Soon, I wish to write a small tutorial under /tutorials folder (in my forked repo) for how to use the algorithms (methods) to compute the model that best fits (in some sense) to the given data. Here is a tentative plan of mine:
Description of the Basics of Curve Fitting
Use Algorithms (in numethods) to compute a polynomial model (we may then enhance later in the repo data-fitting-models or elsewhere) to fit to the given data
Implement a curve_fit using polynomial basis to the methods in numethods so that we may directly use it.
Soon, I wish to write a small tutorial under `/tutorials` folder (in my forked repo) for how to use the algorithms (methods) to compute the model that best fits (in some sense) to the given data. Here is a tentative plan of mine:
1. Description of the Basics of Curve Fitting
2. Use Algorithms (in `numethods`) to compute a *polynomial* model (we may then enhance later in the repo `data-fitting-models` or elsewhere) to fit to the given data
3. Implement a `curve_fit` using polynomial basis to the methods in `numethods` so that we may directly use it.
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Soon, I wish to write a small tutorial under
/tutorialsfolder (in my forked repo) for how to use the algorithms (methods) to compute the model that best fits (in some sense) to the given data. Here is a tentative plan of mine:numethods) to compute a polynomial model (we may then enhance later in the repodata-fitting-modelsor elsewhere) to fit to the given datacurve_fitusing polynomial basis to the methods innumethodsso that we may directly use it.Thanks for the merge.