Linear fit over multiple Rows without using Loops (or polyfit)?

Question

Jason 2022-3-28

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1682534-linear-fit-over-multiple-rows-without-using-loops-or-polyfit

评论： Jason 2022-3-28

Hello, I have a matrix of data

I=[1.8,3,3.6,4.2,4.7,5.3,5.5;3.3,4.2,4.8,5.3,5.8,6.3,6.6;4.5,5.6,6.3,6.8,7.3,7.9,8.2;6.1,6.9,7.5,8,8.6,9,9.4]
I =
    1.8000    3.0000    3.6000    4.2000    4.7000    5.3000    5.5000
    3.3000    4.2000    4.8000    5.3000    5.8000    6.3000    6.6000
    4.5000    5.6000    6.3000    6.8000    7.3000    7.9000    8.2000
    6.1000    6.9000    7.5000    8.0000    8.6000    9.0000    9.4000
    

I would like to get the gradient of a straight line fit through each row.

i.e. I was just going to loop over all the rows something like this:

    x = 1:7
    
    1     2     3     4     5     6     7
    
    y=I(1,:)
    
    p=polyfit(x,y,1)

I then just want to average all those gradients (m's)

I was wondering if there was a better way to do this rather than use polyfit and loops?

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Image Analyst 2022-3-28

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1682534-linear-fit-over-multiple-rows-without-using-loops-or-polyfit#answer_928759

在 MATLAB Online 中打开

No, I don't think so. That way is fine.

I = [1.8,3,3.6,4.2,4.7,5.3,5.5;3.3,4.2,4.8,5.3,5.8,6.3,6.6;4.5,5.6,6.3,6.8,7.3,7.9,8.2;6.1,6.9,7.5,8,8.6,9,9.4]
I = 4×7
    1.8000    3.0000    3.6000    4.2000    4.7000    5.3000    5.5000
    3.3000    4.2000    4.8000    5.3000    5.8000    6.3000    6.6000
    4.5000    5.6000    6.3000    6.8000    7.3000    7.9000    8.2000
    6.1000    6.9000    7.5000    8.0000    8.6000    9.0000    9.4000
[rows, columns] = size(I)
rows = 4
columns = 7
x = 1 : columns;
coefficients = zeros(rows, 2);
for row = 1 : rows
    coefficients(row, :) = polyfit(x, I(row, :), 1);
end
coefficients % Let's see them in the command window:
coefficients = 4×2
    0.6000    1.6143
    0.5393    3.0286
    0.5964    4.2714
    0.5429    5.7571
% Compute means
meanSlope = mean(coefficients(:, 1))
meanSlope = 0.5696
meanOffset = mean(coefficients(:, 2))
meanOffset = 3.6679