public Evaluation(Instances data, CostMatrix costMatrix) throws Exception { m_delegate = new weka.classifiers.evaluation.Evaluation(data, costMatrix); }
public Evaluation(Instances data) throws Exception { m_delegate = new weka.classifiers.evaluation.Evaluation(data); }
public Evaluation(Instances data, CostMatrix costMatrix) throws Exception { m_delegate = new weka.classifiers.evaluation.Evaluation(data, costMatrix); }
public Evaluation(Instances data) throws Exception { m_delegate = new weka.classifiers.evaluation.Evaluation(data); }
BigInteger val = Evaluator.evaluate(new Evaluation() { public int evaluate(int x, int y) { return x - y; } }, new BigInteger("2"), new BigInteger("3"));
} else if (!noCrossValidation) { // CASE 3: CROSS-VALIDATION Random random = new Random(seed); Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { testingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix); Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { testingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix); Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix); Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix); Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix); Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix);
} else if (!noCrossValidation) { // CASE 3: CROSS-VALIDATION Random random = new Random(seed); Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { testingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix); Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { testingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix); Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix); Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix); Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix); Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); if (classifier instanceof weka.classifiers.misc.InputMappedClassifier) { trainingEvaluation = new Evaluation(new Instances(mappedClassifierHeader, 0), costMatrix);
Evaluation eval = new Evaluation(data); EvaluationMetricHelper helper = new EvaluationMetricHelper(eval); boolean maximise = helper.metricIsMaximisable(m_evalMetric); eval = new Evaluation(data); helper.setEvaluation(eval); for (int r = 0; r < numRuns; r++) { eval = new Evaluation(trainingSets[r][i]); helper.setEvaluation(eval); eval.evaluateModel(classifiers[r][i], testSets[r][i]);
Evaluation eval = new Evaluation(data); EvaluationMetricHelper helper = new EvaluationMetricHelper(eval); boolean maximise = helper.metricIsMaximisable(m_evalMetric); eval = new Evaluation(data); helper.setEvaluation(eval); for (int r = 0; r < numRuns; r++) { eval = new Evaluation(trainingSets[r][i]); helper.setEvaluation(eval); eval.evaluateModel(classifiers[r][i], testSets[r][i]);
public void testRegression() throws Exception { Instances inst = new Instances(new StringReader(DATA)); inst.setClassIndex(inst.numAttributes() - 1); Evaluation eval = new Evaluation(inst); for (int i = 0; i < inst.numInstances(); i++) { eval.evaluateModelOnceAndRecordPrediction(PREDS[i], inst.instance(i)); } String standard = eval.toSummaryString(); String info = eval.toClassDetailsString(); weka.test.Regression reg = new weka.test.Regression(getClass()); reg.println(standard); reg.println(info); try { String diff = reg.diff(); if (diff == null) { System.err.println("Warning: No reference available, creating."); } else if (!diff.equals("")) { fail("Regression tst failed. Difference:\n" + diff); } } catch (IOException ex) { fail("Problem during regression testing.\n" + ex); } }
public void testRegression() throws Exception { Instances inst = new Instances(new StringReader(DATA)); inst.setClassIndex(inst.numAttributes() - 1); Evaluation eval = new Evaluation(inst); for (int i = 0; i < inst.numInstances(); i++) { eval.evaluateModelOnceAndRecordPrediction(PREDS[i], inst.instance(i)); } String standard = eval.toSummaryString(); String info = eval.toClassDetailsString(); weka.test.Regression reg = new weka.test.Regression(getClass()); reg.println(standard); reg.println(info); try { String diff = reg.diff(); if (diff == null) { System.err.println("Warning: No reference available, creating."); } else if (!diff.equals("")) { fail("Regression tst failed. Difference:\n" + diff); } } catch (IOException ex) { fail("Problem during regression testing.\n" + ex); } }
Evaluation eval = new Evaluation(trainingData);
m_classifierName.substring(m_classifierName.lastIndexOf(".") + 1, m_classifierName.length()); m_eval = new Evaluation(instance.dataset()); m_eval.useNoPriors(); if (m_windowSize > 0) { m_windowEval = new Evaluation(instance.dataset()); m_windowEval.useNoPriors();
m_OutOfBagEvaluationObject = new Evaluation(m_data);
m_classifierName.substring(m_classifierName.lastIndexOf(".") + 1, m_classifierName.length()); m_eval = new Evaluation(instance.dataset()); m_eval.useNoPriors(); if (m_windowSize > 0) { m_windowEval = new Evaluation(instance.dataset()); m_windowEval.useNoPriors();
m_OutOfBagEvaluationObject = new Evaluation(m_data);