/** * tests whether the scheme alters the training set during training. * * @see CheckAttributeSelection#datasetIntegrity(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, boolean) * @see CheckAttributeSelection#testsPerClassType(int, boolean, boolean) */ public void testDatasetIntegrity() { boolean[] result; int i; for (i = FIRST_CLASSTYPE; i <= LAST_CLASSTYPE; i++) { // does the scheme support this type of class at all? if (!canPredict(i)) continue; result = m_Tester.datasetIntegrity( m_NominalPredictors[i], m_NumericPredictors[i], m_StringPredictors[i], m_DatePredictors[i], m_RelationalPredictors[i], m_multiInstanceHandler, i, m_handleMissingPredictors[i], m_handleMissingClass[i]); if (!result[0] && !result[1]) fail("Dataset is altered during training (" + getClassTypeString(i) + " class)!"); } }
/** * tests whether the scheme alters the training set during training. * * @see CheckAttributeSelection#datasetIntegrity(boolean, boolean, boolean, boolean, boolean, boolean, int, boolean, boolean) * @see CheckAttributeSelection#testsPerClassType(int, boolean, boolean) */ public void testDatasetIntegrity() { boolean[] result; int i; for (i = FIRST_CLASSTYPE; i <= LAST_CLASSTYPE; i++) { // does the scheme support this type of class at all? if (!canPredict(i)) continue; result = m_Tester.datasetIntegrity( m_NominalPredictors[i], m_NumericPredictors[i], m_StringPredictors[i], m_DatePredictors[i], m_RelationalPredictors[i], m_multiInstanceHandler, i, m_handleMissingPredictors[i], m_handleMissingClass[i]); if (!result[0] && !result[1]) fail("Dataset is altered during training (" + getClassTypeString(i) + " class)!"); } }
datasetIntegrity(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, handleMissingPredictors, handleMissingClass);
datasetIntegrity(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, handleMissingPredictors, handleMissingClass);