List of buildClassifier() Examples

Examples of buildClassifier()

org.goai.classification.impl.WekaClassifier.buildClassifier()
Trains classifier with passed date set @param itemClassMap Map
weka.classifiers.Classifier.buildClassifier()
Generates a classifier. Must initialize all fields of the classifier that are not being set via options (ie. multiple calls of buildClassifier must always lead to the same result). Must not change the dataset in any way. @param data set of instances serving as training data @exception Exception if the classifier has not been generated successfully
weka.classifiers.bayes.BayesNet.buildClassifier()
Generates the classifier. @param instances set of instances serving as training data @throws Exception if the classifier has not been generatedsuccessfully
weka.classifiers.bayes.NaiveBayesUpdateable.buildClassifier()
weka.classifiers.functions.LibSVM.buildClassifier()
builds the classifier @param insts the training instances @throws Exception if libsvm classes not in classpath or libsvmencountered a problem
weka.classifiers.functions.LinearRegression.buildClassifier()
Builds a regression model for the given data. @param data the training data to be used for generating thelinear regression function @throws Exception if the classifier could not be built successfully
weka.classifiers.functions.MultilayerPerceptron.buildClassifier()
Call this function to build and train a neural network for the training data provided. @param i The training data. @throws Exception if can't build classification properly.
weka.classifiers.functions.SMO.buildClassifier()
Method for building the classifier. Implements a one-against-one wrapper for multi-class problems. @param insts the set of training instances @throws Exception if the classifier can't be built successfully
weka.classifiers.functions.SPegasos.buildClassifier()
Method for building the classifier. @param data the set of training instances. @throws Exception if the classifier can't be built successfully.
weka.classifiers.meta.FilteredClassifier.buildClassifier()
Build the classifier on the filtered data. @param data the training data @throws Exception if the classifier could not be built successfully
weka.classifiers.misc.InputMappedClassifier.buildClassifier()
Build the classifier @param data the training data to be used for generating thebagged classifier. @throws Exception if the classifier could not be built successfully
weka.classifiers.trees.J48.buildClassifier()
Generates the classifier. @param instances the data to train the classifier with @throws Exception if classifier can't be built successfully

Examples of weka.classifiers.bayes.BayesNet.buildClassifier()

        BayesNet EmptyNet = new BayesNet();
        K2 oSearchAlgorithm = new K2();
        oSearchAlgorithm.setInitAsNaiveBayes(false);
        oSearchAlgorithm.setMaxNrOfParents(0);
        EmptyNet.setSearchAlgorithm(oSearchAlgorithm);
        EmptyNet.buildClassifier(instances);


        BayesNet NBNet = new BayesNet();
        oSearchAlgorithm.setInitAsNaiveBayes(true);
        oSearchAlgorithm.setMaxNrOfParents(1);
        NBNet.setSearchAlgorithm(oSearchAlgorithm);

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Examples of weka.classifiers.bayes.BayesNet.buildClassifier()


        BayesNet NBNet = new BayesNet();
        oSearchAlgorithm.setInitAsNaiveBayes(true);
        oSearchAlgorithm.setMaxNrOfParents(1);
        NBNet.setSearchAlgorithm(oSearchAlgorithm);
        NBNet.buildClassifier(instances);


        // estimate CPTs
        for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
            if (iAttribute != instances.classIndex()) {
                  double w1 = 0.0, w2 = 0.0;

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Examples of weka.classifiers.bayes.BayesNet.buildClassifier()

    try {
      BayesNet b = new BayesNet();
      b.setSearchAlgorithm( new ICSSearchAlgorithm());
      Instances instances = new Instances(new FileReader("C:\\eclipse\\workspace\\weka\\data\\contact-lenses.arff"));
      instances.setClassIndex(instances.numAttributes() - 1);
      b.buildClassifier(instances);
      System.out.println(b.toString());
    } catch (Exception e) {
      e.printStackTrace();
    }
  } // main

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Examples of weka.classifiers.bayes.NaiveBayesUpdateable.buildClassifier()

   */
  public void trainNaiveBayes(Instances data) { 
      NaiveBayesUpdateable nb = new NaiveBayesUpdateable();
      
      try {
      nb.buildClassifier(data);
    } catch (Exception e) {
      e.printStackTrace();
    }
    
      for( int n = 0; n < data.size(); n++ ) {

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Examples of weka.classifiers.bayes.NaiveBayesUpdateable.buildClassifier()

  trainingSets[i] = Filter.useFilter(trainingSets[i], disc);


  trainingSets[i].randomize(r);
  trainingSets[i].stratify(5);
  NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
  fullModel.buildClassifier(trainingSets[i]);


  // add the errors for this branch of the split
  m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
      } else {
  // if fewer than min obj then just count them as errors

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Examples of weka.classifiers.bayes.NaiveBayesUpdateable.buildClassifier()

  trainingSets[i] = Filter.useFilter(trainingSets[i], disc);


  trainingSets[i].randomize(r);
  trainingSets[i].stratify(5);
  NaiveBayesUpdateable fullModel = new NaiveBayesUpdateable();
  fullModel.buildClassifier(trainingSets[i]);


  // add the errors for this branch of the split
  m_errors += NBTreeNoSplit.crossValidate(fullModel, trainingSets[i], r);
      } else {
  for (int j = 0; j < trainingSets[i].numInstances(); j++) {

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Examples of weka.classifiers.functions.LibSVM.buildClassifier()

    public void train(ArrayList<LinkedHashMap<Integer, Double>> trainingSamples, ArrayList<Integer> labels, int maxNumFeats) {
        Instances trainingSet = fillDataSet(trainingSamples, labels, maxNumFeats);


        LibSVM cModel = new LibSVM();
        try {
            cModel.buildClassifier(trainingSet);
            saveTheModel(cModel, maxNumFeats);
            ArffSaver saver = new ArffSaver();
            saver.setInstances(trainingSet);
            saver.setFile(new File(PropertiesGetter.getProperty("TrainingDataSetObject")));
            saver.writeBatch();

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Examples of weka.classifiers.functions.LinearRegression.buildClassifier()

    reducedInst = Filter.useFilter(reducedInst, attributeFilter);
    
    // build a linear regression for the training data using the
    // tested attributes
    LinearRegression temp = new LinearRegression();
    temp.buildClassifier(reducedInst);


    double [] lmCoeffs = temp.coefficients();
    double [] coeffs = new double [m_instances.numAttributes()];


    for (int i = 0; i < lmCoeffs.length - 1; i++) {

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Examples of weka.classifiers.functions.MultilayerPerceptron.buildClassifier()

    // System.out.println(" Structure: " + structure);
     structure.setClassIndex(structure.numAttributes() - 1);
     
     // train NaiveBayes
     MultilayerPerceptron nn = new MultilayerPerceptron();
     nn.buildClassifier(structure);
     
     System.out.println(" Perceptron created1: " + nn);
     
     Attribute att;
     Instance newInstance = new DenseInstance(structure.numAttributes());

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Examples of weka.classifiers.functions.SMO.buildClassifier()

      smo.setC(1.0); //same as default value for jkms
      RBFKernel rbf = new RBFKernel();
      rbf.setGamma(0.1); //same as default value for jkms
      smo.setKernel(rbf);
      
      smo.buildClassifier(train);
      
      
      Evaluation eval = new Evaluation(train);
      eval.evaluateModel(smo, test);

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