Per discussion in #15418, these four files are not algorithms (they are
how-to-use scripts wrapping a deep-learning framework) and dragged in the
heavy keras/tensorflow dependency stack:
- computer_vision/cnn_classification.py
- dynamic_programming/k_means_clustering_tensorflow.py
- machine_learning/lstm/lstm_prediction.py
- neural_network/input_data.py (TF MNIST data loader; nothing imports it)
Also removes the now-orphaned machine_learning/lstm/ package (only
__init__.py + sample_data.csv, which served lstm_prediction.py).
Cleanups:
- Drop keras from pyproject.toml dependencies; regenerate uv.lock
(removes absl-py, h5py, keras, ml-dtypes, namex, optree).
- Remove the pre-release libhdf5-dev install step from build.yml and
sphinx.yml (it existed only because keras needs hdf5).
- Drop the four stale pytest --ignore entries in build.yml.
- Remove the four DIRECTORY.md entries and the empty Lstm heading.
* Added Random Forest Regressor as an additional prediction model.
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* Added Random Forest Regressor to main voting
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* Update run.py
* Update run.py
* Update run.py
Used matplotlib to plot actual vs predicted user count, forecast confidence intervals, outlier thresholds from IQR.
Added logging instead of print because in production, print() is not scalable.
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* updating DIRECTORY.md
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* Add t-SNE implementation and tests for dimensionality reduction
Implemented the t-distributed stochastic neighbor embedding (t-SNE) algorithm in dimensionality_reduction.py, including input validation and a test function.
* Fix Ruff linting errors E501 and EM102 in t-SNE implementation
Resolve line length violation (E501) and f-string literal in exception (EM102) by splitting error message and using variable assignment.
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* Fix issue #12108: Added Ridge Regression to Machine Learning
* Added type hints and minor case improvements
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* Resolved ruff checks
* Added doctests
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* Resolved ruff checks
* Resolved mypy checks
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* Resolved ruff checks
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* updating DIRECTORY.md
* Comments should not force line wrapping
* updating DIRECTORY.md
* Fix feature scaling variable assignment in predictions
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* Add visualization support for linear regression
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* Fix linting and plotting issues
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* Fix spelling issue
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* Add Gaussian negative log likelihood loss algorithm
* Fix boolean conversion in Gaussian NLL loss example
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* added multi arm bandit alg with three strategies to solve it
* added doctest tests
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* corrected test cases
* added return type hinting
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* return typehint for test func updated
* fixed variable name k
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* fixed formatting
* fix1
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* Address Copilot review comments on MAB PR
- Cast rng.integers() results to Python int in EpsilonGreedy and
RandomStrategy select_arm, fixing doctest flakiness from np.int64
- Fix grammar in module docstring and RandomStrategy docstring
- Add missing doctest for Bandit.__init__
- Split test_mab_strategies into a real deterministic assertion-based
test and a separate demo_mab_strategies for the stochastic plot
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* Add: Ordinary Least Squares Regression Algorithm
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* fix: typos
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* Change intercept output to float in examples
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* Add DBSCAN clustering algorithm in machine_learning/
* Fix logic order in dbscan: check cluster membership before assigning
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* Add multinomial Naive Bayes text classification example
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* Add Mean Shift clustering algorithm in machine_learning/
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* Rename lambda variable for clarity in mean_shift.py
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2026-09-14 06:45:45 +02:00
Md Ruman Islamandpre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Add Gaussian Mixture Model (GMM) algorithm
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add trailing newline and finalize Gaussian Mixture Model implementation
* Fix Mypy type alias issue for FloatArray
* Use modern 'type' syntax for FloatArray to satisfy Ruff UP040
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* Add Q-Learning algorithm implementation with epsilon-greedy policy and grid world demo
* Add Q-Learning algorithm implementation with epsilon-greedy policy and grid world demo code
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* bug fixes and linting
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* cls
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* added mini batch gradient descent algo in ml dir
* updated code
* updated code quality
* updated docs
* Refactor parameter documentation in mini_batch_gradient_descent.py
Updated parameter documentation format in mini_batch_gradient_descent.py.
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* Add naive and vectorized implementations of Linear Regression using Gradient Descent
* Add references section to docstrings in linear regression implementations
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* Refactor function signatures for improved readability in linear regression implementation
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* Refactor function signatures for improved readability in linear regression implementation
* Update README sections for dataset inputs and usage instructions in linear regression implementations
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* Add doctests for dataset collection and gradient descent functions
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* Refactor imports and improve README formatting in linear regression scripts
* fix doctests
* Remove linear regression naive implementation script
* Refactor docstring and improve script documentation for clarity
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* Fix formatting in gradient_descent doctest and streamline main function call
* fix doctest
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* Change httpx to httpx2 and update docstring
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Drop machine_learning/xgboost_classifier.py and
machine_learning/xgboost_regressor.py. Both were thin "how-to-use"
wrappers around sklearn's XGBClassifier/XGBRegressor rather than
from-scratch implementations, and the classifier's only doctest was
already disabled (# THIS TEST IS BROKEN!!), so it was never exercised
in CI.
xgboost is one of the heaviest compiled dependencies in the tree (large
wheel, needs OpenMP/libgomp at runtime, no free-threaded wheel yet), and
gradient boosting is already implemented from scratch in
machine_learning/gradient_boosting_classifier.py and
gradient_boosting_regressor.py, so no algorithm coverage is lost.
Removes the xgboost dependency from pyproject.toml, its (and its
xgboost-only transitive dep nvidia-nccl-cu13) entries from uv.lock, and
the two DIRECTORY.md links.
Refs #15081
Re-enable the four scikit-learn machine-learning examples and the
neural-network perceptron that had been disabled (renamed to
.broken.txt / .DISABLED), and modernize them so they import and run
cleanly on current scikit-learn and pass the doctest CI:
machine_learning/gaussian_naive_bayes.py
machine_learning/random_forest_classifier.py
- Replace the removed sklearn.metrics.plot_confusion_matrix with
ConfusionMatrixDisplay.from_estimator (removed in scikit-learn 1.2).
- Drop the artificial time.sleep() calls.
machine_learning/gradient_boosting_regressor.py
machine_learning/random_forest_regressor.py
- Replace the removed load_boston dataset (removed in scikit-learn
1.2 for ethical reasons) with the bundled load_diabetes dataset so
the examples run offline.
- Avoid an unused-variable lint (RUF059).
neural_network/perceptron.py
- Use a dedicated seeded random.Random instance instead of the global
random state, so training is reproducible and thread-safe under the
parallel test runner.
- Cap training at epoch_number epochs so it always terminates even on
non-linearly-separable data (previously an unbounded while True).
- Have training() and sort() return their results instead of printing,
per the contribution guidelines, and update the doctests accordingly.
Requested by @cclauss in #8029; perceptron follow-up to #15206.
* add a code file of the multi-layer perceptron classifier from scrach
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* Delete tqdm in multilayer_perceptron_classifier_from_scratch.py
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* Update multilayer_perceptron_classifier_from_scratch.py
add a code file of the multi-layer perceptron classifier from scrach
* Correct errors in multilayer_perceptron_classifier_from_scratch.py
* Delete machine_learning/multilayer_perceptron_classifier.py
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* deps: migrate from httpx to httpx2 (pydantic's maintained fork)
Mechanical rename of httpx -> httpx2 (API-compatible fork of httpx 0.28.1):
pyproject.toml deps, PEP 723 inline-script headers, and all import/call sites.
Excludes uv.lock (the keeper's allow-list rejects .lock files); the lock
refresh needs a separate maintainer-merged PR.
Refs #15081
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* deps: drop tweepy + migrate remaining requests refs to httpx2
- maths/allocation_number.py: docstring example uses httpx2, not requests
- web_programming/get_imdbtop.py.DISABLED: import httpx2 instead of requests
- remove web_programming/get_user_tweets.py.DISABLED (a Twitter API how-to,
not an algorithm) and drop the tweepy dependency that was its only user and
the last high-level dep pulling in requests
- uv.lock intentionally untouched (keeper allow-list)
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* ci: un-ignore local_weighted_learning doctests in build.yml
machine_learning/local_weighted_learning/local_weighted_learning.py only
imports numpy and matplotlib (both already project dependencies) and its
5 doctests pass headlessly. Removing it from the pytest --ignore list so
the module is covered by CI again.
* fix(local_weighted_learning): use a well-conditioned bandwidth in doctests
The doctests used tau=0.6 on data with feature values ~17-25, so the
Gaussian weights underflowed to ~0 (e.g. 8e-118, 1e-177). That made
X\u1d40WX numerically singular (cond ~5.6e18), so its inverse - and the
resulting predictions - were nondeterministic across numpy/BLAS builds.
That is why the module was on the pytest --ignore list; on the CI numpy
the first prediction came out 0.0 instead of the documented 1.07.
Switch the doctests to tau=5 (cond ~2e2), matching the bandwidth the
module's own main() already uses, and round the outputs so they are
stable across platforms. Deterministic now; removed from --ignore.
`machine_learning/automatic_differentiation.py` was the only module
importing `Self` from `typing_extensions`, behind a `# noqa: UP035`.
Since the repo requires Python >=3.14, `typing.Self` (added in 3.11)
is always available, so import it from the stdlib and drop the noqa.
### Describe your change:
* [x] Add an algorithm?
* [ ] Fix a bug or typo in an existing algorithm?
* [ ] Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
* [ ] Documentation change?
### Checklist:
* [x] I have read [CONTRIBUTING.md](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md).
* [x] This pull request is all my own work -- I have not plagiarized.
* [x] I know that pull requests will not be merged if they fail the automated tests.
* [x] This PR only changes one algorithm file. If not, please split into separate PRs.
* [x] All new Python files are placed inside an existing directory.
* [x] All filenames are in all lowercase characters with no spaces or dashes.
* [x] All functions and variable names follow Python naming conventions.
* [x] All function parameters and return values are annotated with Python type hints.
* [x] All functions have doctests that pass the automated testing.
* [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
* fix: raise ValueError in encode() for non-lowercase input
encode() previously accepted uppercase letters, digits, and other
non-lowercase characters silently, producing incorrect/out-of-range
values (e.g. negative numbers for uppercase letters) instead of
failing. Add input validation using str.islower() and str.isalpha()
to raise a ValueError when the input isn't purely lowercase a-z.
Added a doctest covering the new error case.
* resolved doctest
* Fix Ruff 0.16 lint failures
* Enhance encode function error handling examples
Update error handling in encode function to include examples for mixed case and invalid characters.
* Fix indentation in test_cancer_data function
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* feat: optimizing the prune function at the apriori_algorithm.py archive
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* fix: fixing the unsorted importing statment
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* Added t-SNE with Iris dataset example
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* pre-commit autoupdate 2025-09-11
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Christian Clauss