* added merge interval problem on the array ds
* removed typing which is deprecated
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* Refactor type hints in merge_intervals function
Updated type hints from List to built-in list for compatibility.
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* types(reverse_selection): constrain items to Comparable
Bind reverse_selection_sort's element type to a Comparable Protocol so the
signature says "a list of items that can be compared with each other"
instead of a bare list, and keep the element type in the return.
reverse_subarray only swaps elements and never compares them, so its
TypeVar stays unbounded.
Also adds doctests for a comparable non-int type (strings, floats) and for
the failure mode: mixing non-comparable items must raise TypeError rather
than silently mis-sort. The test battery picks the sort up for the shared
cases and for the rejection check.
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* Add similarity test for square matrices
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* Add joaat function for non-cryptographic hashing
Implement the Jenkins-One-At-A-Time hash function.
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* added lstm algorithm in neural network section.
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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)
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__init__.py + sample_data.csv, which served lstm_prediction.py).
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(removes absl-py, h5py, keras, ml-dtypes, namex, optree).
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sphinx.yml (it existed only because keras needs hdf5).
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* Adding references
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* Suffix Array and LCP Array Implementation
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* Added edmonds_blossom_algorithm.py. For maximum matching in the graph.
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* Initial Commit: Add algorithms to determine peripheral and farthest nodes in a weighted graph
- Implemented Floyd-Warshall algorithm to calculate all-pairs shortest paths,
supporting positive edge weights for weighted graphs.
- Added functions to identify peripheral and far nodes:
- `find_peripheral_node`: Determines the node with maximal eccentricity.
- `find_far_node`: Finds the node with maximal farness, summing shortest-path distances.
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- Covered single-node, fully connected, sparse, cyclic, directed acyclic, and
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- Ensured code meets PEP 8 standards and passed all lint checks with `ruff`.
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This commit provides foundational functionality for graph-theoretical analysis
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* Initial commit: Implement graph centrality algorithms for weighted graphs
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* Implementation of streaming moving average
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* feat: add CRC32 hash algorithm implementation
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* added star delta converter module
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* fixed typo
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* Updates star_delta_conversions.py
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* updated to handle ac impedances
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2026-09-21 13:32:57 +02:00
Gabrielly de S. Pinto Dantaspre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>Christian Clausspre-commit-ci[bot] <pre-commit-ci[bot]@users.noreply.github.com>
* add tests for enigma_machine
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* typo
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* Adds pairwise iteration algorithm
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* updating DIRECTORY.md
* updating DIRECTORY.md
* Improve documentation for pairwise_iteration_tee
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* Correct import and update pairwise test cases
Fix import statement for pairwise in pairwise_iteration_tee function and update test cases.
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Added docstring explaining the use of itertools.tee for independent iterators.
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* Fix Base58 encoding/decoding
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* feat: implements a hex to rgb conversion function
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* added shunt power factor correction in electronics
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* Enhance validation and error handling in PF correction
Added validation for power factors and frequency in shunt inductor and capacitor functions. Improved error handling for edge cases.
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* feat: add softsign activation function
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Updated the reference link for the softsign activation function.
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* Correct implementation and add tests for dfs and bfs
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* feat(project_euler): add solution to problem 60
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* feat(ciphers): add scytale (skytale) transposition cipher with doctests
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* style(ciphers): fix import block formatting (isort I001)
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* Fix issue #12108: Added Ridge Regression to Machine Learning
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* Fix feature scaling variable assignment in predictions
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