Example usage for weka.core.converters ArffLoader getNextInstance

Introduction

In this page you can find the example usage for weka.core.converters ArffLoader getNextInstance.

Prototype

@Override
public Instance getNextInstance(Instances structure) throws IOException 

Source Link

Document

Read the data set incrementally---get the next instance in the data set or returns null if there are no more instances to get.

Usage

From source file:CEP.GenerateStream.java

public void MakeStream() {
    File file = new File("C:\\Users\\Weary\\Documents\\w4ndata\\w4ndata.arff");
    String pc = System.getProperty("user.dir").toString();
    if (pc.contains("gs023850")) {
        file = new File("C:\\Users\\gs023850\\Documents\\w4ndata\\w4ndata.arff");
    }//ww  w . ja  v  a2  s . co m
    try {
        ArffLoader loader = new ArffLoader();
        loader.setFile(file);
        Instances structure = loader.getStructure();
        int j = structure.numAttributes();

        HeaderManager.SetStructure(new Instances(structure));
        Instance current;
        long previousTimeStamp = 0;
        String timeStamp = "0";
        long wait = 0;

        while ((current = loader.getNextInstance(structure)) != null) {
            timeStamp = current.stringValue(0);
            cepRT.sendEvent(current);
            System.out.println("Sending event");
            previousTimeStamp = WaitTime(timeStamp, previousTimeStamp, wait);
        }
    } catch (Exception e) {
        if (e.equals(new FileNotFoundException())) {
            System.out.println("File not found - could not generate stream");
            return;
        } else if (e.equals(new IOException())) {
            System.out.println("Unable to read file");
        } else if (e.equals(new NumberFormatException())) {
            System.out.println("Unable to convert to time to number - bad time");
        } else {
            System.out.println(e.toString());
        }
    }
}

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From source file:com.daniel.convert.IncrementalClassifier.java

License:Open Source License

/**
 * Expects an ARFF file as first argument (class attribute is assumed to be
 * the last attribute).//from ww  w  . ja  va  2s.c om
 * 
 * @param args
 *            the commandline arguments
 * @throws Exception
 *             if something goes wrong
 */
public static BayesNet treinar(String[] args) throws Exception {
    // load data
    ArffLoader loader = new ArffLoader();
    loader.setFile(new File(args[0]));
    Instances structure = loader.getStructure();
    structure.setClassIndex(structure.numAttributes() - 1);

    // train NaiveBayes
    BayesNet BayesNet = new BayesNet();

    Instance current;
    while ((current = loader.getNextInstance(structure)) != null) {
        structure.add(current);
    }
    BayesNet.buildClassifier(structure);

    // output generated model
    // System.out.println(nb);

    // test set
    BayesNet BayesNetTest = new BayesNet();

    // test the model
    Evaluation eTest = new Evaluation(structure);
    // eTest.evaluateModel(nb, structure);
    eTest.crossValidateModel(BayesNetTest, structure, 15, new Random(1));

    // Print the result  la Weka explorer:
    String strSummary = eTest.toSummaryString();
    System.out.println(strSummary);

    return BayesNet;
}

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From source file:cs.man.ac.uk.predict.Predictor.java

License:Open Source License

public static void makePredictionsEnsembleNew(String trainPath, String testPath, String resultPath) {
    System.out.println("Training set: " + trainPath);
    System.out.println("Test set: " + testPath);

    /**// w w w.  j a v  a 2 s  .c  om
     * The ensemble classifiers. This is a heterogeneous ensemble.
     */
    J48 learner1 = new J48();
    SMO learner2 = new SMO();
    NaiveBayes learner3 = new NaiveBayes();
    MultilayerPerceptron learner5 = new MultilayerPerceptron();

    System.out.println("Training Ensemble.");
    long startTime = System.nanoTime();
    try {
        BufferedReader reader = new BufferedReader(new FileReader(trainPath));
        Instances data = new Instances(reader);
        data.setClassIndex(data.numAttributes() - 1);
        System.out.println("Training data length: " + data.numInstances());

        learner1.buildClassifier(data);
        learner2.buildClassifier(data);
        learner3.buildClassifier(data);
        learner5.buildClassifier(data);

        long endTime = System.nanoTime();
        long nanoseconds = endTime - startTime;
        double seconds = (double) nanoseconds / 1000000000.0;
        System.out.println("Training Ensemble completed in " + nanoseconds + " (ns) or " + seconds + " (s).");
    } catch (IOException e) {
        System.out.println("Could not train Ensemble classifier IOException on training data file.");
    } catch (Exception e) {
        System.out.println("Could not train Ensemble classifier Exception building model.");
    }

    try {
        String line = "";

        // Read the file and display it line by line. 
        BufferedReader in = null;

        // Read in and store each positive prediction in the tree map.
        try {
            //open stream to file
            in = new BufferedReader(new FileReader(testPath));

            while ((line = in.readLine()) != null) {
                if (line.toLowerCase().contains("@data"))
                    break;
            }
        } catch (Exception e) {
        }

        // A different ARFF loader used here (compared to above) as
        // the ARFF file may be extremely large. In which case the whole
        // file cannot be read in. Instead it is read in incrementally.
        ArffLoader loader = new ArffLoader();
        loader.setFile(new File(testPath));

        Instances data = loader.getStructure();
        data.setClassIndex(data.numAttributes() - 1);

        System.out.println("Ensemble Classifier is ready.");
        System.out.println("Testing on all instances avaialable.");

        startTime = System.nanoTime();

        int instanceNumber = 0;

        // label instances
        Instance current;

        while ((current = loader.getNextInstance(data)) != null) {
            instanceNumber += 1;
            line = in.readLine();

            double classification1 = learner1.classifyInstance(current);
            double classification2 = learner2.classifyInstance(current);
            double classification3 = learner3.classifyInstance(current);
            double classification5 = learner5.classifyInstance(current);

            // All classifiers must agree. This is a very primitive ensemble strategy!
            if (classification1 == 1 && classification2 == 1 && classification3 == 1 && classification5 == 1) {
                if (line != null) {
                    //System.out.println("Instance: "+instanceNumber+"\t"+line);
                    //System.in.read();
                }
                Writer.append(resultPath, instanceNumber + "\n");
            }
        }

        in.close();

        System.out.println("Test set instances: " + instanceNumber);

        long endTime = System.nanoTime();
        long duration = endTime - startTime;
        double seconds = (double) duration / 1000000000.0;

        System.out.println("Testing Ensemble completed in " + duration + " (ns) or " + seconds + " (s).");
    } catch (Exception e) {
        System.out.println("Could not test Ensemble classifier due to an error.");
    }
}

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From source file:cs.man.ac.uk.predict.Predictor.java

License:Open Source License

public static void makePredictionsEnsembleStream(String trainPath, String testPath, String resultPath) {
    System.out.println("Training set: " + trainPath);
    System.out.println("Test set: " + testPath);

    /**//from   w ww.j  a va2 s . c  o  m
     * The ensemble classifiers. This is a heterogeneous ensemble.
     */
    J48 learner1 = new J48();
    SMO learner2 = new SMO();
    NaiveBayes learner3 = new NaiveBayes();
    MultilayerPerceptron learner5 = new MultilayerPerceptron();

    System.out.println("Training Ensemble.");
    long startTime = System.nanoTime();
    try {
        BufferedReader reader = new BufferedReader(new FileReader(trainPath));
        Instances data = new Instances(reader);
        data.setClassIndex(data.numAttributes() - 1);
        System.out.println("Training data length: " + data.numInstances());

        learner1.buildClassifier(data);
        learner2.buildClassifier(data);
        learner3.buildClassifier(data);
        learner5.buildClassifier(data);

        long endTime = System.nanoTime();
        long nanoseconds = endTime - startTime;
        double seconds = (double) nanoseconds / 1000000000.0;
        System.out.println("Training Ensemble completed in " + nanoseconds + " (ns) or " + seconds + " (s).");
    } catch (IOException e) {
        System.out.println("Could not train Ensemble classifier IOException on training data file.");
    } catch (Exception e) {
        System.out.println("Could not train Ensemble classifier Exception building model.");
    }

    try {
        // A different ARFF loader used here (compared to above) as
        // the ARFF file may be extremely large. In which case the whole
        // file cannot be read in. Instead it is read in incrementally.
        ArffLoader loader = new ArffLoader();
        loader.setFile(new File(testPath));

        Instances data = loader.getStructure();
        data.setClassIndex(data.numAttributes() - 1);

        System.out.println("Ensemble Classifier is ready.");
        System.out.println("Testing on all instances avaialable.");

        startTime = System.nanoTime();

        int instanceNumber = 0;

        // label instances
        Instance current;

        while ((current = loader.getNextInstance(data)) != null) {
            instanceNumber += 1;

            double classification1 = learner1.classifyInstance(current);
            double classification2 = learner2.classifyInstance(current);
            double classification3 = learner3.classifyInstance(current);
            double classification5 = learner5.classifyInstance(current);

            // All classifiers must agree. This is a very primitive ensemble strategy!
            if (classification1 == 1 && classification2 == 1 && classification3 == 1 && classification5 == 1) {
                Writer.append(resultPath, instanceNumber + "\n");
            }
        }

        System.out.println("Test set instances: " + instanceNumber);

        long endTime = System.nanoTime();
        long duration = endTime - startTime;
        double seconds = (double) duration / 1000000000.0;

        System.out.println("Testing Ensemble completed in " + duration + " (ns) or " + seconds + " (s).");
    } catch (Exception e) {
        System.out.println("Could not test Ensemble classifier due to an error.");
    }
}

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From source file:cs.man.ac.uk.predict.Predictor.java

License:Open Source License

public static void makePredictionsJ48(String trainPath, String testPath, String resultPath) {
    /**// w w w  . j a  v a  2 s  . c  o  m
     * The decision tree classifier.
     */
    J48 learner = new J48();

    System.out.println("Training set: " + trainPath);
    System.out.println("Test set: " + testPath);

    System.out.println("Training J48");
    long startTime = System.nanoTime();
    try {
        BufferedReader reader = new BufferedReader(new FileReader(trainPath));
        Instances data = new Instances(reader);
        data.setClassIndex(data.numAttributes() - 1);
        System.out.println("Training data length: " + data.numInstances());
        learner.buildClassifier(data);

        long endTime = System.nanoTime();
        long nanoseconds = endTime - startTime;
        double seconds = (double) nanoseconds / 1000000000.0;
        System.out.println("Training J48 completed in " + nanoseconds + " (ns) or " + seconds + " (s)");
    } catch (IOException e) {
        System.out.println("Could not train J48 classifier IOException on training data file");
    } catch (Exception e) {
        System.out.println("Could not train J48 classifier Exception building model");
    }

    try {
        // Prepare data for testing
        //BufferedReader reader = new BufferedReader( new FileReader(testPath));
        //Instances data = new Instances(reader);
        //data.setClassIndex(data.numAttributes() - 1);

        ArffLoader loader = new ArffLoader();
        loader.setFile(new File(testPath));
        Instances data = loader.getStructure();
        data.setClassIndex(data.numAttributes() - 1);

        System.out.println("J48 Classifier is ready.");
        System.out.println("Testing on all instances avaialable.");
        System.out.println("Test set instances: " + data.numInstances());

        startTime = System.nanoTime();

        int instanceNumber = 0;

        // label instances
        Instance current;

        //for (int i = 0; i < data.numInstances(); i++) 
        while ((current = loader.getNextInstance(data)) != null) {
            instanceNumber += 1;

            //double classification = learner.classifyInstance(data.instance(i));
            double classification = learner.classifyInstance(current);
            //String instanceClass= Double.toString(data.instance(i).classValue());

            if (classification == 1)// Predicted positive, actually negative
            {
                Writer.append(resultPath, instanceNumber + "\n");
            }
        }

        long endTime = System.nanoTime();
        long duration = endTime - startTime;
        double seconds = (double) duration / 1000000000.0;

        System.out.println("Testing J48 completed in " + duration + " (ns) or " + seconds + " (s)");
    } catch (Exception e) {
        System.out.println("Could not test J48 classifier due to an error");
    }
}

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From source file:de.citec.sc.matoll.classifiers.WEKAclassifier.java

public HashMap<Integer, Double> predict(Provenance provenance, Set<String> pattern_lookup,
        Set<String> pos_lookup) throws IOException, Exception {

    /*/*from  w w w. ja  va  2s .  co m*/
    we want predict that the entry is true
    */
    provenance.setAnnotation(1);
    List<Provenance> tmp_prov = new ArrayList<Provenance>();
    tmp_prov.add(provenance);
    writeVectors(tmp_prov, "tmp.arff", pattern_lookup, pos_lookup);

    ArffLoader loader = new ArffLoader();
    loader.setFile(new File("tmp.arff"));
    Instances structure = loader.getStructure();
    structure.setClassIndex(structure.numAttributes() - 1);
    HashMap<Integer, Double> hm = new HashMap<Integer, Double>();

    Instance current;
    while ((current = loader.getNextInstance(structure)) != null) {
        /*
        * value_to_predict
        * can be only 0 or 1, as only two classes are given 
        */

        double value = cls.classifyInstance(current);
        double[] percentage = cls.distributionForInstance(current);

        List<String> result = new ArrayList<String>();
        int prediction = (int) value;
        double distribution = percentage[(int) value];
        hm.put(prediction, distribution);
    }
    return hm;
}

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From source file:gr.demokritos.iit.cpgislanddetection.analysis.VectorSequenceDetector.java

License:Apache License

public VectorSequenceDetector(List<BaseSequence> sequences, List<String> labels)
        throws FileNotFoundException, IOException, Exception {

    //gia ola ta seq
    //gia kathe seq pare to vector me vash ton analyzer 
    //vale kai to label
    //kai update classify

    // load data//from w w w  .  ja  v  a  2s .  c  o m
    ArffLoader loader = new ArffLoader();
    loader.setFile(new File("/Desktop/filesForWeka/2o_peirama/dataForWeka.arff"));
    Instances structure = loader.getStructure();
    // setting class attribute
    structure.setClassIndex(structure.numAttributes() - 1);

    // train NaiveBayes
    NaiveBayesUpdateable nb = new NaiveBayesUpdateable();
    nb.buildClassifier(structure);
    Instance current;
    while ((current = loader.getNextInstance(structure)) != null)
        nb.updateClassifier(current);
}

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From source file:put.semantic.fcanew.ml.WekaClassifier.java

@Override
public void loadExamples(File f) throws IOException {
    ArffLoader l = new ArffLoader();
    l.setFile(f);/*from   w  ww.  j ava 2 s.  co m*/
    Instances structure = l.getStructure();
    Instance i;
    while ((i = l.getNextInstance(structure)) != null) {
        if (!instances.checkInstance(i)) {
            i = convert(i, structure, instances);
        } else {
            i.setDataset(instances);
        }
        if (instances.checkInstance(i)) {
            if (i.classValue() == 0) {
                i.setWeight(getRejectedWeight());
            }
            instances.add(i);
        } else {
            System.err.println("Ignoring incompatible instance");
        }
    }
    updateModel();
    tableModel.fireTableDataChanged();
}

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