#include?
#include?
#include?
#include?
#include?
#include?
#include?
#include?
#include?
#include?“matrix.h“
?
using?namespace?std;
?
const?int?P=100;????????//輸入樣本的數量
vector?X（P）;??//輸入樣本
Matrix?Y（P1）;????????//輸入樣本對應的期望輸出
const?int?M=10;?????????//隱藏層節點數目
vector?center（M）;???????//M個Green函數的數據中心
vector?delta（M）;????????//M個Green函數的擴展常數
Matrix?Green（PM）;?????????//Green矩陣
Matrix?Weight（M1）;???????//權值矩陣
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/*Hermit多項式函數*/
inline?double?Hermit（double?x）{
????return?1.1*（1-x+2*x*x）*exp（-1*x*x/2）;
}
?
/*產生指定區間上均勻分布的隨機數*/
inline?double?uniform（double?floordouble?ceil）{
????return?floor+1.0*rand（）/RAND_MAX*（ceil-floor）;
}
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/*產生區間[floorceil]上服從正態分布N[musigma]的隨機數*/
inline?double?RandomNorm（double?mudouble?sigmadouble?floordouble?ceil）{
????double?xproby;
????do{
????????x=uniform（floorceil）;
????????prob=1/sqrt（2*M_PI*sigma）*exp（-1*（x-mu）*（x-mu）/（2*sigma*sigma））;
????????y=1.0*rand（）/RAND_MAX;
????}while（y>prob）;
????return?x;
}
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/*產生輸入樣本*/
void?generateSample（）{
????for（int?i=0;i????????double?in=uniform（-44）;
????????X[i]=in;
????????Y.put（i0Hermit（in）+RandomNorm（00.1-0.30.3））;
????}
}
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/*尋找樣本離哪個中心最近*/
int?nearest（const?vector&?centerdouble?sample）{
????int?rect=-1;
????double?dist=numeric_limits::max（）;
????for（int?i=0;i????????if（fabs（sample-center[i]）????????????dist=fabs（sample-center[i]）;
????????????rect=i;
????????}
????}
????return?rect;
}
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/*計算簇的質心*/
double?calCenter（const?vector?&g）{
????int?len=g.size（）;
????double?sum=0.0;
????for（int?i=0;i????????sum+=g[i];
????return?sum/len;
}
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/*KMeans聚類法產生數據中心*/
void?KMeans（）{
????assert（P%M==0）;
????vector?>?group（M）;??????????//記錄各個聚類中包含哪些樣本
????double?gap=0.001;???????//聚類中心的改變量小于為個值時，迭代終止
????for（int?i=0;i????????center[i]=X[10*i+3];?????//輸入是均勻分布的，所以我們均勻地選取
????}
????while（1）{
????????for（int?i=0;i?????