public static void main(String[] args) throws Exception {
// We first load the WasteIncinerator bayesian network which has multinomial and Gaussian
// variables.
BayesianNetwork bn = BayesianNetworkLoader.loadFromFile("./networks/WasteIncinerator.bn");
// We recover the relevant variables for this example: Mout which is normally distributed, and W
// which is multinomial.
Variable varMout = bn.getVariables().getVariableByName("Mout");
Variable varW = bn.getVariables().getVariableByName("W");
// First we create an instance of a inference algorithm. In this case, we use the
// ImportanceSampling class.
ImportanceSampling inferenceAlgorithm = new ImportanceSampling();
// Then, we set the BN model
inferenceAlgorithm.setModel(bn);
System.out.println(bn.toString());
// If it exists, we also set the evidence.
Assignment assignment = new HashMapAssignment(1);
assignment.setValue(varW, 0);
inferenceAlgorithm.setEvidence(assignment);
// We can also set to be run in parallel on multicore CPUs
inferenceAlgorithm.setParallelMode(true);
// To perform more than one operation, data should be keep in memory
inferenceAlgorithm.setKeepDataOnMemory(true);
// Then we run inference
inferenceAlgorithm.runInference();
// Then we query the posterior of
System.out.println("P(Mout|W=0) = " + inferenceAlgorithm.getPosterior(varMout));
// Or some more refined queries
System.out.println(
"P(0.7<Mout<6.59 | W=0) = "
+ inferenceAlgorithm.getExpectedValue(varMout, v -> (0.7 < v && v < 6.59) ? 1.0 : 0.0));
// We can also compute the probability of the evidence
System.out.println("P(W=0) = " + Math.exp(inferenceAlgorithm.getLogProbabilityOfEvidence()));
}
@Test
public void testNoFading() throws IOException, ClassNotFoundException {
// load the true WasteIncinerator hugin Bayesian network containing 3 Multinomial and 6 Gaussian
// variables
BayesianNetwork trueBN = BayesianNetworkLoader.loadFromFile("./networks/WasteIncinerator.bn");
System.out.println("\nWasteIncinerator network \n ");
System.out.println(trueBN.getDAG().toString());
System.out.println(trueBN.toString());
// Sampling from trueBN
BayesianNetworkSampler sampler = new BayesianNetworkSampler(trueBN);
sampler.setSeed(0);
// Load the sampled data
DataStream<DataInstance> data = sampler.sampleToDataStream(100000);
// Parameter Learning
MaximumLikelihoodFading maximumLikelihoodFading = new MaximumLikelihoodFading();
maximumLikelihoodFading.setLaplace(false);
maximumLikelihoodFading.setFadingFactor(1.0);
maximumLikelihoodFading.setBatchSize(1000);
maximumLikelihoodFading.setDAG(trueBN.getDAG());
maximumLikelihoodFading.setDataStream(data);
maximumLikelihoodFading.runLearning();
BayesianNetwork bnet = maximumLikelihoodFading.getLearntBayesianNetwork();
// Check if the probability distributions of each node
for (Variable var : trueBN.getVariables()) {
System.out.println("\n------ Variable " + var.getName() + " ------");
System.out.println("\nTrue distribution:\n" + trueBN.getConditionalDistribution(var));
System.out.println("\nLearned distribution:\n" + bnet.getConditionalDistribution(var));
Assert.assertTrue(
bnet.getConditionalDistribution(var)
.equalDist(trueBN.getConditionalDistribution(var), 0.05));
}
// Or check directly if the true and learned networks are equals
Assert.assertTrue(bnet.equalBNs(trueBN, 0.05));
}
