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.Classifier.buildClassifier()

    currentClassifier.buildClassifier(train);
    evaluation.setPriors(train);
    evaluation.evaluateModel(currentClassifier, test);
  }
      } else {
  currentClassifier.buildClassifier(train);
  evaluation = new Evaluation(train);
  evaluation.evaluateModel(currentClassifier, test);
      }


      double error = evaluation.errorRate();

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Examples of weka.classifiers.Classifier.buildClassifier()

  computeStats(data);//Compute training data stats for creating artificial examples
  
  //initialize new committee
  m_Committee = new Vector();
  Classifier newClassifier = m_Classifier;
  newClassifier.buildClassifier(divData);
  m_Committee.add(newClassifier);
  double eComm = computeError(divData);//compute ensemble error
  if(m_Debug) System.out.println("Initialize:\tClassifier "+i+" added to ensemble. Ensemble error = "+eComm);
  
  //repeat till desired committee size is reached OR the max number of iterations is exceeded

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Examples of weka.classifiers.Classifier.buildClassifier()

    trainCopy = Filter.useFilter(trainCopy, delTransform);
    o_Evaluation = new Evaluation(trainCopy);
    String [] oneROpts = { "-B", ""+getMinimumBucketSize()};
    Classifier oneR = Classifier.forName("weka.classifiers.rules.OneR", oneROpts);
    if (m_evalUsingTrainingData) {
      oneR.buildClassifier(trainCopy);
      o_Evaluation.evaluateModel(oneR, trainCopy);
    } else {
      /*      o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", 
              trainCopy, 10, 
              null, new Random(m_randomSeed)); */

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Examples of weka.classifiers.Classifier.buildClassifier()

    try {
      current = Classifier.makeCopy(template);
    } catch (Exception ex) {
      m_Log.logMessage("Problem copying classifier: " + ex.getMessage());
    }
    current.buildClassifier(train);
    Instances test = inst.testCV(numFolds, fold);
    m_Log.statusMessage("Evaluating model for fold "
            + (fold + 1) + "...");
    for (int jj=0;jj<test.numInstances();jj++) {
      processClassifierPrediction(test.instance(jj), current,

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Examples of weka.classifiers.Classifier.buildClassifier()

        try {
    current = Classifier.makeCopy(template);
        } catch (Exception ex) {
    m_Log.logMessage("Problem copying classifier: " + ex.getMessage());
        }
        current.buildClassifier(train);
        eval = new Evaluation(train, costMatrix);
        m_Log.statusMessage("Evaluating on test split...");
       
        if (outputPredictionsText) {
    printPredictionsHeader(outBuff, inst, "test split");

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Examples of weka.classifiers.Classifier.buildClassifier()

    trainCopy = Filter.useFilter(trainCopy, delTransform);
    o_Evaluation = new Evaluation(trainCopy);
    String [] oneROpts = { "-B", ""+getMinimumBucketSize()};
    Classifier oneR = AbstractClassifier.forName("weka.classifiers.rules.OneR", oneROpts);
    if (m_evalUsingTrainingData) {
      oneR.buildClassifier(trainCopy);
      o_Evaluation.evaluateModel(oneR, trainCopy);
    } else {
      /*      o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", 
              trainCopy, 10, 
              null, new Random(m_randomSeed)); */

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Examples of weka.classifiers.Classifier.buildClassifier()


          // We want to randomize the data the same way for every 
          // learning scheme.
    train = newData.trainCV(m_NumXValFolds, j, new Random (1));
    test = newData.testCV(m_NumXValFolds, j);
    currentClassifier.buildClassifier(train);
    evaluation.setPriors(train);
    evaluation.evaluateModel(currentClassifier, test);
  }
      } else {
  currentClassifier.buildClassifier(train);

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

        String[] str = cls.getOptions();
     //   str[2] = "weka.classifiers.bayes.net.search.local.TAN";
     //   str[4] = "";
      //  str[5] = ""; 
        cls.setOptions(str);
      cls.buildClassifier(data);
    } catch (Exception e) {
      e.printStackTrace();
    }
    
    return cls;

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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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