@Test public void testSetHiddenLayers() { int[] expected = new int[]{1, 2, 3}; learner.setHiddenLayers(expected); int[] actual = learner.getHiddenLayers(); Assert.assertTrue(Arrays.equals(expected, actual)); }
public void setHiddenLayers(int[] hiddenLayers) { if (hiddenLayers != null) { for (int value : hiddenLayers) { if (value <= 0) { throw new IllegalArgumentException("Invalid hidden layer topology definition. " + "Number of neurons in hidden layer must be larger than zero."); } } } hiddenLayersTopology = hiddenLayers; }
BPMLL extends MultiLabelLearnerBase { public void setHiddenLayers(int[] hiddenLayers) { if (hiddenLayers != null) { for (int value : hiddenLayers) { if (value <= 0) { throw new IllegalArgumentException("Invalid hidden layer topology definition. " + "Number of neurons in hidden layer must be larger than zero."); } } } hiddenLayersTopology = hiddenLayers; } }
BPMLL extends MultiLabelLearnerBase { public void setHiddenLayers(int[] hiddenLayers) { if (hiddenLayers != null) { for (int value : hiddenLayers) { if (value <= 0) { throw new IllegalArgumentException("Invalid hidden layer topology definition. " + "Number of neurons in hidden layer must be larger than zero."); } } } hiddenLayersTopology = hiddenLayers; } BPMLL();  BPMLL(long randomnessSeed); }
BPMLL extends MultiLabelLearnerBase { public void setHiddenLayers(int[] hiddenLayers) { if (hiddenLayers != null) { for (int value : hiddenLayers) { if (value <= 0) { throw new IllegalArgumentException("Invalid hidden layer topology definition. " + "Number of neurons in hidden layer must be larger than zero."); } } } hiddenLayersTopology = hiddenLayers; } 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 setHiddenLayers(int[] hiddenLayers) { if (hiddenLayers != null) { for (int value : hiddenLayers) { if (value <= 0) { throw new IllegalArgumentException("Invalid hidden layer topology definition. " + "Number of neurons in hidden layer must be larger than zero."); } } } hiddenLayersTopology = hiddenLayers; } 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);  }