Fitting to 4D data

15 次查看（过去 30 天）
Mitch
Mitch 2023-9-13
评论： Mitch 2023-9-14
The fit() function allows fitting a surface to 3D data, where regularly spaced x,y data values specify a "grid" location and the z value specifies a surface "height". The fitted surface can be expressed as a polynomial of up to degree 5 in x and y.
Is there a means of fitting a model (polynomial or otherwise) to 4D data? In this case, the x,y,z data values specify a location (regularly spaced, within a unit cube for example), and w specifies a value at that location. Such data expresses a 3D "field" rather than a surface.
Thanks, mitch

请先登录，再回答此问题。

采纳的回答

Matt J
Matt J 2023-9-13
Yes, you can use lsqcurvefit.
  3 个评论
显示 1更早的评论
Matt J
Matt J 2023-9-14
编辑：Matt J 2023-9-14
Ran in:
Do you know of any multi-dimensional fitting examples I can look at?
Here's an example I just made up. The unknown parameter vector to be recovered is w:
xyz=rand(100,3); %fake x,y,z data
w=[1,2,3]; %ground truth parameters
F=vecnorm(xyz.*w,2,2); %fake dependent data
F=F+randn(size(F))*0.05; %add noise
wfit=lsqcurvefit(@modelFcn,[1,1,1], xyz,F)
Local minimum found. Optimization completed because the size of the gradient is less than the value of the optimality tolerance.
wfit = 1×3
1.0162 1.9904 2.9886
function Fpred=modelFcn(w,xyz)
Fpred=vecnorm(xyz.*w,2,2);
end
Anyway, the point is that lsqcurvefit doesn't care about the dimensions of the data xyz and F. It only cares that your modelFcn returns a prediction Fpred of F as an array the same size as F.
Mitch
Mitch 2023-9-14
Okay, terrific Matt - thanks for that example. I think that will get me started on the right path.
Thanks again, mitch

请先登录，再进行评论。

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Get Started with Curve Fitting Toolbox 的更多信息

标签

产品


版本

R2022b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by