Metadata-Version: 2.1
Name: fake-data-for-learning
Version: 0.4.0
Summary: UNKNOWN
Home-page: UNKNOWN
Author: Paul Larsen
Author-email: munichpavel@gmail.com
License: MIT license
Project-URL: Homepage, https://munichpavel.github.io/fake-data-for-learning/
Project-URL: Bug Tracker, https://github.com/munichpavel/fake-data-for-learning/issues
Project-URL: Documentation, https://munichpavel.github.io/fake-data-docs/html/index.html
Project-URL: Source Code, https://github.com/munichpavel/fake-data-for-learning/
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: networkx (>=2.4)
Requires-Dist: pandas (>=0.25)
Requires-Dist: numpy
Requires-Dist: scikit-learn (>=0.21.3)
Requires-Dist: scipy (>=1.3)
Requires-Dist: xarray
Requires-Dist: pypoman


Interesting fake multivariate data is harder to generate than it should be.
 Textbooks typically give definitions, two standard examples (multinomial and
 multivariate normal) and then proceed to proving theorems and propositions.
 True, one dimensional distributions can be combined, but here as well the
 source of examples is also sparse, e.g. products of distributions or copulas
 (typically Gaussian or t-copulas) applied to these 1-d examples.

For machine learning experimentation, it is useful to have an unlimited supply
 of interesting fake data, where by interesting I mean that we know certain
 properties of the data and want to test if the algorithm can pick this up. A
 great potential source of such data is graphical models.

In the current release, we generate fake data with discrete Bayesian networks
 (also known as directed graphical models).


