Octave中求解算法多项式回归，最小二乘法的问题 [英] Problem in solving algorithm polynomial regression,least squares method in Octave

问题描述

我正在尝试使用最小二乘法实现多项式回归.绘制第三个图形时出现问题，未显示.我认为这是关于公式 y=ax+b 的实现.但就我而言，首先我使用内联函数 polyfit 和 polyval 获得了实验数据值.

I am trying to implement polynomial regression using the least squares method. There was a problem while plotting the 3rd graph, it is not displayed. I think it's about the implementation of the formula y=ax+b. But in my case, in first I got experimental data values using inline functions polyfit and polyval.

x=0:0.1:5;
y=3*x+2;
y1=y+randn(size(y));
k=1;#Polynom
X1=0:0.01:10
B=polyfit(x,y1,k);
Y1=polyval(B,X1);

毕竟，我已经在使用线性模型用最小二乘法求解多项式回归了.

And after all, I am already using a linear model to solve the polynomial regression using the method of least squares.

Y2=Y1'*x+B'; -----this problem formula
subplot(3,2,3);
plot(x,Y1,'-b',X1,y1,'LineWidth');
title('y1=ax+b'); 
xlabel('x'); 
ylabel('y');
grid on;

结果，没有绘制图形.

推荐答案

检查向量的大小:x 和 Y1 的长度不同，X1 和 y1 的长度相同.

check size of the vector: x and Y1 are not same length, same for X1 and y1.

您可能想绘制为:

plot(x,y1,'-b',X1,Y1,'LineWidth', 1);

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