public static void main(String[] args) throws IOException { DataStream<DataInstance> data = eu.amidst.core.utils.DataSetGenerator.generate(0,1000,0,10); DataStreamWriter.writeDataToFile(data, "./datasets/artificialDataset.arff"); // DataStream<DynamicDataInstance> dataDynamic = eu.amidst.dynamic.utils.DataSetGenerator.generate(0,100, 100,0,10); // DataStreamWriter.writeDataToFile(dataDynamic, "./datasets/artificialDatasetDynamic.arff"); } }
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 MixtureOfFactorAnalysers(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()); }
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()); }
DataStream<DataInstance> data = DataSetGenerator.generate(1234,10000,0,1);
public static void main(String[] args) throws WrongConfigurationException { int seed=6236; int nSamples=5000; int nDiscreteVars=5; int nContinuousVars=10; DataStream<DataInstance> data = DataSetGenerator.generate(seed,nSamples,nDiscreteVars,nContinuousVars); String classVarName="DiscreteVar0"; String rootVarName="DiscreteVar1"; TAN model = new TAN(data.getAttributes()); model.setClassName(classVarName); model.setRootVarName(rootVarName); model.updateModel(data); System.out.println(model.getDAG()); System.out.println(); System.out.println(model.getModel()); }
public static void main(String[] args) throws WrongConfigurationException { DataStream<DataInstance> data = DataSetGenerator.generate(1234,500, 0, 4); MultivariateGaussianDistribution MGD = new MultivariateGaussianDistribution(data.getAttributes()); MGD.updateModel(data); for (DataOnMemory<DataInstance> batch : data.iterableOverBatches(100)) { MGD.updateModel(batch); } System.out.println(MGD.getModel()); /* try { DataStreamWriter.writeDataToFile(data, "tmp/gmm2vars.arff"); } catch (IOException e) { e.printStackTrace(); } */ } }
DataStream<DataInstance> data = DataSetGenerator.generate(1,10,5,5);
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(); } */ } }
public static void main(String[] args) { DataStream<DataInstance> data = DataSetGenerator.generate(1234,500, 1, 5);
public static void main(String[] args) throws WrongConfigurationException { DataStream<DataInstance> data = DataSetGenerator.generate(1, 1000, 5, 6);
public static void main(String[] args) { //Generate the data stream using the class DataSetGenerator DataStream<DataInstance> data = DataSetGenerator.generate(1,10,5,5); //Filter example: print only instances such that DiscreteVar0 = 1.0 data.filter(d -> d.getValue(data.getAttributes().getAttributeByName("DiscreteVar0")) == 1) .forEach(d -> System.out.println(d)); //Map example: new DataStream in which each instance has been multiplyed by 10 data.map(d -> { Attribute gaussianVar0 = d.getAttributes().getAttributeByName("GaussianVar0"); d.setValue(gaussianVar0, d.getValue(gaussianVar0)*10); return d; }).forEach(d -> System.out.println(d)); } }
public static void main(String[] args) throws WrongConfigurationException { DataStream<DataInstance> data = DataSetGenerator.generate(1234,500, 1, 3);
DataStream<DataInstance> data = DataSetGenerator.generate(1234,500, 2, 3);
public static void main(String[] args) throws WrongConfigurationException { DataStream<DataInstance> data = DataSetGenerator.generate(1234,1000, 1, 10); System.out.println(data.getAttributes().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()); } }