@Test(expected = IllegalArgumentException.class) public void testSetWeightsDecayRegularization_InvalidUpperBound() { learner.setWeightsDecayRegularization(1.1); }
public void setWeightsDecayRegularization(double weightsDecayCost) { if (weightsDecayCost <= 0 || weightsDecayCost > 1) { throw new IllegalArgumentException("The weights decay regularization cost " + "term must be greater than 0 and no more than 1. " + "The passed value is : " + weightsDecayCost); } this.weightsDecayCost = weightsDecayCost; }
BPMLL extends MultiLabelLearnerBase { public void setWeightsDecayRegularization(double weightsDecayCost) { if (weightsDecayCost <= 0 || weightsDecayCost > 1) { throw new IllegalArgumentException("The weights decay regularization cost " + "term must be greater than 0 and no more than 1. " + "The passed value is : " + weightsDecayCost); } this.weightsDecayCost = weightsDecayCost; } }
BPMLL extends MultiLabelLearnerBase { public void setWeightsDecayRegularization(double weightsDecayCost) { if (weightsDecayCost <= 0 || weightsDecayCost > 1) { throw new IllegalArgumentException("The weights decay regularization cost " + "term must be greater than 0 and no more than 1. " + "The passed value is : " + weightsDecayCost); } this.weightsDecayCost = weightsDecayCost; } BPMLL();  BPMLL(long randomnessSeed); }
BPMLL extends MultiLabelLearnerBase { public void setWeightsDecayRegularization(double weightsDecayCost) { if (weightsDecayCost <= 0 || weightsDecayCost > 1) { throw new IllegalArgumentException("The weights decay regularization cost " + "term must be greater than 0 and no more than 1. " + "The passed value is : " + weightsDecayCost); } this.weightsDecayCost = weightsDecayCost; } 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 setWeightsDecayRegularization(double weightsDecayCost) { if (weightsDecayCost <= 0 || weightsDecayCost > 1) { throw new IllegalArgumentException("The weights decay regularization cost " + "term must be greater than 0 and no more than 1. " + "The passed value is : " + weightsDecayCost); } this.weightsDecayCost = weightsDecayCost; } 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);  }