neuroph简单的感知器测试

这是一个感知器学习AND规则的例子， 抄自NEU的Sample

首先定义TrainingSet， 两个参数分别表示输入向量SIZE和输出向量SIZE，看代码：

 /**
         * Creates an instance of new empty training set
         * 
         * @param inputVectorSize
         * @param outputVectorSize
         */
        public TrainingSet(int inputVectorSize, int outputVectorSize) {
		this.elements = new Vector<TrainingElement>();
                this.inputVectorSize = inputVectorSize;
                this.outputVectorSize = outputVectorSize;
	}

然后定义一个感知器， 两个参数分别表示输入层的元数和输出的元数， 代码：

/**
	 * Creates new Perceptron with specified number of neurons in input and
	 * output layer, with Step trqansfer function
	 * 
	 * @param inputNeuronsCount
	 *            number of neurons in input layer
	 * @param outputNeuronsCount
	 *            number of neurons in output layer
	 */
	public Perceptron(int inputNeuronsCount, int outputNeuronsCount) {
		this.createNetwork(inputNeuronsCount, outputNeuronsCount, TransferFunctionType.STEP);
	}

然后在当前线程启动学习过程。 对于需要异步执行的，提供了learnInNewThread来满足需求。
训练网络


验证输出。

public void testPerceptron() {
        TrainingSet trainingSet = new TrainingSet(2, 1);
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 0}, new double[]{0}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{0, 1}, new double[]{0}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 0}, new double[]{0}));
        trainingSet.addElement(new SupervisedTrainingElement(new double[]{1, 1}, new double[]{1}));

        NeuralNetwork myPerceptron = new Perceptron(2, 1);
        myPerceptron.learnInSameThread(trainingSet);
        System.out.println("Testing trained perceptron");
        neuralNetwork(myPerceptron, trainingSet);
        myPerceptron.save("mySamplePerceptron.nnet");
        
        
        NeuralNetwork loadedPerceptron = NeuralNetwork.load("mySamplePerceptron.nnet");
        System.out.println("Testing loaded perceptron");
        neuralNetwork(loadedPerceptron, trainingSet);
	}
	
	
	
	private void neuralNetwork(NeuralNetwork neuralNet, TrainingSet trainingSet) {
	
	    for(TrainingElement trainingElement : trainingSet.trainingElements()) {
	        neuralNet.setInput(trainingElement.getInput());
	        neuralNet.calculate();
	        Vector<Double> networkOutput = neuralNet.getOutput();
	
	        double d1 = trainingElement.getInput().get(0);
	        double d2 = trainingElement.getInput().get(1);
	        
	        double result = networkOutput.get(0);
	        
	        boolean b1 = d1 == 1.0;
	        boolean b2 = d2 == 1.0;
	        
	        boolean r1 = result == 1.0;
	        
	        assertEquals(b1 & b2, r1);
	    }
	}