Examples of weka.classifiers.Classifier.buildClassifier()

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

  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

View Full Code Here

    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)); */

View Full Code Here

    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,

View Full Code Here

        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");

View Full Code Here

    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)); */

View Full Code Here


          // 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);

View Full Code Here

    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();

View Full Code Here

    try {
      current = AbstractClassifier.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++) {
      plotInstances.process(test.instance(jj), current, eval);

View Full Code Here

        try {
    current = AbstractClassifier.makeCopy(template);
        } catch (Exception ex) {
    m_Log.logMessage("Problem copying classifier: " + ex.getMessage());
        }
        current.buildClassifier(train);
        eval = new Evaluation(train, costMatrix);
        
        // make adjustments if the classifier is an InputMappedClassifier
              eval = setupEval(eval, classifier, train, costMatrix, 
                  plotInstances, classificationOutput, false);

View Full Code Here

                                Classifier cpyCls1 = Classifier.makeCopy(currentClassifier);
                                Classifier cpyCls2 = Classifier.makeCopy(currentClassifier);
                                Evaluation eval = new Evaluation(randData)  ;


                                eval.crossValidateModel(cpyCls1, randData, folds, rand);   // actual randomization happens here
                                cpyCls2.buildClassifier(randData);


                                avgRoc += eval.weightedAreaUnderROC();
                                avgAcc += eval.pctCorrect();

View Full Code Here

0 1 2 3

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.