Matlab Curve Fitting Algorithm
조회 수: 4 (최근 30일)
이전 댓글 표시
I was trying to solve a surface fitting problem where I had two inputs [X1 X2] used to predict a third quantity Y that occupied the range [1,0). I initially created a very simple gradient descent script from scratch in Python. It was a traditional gradient descent with RMSE as the cost function. After playing aorund with different learning rates and starting guesses (learning along the way that the problem/solution seemed to be extrmeley sensitive to the learning rate and would easily diverge) the best result I was able to get was .05 RMSE.
I tried the matlab fit function next, with 'poly11' fit type and it found a surface with .0045 RMSE (1 order magnitude better than I achieved). It's not surprising to me that Matlab has a more sophisticated curve fitting algorithm than the rudamentary one I wrote up, but does anyone have an idea of what additional tricks fit() may be using that I'm not?
댓글 수: 0
채택된 답변
Matt J
2019년 9월 5일
편집: Matt J
2019년 9월 5일
If you didn't specify any lower/upper bounds in the fitoptions, then the 'poly11' fitting task has the form of an unconstrained linear-least squares problem and has a closed-form, linear algebraic solution. It is likely that no iterations were done by fit() it all, but rather it probably just solved an appropriate system of linear equations.
댓글 수: 4
추가 답변 (0개)
참고 항목
카테고리
Help Center 및 File Exchange에서 Get Started with Curve Fitting Toolbox에 대해 자세히 알아보기
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!