?? main.cpp
字號:
#include "head.h"
/*************************************************************
* 訓練網絡樣本輸入的設置 * *
**************************************************************/
void teachinginputset(int m,int interval)
{
data[m].input[0]=loadpointer[m-1][interval];
//data[m].input[1]=loadpointer[m-2][interval];
data[m].input[1]=pricepointer[m-1][interval];
//data[m].input[2]=pricepointer[m-2][interval];
// data[m].input[2]=loadpointer[m-7][interval];
// data[m].input[3]=pricepointer[m-7][interval];
if(interval==0)
{
data[m].input[2]=pricepointer[m-1][interval+N-1];
data[m].input[3]=loadpointer[m-1][interval+N-1];
// data[m].input[8]=pricepointer[m-1][interval+N-2];
// data[m].input[9]=loadpointer[m-1][interval+N-2];
}
else if(interval==1)
{
data[m].input[2]=pricepointer[m][interval-1];
data[m].input[3]=loadpointer[m][interval-1];
// data[m].input[8]=pricepointer[m-1][interval+N-2];
// data[m].input[9]=loadpointer[m-1][interval+N-2];
}
else if(interval>=2)
{
data[m].input[2]=pricepointer[m][interval-1];
data[m].input[3]=loadpointer[m][interval-1];
// data[m].input[8]=pricepointer[m][interval-2];
// data[m].input[9]=loadpointer[m][interval-2];
}
data[m].input[4]=loadpointer[m][interval];
data[m].teach=pricepointer[m][interval];
}
/*************************************************************
* 測試網絡樣本輸入的設置 * *
**************************************************************/
void testinginputset(int m,int interval)
{
data[m].input[0]=loadpointer[m-1][interval];
//data[m].input[1]=loadpointer[m-2][interval];
data[m].input[1]=pricepointer[m-1][interval];
// data[m].input[3]=pricepointer[m-2][interval];
// data[m].input[2]=loadpointer[m-7][interval];
// data[m].input[3]=pricepointer[m-7][interval];
if(interval==0)
{
data[m].input[2]=pricepointer[m][interval+N-1];
data[m].input[3]=nextdayload[interval+N-1];
// data[m].input[8]=pricepointer[m-1][interval+N-2];
// data[m].input[9]=loadpointer[m-1][interval+N-2];
}
if(interval==1)
{
data[m].input[2]=pricepointer[m][interval-1];
data[m].input[3]=nextdayload[interval-1];
// data[m].input[8]=pricepointer[m-1][interval+N-2];
// data[m].input[9]=loadpointer[m-1][interval+N-2];
}
else if(interval>=2)
{
data[m].input[2]=pricepointer[m][interval-1];
data[m].input[3]=nextdayload[interval-1];
// data[m].input[8]=pricepointer[m][interval-2];
// data[m].input[9]=loadpointer[m][interval-2];
}
data[m].input[4]=nextdayload[interval];
data[m].teach=goalprice[interval];
}
/*************************************************************
* GLVQ 算法 * *
**************************************************************/
double t[daynumber][IN];
double t_temp[daynumber][IN];
void initial(int m,int interval)
{
/*
data[m].input[0]=loadpointer[m-1][interval];
//data[m].input[1]=loadpointer[m-2][interval];
data[m].input[1]=pricepointer[m-1][interval];
//data[m].input[2]=pricepointer[m-2][interval];
// data[m].input[2]=loadpointer[m-7][interval];
// data[m].input[3]=pricepointer[m-7][interval];
if(interval==0)
{
data[m].input[2]=pricepointer[m-1][interval+N-1];
data[m].input[3]=loadpointer[m-1][interval+N-1];
// data[m].input[8]=pricepointer[m-1][interval+N-2];
// data[m].input[9]=loadpointer[m-1][interval+N-2];
}
else if(interval==1)
{
data[m].input[2]=pricepointer[m][interval-1];
data[m].input[3]=loadpointer[m][interval-1];
// data[m].input[8]=pricepointer[m-1][interval+N-2];
// data[m].input[9]=loadpointer[m-1][interval+N-2];
}
else if(interval>=2)
{
data[m].input[2]=pricepointer[m][interval-1];
data[m].input[3]=nextdayload[interval-1];
// data[m].input[8]=pricepointer[m][interval-2];
// data[m].input[9]=loadpointer[m][interval-2];
}
data[m].input[4]=nextdayload[interval];
for(int i=0;i<IN;i++)
{
t[0][i]=data[m].input[i];
//cout<<"t[0]["<<i<<"]="<<t[0][i]<<endl;
}
for(i=0;i<IN;i++)
{
t[0][i]=fabs(((double)rand()/32767.0)*2-1);
//cout<<"t[0]["<<i<<"]="<<t[0][i]<<endl;
}
*/
for(int j=0;j<classifynumber;j++)
for(i=0;i<IN;i++)
{
t[j][i]=fabs(((double)rand()/32767.0)*2-1);
// cout<<"t["<<j<<"]["<<i<<"]="<<t[j][i]<<endl;
}
}
int similarmatching(int m)
{
int cell;
double distance=0.0;
double dmin=1000;
for(j=0;j<classifynumber;j++)
{
//distance=0.0;
for(int i=0;i<IN;i++)
distance+=(data[m].input[i]-t[j][i])*(data[m].input[i]-t[j][i]);
distance=sqrt(distance);
if(distance<dmin)
{
dmin=distance;
cell=j;
}
distance=0.0;
}
//cout<<"cell="<<cell<<endl;
return cell;
}
int correctweight(double z,int k)//m 分類數，z學習效率，k為第k個學習模式；
{
int index;
index=similarmatching(k);
for(int i=0;i<IN;i++)
t[index][i]=t[index][i]+z*(data[k].input[i]-t[index][i]);
//cout<<"t["<<j<<"]["<<i<<"]="<<t[j][i]<<endl;
return index;
}
double studyvelocity;
int timeslimit=200;
double error_lvq=1e-5;
void GLVQ(int interval,int d[classifynumber][daynumber])
{
srand((unsigned)time(NULL));
int lop=0;
int k[classifynumber];
studyvelocity=0.8;
initial();
double dis;
while(lop<timeslimit)
{
dis=0.0;
for(int j=0;j<classifynumber;j++)
for(int i=0;i<IN;i++)
t_temp[j][i]=t[j][i];
lop++;
for(i=0;i<classifynumber;i++)
k[i]=0;
for(int m=1;m<daynumber;m++)
{
int flag;
flag=correctweight(studyvelocity,m);
d[flag][k[flag]]=m;
k[flag]=k[flag]+1;
}
for(int i=k[flag];i<daynumber;i++)
d[flag][i]=0;
studyvelocity=studyvelocity*(1-(double)lop/timeslimit);
//cout<<"studyvelocity="<<studyvelocity<<endl;
for(j=0;j<classifynumber;j++)
for(int i=0;i<IN;i++)
dis+=(t_temp[j][i]-t[j][i])*(t_temp[j][i]-t[j][i]);
dis=sqrt(dis);
if(dis<error_lvq) break;
dis=0.0;
}
}
int chosedata()
{
}
/*************************************************************
* BP 算法 * *
**************************************************************/
//-----------------------------------------------------------
//初始化. 將連接權值、偏置系數置為(-1,1)之間的隨機數
// cout<<"輸出w[i][j],unitnumber_b[i],v[i],y_b的初始值"<<endl;
void initializing()
{
srand((unsigned)time(NULL));
for(i=0; i<unitnumber; i++)
for(j=0; j<IN-1; j++)
{
w[i][j]=((double)rand()/32767.0)*2-1;
// cout<<"w["<<i+1<<"]["<<j+1<<"]="<<w[i][j]<<endl;
};
for(i=0; i<unitnumber; i++)
for(j=0; j<IN-1; j++)
{
w0[i][j] = 0.0;
};
for(i=0; i<unitnumber; i++)
{
hn_b[i] = ((double)rand()/32767.0)*2-1;
// cout<<"unitnumber_b["<<i+1<<"]="<<unitnumber_b[i]<<endl;
};
for(i=0; i<unitnumber; i++)
{
v[i] = ((double)rand()/32767.0)*2-1;
// cout<<"v["<<i+1<<"]="<<v[i]<<endl;
}
for(i=0; i<unitnumber; i++)
{
v0[i] =0.0;
}
y_b = ((double)rand()/32767.0)*2-1;
//cout<<"y_b="<<y_b<<endl;
}
void layer_in(int m) //樣本m輸入層的輸出
{
//輸入正向傳播
for(j=0; j<IN; j++) //設置輸入層的輸出
xout[j]=t[m][j];
}
void hnout()
{
for(i=0; i<unitnumber; i++) //計算隱含層的輸出
{
sumtemp = 0.0;
for(j=0; j<IN; j++)
sumtemp += w[i][j] * xout[j];
sumtemp +=hn_b[i];
hn_out[i]=1/(1+exp(-sumtemp)); //隱含層作用函數為y=1/(1+exp(-sumtemp)
}
}
void yout()
{
sumtemp = 0.0;
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