fuzzy linear regression

[Allison Campbell]

Hello,

I'd like to use sk-fuzzy (and contribute to it, when I get the hang of it) for linear regression.  I'm used to the linear optimizer packages in python like cvxpy, and for linear optimization, it's trivial to break out the optimization into two problems -- the bound for the upper spread, and the bound for the lower spread.

I've worked through the tipping problem (hard and easy), and I still can't wrap my head around how to use skfuzzy for fuzzy linear regression.  Also read through Tanaka's papers, and it's not adding up.

Josh (et al), and direction?

Best,

Allison

[Joshua Warner]

Hi Allison,

Scikit-Fuzzy at present has a toolkit of useful functions exposed at package base (you've used these in the hard tipping problem) and then a higher-level, much more user friendly object oriented interface in the `skfuzzy.control` submodule for building and working with fuzzy systems.  The pieces of the package that will help in fuzzy linear regression robust definitions of fuzzy numbers (symmetric and asymmetric) based on NumPy arrays, as well as the infrastructure built around fuzzy inference for interpolation and exact calculations with/integration of sparse underlying arrays.

I have not worked with fuzzy linear regression directly, but certainly would approve of fuzzy linear regression joining the package.  Fuzzy linear regression seems like an interesting problem, though some functionality will need to be built to enable it.  Big picture, it would likely make the most sense to add this in its own submodule similar to `skfuzzy.control` (perhaps `skfuzzy.regression` or `skfuzzy.fuzzyreg`).

I hope that's helpful.  Checking out other open source implementations of fuzzy linear regression may also be of assistance.

Josh

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