PyUp Safety actively tracks 232,000 Python packages for vulnerabilities and notifies you when to upgrade.
PySINDy now requires Scikit-learn version 0.23 instead of 0.21. There are also some minor bug fixes included in this release related to checking when optimizers have been fit.
This release makes the `SINDy.score` function better conform to the call signature of metrics from `sklearn.metrics`. Resolves 80.
Fixes a minor bug in the `CustomLibrary` class for earlier releases of Scikit-learn (e.g. 0.21). Scikit-learn version 0.23 (the latest version) does not have this issue.
This is PySINDy's first major release, including source code, documentation-generating scripts, examples, unit tests, and a markdown version of the PySINDy JOSS submission. This release includes a DOI via Zenodo: [![DOI](https://zenodo.org/badge/186055899.svg)](https://zenodo.org/badge/latestdoi/186055899)
Removes an extraneous print statement put in the code for debugging purposes.
* Expand allowable set of control input functions accepted by the `u` keyword argument of `SINDy.simulate` to include interpolating functions * Relax optimizer complexity test
Documentation improvements: - Fixed formatting of class attributes on documentation site - Better code formatting for `SINDy` method docstrings - Add missing class attributes to docstrings Code improvements: - More robust complexity test - Add missing attributes to `SINDy` class, optimizers, and feature libraries
This release adds functionality for performing SINDy with control inputs (SINDYc). It also implements concatenation of feature libraries via the + operator. Minor changes: * SINDy constructor properly instantiates `optimizer`, `differentiation_method`, and `feature_library` * improved discrete time input handling * Updated README * Various documentation typos fixed
Fix syntax errors in rst for the previous long description for PyPI.
The main changes in this release are all documentation-related. In particular we added information on the mathematical underpinnings behind the SINDy method in the README and created a new example notebook walking through the math in more detail.
Update the way optimizers are handled Previously optimizers needed to inherit from our base class `BaseOptimizer`. In this version users can more easily pass in their own optimizers, which are then wrapped in a custom class performing some postprocessing steps. As a result of this change, we have removed the `LASSO` and `ElasticNet` optimizers since their corresponding sklearn objects can now be passed into the `SINDy` object directly. Minor changes - Added quiet mode to `SINDy.fit` - Use L2 regularization by default for `STLSQ`
Changes in this version: - Optimizers are properly initialized so that copies created by `MultiOutputClassifier` share the same properties - Added documentation - Example notebooks conform to PEP8 - A dummy library, `IdentityLibrary` , was implemented so that fully custom features can be used - Additional unit tests to improve coverage - Optimizers have `unbias` option to improve performance
Release Pipeline Test