@Test(expected = IllegalArgumentException.class) public void testSetTrainigEpochs_WithInvalid() { learner.setTrainingEpochs(0); }
public void setTrainingEpochs(int epochs) { if (epochs <= 0) { throw new IllegalArgumentException("The number of training epochs must be greater than zero. " + "Entered value is : " + epochs); } this.epochs = epochs; }
BPMLL extends MultiLabelLearnerBase { public void setTrainingEpochs(int epochs) { if (epochs <= 0) { throw new IllegalArgumentException("The number of training epochs must be greater than zero. " + "Entered value is : " + epochs); } this.epochs = epochs; } }
BPMLL extends MultiLabelLearnerBase { public void setTrainingEpochs(int epochs) { if (epochs <= 0) { throw new IllegalArgumentException("The number of training epochs must be greater than zero. " + "Entered value is : " + epochs); } this.epochs = epochs; } BPMLL();  BPMLL(long randomnessSeed); }
BPMLL extends MultiLabelLearnerBase { public void setTrainingEpochs(int epochs) { if (epochs <= 0) { throw new IllegalArgumentException("The number of training epochs must be greater than zero. " + "Entered value is : " + epochs); } this.epochs = epochs; } 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 setTrainingEpochs(int epochs) { if (epochs <= 0) { throw new IllegalArgumentException("The number of training epochs must be greater than zero. " + "Entered value is : " + epochs); } this.epochs = epochs; } 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);  }