@Test public void testReset(){ testFeedForward(); neuralNet.reset(); testGetOutput(); }
public void reset() { currentNetOutput = null; for (List<Neuron> layer : layers) { for (Neuron neuron : layer) { neuron.reset(); } } }
BasicNeuralNet implements NeuralNet, Serializable { public void reset() { currentNetOutput = null; for (List<Neuron> layer : layers) { for (Neuron neuron : layer) { neuron.reset(); } } } }
BasicNeuralNet implements NeuralNet, Serializable { public void reset() { currentNetOutput = null; for (List<Neuron> layer : layers) { for (Neuron neuron : layer) { neuron.reset(); } } } BasicNeuralNet(int[] netTopology, double biasInput,
            Class<? extends ActivationFunction> activationFunction, Random random); }
BasicNeuralNet implements NeuralNet, Serializable { public void reset() { currentNetOutput = null; for (List<Neuron> layer : layers) { for (Neuron neuron : layer) { neuron.reset(); } } } 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 void reset() { currentNetOutput = null; for (List<Neuron> layer : layers) { for (Neuron neuron : layer) { neuron.reset(); } } } 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();  }