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[pre-commit.ci] pre-commit autoupdate (#8177)

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* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/charliermarsh/ruff-pre-commit: v0.0.254 → v0.0.255](https://github.com/charliermarsh/ruff-pre-commit/compare/v0.0.254...v0.0.255)
- [github.com/pre-commit/mirrors-mypy: v1.0.1 → v1.1.1](https://github.com/pre-commit/mirrors-mypy/compare/v1.0.1...v1.1.1)
- [github.com/codespell-project/codespell: v2.2.2 → v2.2.4](https://github.com/codespell-project/codespell/compare/v2.2.2...v2.2.4)

* updating DIRECTORY.md

* Fixes for new version of codespell

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>
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pre-commit-ci[bot]
2023-03-13 23:18:35 +01:00
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commit 8959211100
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2
machine_learning/sequential_minimum_optimization.py
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@ -569,7 +569,7 @@ def plot_partition_boundary(
"""
We can not get the optimum w of our kernel svm model which is different from linear
svm. For this reason, we generate randomly distributed points with high desity and
prediced values of these points are calculated by using our tained model. Then we
prediced values of these points are calculated by using our trained model. Then we
could use this prediced values to draw contour map.
And this contour map can represent svm's partition boundary.
"""