@Test public void testRandomness(){ neuralNet = new BasicNeuralNet(NET_TOPOLOGY, BIAS, ACT_FUNCT_CLASS, new Random(10)); double[] weights = neuralNet.getLayerUnits(1).get(0).getWeights(); neuralNet = new BasicNeuralNet(NET_TOPOLOGY, BIAS, ACT_FUNCT_CLASS, new Random(10)); Assert.assertTrue(Arrays.equals(weights, neuralNet.getLayerUnits(1).get(0).getWeights())); neuralNet = new BasicNeuralNet(NET_TOPOLOGY, BIAS, ACT_FUNCT_CLASS, null); weights = neuralNet.getLayerUnits(1).get(0).getWeights(); neuralNet = new BasicNeuralNet(NET_TOPOLOGY, BIAS, ACT_FUNCT_CLASS, null); Assert.assertFalse(Arrays.equals(weights, neuralNet.getLayerUnits(1).get(0).getWeights())); }
public List<Neuron> getLayerUnits(int layerIndex) { return Collections.unmodifiableList(layers.get(layerIndex)); }
BasicNeuralNet implements NeuralNet, Serializable { public List<Neuron> getLayerUnits(int layerIndex) { return Collections.unmodifiableList(layers.get(layerIndex)); } }
BasicNeuralNet implements NeuralNet, Serializable { public List<Neuron> getLayerUnits(int layerIndex) { return Collections.unmodifiableList(layers.get(layerIndex)); } BasicNeuralNet(int[] netTopology, double biasInput,
            Class<? extends ActivationFunction> activationFunction, Random random); }
BasicNeuralNet implements NeuralNet, Serializable { public List<Neuron> getLayerUnits(int layerIndex) { return Collections.unmodifiableList(layers.get(layerIndex)); } BasicNeuralNet(int[] netTopology, double biasInput,
            Class<? extends ActivationFunction> activationFunction, Random random); List<Neuron> getLayerUnits(int layerIndex); int getLayersCount(); double[] feedForward(final double[] inputPattern); double[] getOutput(); void reset(); int getNetInputSize(); int getNetOutputSize(); }
BasicNeuralNet implements NeuralNet, Serializable { public List<Neuron> getLayerUnits(int layerIndex) { return Collections.unmodifiableList(layers.get(layerIndex)); } BasicNeuralNet(int[] netTopology, double biasInput,
            Class<? extends ActivationFunction> activationFunction, Random random); List<Neuron> getLayerUnits(int layerIndex); int getLayersCount(); double[] feedForward(final double[] inputPattern); double[] getOutput(); void reset(); int getNetInputSize(); int getNetOutputSize();  }