@Test public void testCalculate() { BasicNetwork network = TestBasicNetwork.buildSimpleXOR(); double[] out1 = network.computeRegression(new double[] {0.0, 0.0}); Assert.assertEquals(0.34688637738116557, out1[0], AIFH.DEFAULT_PRECISION); double[] out2 = network.computeRegression(new double[] {1.0, 0.0}); Assert.assertEquals(0.32943376685512565, out2[0], AIFH.DEFAULT_PRECISION); Assert.assertEquals(1,out2.length); }
@Override public double[] computeRegression(double[] input) { if( input.length!=getInputCount()) { throw new AIFHError("Invalid input count("+ input.length+"), this network is designed for: " + getInputCount()); } double[] output = new double[getOutputCount()]; compute(input,output); return output; }
BasicNetwork implements RegressionAlgorithm, ClassificationAlgorithm { @Override public double[] computeRegression(double[] input) { if( input.length!=getInputCount()) { throw new AIFHError("Invalid input count("+ input.length+"), this network is designed for: " + getInputCount()); } double[] output = new double[getOutputCount()]; compute(input,output); return output; } }
BasicNetwork implements RegressionAlgorithm, ClassificationAlgorithm { @Override public double[] computeRegression(double[] input) { if( input.length!=getInputCount()) { throw new AIFHError("Invalid input count("+ input.length+"), this network is designed for: " + getInputCount()); } double[] output = new double[getOutputCount()]; compute(input,output); return output; } BasicNetwork(); }
BasicNetwork implements RegressionAlgorithm, ClassificationAlgorithm { @Override public double[] computeRegression(double[] input) { if( input.length!=getInputCount()) { throw new AIFHError("Invalid input count("+ input.length+"), this network is designed for: " + getInputCount()); } double[] output = new double[getOutputCount()]; compute(input,output); return output; } BasicNetwork(); void compute(final double[] input, final double[] output); int getNeuronCount(); int getEncodeLength(); int getInputCount(); double[] getLayerOutput(); int getOutputCount(); double[] getWeights(); void setInputCount(final int inputCount); void setOutputCount(final int outputCount); double[] getLayerSums(); double getWeight(final int fromLayer,
                            final int fromNeuron,
                            final int toNeuron); void validateNeuron(final int targetLayer, final int neuron); int getLayerTotalNeuronCount(final int l); void addLayer(Layer layer); void finalizeStructure(); void clearOutput(); void setWeight(final int fromLayer, final int fromNeuron,
                          final int toNeuron, final double value); void reset(); List<Layer> getLayers(); @Override double[] computeRegression(double[] input); @Override double[] getLongTermMemory(); Layer getNextLayer(Layer layer); Layer getPreviousLayer(Layer layer); @Override int computeClassification(double[] input); boolean isNetworkTraining(); void setNetworkTraining(boolean networkTraining); }
BasicNetwork implements RegressionAlgorithm, ClassificationAlgorithm { @Override public double[] computeRegression(double[] input) { if( input.length!=getInputCount()) { throw new AIFHError("Invalid input count("+ input.length+"), this network is designed for: " + getInputCount()); } double[] output = new double[getOutputCount()]; compute(input,output); return output; } BasicNetwork(); void compute(final double[] input, final double[] output); int getNeuronCount(); int getEncodeLength(); int getInputCount(); double[] getLayerOutput(); int getOutputCount(); double[] getWeights(); void setInputCount(final int inputCount); void setOutputCount(final int outputCount); double[] getLayerSums(); double getWeight(final int fromLayer,
                            final int fromNeuron,
                            final int toNeuron); void validateNeuron(final int targetLayer, final int neuron); int getLayerTotalNeuronCount(final int l); void addLayer(Layer layer); void finalizeStructure(); void clearOutput(); void setWeight(final int fromLayer, final int fromNeuron,
                          final int toNeuron, final double value); void reset(); List<Layer> getLayers(); @Override double[] computeRegression(double[] input); @Override double[] getLongTermMemory(); Layer getNextLayer(Layer layer); Layer getPreviousLayer(Layer layer); @Override int computeClassification(double[] input); boolean isNetworkTraining(); void setNetworkTraining(boolean networkTraining);  }