@Test public void testSetNormalizeAttributes() { learner.setNormalizeAttributes(false); Assert.assertFalse(learner.getNormalizeAttributes()); }
public void setNormalizeAttributes(boolean normalize) { normalizeAttributes = normalize; }
BPMLL extends MultiLabelLearnerBase { public void setNormalizeAttributes(boolean normalize) { normalizeAttributes = normalize; } }
BPMLL extends MultiLabelLearnerBase { public void setNormalizeAttributes(boolean normalize) { normalizeAttributes = normalize; } BPMLL();  BPMLL(long randomnessSeed); }
BPMLL extends MultiLabelLearnerBase { public void setNormalizeAttributes(boolean normalize) { normalizeAttributes = normalize; } BPMLL();  BPMLL(long randomnessSeed); void setHiddenLayers(int[] hiddenLayers); int[] getHiddenLayers(); void setLearningRate(double learningRate); double getLearningRate(); void setWeightsDecayRegularization(double weightsDecayCost); double getWeightsDecayRegularization(); void setTrainingEpochs(int epochs); int getTrainingEpochs(); void setNormalizeAttributes(boolean normalize); boolean getNormalizeAttributes(); String globalInfo(); @Override TechnicalInformation getTechnicalInformation(); MultiLabelOutput makePredictionInternal(Instance instance); }
BPMLL extends MultiLabelLearnerBase { public void setNormalizeAttributes(boolean normalize) { normalizeAttributes = normalize; } BPMLL();  BPMLL(long randomnessSeed); void setHiddenLayers(int[] hiddenLayers); int[] getHiddenLayers(); void setLearningRate(double learningRate); double getLearningRate(); void setWeightsDecayRegularization(double weightsDecayCost); double getWeightsDecayRegularization(); void setTrainingEpochs(int epochs); int getTrainingEpochs(); void setNormalizeAttributes(boolean normalize); boolean getNormalizeAttributes(); String globalInfo(); @Override TechnicalInformation getTechnicalInformation(); MultiLabelOutput makePredictionInternal(Instance instance);  }