C語言實現單層感知器

根據perceptron 學習規則（Rosenblatt 提出 ）用C語言實現單層感知器。代碼如下。

#include<stdio.h>#include<stdlib.h>int nTrain=7;  //訓練樣本數量int nInput=3; //訓練樣本維度double delta=0.1;  //學習速率int nTest=1; //測試樣本數量int maxItre =100;typedef struct slp {int input[3];int output;} slp_testData;double com_output( int *input,double *weight){double sum=0.0;for(int i=0;i <nInput;i++){sum= sum + (input[i] * weight[i]);}sum=sum - weight[nInput]*1.0;return sum;}//分類函數int classOutPerceptron(double output){if (output >= 0)return 1 ;if (output < 0 )return -1;}//計算誤差int com_err(slp_testData *trainData,double *weight){int err=0,i;for(i=0;i<nTrain;i++){err = err+ trainData[i].output - classOutPerceptron(com_output(trainData[i].input,weight));}return err;}int main(void){int i ,j,k,tempResu ;slp_testData trainData[7]={{{1,0,0},-1},{{1,0,1},1},{{1,1,0},1},{{1,1,1},1},{{0,0,1},-1},{{0,1,0},-1},{{0,1,1},1},};slp_testData testData[1]={{{0,0,0},-1},};double weights[4]={0.0,0.0,0.0,0.4};  //賦值權重，最後一位是位移for(k=0;k<maxItre;k++){for(i=0;i<nTrain;i++){tempResu = classOutPerceptron(com_output(trainData[i].input,weights));for (j=0;j<nInput;j++){weights[j] = weights[j] + ( delta*(trainData[i].output - tempResu)*trainData[i].input[j]);}weights[nInput] = weights[nInput] + delta*(trainData[i].output - tempResu);}}for (i=0;i<nTrain;i++)        {                printf("train[%d] .. %d\n",i,classOutPerceptron(com_output(trainData[i].input,weights)));        }for (i=0;i<nTest;i++){printf("test[%d] .. %d\n",i,classOutPerceptron(com_output(testData[i].input,weights)));}return 0;}

測試效果圖如下：