void trainNetwork(int sampleCnt) {
int i, c = 0;
do {
e = 0;
for (i = 0; i < sampleCnt; ++i) {
computO(i);
//for (j = 0; j < OUTPUT_CNT; ++j)
e += fabs(networkOutput[realNumIndex[i]] - sampleOutput[i*OUTPUT_CNT + realNumIndex[i]]);
backUpdate(i);
}
//printf("%d %lf\n", c, e / sampleCnt);
c++;
} while (c<TRAIN_CNT && e / sampleCnt>0.01);
//printArrDbe(networkOutput, FEATURES_CNT);
}
void trainNetwork(){ //训练神经网络
printf("开始训练神经网络\n");
int i,c=0,j;
int check;
do{
e=0;
for (i = 0; i < Data; ++i){
comput(i);
check=1;
for (j = 0; j < Out; ++j)
if (OutputData[j]!=d_out[i][j]) check=0;
if (check!=1) e++;
backUpdate(i);
// printf("%d %d %d\n",OutputData[0],OutputData[1],OutputData[2]);
// printf("%d %d %d\n\n\n",d_out[i][0],d_out[i][1],d_out[i][2]);
}
c++;
printf("第%d次训练网络，误差精度为：%f\n",c,1.0*e/Data);
}while(c<TrainC && 1.0*e/Data>0.001);
printf("训练神经网络完毕，读取测试样本\n");
}
