* Harmonic Geometric and P-Series Added
* Editing comments
* Update and rename series/Geometric_Series.py to maths/series/geometric_series.py
* Update and rename series/Harmonic_Series.py to maths/series/harmonic_series.py
* Update and rename series/P_Series.py to maths/series/p_series.py
* Changed as suggested
Now return in same format as oct() returns
* Slight change
* Fixed issue #1368, return values for large number now is fixed and does not return in scientific notation
* Update decimal_to_octal.py
* Adding doctests into <gaussian_distribution> function
* Adding doctests into <y_generator> function
* Adding doctests into <calculate_mean> function
* Adding doctests into <calculate_probabilities> function
* Adding doctests into <calculate_variance> function
* Adding doctests into <predict_y_values> function
* Adding doctests into <accuracy> function
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* Update convex_hull.py
* Update convex_hull.py
* Simplify sudoku.is_completed() using builtin all()
Simplify __sudoku.is_completed()__ using Python builtin function [__all()__](https://docs.python.org/3/library/functions.html#all).
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* Update sudoku.py
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* Old style exception -> new style for Python 3
* updating DIRECTORY.md
* Update convex_hull.py
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* e.args[0] = "msg"
* ValueError: could not convert string to float: 'pi'
* Update convex_hull.py
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* improved prime numbers implementation
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* fix type hint
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* fix doctests
* updating DIRECTORY.md
* added prime tests with negative numbers
* using for instead filter
* updating DIRECTORY.md
* Remove unused typing.List
* Remove tab indentation
* print("Sorted order is:", " ".join(a))
* add the index calculation at difital_image_processing file
* make changes at index_calculation
* update the variables to self variables at functions
* update the word wrap in comments at index_calculation
* add the hamming code algorithm
* Wrap long lines
* converting generator object to a list object
* Refactor: converting generator object to a list object
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* Adding new file to the machine_learning directory
* Adding initial documentation
* importing modules
* Adding Normal_gen function
* Adding Y_gen function
* Adding mean_calc function
* Adding prob_calc function
* Adding var_calc function
* Adding predict function
* Adding accuracy function
* Adding main function
* Renaming LDA file
* Adding requested changes
* Renaming some of functions
* Refactoring str.format() statements to f-string
* Removing unnecessary list objects inside two functions
* changing code style in some lines
* Fixing y_generator function
* Refactoring 'predict_y_values' function by using list comprehensions
* Changing code style in import statements
* Refactoring CLI code block
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* No lines longer than 88 characters
* Update singly_linked_list.py
printing current.data rather than node address in __repr__ for a more readable print statement
* eval(repr(c)) == c
The output of `__repr__()` _should look like a valid Python expression that could be used to recreate an object with the same value_.
https://docs.python.org/3.4/reference/datamodel.html#object.__repr__
* += --> +
* Update gaussian_filter.py
Changed embedded for loops with product. This way range(dst_height) is called only once, instead of being called $dst_height.
* Update gaussian_filter.py
fixed missing width
* Fix out of range error in bellman_ford.py
* Update bellman_ford.py
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* Enhance the print function
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* Adds, append, len, print operations for circular linked list
* Adds, prepend support
* Adds, delete from front of the list
* Adds, delete_rear support
* Adds, method documentations
* Adds, type checking and doctests
* Updates doctest for delete ops
* Addressing requested changes
* Removes unused import
* Fixes failing doctests
* Minor modifications...
* Remove code with side effects from main
When running tests withy pytest, some modules execute code in main scope
and open plot or browser windows.
Moves such code under `if __name__ == "__main__"`.
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I revised my implementation and found out that I have miss a inner loop for t. x and y should be recalculated everytime when t is divisble by 2. I have also included a more readble source for this algorithm.
