encog(一)

encog库主要是一个包含神经网络算法的java库。

最基本的前馈神经网络BasicNetwork类是前馈神经网络。

以下是BasicNetwork类的方法

void	addLayer(Layer layer) 向网络中添加新层
void	addWeight(int fromLayer, int fromNeuron, int toNeuron, double value)    改变权重。 W[第fromLayer+1层](fromNeuron+1,toNeuronr+2)=W[第fromLayer+1层](fromNeuron+1,toNeuronr+2)+value network.addWeight(0, 1, 2, -1);  //将第一层的第二个神经元连接到第二层的第三个神经元的权重增加-1
double	calculateError(MLDataSet data) Calculate the error for this neural network.
int	calculateNeuronCount() Calculate the total number of neurons in the network across all layers.
int	classify(MLData input) Classify the input into a group.
void	clearContext() Clear any data from any context layers.
Object	clone() Return a clone of this neural network.
void	compute(double[] input, double[] output) Compute the output for this network.
MLData	compute(MLData input) Compute the output for a given input to the neural network.
void	decodeFromArray(double[] encoded) Decode an array to this object.
String	dumpWeights()
void	enableConnection(int fromLayer, int fromNeuron, int toNeuron, boolean enable) 关闭或开启网络中的某个连接
int	encodedArrayLength()
void	encodeToArray(double[] encoded) Encode the object to the specified array.
boolean	equals(BasicNetwork other, int precision) Determine if this neural network is equal to another.
boolean	equals(Object other) Compare the two neural networks.
ActivationFunction	getActivation(int layer) Get the activation function for the specified layer.
String	getFactoryArchitecture()
String	getFactoryType()
FlatNetwork	getFlat()
int	getInputCount()
double	getLayerBiasActivation(int l) Get the bias activation for the specified layer.
int	getLayerCount()
int	getLayerNeuronCount(int l) Get the neuron count.
double	getLayerOutput(int layer, int neuronNumber) Get the layer output for the specified neuron.
int	getLayerTotalNeuronCount(int l) Get the total (including bias and context) neuron cont for a layer.
int	getOutputCount()
NeuralStructure	getStructure()
double	getWeight(int fromLayer, int fromNeuron, int toNeuron) Get the weight between the two layers.
int	hashCode() Generate a hash code.
boolean	isConnected(int layer, int fromNeuron, int toNeuron) Determine if the specified connection is enabled.
boolean	isLayerBiased(int l) Determine if the specified layer is biased.
void	reset() 随机设置网络权重
void	reset(int seed) 通过随机数种子随机设置网络权重
void	setBiasActivation(double activation) Sets the bias activation for every layer that supports bias.
void	setLayerBiasActivation(int l, double value) Set the bias activation for the specified layer.
void	setWeight(int fromLayer, int fromNeuron, int toNeuron, double value) Set the weight between the two specified neurons.
String	toString()
void	updateProperties() Update any objeccts when a property changes.
void	validateNeuron(int targetLayer, int neuron) Validate the the specified targetLayer and neuron are valid.
int	winner(MLData input) Determine the winner for the specified input.

 BasicLayer类是最基本的层

构造函数

BasicLayer(ActivationFunction activationFunction, boolean hasBias, int neuronCount)

第一个参数:激活函数类型

第二个参数:是否有偏置节点

第三个参数:神经元个数

BasicLayer(int neuronCount)

Construct this layer with a sigmoid activation function.

返回值	成员函数
ActivationFunction	getActivationFunction()
BasicNetwork	getNetwork()
int	getNeuronCount()
void	setNetwork(BasicNetwork network) Set the network for this layer.

import org.encog.Encog;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;

/**
神经网络实现异或逻辑
 */
public class text {

    /**
     * The input necessary for XOR.
     */
    public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
            { 0.0, 1.0 }, { 1.0, 1.0 } };

    /**
     * The ideal data necessary for XOR.
     */
    public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };
    
    /**
     * The main method.
     * @param args No arguments are used.
     */
    public static void main(final String args[]) {
        
        // create a neural network, without using a factory
        BasicNetwork network = new BasicNetwork();
        network.addLayer(new BasicLayer(null,true,2));//输入层
        network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));//隐层1
        network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));//隐层2
        network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));//输出层
        network.getStructure().finalizeStructure();
        network.reset();
        
        // create training data
        MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
        
        // train the neural network
        final ResilientPropagation train = new ResilientPropagation(network, trainingSet);

        int epoch = 1;

        do {
            train.iteration();
            System.out.println("Epoch #" + epoch + " Error:" + train.getError());
            epoch++;
        } while(train.getError() > 0.01);
        train.finishTraining();

        // test the neural network
        System.out.println("Neural Network Results:");
        for(MLDataPair pair: trainingSet ) {
            final MLData output = network.compute(pair.getInput());
            System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
                    + ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
        }
        
        Encog.getInstance().shutdown();
    }
}