List of crossValidateModel() Examples

Examples of crossValidateModel()

weka.classifiers.Evaluation.crossValidateModel()
Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances. Now performs a deep copy of the classifier before each call to buildClassifier() (just in case the classifier is not initialized properly). @param classifier the classifier with any options set. @param data the data on which the cross-validation is to beperformed @param numFolds the number of folds for the cross-validation @param random random number generator for randomization @param forPredictionsPrinting varargs parameter that, if supplied, isexpected to hold a weka.classifiers.evaluation.output.prediction.AbstractOutput object @throws Exception if a classifier could not be generatedsuccessfully or the class is not defined

Examples of weka.classifiers.Evaluation.crossValidateModel()

          AbstractClassifier aClassifier = classifiers[curCfr];


          Evaluation eval = new Evaluation(data);


          Long millis = System.currentTimeMillis();
          eval.crossValidateModel(aClassifier, data, numFolds, new Random(curRun));
          long elapsedTime = System.currentTimeMillis() - millis;


          double aucSum = 0.0;
          double sumClassProps = 0;
          for (int c = 0; c < data.numClasses(); c++) {

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Examples of weka.classifiers.Evaluation.crossValidateModel()

            }
            eTest = new Evaluation(trainingSet);
            if (numFolds > trainingSet.numInstances()) {
                numFolds = trainingSet.numInstances();
            }
            eTest.crossValidateModel(cModel, trainingSet, numFolds, new Random(1));
        } catch (Exception ex) {
            Logger.getLogger(WekaWrapper.class.getName()).log(Level.SEVERE, null, ex);
            System.out.println(ex.toString());
        }
        return eTest;

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Examples of weka.classifiers.Evaluation.crossValidateModel()

            if(mFoldsInput != null) {
              Integer folds = TupleUtilities.getNumber(readBlock(mFoldsInput), INPUT_FOLDS);
              Evaluation ev = new Evaluation(dataset);
              if(mSeedInput != null) {
                Integer seed = TupleUtilities.getNumber(readBlock(mSeedInput), INPUT_SEED);
                ev.crossValidateModel(classifier, dataset, folds, new Random(seed));
              }
              else 
                ev.crossValidateModel(classifier, dataset, folds, new Random(123));
              if(mEvaluationOutput != null) {
                mEvaluationOutput.write(ev.toSummaryString());

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Examples of weka.classifiers.Evaluation.crossValidateModel()

              if(mSeedInput != null) {
                Integer seed = TupleUtilities.getNumber(readBlock(mSeedInput), INPUT_SEED);
                ev.crossValidateModel(classifier, dataset, folds, new Random(seed));
              }
              else 
                ev.crossValidateModel(classifier, dataset, folds, new Random(123));
              if(mEvaluationOutput != null) {
                mEvaluationOutput.write(ev.toSummaryString());
                LOG.debug("BuildClassifier evaluation: " + ev.toSummaryString());
              }
            }

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Examples of weka.classifiers.Evaluation.crossValidateModel()

      o_Evaluation.evaluateModel(oneR, trainCopy);
    } else {
      /*      o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", 
              trainCopy, 10, 
              null, new Random(m_randomSeed)); */
      o_Evaluation.crossValidateModel(oneR, trainCopy, m_folds, new Random(m_randomSeed));
    }
    errorRate = o_Evaluation.errorRate();
    return  (1 - errorRate)*100.0;
  }

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Examples of weka.classifiers.Evaluation.crossValidateModel()

    // setup classifier
    classifier = (Classifier) setup(getClassifier(), x, y);


    // evaluate
    eval = new Evaluation(data);
    eval.crossValidateModel(classifier, data, cv, new Random(getSeed()));
    performances.add(new Performance(values, eval));
    
    // add to cache
    m_Cache.add(cv, new Performance(values, eval));
  }

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Examples of weka.classifiers.Evaluation.crossValidateModel()

      o_Evaluation.evaluateModel(oneR, trainCopy);
    } else {
      /*      o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", 
              trainCopy, 10, 
              null, new Random(m_randomSeed)); */
      o_Evaluation.crossValidateModel(oneR, trainCopy, m_folds, new Random(m_randomSeed));
    }
    errorRate = o_Evaluation.errorRate();
    return  (1 - errorRate)*100.0;
  }

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Examples of weka.classifiers.Evaluation.crossValidateModel()

    // setup classifier
    classifier = (Classifier) setup(getClassifier(), x, y);


    // evaluate
    eval = new Evaluation(data);
    eval.crossValidateModel(classifier, data, cv, new Random(getSeed()));
    performances.add(new Performance(values, eval));
    
    // add to cache
    m_Cache.add(cv, new Performance(values, eval));
  }

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Examples of weka.classifiers.Evaluation.crossValidateModel()

      o_Evaluation.evaluateModel(oneR, trainCopy);
    } else {
      /*      o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", 
              trainCopy, 10, 
              null, new Random(m_randomSeed)); */
      o_Evaluation.crossValidateModel(oneR, trainCopy, m_folds, new Random(m_randomSeed));
    }
    errorRate = o_Evaluation.errorRate();
    return  (1 - errorRate)*100.0;
  }

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Examples of weka.classifiers.Evaluation.crossValidateModel()

            try {
                train.setClassIndex(1);
                Evaluation ev = new Evaluation(train);
                if (test == null) {
                    // use 5-fold cross-validation
                    ev.crossValidateModel(classifier, train, Math.min(train.numInstances(), 5), new Random(42));
                } else {
                    test.setClassIndex(1);
                    classifier.buildClassifier(train);
                    ev.evaluateModel(classifier, test);
                }

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