#include "comm_def_col.h"

#pragma once

class NNconnection

{

NNconnection(void);

~NNconnection(void);

public:

//连接线的权重在整个权重向量中的索引

unsigned weightIdx;

//连接的前面一层结点的索引

unsigned neuralIdx;

};

class NNneural

{

NNneural(void);

~NNneural(void);

public:

//输出

double output;

//若干连线

vector<NNconnection> m_connection;

};

class NNlayer

{

~NNlayer(void);

public:

NNlayer(){ preLayer = NULL; }

NNlayer *preLayer;

vector<NNneural> m_neurals;

vector<double> m_weights;

void addNeurals(unsigned num, unsigned preNumNeurals);

void backPropagate(vector<double>& dErrWrtDxn, vector<double>& dErrWrtDxnm, double eta);

};

class NeuralNetwork

{

NeuralNetwork(void);

~NeuralNetwork(void);

private:

unsigned nLayer; // 网络层数

vector<unsigned> nodes; // 每层的结点数

vector<double> actualOutput; // 每次迭代的输出结果

double etaLearningRate; // 权值学习率

unsigned iterNum; // 迭代次数

public:

vector<NNlayer*> m_layers; // 整个网络层

void create(unsigned num_layers,unsigned * ar_nodes); // 创建网络

void initializeNetwork(); // 初始化网络，包括设置权值等

void forwardCalculate(vector<double>& invect,vector<double>& outvect); // 向前计算

void backPropagate(vector<double>& tVect,vector<double>& oVect); ///反向传播

void train(vector<vector<double>>& inputVect,vector<vector<double>>& outputVect); //训练

void classifer(vector<double>& inVect,vector<double>& outVect); // 分类

};