Example usage for weka.classifiers.functions LinearRegression LinearRegression

List of usage examples for weka.classifiers.functions LinearRegression LinearRegression

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Introduction

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

Prototype

public LinearRegression()

Source Link

Usage

From source file:task2.java

/**
 * Processes requests for both HTTP <code>GET</code> and <code>POST</code>
 * methods./*ww w  .  j  av  a 2 s  .c  om*/
 *
 * @param request servlet request
 * @param response servlet response
 * @throws ServletException if a servlet-specific error occurs
 * @throws IOException if an I/O error occurs
 */
protected void processRequest(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    response.setContentType("text/html;charset=UTF-8");
    try (PrintWriter out = response.getWriter()) {
        /* TODO output your page here. You may use following sample code. */
        out.println("<!DOCTYPE html>");
        out.println("<html>");
        out.println("<head>");
        out.println("<title>Servlet selection</title>");
        out.println("</head>");
        out.println("<body>");
        CSVLoader loader = new CSVLoader();
        loader.setSource(new File("C:/Users//Raguvinoth/Desktop/5339.csv"));
        Instances data = loader.getDataSet();

        //Save ARFF
        ArffSaver saver = new ArffSaver();
        saver.setInstances(data);
        saver.setFile(new File("\"C:/Users/Raguvinoth/Desktop/5339_converted.arff"));
        saver.writeBatch();

        BufferedReader reader = new BufferedReader(
                new FileReader("C://Users//Raguvinoth//Desktop//weka1//5339_nominal.arff"));
        Instances data1 = new Instances(reader);

        if (data1.classIndex() == -1)
            data1.setClassIndex(data1.numAttributes() - 14);
        // 1. meta-classifier
        // useClassifier(data);

        // 2. AttributeSelector
        try {
            AttributeSelection attsel = new AttributeSelection();
            GreedyStepwise search = new GreedyStepwise();
            CfsSubsetEval eval = new CfsSubsetEval();
            attsel.setEvaluator(eval);
            attsel.setSearch(search);
            attsel.SelectAttributes(data);
            int[] indices = attsel.selectedAttributes();

            System.out.println("selected attribute indices:\n" + Utils.arrayToString(indices));
            System.out.println("\n 4. Linear-Regression on above selected attributes");
            long time1 = System.currentTimeMillis();
            long sec1 = time1 / 1000;
            BufferedReader reader1 = new BufferedReader(
                    new FileReader("C://Users//Raguvinoth//Desktop//weka1//5339_linear2.arff"));
            Instances data2 = new Instances(reader1);
            data2.setClassIndex(0);
            LinearRegression lr = new LinearRegression();
            lr.buildClassifier(data2);

            System.out.println(lr.toString());
            long time2 = System.currentTimeMillis();
            long sec2 = time2 / 1000;
            long timeTaken = sec2 - sec1;
            System.out.println("Total time taken for building the model: " + timeTaken + " seconds");

            for (int i = 0; i < 5; i++) {
                out.println("<p>" + "selected attribute indices:\n" + Utils.arrayToString(indices[i]) + "</p>");
            }
            out.println("<p>" + "\n 4. Linear-Regression on above selected attributes" + "</p>");
            out.println("<p>" + lr.toString() + "</p>");
            out.println("<p>" + "Total time taken for building the model: " + timeTaken + " seconds" + "</p>");
            out.println("</body>");
            out.println("</html>");
        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }
}

From source file:boa.aggregators.LinearRegressionAggregator.java

License:Apache License

/** {@inheritDoc} */
@Override//w  w  w.ja v  a2 s.  c o  m
public void finish() throws IOException, InterruptedException {
    int NumOfAttributes = this.getVectorSize();
    List<Attribute> attribute = new ArrayList<Attribute>();
    FastVector fvAttributes = new FastVector(NumOfAttributes);

    for (int i = 0; i < NumOfAttributes; i++) {
        attribute.add(new Attribute("Attribute" + i));
        fvAttributes.addElement(attribute.get(i));
    }

    Instances trainingSet = new Instances("LinearRegression", fvAttributes, 1);
    trainingSet.setClassIndex(NumOfAttributes - 1);

    for (List<Double> vector : this.vectors.values()) {
        Instance instance = new Instance(NumOfAttributes);
        for (int i = 0; i < vector.size(); i++) {
            instance.setValue((Attribute) fvAttributes.elementAt(i), vector.get(i));
        }
        trainingSet.add(instance);
    }

    try {
        this.model = new LinearRegression();
        this.model.setOptions(options);
        this.model.buildClassifier(trainingSet);
    } catch (Exception ex) {
    }

    this.saveModel(this.model);
}

From source file:br.com.ufu.lsi.rebfnetwork.RBFNetwork.java

License:Open Source License

/**
 * Returns default capabilities of the classifier, i.e.,  and "or" of
 * Logistic and LinearRegression.//from   ww w  . ja va 2 s .  c  o m
 *
 * @return      the capabilities of this classifier
 * @see         Logistic
 * @see         LinearRegression
 */
public Capabilities getCapabilities() {
    Capabilities result = new Logistic().getCapabilities();
    result.or(new LinearRegression().getCapabilities());
    Capabilities classes = result.getClassCapabilities();
    result.and(new SimpleKMeans().getCapabilities());
    result.or(classes);
    return result;
}

From source file:br.com.ufu.lsi.rebfnetwork.RBFNetwork.java

License:Open Source License

/**
 * Builds the classifier//from   w  w w.  j a va  2s  .  c  o  m
 *
 * @param instances the training data
 * @throws Exception if the classifier could not be built successfully
 */
public void buildClassifier(Instances instances) throws Exception {

    // can classifier handle the data?
    getCapabilities().testWithFail(instances);

    // remove instances with missing class
    instances = new Instances(instances);
    instances.deleteWithMissingClass();

    // only class? -> build ZeroR model
    if (instances.numAttributes() == 1) {
        System.err.println(
                "Cannot build model (only class attribute present in data!), " + "using ZeroR model instead!");
        m_ZeroR = new weka.classifiers.rules.ZeroR();
        m_ZeroR.buildClassifier(instances);
        return;
    } else {
        m_ZeroR = null;
    }

    m_standardize = new Standardize();
    m_standardize.setInputFormat(instances);
    instances = Filter.useFilter(instances, m_standardize);

    SimpleKMeans sk = new SimpleKMeans();
    sk.setNumClusters(m_numClusters);
    sk.setSeed(m_clusteringSeed);
    MakeDensityBasedClusterer dc = new MakeDensityBasedClusterer();
    dc.setClusterer(sk);
    dc.setMinStdDev(m_minStdDev);
    m_basisFilter = new ClusterMembership();
    m_basisFilter.setDensityBasedClusterer(dc);
    m_basisFilter.setInputFormat(instances);
    Instances transformed = Filter.useFilter(instances, m_basisFilter);

    if (instances.classAttribute().isNominal()) {
        m_linear = null;
        m_logistic = new Logistic();
        m_logistic.setRidge(m_ridge);
        m_logistic.setMaxIts(m_maxIts);
        m_logistic.buildClassifier(transformed);
    } else {
        m_logistic = null;
        m_linear = new LinearRegression();
        m_linear.setAttributeSelectionMethod(
                new SelectedTag(LinearRegression.SELECTION_NONE, LinearRegression.TAGS_SELECTION));
        m_linear.setRidge(m_ridge);
        m_linear.buildClassifier(transformed);
    }
}

From source file:cn.ict.zyq.bestConf.COMT2.COMT2.java

License:Open Source License

@Override
public Capabilities getCapabilities() {
    return new LinearRegression().getCapabilities();
}

From source file:com.github.fracpete.multisearch.optimize.PLSFilterAndLinearRegression.java

License:Open Source License

/**
 * The first parameter must be dataset,//from   www  . ja  v  a 2s .c  o m
 * the (optional) second the class index (1-based, 'first' and 'last'
 * also supported).
 *
 * @param args   the commandline options
 * @throws Exception   if optimization fails for some reason
 */
public static void main(String[] args) throws Exception {
    if (args.length == 0) {
        System.err.println("\nUsage: PLSFilterAndLinearRegression <dataset> [classindex]\n");
        System.exit(1);
    }

    // load data
    Instances data = ExampleHelper.loadData(args[0], (args.length > 1) ? args[1] : null);

    // configure classifier we want to optimize
    PLSFilter pls = new PLSFilter();
    LinearRegression lr = new LinearRegression();
    FilteredClassifier fc = new FilteredClassifier();
    fc.setClassifier(lr);
    fc.setFilter(pls);
    // required for Weka > 3.7.13
    fc.setDoNotCheckForModifiedClassAttribute(true);

    // configure multisearch
    // 1. number of components
    ListParameter numComp = new ListParameter();
    numComp.setProperty("filter.numComponents");
    numComp.setList("2 5 7");
    // 2. ridge
    MathParameter ridge = new MathParameter();
    ridge.setProperty("classifier.ridge");
    ridge.setBase(10);
    ridge.setMin(-5);
    ridge.setMax(1);
    ridge.setStep(1);
    ridge.setExpression("pow(BASE,I)");
    // assemble everything
    MultiSearch multi = new MultiSearch();
    multi.setClassifier(fc);
    multi.setSearchParameters(new AbstractParameter[] { numComp, ridge });
    SelectedTag tag = new SelectedTag(DefaultEvaluationMetrics.EVALUATION_RMSE,
            new DefaultEvaluationMetrics().getTags());
    multi.setEvaluation(tag);

    // output configuration
    System.out.println("\nMultiSearch commandline:\n" + Utils.toCommandLine(multi));

    // optimize
    System.out.println("\nOptimizing...\n");
    multi.buildClassifier(data);
    System.out.println("Best setup:\n" + Utils.toCommandLine(multi.getBestClassifier()));
    System.out.println("Best parameters: " + multi.getGenerator().evaluate(multi.getBestValues()));
}

From source file:com.github.fracpete.multisearch.setupgenerator.PLSFilterAndLinearRegression.java

License:Open Source License

/**
 * Outputs the commandlines.//from www. j ava 2 s  . co  m
 *
 * @param args   the commandline options
 * @throws Exception   if setup generator fails for some reason
 */
public static void main(String[] args) throws Exception {
    // configure classifier we want to generate setups for
    PLSFilter pls = new PLSFilter();
    LinearRegression lr = new LinearRegression();
    FilteredClassifier fc = new FilteredClassifier();
    fc.setClassifier(lr);
    fc.setFilter(pls);
    // required for Weka > 3.7.13
    fc.setDoNotCheckForModifiedClassAttribute(true);

    // configure generator
    // 1. number of components
    ListParameter numComp = new ListParameter();
    numComp.setProperty("filter.numComponents");
    numComp.setList("2 5 7");
    // 2. ridge
    MathParameter ridge = new MathParameter();
    ridge.setProperty("classifier.ridge");
    ridge.setBase(10);
    ridge.setMin(-5);
    ridge.setMax(1);
    ridge.setStep(1);
    ridge.setExpression("pow(BASE,I)");
    // assemble everything
    SetupGenerator generator = new SetupGenerator();
    generator.setBaseObject(fc);
    generator.setParameters(new AbstractParameter[] { numComp, ridge });

    // output configuration
    System.out.println("\nSetupgenerator commandline:\n" + Utils.toCommandLine(generator));

    // output commandlines
    System.out.println("\nCommandlines:\n");
    Enumeration<Serializable> enm = generator.setups();
    while (enm.hasMoreElements())
        System.out.println(Utils.toCommandLine(enm.nextElement()));
}

From source file:com.github.r351574nc3.amex.assignment2.App.java

License:Open Source License

/**
 * Trains the model using a {@link LinearRegression} classifier.
 *
 * @throws an Exception/*from  w w w  .  ja  v  a 2 s  . c  o  m*/
 */
public void train() throws Exception {
    setClassifier(new LinearRegression());
    getClassifier().buildClassifier(getTrained());
}

From source file:controller.MineroControler.java

public String regresionLineal() {
    BufferedReader breader = null;
    Instances datos = null;/* ww w  . j  a v a  2 s  .  c  o  m*/
    breader = new BufferedReader(fuente_arff);
    try {
        datos = new Instances(breader);
        datos.setClassIndex(datos.numAttributes() - 1); // clase principal, ltima en atributos
    } catch (IOException ex) {
        System.err.println("Problemas al intentar cargar los datos");
    }

    LinearRegression regresionL = new LinearRegression();
    try {

        regresionL.buildClassifier(datos);

        Instance nuevaCal = datos.lastInstance();
        double calif = regresionL.classifyInstance(nuevaCal);

        setValorCalculado(new Double(calif));

    } catch (Exception ex) {
        System.err.println("Problemas al clasificar instancia");
    }

    return regresionL.toString();
}

From source file:data.Regression.java

public int regression(String fileName) {

    String arffName = FileTransfer.transfer(fileName);

    try {//from   w w w . ja va 2  s. com
        //load data
        Instances data = new Instances(new BufferedReader(new FileReader(arffName)));
        data.setClassIndex(data.numAttributes() - 1);
        //build model
        LinearRegression model = new LinearRegression();
        model.buildClassifier(data);
        //the last instance with missing class is not used
        System.out.println(model);
        //classify the last instance
        Instance num = data.lastInstance();
        int people = (int) model.classifyInstance(num);
        System.out.println("NumOfEnrolled (" + num + "): " + people);
        return people;
    } catch (Exception e) {
        e.printStackTrace();
        System.out.println("Regression fail");
    }
    return 0;
}