Scikit-fuzzy

This minor point release brings a number of improvements, most importantly compatibility with Python 3.7. Also:

* The number of membership functions is no longer restricted in `automf` (scikit-fuzzy206)
* Improvements to the documentation, especially for Consequents

In addition to the source release here, 0.4.1 is now live on PyPi.

Install/upgrade with

`pip install -U scikit-fuzzy`

This major point release brings bug fixes, efficiency improvements, and quality of life improvements.

* A bug was fixed in the `lom`/`som` methods; negative range now fully supported (189)
* Significant efficiency gains by streamlined linear algebra (187) (156)
* Names of nodes in the control system graph can now be displayed (166)
* Numerical improvement to `cmeans` for values of `m` close to 1.0 (154)

In addition to the source release here, 0.4.0 is now live on PyPi.

Install/upgrade with

`pip install -U scikit-fuzzy`

This minor point release brings a significant new feature and minor fixes/compatibility enhancements:

Major feature:

* Arrays are now accepted as system inputs; all inputs must have the same shape. The output matches this shape. This is dramatically more computationally efficient for repeated runs on existing data - within the limits of your system memory. (see 141)

Major fix

* Fixed the mathematical definition of `skfuzzy.gaussmf`; this is a potentially breaking change but the results are now correct (see 147).

Minor fixes

* NetworkX 2.0 forward compatibility (see 149 and 150)
* Improve and harmonize the .view() methods in skfuzzy.control (see 142 and 148)
* Update examples to use the above

This major point release includes a number of improvements, primarily to the `skfuzzy.control` submodule. A significant bug was squashed, the entire system is markedly more performant, and users can now more intuitively control the aggregation method.

* Fixed a memory leak on repeated, cached simulations not entirely flushing the cache (120)
* AND/OR aggregation methods are now directly exposed to the user (126)
* `np.interp` is used under the hood, resulting in major performance improvements (130)
* System visualization commands uniformly return both Matplotlib fig/axis objects, and better documented (133/136)

Thanks for using Scikit-Fuzzy, your reports keep making the package better!

This major point release brings many improvements to Scikit-Fuzzy, notably
- **New fuzzy control system API**
- Explore the new features located in `skfuzzy.control`. Recommended import statement is `import skfuzzy.control as ctrl` - note these new classes are not brought into the main `skfuzzy` namespace, they live only in `skfuzzy.control`.
- Designing complicated fuzzy control systems is now elegant, expressive, and Pythonic
- Peruse the new examples (reworked tipping problem and an advanced system) in the gallery
- New, more accurate defuzzification calculations result in higher accuracy with even sparser systems
- Significantly improved documentation and test coverage.