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필터 지우기

Plotting the outcome of a 3D fit

조회 수: 7 (최근 30일)
Saeid
Saeid 2023년 8월 21일
댓글: Saeid 2023년 8월 21일
I have a set of data in the form [X Y Z] where X, Y and Z are column arrays. I can plot these data using scatter3. However, when I do the fit and then try to plot a surface of the fit with new Xf & Yf values in a range that is a bit beyond the original X & Y, I try this:
XYZFitCoefficinets=fit([X Y],Z,"poly22")
Xf=linspace(0,30,100)'; Yf=linspace(40,140,100)';
Zf=feval(XYZFitCoefficinets,[Xf, Yf])
I get another column array that just shows the calculated points, whereas I need a surface. I know that I need to use the surf function and turn Xf and Yf into mesh grids, but apparently it is not possible to use the feval with meshgridded X & Y. So what can i do?

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Torsten
Torsten 2023년 8월 21일
편집: Torsten 2023년 8월 21일
x = 0:0.1:1;
y = -1:0.1:1;
[X,Y] = meshgrid(x,y);
Z = X.^2+Y.^2+0.1*(-1+2*rand(size(X)));
[Xout,Yout,Zout] = prepareSurfaceData(X,Y,Z);
S = fit([Xout,Yout],Zout,"poly22")
Linear model Poly22: S(x,y) = p00 + p10*x + p01*y + p20*x^2 + p11*x*y + p02*y^2 Coefficients (with 95% confidence bounds): p00 = 0.01879 (-0.001415, 0.039) p10 = -0.02596 (-0.1119, 0.05996) p01 = -0.007346 (-0.02993, 0.01523) p20 = 1.018 (0.9357, 1.101) p11 = 0.009983 (-0.02819, 0.04815) p02 = 0.9853 (0.963, 1.008)
xq = 0:0.01:0.5;
yq = -1:0.01:0;
[Xq,Yq] = meshgrid(xq,yq);
S(Xq,Yq)
ans = 101×51
1.0115 1.0112 1.0112 1.0113 1.0117 1.0122 1.0130 1.0140 1.0151 1.0165 1.0181 1.0199 1.0218 1.0240 1.0264 1.0290 1.0318 1.0348 1.0380 1.0414 1.0450 1.0489 1.0529 1.0571 1.0615 1.0662 1.0710 1.0760 1.0813 1.0867 0.9918 0.9915 0.9915 0.9916 0.9920 0.9926 0.9933 0.9943 0.9955 0.9968 0.9984 1.0002 1.0022 1.0044 1.0067 1.0093 1.0121 1.0151 1.0184 1.0218 1.0254 1.0292 1.0332 1.0374 1.0419 1.0465 1.0513 1.0564 1.0616 1.0671 0.9723 0.9721 0.9720 0.9722 0.9725 0.9731 0.9738 0.9748 0.9760 0.9774 0.9789 0.9807 0.9827 0.9849 0.9873 0.9899 0.9927 0.9957 0.9989 1.0023 1.0059 1.0097 1.0138 1.0180 1.0224 1.0270 1.0319 1.0369 1.0422 1.0476 0.9530 0.9528 0.9527 0.9529 0.9532 0.9538 0.9546 0.9555 0.9567 0.9581 0.9597 0.9614 0.9634 0.9656 0.9680 0.9706 0.9734 0.9764 0.9796 0.9830 0.9866 0.9905 0.9945 0.9987 1.0031 1.0078 1.0126 1.0177 1.0229 1.0283 0.9339 0.9337 0.9336 0.9338 0.9341 0.9347 0.9355 0.9364 0.9376 0.9390 0.9406 0.9424 0.9443 0.9465 0.9489 0.9515 0.9543 0.9573 0.9605 0.9640 0.9676 0.9714 0.9754 0.9796 0.9841 0.9887 0.9935 0.9986 1.0038 1.0093 0.9150 0.9148 0.9147 0.9149 0.9153 0.9158 0.9166 0.9176 0.9187 0.9201 0.9217 0.9235 0.9255 0.9277 0.9300 0.9326 0.9355 0.9385 0.9417 0.9451 0.9487 0.9525 0.9565 0.9608 0.9652 0.9698 0.9747 0.9797 0.9850 0.9904 0.8964 0.8961 0.8961 0.8962 0.8966 0.8971 0.8979 0.8989 0.9000 0.9014 0.9030 0.9048 0.9068 0.9090 0.9114 0.9140 0.9168 0.9198 0.9230 0.9264 0.9300 0.9338 0.9379 0.9421 0.9465 0.9512 0.9560 0.9611 0.9663 0.9718 0.8779 0.8776 0.8776 0.8777 0.8781 0.8786 0.8794 0.8804 0.8816 0.8829 0.8845 0.8863 0.8883 0.8905 0.8929 0.8955 0.8983 0.9013 0.9045 0.9079 0.9115 0.9154 0.9194 0.9236 0.9281 0.9327 0.9375 0.9426 0.9478 0.9533 0.8595 0.8593 0.8593 0.8594 0.8598 0.8603 0.8611 0.8621 0.8633 0.8646 0.8662 0.8680 0.8700 0.8722 0.8746 0.8772 0.8800 0.8830 0.8862 0.8896 0.8933 0.8971 0.9011 0.9053 0.9098 0.9144 0.9193 0.9243 0.9296 0.9350 0.8414 0.8412 0.8412 0.8413 0.8417 0.8422 0.8430 0.8440 0.8452 0.8465 0.8481 0.8499 0.8519 0.8541 0.8565 0.8591 0.8619 0.8649 0.8681 0.8716 0.8752 0.8790 0.8830 0.8873 0.8917 0.8963 0.9012 0.9062 0.9115 0.9169
surf(Xq,Yq,S(Xq,Yq))
  댓글 수: 1
Saeid
Saeid 2023년 8월 21일
Thank you Torsten, that's exactly what I wanted!

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