* Add type hints to md5.py
* Rename some vars to snake case
* Specify functions imported from math
* Rename vars and functions to be more descriptive
* Make tests from test function into doctests
* Clarify more var names
* Refactor some MD5 code into preprocess function
* Simplify loop indices in get_block_words
* Add more detailed comments, docs, and doctests
* updating DIRECTORY.md
* updating DIRECTORY.md
* updating DIRECTORY.md
* updating DIRECTORY.md
* updating DIRECTORY.md
* Add type hints to md5.py
* Rename some vars to snake case
* Specify functions imported from math
* Rename vars and functions to be more descriptive
* Make tests from test function into doctests
* Clarify more var names
* Refactor some MD5 code into preprocess function
* Simplify loop indices in get_block_words
* Add more detailed comments, docs, and doctests
* updating DIRECTORY.md
* updating DIRECTORY.md
* updating DIRECTORY.md
* updating DIRECTORY.md
* Convert str types to bytes
* Add tests comparing md5_me to hashlib's md5
* Replace line-break backslashes with parentheses
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* Remove python_version < "3.11" for tensorflow
* Reenable neural_network/input_data.py_tf
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* updating DIRECTORY.md
* Fix mypy errors in lu_decomposition.py
* Replace for-loops with comprehensions
* Add explanation of LU decomposition and extra doctests
Add an explanation of LU decomposition with conditions for when an LU
decomposition exists
Add extra doctests to handle each of the possible conditions for when a
decomposition exists/doesn't exist
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* Update receive_file.py
Here are the changes I made:
Added the main() function and called it from if __name__ == "__main__" block. This makes it easier to test the code and import it into other programs.
Added socket.AF_INET as the first argument to socket.socket(). This specifies the address family to be used, which is necessary when using connect().
Changed print(f"{data = }") to print("Received:", len(data), "bytes"). This makes it clearer what's happening and how much data is being received.
Changed the final print statement to "Successfully received the file". This makes it more accurate and descriptive.
Moved the import statement to the top of the file. This is a common convention in Python.
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* added kadane's algorithm directory with one problem's solution.
* added type hints
* Rename kaadne_algorithm/max_product_subarray.py to dynamic_programming/max_product_subarray.py
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* Reduce the complexity of graphs/bi_directional_dijkstra.py
* Try to lower the --max-complexity threshold in the file .flake8
* Lower the --max-complexity threshold in the file .flake8
* updating DIRECTORY.md
* updating DIRECTORY.md
* Try to lower max-complexity
* Try to lower max-complexity
* Try to lower max-complexity
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* add circular convolution
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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.
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* updating DIRECTORY.md
* Fix mypy errors in lorentz_transformation_four_vector.py
* Remove unused symbol vars
* Add function documentation and rewrite algorithm explanation
Previous explanation was misleading, as the code only calculates Lorentz
transformations for movement in the x direction (0 velocity in the y and
z directions) and not movement in any direction
* updating DIRECTORY.md
* Update error message for speed
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* add maths/Gcd of N Numbers
* add maths/Gcd of N Numbers
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* more pythonic
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* merged
* merged
* more readable
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* feat: Add `fib_recursive_cached` func
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* updating DIRECTORY.md
* Update sierpinski_triangle.py header doc
* Remove unused PROGNAME var in sierpinski_triangle.py
The PROGNAME var was used to print an image description in the reference
code that this implementation was taken from, but it's entirely unused
here
* Refactor triangle() function to not use list of vertices
Since the number of vertices is always fixed at 3, there's no need to
pass in the vertices as a list, and it's clearer to give the vertices
distinct names rather than index them from the list
* Refactor sierpinski_triangle.py to use tuples
Tuples make more sense than lists for storing coordinate pairs
* Flip if-statement condition in sierpinski_triangle.py to avoid nesting
* Add type hints to sierpinski_triangle.py
* Add doctests to sierpinski_triangle.py
* Fix return types in doctests
* Update fractals/sierpinski_triangle.py
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* updating DIRECTORY.md
* Format local_weighted_learning.py doctests for clarity
* Refactor local_weighted_learning.py to use np.array instead of np.mat
The np.matrix class is planned to be eventually depreciated in favor of
np.array, and current use of the class raises warnings in pytest
* Update local_weighted_learning.py documentation
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