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[pre-commit.ci] pre-commit autoupdate (#8177)
* [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 --------- 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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@ -44,7 +44,7 @@ repos:
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- --py311-plus
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- repo: https://github.com/charliermarsh/ruff-pre-commit
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rev: v0.0.254
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rev: v0.0.255
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hooks:
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- id: ruff
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args:
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*flake8-plugins
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v1.0.1
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rev: v1.1.1
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hooks:
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- id: mypy
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args:
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@ -79,11 +79,11 @@ repos:
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additional_dependencies: [types-requests]
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- repo: https://github.com/codespell-project/codespell
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rev: v2.2.2
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rev: v2.2.4
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hooks:
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- id: codespell
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args:
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- --ignore-words-list=ans,crate,damon,fo,followings,hist,iff,mater,secant,som,sur,tim,zar
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- --ignore-words-list=3rt,ans,crate,damon,fo,followings,hist,iff,kwanza,mater,secant,som,sur,tim,zar
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exclude: |
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(?x)^(
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ciphers/prehistoric_men.txt |
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@ -334,6 +334,7 @@
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## Electronics
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* [Builtin Voltage](electronics/builtin_voltage.py)
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* [Carrier Concentration](electronics/carrier_concentration.py)
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* [Circular Convolution](electronics/circular_convolution.py)
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* [Coulombs Law](electronics/coulombs_law.py)
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* [Electric Conductivity](electronics/electric_conductivity.py)
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* [Electric Power](electronics/electric_power.py)
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@ -569,7 +569,7 @@ def plot_partition_boundary(
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"""
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We can not get the optimum w of our kernel svm model which is different from linear
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svm. For this reason, we generate randomly distributed points with high desity and
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prediced values of these points are calculated by using our tained model. Then we
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prediced values of these points are calculated by using our trained model. Then we
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could use this prediced values to draw contour map.
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And this contour map can represent svm's partition boundary.
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"""
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Lorentz transformations describe the transition between two inertial reference
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frames F and F', each of which is moving in some direction with respect to the
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other. This code only calculates Lorentz transformations for movement in the x
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direction with no spacial rotation (i.e., a Lorentz boost in the x direction).
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direction with no spatial rotation (i.e., a Lorentz boost in the x direction).
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The Lorentz transformations are calculated here as linear transformations of
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four-vectors [ct, x, y, z] described by Minkowski space. Note that t (time) is
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multiplied by c (the speed of light) in the first entry of each four-vector.
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