result[current++] = new Double(eval.incorrect()); result[current++] = new Double(eval.unclassified()); result[current++] = new Double(eval.pctCorrect()); result[current++] = new Double(eval.pctIncorrect()); result[current++] = new Double(eval.pctUnclassified());
result[current++] = new Double(eval.incorrect()); result[current++] = new Double(eval.unclassified()); result[current++] = new Double(eval.pctCorrect()); result[current++] = new Double(eval.pctIncorrect()); result[current++] = new Double(eval.pctUnclassified());
accyScore[curCfr][curRun-1] = eval.pctCorrect(); timeScore[curCfr][curRun-1] = elapsedTime; eval.pctCorrect(), elapsedTime));
accyScore[curCfr][curRun-1] = eval.pctCorrect(); timeScore[curCfr][curRun-1] = elapsedTime; eval.pctCorrect(), elapsedTime));
public static void evaluate(Dl4jMlpClassifier clf, Instances data, double minPerfomance) throws Exception { Instances[] split = TestUtil.splitTrainTest(data); Instances train = split[0]; Instances test = split[1]; clf.buildClassifier(train); Evaluation trainEval = new Evaluation(train); trainEval.evaluateModel(clf, train); Evaluation testEval = new Evaluation(train); testEval.evaluateModel(clf, test); final double testPctCorrect = testEval.pctCorrect(); final double trainPctCorrect = trainEval.pctCorrect(); log.info("Train: {}, Test: {}", trainPctCorrect, testPctCorrect); boolean success = testPctCorrect > minPerfomance && trainPctCorrect > minPerfomance; log.info("Success: " + success); log.info(clf.getModel().conf().toYaml()); Assert.assertTrue("Performance was < " + minPerfomance + ". TestPctCorrect: " + testPctCorrect +", TrainPctCorrect: " + trainPctCorrect, success); }
eval[i] = new weka.classifiers.Evaluation(filteredTrainData); eval[i].evaluateModel(indepModel, filteredTestData); acc1[i] = eval[i]. pctCorrect(); eval2[i] = new weka.classifiers.Evaluation(filteredTrainData2); eval2[i].evaluateModel(depModel, filteredTestData2); acc2[i] = eval2[i]. pctCorrect();
result[current++] = new Double(eval.incorrect()); result[current++] = new Double(eval.unclassified()); result[current++] = new Double(eval.pctCorrect()); result[current++] = new Double(eval.pctIncorrect()); result[current++] = new Double(eval.pctUnclassified());
results.put(PCT_CORRECT, eval.pctCorrect()); results.put(PCT_INCORRECT, eval.pctIncorrect()); results.put(PCT_UNCLASSIFIED, eval.pctUnclassified());
result[current++] = new Double(eval.incorrect()); result[current++] = new Double(eval.unclassified()); result[current++] = new Double(eval.pctCorrect()); result[current++] = new Double(eval.pctIncorrect()); result[current++] = new Double(eval.pctUnclassified());
/** * Test datasets with class meta data that is not in lexicographic order. * * @throws Exception Something went wrong. */ @Test public void testMixedClassOrder() throws Exception { String prefix = "src/test/resources/nominal/"; // Get data Instances testProb = DatasetLoader.loadArff(prefix + "mnist.meta.minimal.arff"); Instances testProbInverse = DatasetLoader.loadArff(prefix + "mnist.meta.minimal.mixed-class-meta-data.arff"); Evaluation evalNormal = eval(testProb); Evaluation evalMixed = eval(testProbInverse); // Compare accuracy Assert.assertEquals(evalNormal.pctCorrect(), evalMixed.pctCorrect(), 1e-7); Assert.assertEquals(evalNormal.pctIncorrect(), evalMixed.pctIncorrect(), 1e-7); }
case EVAL_DEFAULT: if (m_classIsNominal) { return m_evaluation.pctCorrect(); return m_evaluation.pctCorrect(); case EVAL_RMSE: return -m_evaluation.rootMeanSquaredError();
case EVAL_DEFAULT: if (m_classIsNominal) { return m_evaluation.pctCorrect(); return m_evaluation.pctCorrect(); case EVAL_RMSE: return -m_evaluation.rootMeanSquaredError();
return (1 - StrictMath.abs(m_Evaluation.correlationCoefficient()) + m_Evaluation.rootRelativeSquaredError() + m_Evaluation.relativeAbsoluteError()); case DefaultEvaluationMetrics.EVALUATION_ACC: return m_Evaluation.pctCorrect(); case DefaultEvaluationMetrics.EVALUATION_KAPPA: return m_Evaluation.kappa();