/** * Evaluates the supplied distribution on a single instance. * * @param dist the supplied distribution * @param instance the test instance to be classified * @return the prediction * @throws Exception if model could not be evaluated successfully */ public double evaluateModelOnceAndRecordPrediction(double[] dist, Instance instance) throws Exception { return m_delegate.evaluateModelOnceAndRecordPrediction(dist, instance); }
/** * Evaluates the supplied distribution on a single instance. * * @param dist the supplied distribution * @param instance the test instance to be classified * @return the prediction * @throws Exception if model could not be evaluated successfully */ public double evaluateModelOnceAndRecordPrediction(double[] dist, Instance instance) throws Exception { return m_delegate.evaluateModelOnceAndRecordPrediction(dist, instance); }
/** * Evaluates the classifier on a single instance and records the prediction. * * @param classifier machine learning classifier * @param instance the test instance to be classified * @return the prediction made by the clasifier * @throws Exception if model could not be evaluated successfully or the data * contains string attributes */ public double evaluateModelOnceAndRecordPrediction(Classifier classifier, Instance instance) throws Exception { return m_delegate .evaluateModelOnceAndRecordPrediction(classifier, instance); }
/** * Evaluates the classifier on a single instance and records the prediction. * * @param classifier machine learning classifier * @param instance the test instance to be classified * @return the prediction made by the clasifier * @throws Exception if model could not be evaluated successfully or the data * contains string attributes */ public double evaluateModelOnceAndRecordPrediction(Classifier classifier, Instance instance) throws Exception { return m_delegate .evaluateModelOnceAndRecordPrediction(classifier, instance); }
m_eval.evaluateModelOnceAndRecordPrediction(m_classifier, inst); } else { m_eval.evaluateModelOnce(m_classifier, inst);
Classifier nbTree = (Classifier)SerializationHelper.read(Model) as NBTree; Instances testDataSet = new Instances(new BufferedReader(new FileReader(arff))); testDataSet.setClassIndex(10); Evaluation evaluation = new Evaluation(testDataSet); for (int i = 0; i < testDataSet.numInstances(); i++) { Instance instance = testDataSet.instance(i); evaluation.evaluateModelOnceAndRecordPrediction(nbTree, instance); } foreach (object o in evaluation.predictions().toArray()) { NominalPrediction prediction = o as NominalPrediction; if (prediction != null) { double[] distribution = prediction.distribution(); double predicted = prediction.predicted(); } }
trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train)); testingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, testSource.nextElement(test)); trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train)); trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train)); trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train));
trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train)); testingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, testSource.nextElement(test)); trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train)); trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train)); trainingEvaluation.evaluateModelOnceAndRecordPrediction(classifier, trainSource.nextElement(train));
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); } }
for (int i = 0; i < test.numInstances(); i++) { if (m_predFrac > 0) { m_eval.evaluateModelOnceAndRecordPrediction(m_classifier, test.instance(i)); } else {
if (!instance.classIsMissing()) { if (m_outputInfoRetrievalStats) { m_eval.evaluateModelOnceAndRecordPrediction(dist, instance); } else { m_eval.evaluateModelOnce(dist, instance);
if (!instance.classIsMissing()) { if (m_outputInfoRetrievalStats) { m_eval.evaluateModelOnceAndRecordPrediction(dist, instance); } else { m_eval.evaluateModelOnce(dist, instance);
evaluateModelOnceAndRecordPrediction(classifier, data.instance(i)); if (classificationOutput != null) { classificationOutput.printClassification(classifier,
evaluateModelOnceAndRecordPrediction(classifier, data.instance(i)); if (classificationOutput != null) { classificationOutput.printClassification(classifier,