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Updated testing from Ubuntu 18+22 to 20+22. #1485

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Jul 25, 2023
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4 changes: 2 additions & 2 deletions .github/workflows/unit-test-os-coverage.yml
Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,8 @@ jobs:

strategy:
matrix:
os: [ubuntu-18.04, macos-latest, windows-latest]
# Note: Ubuntu 20 is checked by other tests
os: [ubuntu-20.04, macos-latest, windows-latest]
# Note: Ubuntu 22 is checked by other tests

steps:
- uses: actions/checkout@v3
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4 changes: 2 additions & 2 deletions CHANGELOG.md
Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,6 @@ All notable changes to this project will be documented in this file.

## Unreleased

- [#1479](https://github.com/pints-team/pints/pull/1479) PINTS is no longer tested on Python 3.6.

### Added
- [#1466](https://github.com/pints-team/pints/pull/1466) Added a `TransformedRectangularBoundaries` class that preserves the `RectangularBoundaries` methods after transformation.
- [#1462](https://github.com/pints-team/pints/pull/1461) The `OptimisationController` now has a stopping criterion `max_evaluations`.
Expand All @@ -19,6 +17,8 @@ All notable changes to this project will be documented in this file.
- [#1378](https://github.com/pints-team/pints/pull/1378) Added a class `pints.LogNormalLogLikelihood`.

### Changed
- [#1485](https://github.com/pints-team/pints/pull/1485) PINTS is no longer tested on Ubuntu 18.04 LTS.
- [#1479](https://github.com/pints-team/pints/pull/1479) PINTS is no longer tested on Python 3.6.
- [#1479](https://github.com/pints-team/pints/pull/1479) The `asyncio.coroutine` decorators have been removed from all of NUTS's coroutines in order to be compatible with Python 3.11.
- [#1466](https://github.com/pints-team/pints/pull/1466) `Transformation.convert_boundaries` will now return a `TransformedRectangularBoundaries` object if the transformation is element-wise and the given boundaries extend `RectangularBoundaries`.
- [#1458](https://github.com/pints-team/pints/pull/1458) The `GradientDescent` optimiser now sets its default learning rate as `min(sigma0)` (it can still be changed afterwards with `set_learning_rate()`).
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