Fokl

Highlights

- clean() split into _normalize() and _format() to provide more user-friendly normalization methods (e.g., new keywords minmax and pillow, ability to pass non-formatted non-normalized inputs to evaluate())
- automatic tests of package now included in repo
- isotherm benchmark comparison added as example
- FoKL-to-Pyomo method received significant improvements (e.g., user-definable variable names, normalization within Pyomo s.t. variable name refers to true scale value)
- multiple GP models in Pyomo added as example
- updated documentation to reflect current changes

What's Changed
* v3.3.0 by jakekrell in https://github.com/ESMS-Group-Public/FoKL-GPy/pull/23


**Full Changelog**: https://github.com/ESMS-Group-Public/FoKL-GPy/compare/v3.2.4...v3.3.0

- patch in 'to_pyomo'

What's Changed
* Optimized scaling of "Bernoulli Polynomials" basis functions to best align with "Cubic Splines" basis functions while preserving eigenvalue ratios of BSS-ANOVA kernel approaching infinite resolution; documentation for this method stored as 'docs/_dev/basis_functions/bernoulli_polynomials/main.ipynb'

For Developers
* FoKL v3.2.3 by jakekrell in https://github.com/ESMS-Group-Public/FoKL-GPy/pull/19

**Full Changelog**: https://github.com/ESMS-Group-Public/FoKL-GPy/compare/v3.2.2...v3.2.3

What's Changed
- Pyomo example using simple toy problem to demonstrate nonlinear optimization of a GP model trained by FoKL
- Optimization of model attributes added so *.fokl* files require less memory
- Support for large datasets added, where 16-bit or 32-bit floating point numbers (after normalization) are acceptable

For Developers
* FoKL v3.2.2 by jakekrell in https://github.com/ESMS-Group-Public/FoKL-GPy/pull/18

- Updated Dependencies to include Pyomo
- Removed redundant files

- kernel using orthogonal Bernoulli polynomials as basis functions is now included (primarily for Pyomo application)
- some tutorials added
- README extensively updated/overhauled with more thorough documentation
- several minor bug fixes / patch updates
- original research paper included as PDF
- structure of 'clean' method modified slightly to allow auto normalization and formatting of inputs without data
- option to pass dataset into 'clean' method from within argument of 'fit' and 'evaluate' methods
- folder '_dev' added to begin collecting past and present useful materials for development to have for future reference
- list of dependencies added