public static void main(String[] args) throws WrongConfigurationException { int seed=6236; int nSamples=5000; int nContinuousVars=10; DataStream<DataInstance> data = DataSetGenerator.generate(seed,nSamples,0,nContinuousVars); Model model = new FactorAnalysis(data.getAttributes()); System.out.println(model.getDAG()); model.updateModel(data); // for (DataOnMemory<DataInstance> batch : data.iterableOverBatches(1000)) { // model.updateModel(batch); // } System.out.println(model.getModel()); System.out.println(model.getPosteriorDistribution("LatentVar0").toString()); }
public static void main(String[] args) throws WrongConfigurationException { DataStream<DataInstance> data = DataSetGenerator.generate(1234,500, 0, 1); GaussianMixture GMM = new GaussianMixture(data.getAttributes()); GMM.setDiagonal(false); GMM.setNumStatesHiddenVar(2); GMM.updateModel(data); for (DataOnMemory<DataInstance> batch : data.iterableOverBatches(100)) { GMM.updateModel(batch); } System.out.println(GMM.getModel()); System.out.println(GMM.getDAG()); System.out.println("HiddenVar"); System.out.println(GMM.getPosteriorDistribution("HiddenVar").toString()); /* try { DataStreamWriter.writeDataToFile(data, "tmp/gmm2vars.arff"); } catch (IOException e) { e.printStackTrace(); } */ } }
model.updateModel(batch); System.out.println(model.getPosteriorDistribution("GlobalHidden_0"). toString());
public static void main(String[] args) throws WrongConfigurationException { DataStream<DataInstance> data = DataSetGenerator.generate(0,1000, 0, 10); System.out.println(data.getAttributes().toString()); String className = "GaussianVar0"; BayesianLinearRegression BLR = new BayesianLinearRegression(data.getAttributes()) .setClassName(className) .setWindowSize(100) .setDiagonal(false); BLR.updateModel(data); for (DataOnMemory<DataInstance> batch : data.iterableOverBatches(100)) { BLR.updateModel(batch); } System.out.println(BLR.getModel()); System.out.println(BLR.getDAG()); List<DataInstance> dataTest = data.stream().collect(Collectors.toList()).subList(0,5); for(DataInstance d : dataTest) { Assignment assignment = new HashMapAssignment(BLR.getModel().getNumberOfVars()-1); for (int i=0; i<BLR.getModel().getNumberOfVars(); i++) { Variable v = BLR.getModel().getVariables().getVariableById(i); if(!v.equals(BLR.getClassVar())) assignment.setValue(v,d.getValue(v)); } UnivariateDistribution posterior = InferenceEngine.getPosterior(BLR.getClassVar(), BLR.getModel(),assignment); System.out.println(posterior.toString()); } }
System.out.println("["+instance.getSequenceID()+","+instance.getTimeID()+"]"+dist.toString()); distAhead = InferenceEngineForDBN.getPredictivePosterior(targetVar,1); System.out.println("PP: "+distAhead.toString()); System.out.println("["+instance.getSequenceID()+","+instance.getTimeID()+"]"+dist.toString()); distAhead = InferenceEngineForDBN.getPredictivePosterior(targetVar,1); System.out.println("PP: "+distAhead.toString()); System.out.println("["+instance.getSequenceID()+","+instance.getTimeID()+"]"+dist.toString()); distAhead = InferenceEngineForDBN.getPredictivePosterior(targetVar,1); System.out.println("PP: "+distAhead.toString());
System.out.println((inferenceForBN.getPosterior(ClassVar)).toString());