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.
* ty: un-ignore invalid-type-form
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* Add gramian algorithm
* Update docstring with additional references
Added references to Gram matrices and neural style transfer in the docstring.
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* improve computer-vision-readme
* Clean up blank lines in README.md
Removed unnecessary blank lines in the README.
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* Fix CNN prediction threshold for binary classification
* Use ternary operator for binary prediction
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* docs: enhance README with table of contents, installation guide, and features section
- Add comprehensive table of contents for easy navigation
- Include detailed installation steps with virtual environment setup
- Add usage examples showing how to run and import algorithms
- Create features section listing all algorithm categories
- Add explicit license section with MIT License information
- Expand contributing section with quick start guide
- Add about section explaining repository purpose
Fixes#13111
* docs: enhance README with table of contents, installation guide, and features section
- Add comprehensive table of contents for easy navigation
- Include detailed installation steps with virtual environment setup
- Add usage examples showing how to run and import algorithms
- Create features section listing all algorithm categories
- Add explicit license section with MIT License information
- Expand contributing section with quick start guide
- Add about section explaining repository purpose
Fixes#13111
* feat: add Vision Transformer (ViT) implementation for image classification
- Implement complete ViT architecture with patch embedding
- Add positional encoding with learnable CLS token
- Include scaled dot-product attention mechanism
- Implement transformer encoder blocks with layer normalization
- Add feed-forward network with GELU activation
- Include comprehensive docstrings and type hints
- Add doctests for all functions
- Provide example usage demonstrating the complete pipeline
Fixes#13326
* fix: resolve ruff linting errors in vision_transformer.py
- Replace Optional with X | None syntax (UP045)
- Use np.random.Generator instead of legacy np.random methods (NPY002)
- Fix line length violations (E501)
- Assign f-string literals to variables in exceptions (EM102)
- Remove unused variables and parameters (RUF059, F841)
- Add noqa comment for intentionally unused API parameter
- All doctests still pass successfully
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* refactor: use descriptive parameter names instead of 'x'
- Rename 'x' to 'embeddings' in layer_norm, feedforward_network,
and transformer_encoder_block functions
- Update all docstring examples to use 'embeddings'
- Improves code readability per algorithms-keeper bot feedback
- Fix noqa comment placement for unused num_heads parameter
- All doctests and ruff checks pass
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* updating DIRECTORY.md
* Remove redundant sections from README.md
Removed sections on Table of Contents, About, Features, and Contributing from README.
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* [pre-commit.ci] pre-commit autoupdate
updates:
- [github.com/astral-sh/ruff-pre-commit: v0.8.6 → v0.9.1](https://github.com/astral-sh/ruff-pre-commit/compare/v0.8.6...v0.9.1)
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* Update maths/dual_number_automatic_differentiation.py
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* No <fin-streamer> tag with the specified data-test attribute found.
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* Add files via upload
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* Update intensity-based_segmentation.py
* Update and rename intensity-based_segmentation.py to intensity_based_segmentation.py
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* [0, 1, 1]], dtype=int32)
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* DRAFT: GitHub Actions: Test on Python 3.12
Repeats #8777
* #8777
Some of our dependencies will not be ready yet.
* Python 3.12: Disable qiskit and tensorflow algorithms
* updating DIRECTORY.md
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* Create haralick_descriptors
* Working on creating Unit Testing for Haralick Descriptors module
* Type hinting for Haralick descriptors
* Fixed docstrings, unit testing and formatting choices
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* Fixed line size formatting
* Added final doctests
* Changed main callable
* Updated requirements.txt
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* Update computer_vision/haralick_descriptors.py
No! What if the Kernel is empty?
Example:
>>> kernel = np.zeros((1))
>>> kernel or np.ones((3, 3))
array([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]])
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* Undone wrong commit
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* Fix ruff errors in haralick_descriptors.py
* Add type hint to haralick_descriptors.py to fix ruff error
* Update haralick_descriptors.py
* Update haralick_descriptors.py
* Update haralick_descriptors.py
* Update haralick_descriptors.py
* Try to fix mypy errors in haralick_descriptors.py
* Update haralick_descriptors.py
* Fix type hint in haralick_descriptors.py
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* add torch to requirements
* add lenet architecture in pytorch
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* add type hints
* remove file
* add type hints
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* update variable name
* add fail test
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* add newline
* reformatting
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* pre-commit: Upgrade psf/black for stable style 2023
Updating https://github.com/psf/black ... updating 22.12.0 -> 23.1.0 for their `2023 stable style`.
* https://github.com/psf/black/blob/main/CHANGES.md#2310
> This is the first [psf/black] release of 2023, and following our stability policy, it comes with a number of improvements to our stable style…
Also, add https://github.com/tox-dev/pyproject-fmt and https://github.com/abravalheri/validate-pyproject to pre-commit.
I only modified `.pre-commit-config.yaml` and all other files were modified by pre-commit.ci and psf/black.
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* Updated documentation of cnn_classification.py for much better understanding
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* Update computer_vision/cnn_classification.py
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* The black formatter is no longer beta
* pre-commit autoupdate
* pre-commit autoupdate
* Remove project_euler/problem_145 which is killing our CI tests
* updating DIRECTORY.md
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* add 5 README files
* empty commit to (hopefully) get rid of the `test-are-failling` label
* Update ciphers/README.md
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* Update conversions/README.md
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* ADD the algorithms of image augmentation
* ADD the algorithms of image augmentation
* ADD the algorithms of image augmentation
* ADD the algorithms of image augmentation
* ADD the algorithms of image augmentation
* ADD the algorithms of image augmentation
* UPDATE format code
* UPDATE format and recode structure
* UPDATE format import library
* UPDATE code structure
* Fix all checks have failded
* FIX variable format
* FIX variable format
* FIX variable format
* FIX code structure
* FIX code structure
* FIX code structure
* FIX code structure
* Fix all errors mentioned in pre-commit run:
- Fix end of file
- Remove trailing whitespace
- Fix files with black
- Fix imports with isort
* Fix errors
* Added Lstm example for stock predection
* Changes after review
* changes after build failed
* Add Kiera’s to requirements.txt
* requirements.txt: Add keras and tensorflow
* psf/black
* haris corner detection
* fixup! Format Python code with psf/black push
* changes after review
* changes after review
* fixup! Format Python code with psf/black push
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