Example usage for weka.classifiers.functions SMO classifyInstance

Introduction

In this page you can find the example usage for weka.classifiers.functions SMO classifyInstance.

Prototype

@Override
public double classifyInstance(Instance instance) throws Exception 

Source Link

Document

Classifies the given test instance.

Usage

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);

    /**/*from   ww w  .  ja  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  w  w .  ja va  2  s .  co  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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