神经网络

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搞数据挖掘编的神经网络，用于分类预测。

#include <iostream>#include <fstream>#include <string>#include <cstring>#include <stack>#include <math.h>#include <vector>using namespace std;const double l=0.1;//学习率const int trainItemNumber=20000;//训练对的数目typedef double** iMatrixPointer;int HIDDENLAYERNUMBER;int* HIDDENLAYERNUMBERARR;int* inputLayer;//输入层iMatrixPointer* weightArray;struct hiddenNode{double bias;double outputval;double err;};hiddenNode** hiddenLayer;//隐藏层hiddenNode* outputLayer;//输出层struct item{int* inputArray;int category;};vector<item> ncItems;vector<item> changeItems;vector<item> controlItems;//通过用户输入获取参数void processInput(){//获取隐含层层数cout<<"请输入隐藏层层数:";cin>>HIDDENLAYERNUMBER;cout<<endl;weightArray = new iMatrixPointer[HIDDENLAYERNUMBER+1];//获取每层的结点数HIDDENLAYERNUMBERARR = new int[HIDDENLAYERNUMBER];for(int i=1;i<=HIDDENLAYERNUMBER;i++){cout<<"输入第"<<i<<"层的结点个数:";cin>>HIDDENLAYERNUMBERARR[i-1];cout<<endl;}}void InitializeMatrix(){int layerIndex;for(layerIndex=0;layerIndex<=HIDDENLAYERNUMBER;layerIndex++){int currNumber,nextNumber;if(layerIndex==0)currNumber=14;elsecurrNumber=HIDDENLAYERNUMBERARR[layerIndex-1];if(layerIndex==HIDDENLAYERNUMBER)nextNumber=2;elsenextNumber=HIDDENLAYERNUMBERARR[layerIndex];iMatrixPointer matrix;//构造数组matrix = new double*[currNumber];for(int i=0;i<currNumber;i++){matrix[i]=new double[nextNumber];}//初始化for(int i=0;i<currNumber;i++)for(int j=0;j<nextNumber;j++)matrix[i][j]=0.5;//给一个定值_____0.5_______weightArray[layerIndex]=matrix;}}//初始化隐藏层void InitializeHiddenLayer(){hiddenLayer = new hiddenNode*[HIDDENLAYERNUMBER];for(int hLayerIndex=0;hLayerIndex<HIDDENLAYERNUMBER;hLayerIndex++){int iLayerNodeNum = HIDDENLAYERNUMBERARR[hLayerIndex];hiddenNode* nodeArray = new hiddenNode[iLayerNodeNum];for(int hNodeIndex=0;hNodeIndex<iLayerNodeNum;hNodeIndex++){nodeArray[hNodeIndex].bias = 0.5;//给一个定值_____0.5_______nodeArray[hNodeIndex].outputval = 0;nodeArray[hNodeIndex].err=0;}hiddenLayer[hLayerIndex]=nodeArray;}}//初始化神经网络void InitializeModel(){InitializeMatrix();//初始化输入层inputLayer = new int[14];for(int i=0;i<14;i++)inputLayer[i]=0;//初始化隐藏层InitializeHiddenLayer();//初始化输出层outputLayer = new hiddenNode[2];outputLayer[0].bias=0.5;//给一个定值_____0.5_______outputLayer[0].outputval=0;outputLayer[0].err=0;outputLayer[1].bias=0.5;//给一个定值_____0.5_______outputLayer[1].outputval=0;outputLayer[1].err=0;}void forwardInput(int inputArray[]){//处理输入层for(int i=0;i<14;i++){inputLayer[i]=inputArray[i];}//处理隐藏层for(int mi=0;mi<HIDDENLAYERNUMBER;mi++){int currNum=HIDDENLAYERNUMBERARR[mi];//当前层结点个数if(mi==0){//第一层隐藏层for(int nindex=0;nindex<currNum;nindex++){//对于第一隐藏层每个结点进行计算iMatrixPointer inputMatrix=weightArray[0];//输入--第一隐藏层权重数组double sum=0;for(int findex=0;findex<14;findex++){//取输入层结点累加sum+=(double)inputLayer[findex]*inputMatrix[findex][nindex];}hiddenLayer[0][nindex].outputval=sum;//记录输出值}}else{iMatrixPointer prev_currMatrix=weightArray[mi];int prevNodeNum=HIDDENLAYERNUMBERARR[mi-1];for(int curindex=0;curindex<currNum;curindex++){//对于当前每个结点进行计算double sum=0;for(int previndex=0;previndex<prevNodeNum;previndex++){double prevNWeight=prev_currMatrix[previndex][curindex];double prevNOutput=hiddenLayer[mi-1][previndex].outputval;sum+=prevNWeight*prevNOutput;}//加上当前结点的偏倚值sum+=hiddenLayer[mi][curindex].bias;//激励函数double output=1/(1+exp(-sum));//保存输出hiddenLayer[mi][curindex].outputval=output;}}//else}//for//处理输出层int lastHiddenNumber=HIDDENLAYERNUMBERARR[HIDDENLAYERNUMBER-1];iMatrixPointer prev_currMatrix=weightArray[HIDDENLAYERNUMBER];for(int curindex=0;curindex<2;curindex++){double sum=0;for(int previndex=0;previndex<lastHiddenNumber;previndex++){double prevNWeight=prev_currMatrix[previndex][curindex];double prevNOutput=hiddenLayer[HIDDENLAYERNUMBER-1][previndex].outputval;sum+=prevNWeight*prevNOutput;}sum+=outputLayer[curindex].bias;double output=1/(1+exp(-sum));outputLayer[curindex].outputval=output;}}void trainOneItem(int inputArray[],int category){//向前传播输入forwardInput(inputArray);//后向传播误差double err;//输出层double bit[2];switch(category){case 2:bit[0]=1;bit[1]=0;break;case 1:bit[0]=0;bit[1]=1;break;case 0:bit[0]=0;bit[1]=0;break;}for(int oindex=0;oindex<2;oindex++){double output=outputLayer[oindex].outputval;double cbit=bit[oindex];err=output*(1-output)*(cbit-output);outputLayer[oindex].err=err;}//计算隐藏层误差for(int hindex=HIDDENLAYERNUMBER-1;hindex>=0;hindex--){int nodeNumber=HIDDENLAYERNUMBERARR[hindex];for(int nindex=0;nindex<nodeNumber;nindex++){double curNodeOut=hiddenLayer[hindex][nindex].outputval;//取当前结点的输出double errsum=0;//后节点的误差权重积的和if(hindex==HIDDENLAYERNUMBER-1)//最后一层隐藏层{//取输出层信息for(int oindex=0;oindex<2;oindex++){double oerr=outputLayer[oindex].err;iMatrixPointer omatrix=weightArray[HIDDENLAYERNUMBER];double oweight=omatrix[nindex][oindex];errsum+=oerr*oweight;}}else{//取下一层信息iMatrixPointer cur_nextMatrix=weightArray[hindex+1];int nextLayerNodeNum=HIDDENLAYERNUMBERARR[hindex+1];for(int nextindex=0;nextindex<nextLayerNodeNum;nextindex++){double nerr=hiddenLayer[hindex+1][nextindex].err;double nweight=cur_nextMatrix[nindex][nextindex];errsum+=nerr*nweight;}}double curerr=curNodeOut*(1-curNodeOut)*errsum;hiddenLayer[hindex][nindex].err=curerr;}//for}//for//更新权重for(int lindex=HIDDENLAYERNUMBER;lindex>=0;lindex--){iMatrixPointer matrix=weightArray[lindex];int curNum,nextNum;if(lindex==HIDDENLAYERNUMBER)//隐藏层最后一层{nextNum=2;curNum=HIDDENLAYERNUMBERARR[HIDDENLAYERNUMBER-1];for(int ci=0;ci<curNum;ci++){for(int ni=0;ni<nextNum;ni++){double nerr=outputLayer[ni].err;double coutput=hiddenLayer[HIDDENLAYERNUMBER-1][ci].outputval;double deltaw=l*nerr*coutput;matrix[ci][ni]+=deltaw;}}}else if(lindex==0)//输入层{curNum=14;nextNum=HIDDENLAYERNUMBERARR[0];for(int ci=0;ci<curNum;ci++){for(int ni=0;ni<nextNum;ni++){double nerr=hiddenLayer[0][ni].err;double coutput=inputLayer[ci];double deltaw=l*nerr*coutput;matrix[ci][ni]+=deltaw;}}}else{curNum=HIDDENLAYERNUMBERARR[lindex-1];nextNum=HIDDENLAYERNUMBERARR[lindex];for(int ci=0;ci<curNum;ci++){for(int ni=0;ni<nextNum;ni++){double nerr=hiddenLayer[lindex][ni].err;double coutput=hiddenLayer[lindex-1][ci].outputval;double deltaw=l*nerr*coutput;matrix[ci][ni]+=deltaw;}}}}//更新偏倚for(int lindex=HIDDENLAYERNUMBER+1;lindex>=1;lindex--){if(lindex==HIDDENLAYERNUMBER+1)//输出层{for(int i=0;i<2;i++){double delta=l*outputLayer[i].err;outputLayer[i].bias+=delta;}}else{int number=HIDDENLAYERNUMBERARR[lindex-1];for(int i=0;i<number;i++){double delta=l*hiddenLayer[lindex-1][i].err;hiddenLayer[lindex-1][i].bias+=delta;}}}}void training(){//读文件ifstream fin("TrainForm.txt");if(!fin.is_open()){cerr<<"打开文件错误"<<endl;exit(0);}char buf[80];fin.getline(buf,80);char* token="/t";while(fin.getline(buf,80)){int index=0;int lineInput[14];int lineIndex=0;char* result = strtok(buf,token);int category;while(result!=NULL){index++;if(index==5){if(strcmp(result,"nc")==0)category=0;else if(strcmp(result,"control")==0)category=1;else if(strcmp(result,"change")==0)category=2;}if(index!=1&&index!=5){int num=atoi(result);//十进制转成二进制stack<int> s;while(num!=0){s.push(num%2);num/=2;}if(index==2||index==4){if(s.size()!=5){int vac=5-s.size();for(int i=0;i<vac;i++)s.push(0);}}else if(index==3){if(s.size()!=4){int vac=4-s.size();for(int i=0;i<vac;i++)s.push(0);}}while(!s.empty()){lineInput[lineIndex]=s.top();s.pop();lineIndex++;}}result=strtok(NULL,token);}//whileitem i;i.inputArray=new int[14];i.inputArray=lineInput;i.category=category;if(category==0)ncItems.push_back(i);else if(category==1)controlItems.push_back(i);else if(category==2)changeItems.push_back(i);//trainOneItem(lineInput,category);fin.getline(buf,80);}//whilefor(int i=0;i<trainItemNumber;i++){//从nc,control,change中各取一条进行训练int ncNumber,changeNumber,controlNumber,ncIndex,changeIndex,controlIndex;ncNumber=ncItems.size();changeNumber=changeItems.size();controlNumber=controlItems.size();ncIndex=rand()%ncNumber;changeIndex=rand()%changeNumber;controlIndex=rand()%controlNumber;item ncit=ncItems.at(ncIndex);item controlit=controlItems.at(controlIndex);item changeit =changeItems.at(changeIndex);trainOneItem(ncit.inputArray,ncit.category);trainOneItem(changeit.inputArray,changeit.category);trainOneItem(controlit.inputArray,controlit.category);}}void forecastOneItem(int inputArr[]){forwardInput(inputArr);cout<<outputLayer[0].outputval<<":"<<outputLayer[1].outputval<<endl;}int main(){processInput();InitializeModel();training();int arr1[]={00101010000100};//changeforecastOneItem(arr1);int arr2[]={00101100100011};//ncforecastOneItem(arr2);int arr3[]={00001100100011};//controlforecastOneItem(arr3);return 0;}