AbstractPipeline.java:234)
	at java.util.stream.DoublePipeline.collect(DoublePipeline.java:476)
	at java.util.stream.DoublePipeline.sum(DoublePipeline.java:388)
	at eu.amidst.core.inference.messagepassing.VMP.computeLogProbabilityOfEvidence(VMP.java:144)
	at eu.amidst.core.inference.messagepassing.VMP.testConvergence(VMP.java:118)
	at eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm.runInference(MessagePassingAlgorithm.java:195)
	at eu.amidst.dynamic.inference.FactoredFrontierForDBN.runInference(FactoredFrontierForDBN.java:144)
	at eu.amidst.tutorial.usingAmidst.inference.DynModelInference.main(DynModelInference.java:70)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:497)
	at com.intellij.rt.execution.application.AppMain.main(AppMain.java:144)

The full code:

	Random rand = new Random(0);

		String path = "datasets/dynamic/noclassdata/";


		DataStream<DynamicDataInstance> data = DynamicDataStreamLoader.open(path+"data0.arff");

		//Learn the model
		DynamicModel model =
				new KalmanFilter(data.getAttributes())
				.setNumHidden(2);

		//Learn the distributions
		model.updateModel(data);

		//Obtain the learned dynamic BN
		DynamicBayesianNetwork dbn = model.getModel();

		// Print the dynamic BN and save it
		System.out.println(dan);


		//Select the inference algorithm
		InferenceAlgorithmForDBN infer = new FactoredFrontierForDBN(new VMP()); // new ImportanceSampling(),  new VMP(),
		infer.setModel(dan);

		// Set the Variables of interest
		Variable varTarget = dbn.getDynamicVariables().getVariableByName("gaussianHiddenVar1");

		for(int t=0; t<10; t++) {
			// Set the evidence
			HashMapDynamicAssignment assignment = new HashMapDynamicAssignment(2);
			assignment.setValue(dbn.getDynamicVariables().getVariableByName("GaussianVar9"), rand.nextDouble());
			assignment.setValue(dbn.getDynamicVariables().getVariableByName("GaussianVar8"), rand.nextDouble());
			assignment.setTimeID(t);

			// Run the inference
			infer.addDynamicEvidence(assignment);
			infer.runInference();

			// Get the posterior at current instant of time
			Distribution posterior_t = infer.getFilteredPosterior(varTarget);
			System.out.println("t="+t+" "+posterior_t);

			// Get the posterior in the future
			Distribution posterior_t_1 = infer.getPredictivePosterior(varTarget, 1);
			System.out.println("t="+t+"+1 "+posterior_t_1);

		}