@Test() public void testSetConvertNominalToBinary() { learner.setConvertNominalToBinary(false); Assert.assertFalse(learner.getConvertNominalToBinary()); }
public void setConvertNominalToBinary(boolean convert) { convertNomToBin = convert; }
MMPLearner extends MultiLabelLearnerBase { public void setConvertNominalToBinary(boolean convert) { convertNomToBin = convert; } }
MMPLearner extends MultiLabelLearnerBase { public void setConvertNominalToBinary(boolean convert) { convertNomToBin = convert; } MMPLearner();  MMPLearner(RankingLossFunction lossMeasure, MMPUpdateRuleType modelUpdateRule);  MMPLearner(RankingLossFunction lossMeasure, MMPUpdateRuleType modelUpdateRule, long randomnessSeed); }
MMPLearner extends MultiLabelLearnerBase { public void setConvertNominalToBinary(boolean convert) { convertNomToBin = convert; } MMPLearner();  MMPLearner(RankingLossFunction lossMeasure, MMPUpdateRuleType modelUpdateRule);  MMPLearner(RankingLossFunction lossMeasure, MMPUpdateRuleType modelUpdateRule, long randomnessSeed); void setTrainingEpochs(int epochs); int getTrainingEpochs(); void setConvertNominalToBinary(boolean convert); boolean getConvertNominalToBinary(); @Override boolean isUpdatable(); @Override MultiLabelOutput makePredictionInternal(Instance instance); @Override TechnicalInformation getTechnicalInformation(); String globalInfo(); }
MMPLearner extends MultiLabelLearnerBase { public void setConvertNominalToBinary(boolean convert) { convertNomToBin = convert; } MMPLearner();  MMPLearner(RankingLossFunction lossMeasure, MMPUpdateRuleType modelUpdateRule);  MMPLearner(RankingLossFunction lossMeasure, MMPUpdateRuleType modelUpdateRule, long randomnessSeed); void setTrainingEpochs(int epochs); int getTrainingEpochs(); void setConvertNominalToBinary(boolean convert); boolean getConvertNominalToBinary(); @Override boolean isUpdatable(); @Override MultiLabelOutput makePredictionInternal(Instance instance); @Override TechnicalInformation getTechnicalInformation(); String globalInfo();  }