* Code Upload
* Code Upload
* Delete n_possible_bst
* Find the N Possible Binary Tree and Binary Tree from given Nth Number of Node.
* Update in Test
* Update and rename n_possible_bst.py to number_of_possible_binary_trees.py
Co-authored-by: Christian Clauss <cclauss@me.com>
* added hill climbing algorithm
* Shorten long lines, streamline get_neighbors()
* Update hill_climbing.py
* Update and rename optimization/hill_climbing.py to searches/hill_climbing.py
Co-authored-by: Christian Clauss <cclauss@me.com>
* Code Upload
* Code Upload
* Delete n_possible_bst
* Find the N Possible Binary Tree and Binary Tree from given Nth Number of Node.
* Update in Test
* Update and rename n_possible_bst.py to number_of_possible_binary_trees.py
Co-authored-by: Christian Clauss <cclauss@me.com>
* update volumes with type hints + some refactoring
* added docstrings
* Use float instead of ints in doctest results
Co-authored-by: Christian Clauss <cclauss@me.com>
* Dijkstra's Bankers algorithm
@bluedistro, Your review please. A second shot at #1645
Implementation of the Dijkstra's Banker's algorithm with test examples and a comprehensible description.
* fixup! Format Python code with psf/black push
* Delete back_propagation_neural_network.py
* Create back_propagation_neural_network.py
* fixup! Format Python code with psf/black push
* Added binary exponentiaion with respect to modulo
* Added miller rabin: the probabilistic primality test for large numbers
* Removed unused import
* Added test for miller_rabin
* Add test to binary_exp_mod
* Removed test parameter to make Travis CI happy
* unittest.main() # doctest: +ELLIPSIS ...
* Update binary_exp_mod.py
* Update binary_exp_mod.py
* Update miller_rabin.py
* from .prime_check import prime_check
Co-authored-by: Christian Clauss <cclauss@me.com>
* Stack using double linked list
* Test with doctests
* Update stack_using_dll.py
* Update stack_using_dll.py
* Update stack_using_dll.py
Co-authored-by: Christian Clauss <cclauss@me.com>
* added roman to integer conversion (LeetCode No. 13)
* updated directory to include Roman to Integer
* Delete DIRECTORY.md
* Update roman_to_integer.py
Co-authored-by: Christian Clauss <cclauss@me.com>
* new file *iterating_through_submasks* is added in dynamic_programming section
* no changes
* *iterating_through_submasks.py is added in dynamic_programming
* iterating_through_submasks is added with doctests
* iterating_through_submasks.py is added in dynamic_programming
* changes made in *iterating_through_submasks.py
* changes made in *iterating_through_submasks.py
* updated
* *other/integeration_by_simpson_approx.py added
* *other/integeration_by_simpson_approx.py Added for integeration
* Delete iterating_through_submasks.py
* Delete DIRECTORY.md
* Revert "updated"
This reverts commit 73456f85de.
* changes made *integeration_by_simpson_approx.py
* update2
Co-authored-by: Christian Clauss <cclauss@me.com>
* new file *iterating_through_submasks* is added in dynamic_programming section
* no changes
* *iterating_through_submasks.py is added in dynamic_programming
* iterating_through_submasks is added with doctests
* iterating_through_submasks.py is added in dynamic_programming
* changes made in *iterating_through_submasks.py
* changes made in *iterating_through_submasks.py
* updated
* 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
* fixup! Format Python code with psf/black push
* 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).
* fixup! Format Python code with psf/black push
* Update sudoku.py
* fixup! Format Python code with psf/black push
* Old style exception -> new style for Python 3
* updating DIRECTORY.md
* Update convex_hull.py
* fixup! Format Python code with psf/black push
* e.args[0] = "msg"
* ValueError: could not convert string to float: 'pi'
* Update convex_hull.py
* fixup! Format Python code with psf/black push
* improved prime numbers implementation
* fixup! Format Python code with psf/black push
* fix type hint
* fixup! Format Python code with psf/black push
* 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
* fixup! Format Python code with psf/black push
* 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
* fixup! Format Python code with psf/black push
* 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__
* += --> +
