public static void main(String[] args) throws Exception{ int nOfDisc; int nOfCont; DataStream<DynamicDataInstance> dataGaussians = null; String path = "datasets/simulated/"; nOfCont = 3; nOfDisc = 2; dataGaussians = DataSetGenerator.generate(1,1000,nOfDisc,nOfCont); DataStreamWriter.writeDataToFile(dataGaussians, path+"exampleDS_d"+nOfDisc+"_c"+nOfCont+".arff"); nOfCont = 5; nOfDisc = 0; dataGaussians = DataSetGenerator.generate(1,10000,nOfDisc,nOfCont); DataStreamWriter.writeDataToFile(dataGaussians, path+"exampleDS_d"+nOfDisc+"_c"+nOfCont+".arff"); dataGaussians = DataSetGenerator.generate(1,50,nOfDisc,nOfCont); DataStreamWriter.writeDataToFile(dataGaussians, path+"exampleDS_d"+nOfDisc+"_c"+nOfCont+"_small.arff"); nOfCont = 0; nOfDisc = 5; dataGaussians = DataSetGenerator.generate(1,1000,nOfDisc,nOfCont); DataStreamWriter.writeDataToFile(dataGaussians, path+"exampleDS_d"+nOfDisc+"_c"+nOfCont+".arff"); }
public static void main(String[] args) throws Exception{ int nContinuousAttributes=0; int nDiscreteAttributes=5; String names[] = {"SEQUENCE_ID", "TIME_ID","DEFAULT","Income","Expenses","Balance","TotalCredit"}; String path = "datasets/simulated/"; int nSamples=1000; String filename="bank_data_test"; int seed = filename.hashCode(); //Generate random dynamic data DataStream<DynamicDataInstance> data = DataSetGenerator.generate(seed,nSamples,nDiscreteAttributes,nContinuousAttributes); List<Attribute> list = new ArrayList<Attribute>(); //Replace the names IntStream.range(0, data.getAttributes().getNumberOfAttributes()) .forEach(i -> { Attribute a = data.getAttributes().getFullListOfAttributes().get(i); StateSpaceType s = a.getStateSpaceType(); Attribute a2 = new Attribute(a.getIndex(), names[i],s); list.add(a2); }); //New list of attributes Attributes att2 = new Attributes(list); List<DynamicDataInstance> listData = data.stream().collect(Collectors.toList()); //Datastream with the new attribute names DataStream<DynamicDataInstance> data2 = new DataOnMemoryListContainer<DynamicDataInstance>(att2,listData); //Write to a single file DataStreamWriter.writeDataToFile(data2, path+filename+".arff"); }
public static void main(String[] args) { DataStream<DynamicDataInstance> dataHybrid= DataSetGenerator.generate(1,1000,3,10); DataStream<DynamicDataInstance> dataGaussians = DataSetGenerator.generate(1,1000,0,10); //DataStream<DynamicDataInstance> data = DynamicDataStreamLoader // .loadFromFile("datasets/syntheticDataVerdandeScenario3.arff"); System.out.println("------------------Factorial HMM (diagonal matrix) from streaming------------------"); FactorialHMM factorialHMM = new FactorialHMM(dataHybrid.getAttributes()); System.out.println(factorialHMM.getDynamicDAG()); factorialHMM.updateModel(dataHybrid); System.out.println(factorialHMM.getModel()); System.out.println("------------------Factorial HMM (full cov. matrix) from streaming------------------"); factorialHMM = new FactorialHMM(dataGaussians.getAttributes()); factorialHMM.setDiagonal(false); System.out.println(factorialHMM.getDynamicDAG()); factorialHMM.updateModel(dataGaussians); System.out.println(factorialHMM.getModel()); System.out.println("------------------Factorial HMM (diagonal matrix) from batches------------------"); factorialHMM = new FactorialHMM(dataHybrid.getAttributes()); System.out.println(factorialHMM.getDynamicDAG()); for (DataOnMemory<DynamicDataInstance> batch : dataHybrid.iterableOverBatches(100)) { factorialHMM.updateModel(batch); } System.out.println(factorialHMM.getModel()); }
public static void main(String[] args) { DataStream<DynamicDataInstance> dataGaussians = DataSetGenerator.generate(1,1000,0,10); DataStream<DynamicDataInstance> dataHybrid = DataSetGenerator.generate(1,1000,2,10); //DataStream<DynamicDataInstance> data = DynamicDataStreamLoader // .loadFromFile("datasets/syntheticDataVerdandeScenario3.arff"); System.out.println("------------------HMM (diagonal matrix) from streaming------------------"); HiddenMarkovModel HMM = new HiddenMarkovModel(dataHybrid.getAttributes()); System.out.println(HMM.getDynamicDAG()); HMM.updateModel(dataHybrid); System.out.println(HMM.getModel()); System.out.println("------------------HMM (full cov. matrix) from streaming------------------"); HMM = new HiddenMarkovModel(dataGaussians.getAttributes()); HMM.setDiagonal(false); System.out.println(HMM.getDynamicDAG()); HMM.updateModel(dataGaussians); System.out.println(HMM.getModel()); System.out.println("------------------HMM (diagonal matrix) from batches------------------"); HMM = new HiddenMarkovModel(dataHybrid.getAttributes()); System.out.println(HMM.getDynamicDAG()); for (DataOnMemory<DynamicDataInstance> batch : dataHybrid.iterableOverBatches(100)) { HMM.updateModel(batch); } System.out.println(HMM.getModel()); }
public static void main(String[] args) { DataStream<DynamicDataInstance> dataHybrid= DataSetGenerator.generate(1,1000,3,10); DataStream<DynamicDataInstance> dataGaussians = DataSetGenerator.generate(1,1000,0,10); //DataStream<DynamicDataInstance> data = DynamicDataStreamLoader // .loadFromFile("datasets/syntheticDataVerdandeScenario3.arff"); System.out.println("------------------Auto-Regressive HMM (diagonal matrix) from streaming------------------"); AutoRegressiveHMM autoRegressiveHMM = new AutoRegressiveHMM(dataHybrid.getAttributes()); System.out.println(autoRegressiveHMM.getDynamicDAG()); autoRegressiveHMM.updateModel(dataHybrid); System.out.println(autoRegressiveHMM.getModel()); System.out.println("------------------Auto-Regressive HMM (full cov. matrix) from streaming------------------"); autoRegressiveHMM = new AutoRegressiveHMM(dataGaussians.getAttributes()); autoRegressiveHMM.setDiagonal(false); System.out.println(autoRegressiveHMM.getDynamicDAG()); autoRegressiveHMM.updateModel(dataGaussians); System.out.println(autoRegressiveHMM.getModel()); System.out.println("------------------Auto-Regressive HMM (diagonal matrix) from batches------------------"); autoRegressiveHMM = new AutoRegressiveHMM(dataHybrid.getAttributes()); System.out.println(autoRegressiveHMM.getDynamicDAG()); for (DataOnMemory<DynamicDataInstance> batch : dataHybrid.iterableOverBatches(100)) { autoRegressiveHMM.updateModel(batch); } System.out.println(autoRegressiveHMM.getModel()); } }
public static void main(String[] args) throws Exception{ int nContinuousAttributes=4; int nDiscreteAttributes=1; String names[] = {"SEQUENCE_ID", "TIME_ID","Default","Income","Expenses","Balance","TotalCredit"}; String path = "datasets/simulated/"; int nSamples=1000; int seed = 11234; String filename="bank_data_test"; //Generate random dynamic data DataStream<DynamicDataInstance> data = DataSetGenerator.generate(seed,nSamples,nDiscreteAttributes,nContinuousAttributes); List<Attribute> list = new ArrayList<Attribute>(); //Replace the names IntStream.range(0, data.getAttributes().getNumberOfAttributes()) .forEach(i -> { Attribute a = data.getAttributes().getFullListOfAttributes().get(i); StateSpaceType s = a.getStateSpaceType(); Attribute a2 = new Attribute(a.getIndex(), names[i],s); list.add(a2); }); //New list of attributes Attributes att2 = new Attributes(list); List<DynamicDataInstance> listData = data.stream().collect(Collectors.toList()); //Datastream with the new attribute names DataStream<DynamicDataInstance> data2 = new DataOnMemoryListContainer<DynamicDataInstance>(att2,listData); //Write to a single file DataStreamWriter.writeDataToFile(data2, path+filename+".arff"); }
public static void main(String[] args) { DataStream<DynamicDataInstance> dataGaussians = DataSetGenerator.generate(1,1000,0,10); //DataStream<DynamicDataInstance> data = DynamicDataStreamLoader // .loadFromFile("datasets/syntheticDataVerdandeScenario3.arff"); System.out.println("------------------SKF (diagonal matrix) from streaming------------------"); SwitchingKalmanFilter SKF = new SwitchingKalmanFilter(dataGaussians.getAttributes()); System.out.println(SKF.getDynamicDAG()); SKF.updateModel(dataGaussians); System.out.println(SKF.getModel()); System.out.println("------------------SKF (full cov. matrix) from streaming------------------"); SKF = new SwitchingKalmanFilter(dataGaussians.getAttributes()); SKF.setDiagonal(false); System.out.println(SKF.getDynamicDAG()); SKF.updateModel(dataGaussians); System.out.println(SKF.getModel()); System.out.println("------------------SKF (diagonal matrix) from batches------------------"); SKF = new SwitchingKalmanFilter(dataGaussians.getAttributes()); System.out.println(SKF.getDynamicDAG()); for (DataOnMemory<DynamicDataInstance> batch : dataGaussians.iterableOverBatches(100)) { SKF.updateModel(batch); } System.out.println(SKF.getModel()); } }
public static void main(String[] args) { DataStream<DynamicDataInstance> data = DataSetGenerator.generate(1,1000,3,3);
public static void main(String[] args) { DataStream<DynamicDataInstance> dataGaussians = DataSetGenerator.generate(1,1000,0,10);
public static void main(String[] args) throws WrongConfigurationException { DataStream<DynamicDataInstance> data = DataSetGenerator.generate(1,1000, 1, 10);
DataStream<DynamicDataInstance> data = DataSetGenerator.generate(1,1000,nDiscreteAttributes,nContinuousAttributes); List<Attribute> list = new ArrayList<Attribute>();