@Test public void testNumOutputs() { assertTrue(mlp.numOutputs() == 1); }
@Override public int numOutputs() { return secondConnectionLayer[0].length; }
MLP implements Network { @Override public int numOutputs() { return secondConnectionLayer[0].length; } }
MLP implements Network { @Override public int numOutputs() { return secondConnectionLayer[0].length; } MLP(MLPGenotype genotype);  MLP(int numberOfInputs, int numberOfHidden, int numberOfOutputs);  MLP(double[][] firstConnectionLayer, double[][] secondConnectionLayer);  MLP(double[][] firstConnectionLayer, double[][] secondConnectionLayer, int numberOfHidden,
			int numberOfOutputs); }
MLP implements Network { @Override public int numOutputs() { return secondConnectionLayer[0].length; } MLP(MLPGenotype genotype);  MLP(int numberOfInputs, int numberOfHidden, int numberOfOutputs);  MLP(double[][] firstConnectionLayer, double[][] secondConnectionLayer);  MLP(double[][] firstConnectionLayer, double[][] secondConnectionLayer, int numberOfHidden,
			int numberOfOutputs); @Override /** * returns number of inputs */ int numInputs(); @Override /** * returns number of outputs */ int numOutputs(); @Override /** * processes inputs via propagation */ double[] process(double[] inputs); @Override /** * Clears MLP so SRN can be reused */ void flush(); @Override /** * Unwritten method, will become useful * once mulitask learning is developed for MLPs */ boolean isMultitask(); @Override /** * Chooses the node corresponding to the input */ void chooseMode(int mode); double[] propagate(double[] inputIn); final void initializeAllLayersRandom(); MLP copy(); double sum(); void println(); double backPropagate(double[] targetOutputs); String info(); @Override /** * default toString method */ String toString(); @Override /** * Not supported yet */ int effectiveNumOutputs(); @Override /** * not supported yet */ double[] moduleOutput(int mode); @Override /** * not supported yet */ int lastModule(); @Override /** *not supported yet */ int numModules(); @Override /** * not supported yet */ int[] getModuleUsage(); }
MLP implements Network { @Override public int numOutputs() { return secondConnectionLayer[0].length; } MLP(MLPGenotype genotype);  MLP(int numberOfInputs, int numberOfHidden, int numberOfOutputs);  MLP(double[][] firstConnectionLayer, double[][] secondConnectionLayer);  MLP(double[][] firstConnectionLayer, double[][] secondConnectionLayer, int numberOfHidden,
			int numberOfOutputs); @Override /** * returns number of inputs */ int numInputs(); @Override /** * returns number of outputs */ int numOutputs(); @Override /** * processes inputs via propagation */ double[] process(double[] inputs); @Override /** * Clears MLP so SRN can be reused */ void flush(); @Override /** * Unwritten method, will become useful * once mulitask learning is developed for MLPs */ boolean isMultitask(); @Override /** * Chooses the node corresponding to the input */ void chooseMode(int mode); double[] propagate(double[] inputIn); final void initializeAllLayersRandom(); MLP copy(); double sum(); void println(); double backPropagate(double[] targetOutputs); String info(); @Override /** * default toString method */ String toString(); @Override /** * Not supported yet */ int effectiveNumOutputs(); @Override /** * not supported yet */ double[] moduleOutput(int mode); @Override /** * not supported yet */ int lastModule(); @Override /** *not supported yet */ int numModules(); @Override /** * not supported yet */ int[] getModuleUsage(); public double[][] firstConnectionLayer; public double[][] secondConnectionLayer; public double learningRate; }