// require java 1.8

// jar:  D:\Neuroph\neuroph-2.93b\neuroph-2.93b\neuroph-core-2.93.jar

// java.lang.ClassNotFoundException: org.slf4j.LoggerFactory

// 这种情况，一般是在lib包下缺少两个jar文件，这两个jar文件是：slf4j-api-1.5.0和slf4j-log4j12-1.5.0，他们的位置在：

// spring-framework-2.5.6\lib\slf4j文件夹下。

// https://www.slf4j.org/

import java.util.Arrays;

import org.neuroph.core.NeuralNetwork;

import org.neuroph.nnet.Perceptron;

import org.neuroph.core.data.DataSet;

import org.neuroph.core.data.DataSetRow;

/**

* This sample shows how to create, train, save and load simple Perceptron neural network

*/

public class PerceptronSample {

    public static void main(String args[]) {

        // create training set (logical AND function)

        DataSet trainingSet = new DataSet(2, 1);

        trainingSet.addRow(new DataSetRow(new double[]{0, 0}, new double[]{0}));

        trainingSet.addRow(new DataSetRow(new double[]{0, 1}, new double[]{0}));

        trainingSet.addRow(new DataSetRow(new double[]{1, 0}, new double[]{0}));

        trainingSet.addRow(new DataSetRow(new double[]{1, 1}, new double[]{1}));

        // create perceptron neural network

        NeuralNetwork myPerceptron = new Perceptron(2, 1);

        // learn the training set

        myPerceptron.learn(trainingSet);

        // test perceptron

        System.out.println("Testing trained perceptron");

        testNeuralNetwork(myPerceptron, trainingSet);

        // save trained perceptron

        myPerceptron.save("mySamplePerceptron.nnet");

        // load saved neural network

        NeuralNetwork loadedPerceptron = NeuralNetwork.createFromFile("mySamplePerceptron.nnet");

        // test loaded neural network

        System.out.println("Testing loaded perceptron");

        testNeuralNetwork(loadedPerceptron, trainingSet);

    }

    public static void testNeuralNetwork(NeuralNetwork nnet, DataSet tset) {

        for(DataSetRow dataRow : tset.getRows()) {

            nnet.setInput(dataRow.getInput());

            nnet.calculate();

            double[ ] networkOutput = nnet.getOutput();

            System.out.print("Input: " + Arrays.toString(dataRow.getInput()) );

            System.out.println(" Output: " + Arrays.toString(networkOutput) );

        }

    }

}