matlab和C语言实现最小二乘法

N = 8;
x = [1   2   3    4   5  6   7   8  ];
y = [67 84 102  120 137 155 172 190];
subplot(2,1,1);
plot(x,y,‘*‘);
% 图形的一些设置
xlabel(‘时间（秒）‘);
ylabel(‘位移（米）‘);
title(‘原始数据离散点‘)  
grid on
subplot(2,1,2);
p = polyfit(x,y,1); %得出P就是线性拟合的系数
% 0:0.01:9    
x1 = 0:1:N;  %起始为0，终点为N，步长1
y1 = polyval(p,x1);
plot(x,y,‘*‘,x1,y1,‘r‘)
xlabel(‘时间（秒）‘);
ylabel(‘位移（米）‘);
title(‘红线为最小二乘法拟合‘)  
grid on

sumxyji =sum(x.*y); %向量内积
sumx = sum(x);
sumy = sum(y);
sumxx = sum(x.*x);
k = (N*sumxyji - sumx*sumy)/(N*sumxx-sumx*sumx)
b = (sumy-k*sumx)/N

#include <stdio.h>
#include <stdlib.h>


//函数功能:进行最小二乘曲线拟合(拟合y=a0+a1*x),计算出对应的系数a
//参数说明:
//      n:      给定数据点的个数
//      x[]:    存放给定n个数据点的X坐标
//      y[]:    存放给定n个数据点的Y坐标
//      k,b:    拟合多项式的系数,表示多项式的k,b
void polyfit(int n,double x[],double y[],double &k,double &b)
{
    
    
    int i,j;
    double sumxymultiply = 0.0;
    double sumx   = 0.0;
    double sumy   = 0.0;
    double sumxx   = 0.0;
     for (i=0;i<n;i++)
    {
        sumx += x[i];
        sumy += y[i];
        sumxymultiply += (x[i]*y[i]);
        sumxx += (x[i]*x[i]);
    }
    
    k = (n*sumxymultiply - sumx*sumy)/(n*sumxx - sumx*sumx);
    b = (sumy-k*sumx)/n;
}

void printArr(double *arr,int n)
{
    for(int i=0;i<n;++i)
        printf("%lf ",arr[i]);
    
    printf("\n");
}
int main()
{
    const int N = 8;
      

    double x[N] = {1,2,3, 4,5,6,7,8};
    double y[N] = {67,84,102,120,137,155,172,190};
    double k,b;
    
    polyfit(N,x,y,k,b);
    printf("%lf %lf\n",k,b);
    
 
    return 0;
}