@Test public void testGetOutput(){ double[] zeroArray = new double[NET_TOPOLOGY[NET_TOPOLOGY.length - 1]]; Arrays.fill(zeroArray, 0); assertTrue("Network output is not as expected.", Arrays.equals(zeroArray, neuralNet.getOutput())); }
public double[] getOutput() { if (currentNetOutput == null) { return new double[netOutputDim]; } return currentNetOutput; }
BasicNeuralNet implements NeuralNet, Serializable { public double[] getOutput() { if (currentNetOutput == null) { return new double[netOutputDim]; } return currentNetOutput; } }
BasicNeuralNet implements NeuralNet, Serializable { public double[] getOutput() { if (currentNetOutput == null) { return new double[netOutputDim]; } return currentNetOutput; } BasicNeuralNet(int[] netTopology, double biasInput,
            Class<? extends ActivationFunction> activationFunction, Random random); }
BasicNeuralNet implements NeuralNet, Serializable { public double[] getOutput() { if (currentNetOutput == null) { return new double[netOutputDim]; } return currentNetOutput; } 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 double[] getOutput() { if (currentNetOutput == null) { return new double[netOutputDim]; } return currentNetOutput; } 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();  }