Fix long line, tests (TheAlgorithms#2123)

* Fix long line

* updating DIRECTORY.md

* Add doctest

* ...

* ...

* Update tabu_search.py

* space

* Fix doctest

    >>> find_neighborhood(['a','c','b','d','e','a'])  # doctest: +NORMALIZE_WHITESPACE
    [['a','e','b','d','c','a',90], [['a','c','d','b','e','a',90],
     ['a','d','b','c','e','a',93], ['a','c','b','e','d','a',102],
     ['a','c','e','d','b','a',113], ['a','b','c','d','e','a',93]]

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: John Law <[email protected]>

Author: 3 people

DIRECTORY.md

@@ -18,6 +18,7 @@
   * [Hamiltonian Cycle](https://github.com/TheAlgorithms/Python/blob/master/backtracking/hamiltonian_cycle.py)
   * [Minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py)
   * [N Queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py)
+  * [Rat In Maze](https://github.com/TheAlgorithms/Python/blob/master/backtracking/rat_in_maze.py)
   * [Sudoku](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sudoku.py)
   * [Sum Of Subsets](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sum_of_subsets.py)
 

digital_image_processing/resize/resize.py

@@ -50,7 +50,7 @@ def get_y(self, y: int) -> int:
         :param y: Destination X coordinate
         :return: Parent X coordinate based on `y ratio`
         >>> nn = NearestNeighbour(imread("digital_image_processing/image_data/lena.jpg",
-                                         1), 100, 100)
+        ...                              1), 100, 100)
         >>> nn.ratio_y = 0.5
         >>> nn.get_y(4)
         2

divide_and_conquer/convex_hull.py

@@ -325,10 +325,10 @@ def convex_hull_recursive(points):
     >>> convex_hull_recursive([[0, 0], [1, 0], [10, 0]])
     [(0.0, 0.0), (10.0, 0.0)]
     >>> convex_hull_recursive([[-1, 1],[-1, -1], [0, 0], [0.5, 0.5], [1, -1], [1, 1],
-                               [-0.75, 1]])
+    ...                        [-0.75, 1]])
     [(-1.0, -1.0), (-1.0, 1.0), (1.0, -1.0), (1.0, 1.0)]
     >>> convex_hull_recursive([(0, 3), (2, 2), (1, 1), (2, 1), (3, 0), (0, 0), (3, 3),
-                               (2, -1), (2, -4), (1, -3)])
+    ...                        (2, -1), (2, -4), (1, -3)])
     [(0.0, 0.0), (0.0, 3.0), (1.0, -3.0), (2.0, -4.0), (3.0, 0.0), (3.0, 3.0)]
 
     """

maths/gamma.py

@@ -29,7 +29,8 @@ def gamma(num: float) -> float:
     40320.0
 
     >>> from math import gamma as math_gamma
-    >>> all(gamma(i)/math_gamma(i) <= 1.000000001 and abs(gamma(i)/math_gamma(i)) > .99999999 for i in range(1, 50))
+    >>> all(.99999999 < gamma(i) / math_gamma(i) <= 1.000000001
+    ...     for i in range(1, 50))
     True
 
 

searches/tabu_search.py

@@ -139,10 +139,19 @@ def find_neighborhood(solution, dict_of_neighbours):
         from the solution that the method took as an input
 
     Example:
-    >>> find_neighborhood(['a','c','b','d','e','a'])  # doctest: +NORMALIZE_WHITESPACE
-    [['a','e','b','d','c','a',90], [['a','c','d','b','e','a',90],
-     ['a','d','b','c','e','a',93], ['a','c','b','e','d','a',102],
-     ['a','c','e','d','b','a',113], ['a','b','c','d','e','a',93]]
+    >>> find_neighborhood(['a', 'c', 'b', 'd', 'e', 'a'],
+    ...                   {'a': [['b', '20'], ['c', '18'], ['d', '22'], ['e', '26']],
+    ...                    'c': [['a', '18'], ['b', '10'], ['d', '23'], ['e', '24']],
+    ...                    'b': [['a', '20'], ['c', '10'], ['d', '11'], ['e', '12']],
+    ...                    'e': [['a', '26'], ['b', '12'], ['c', '24'], ['d', '40']],
+    ...                    'd': [['a', '22'], ['b', '11'], ['c', '23'], ['e', '40']]}
+    ...                   )  # doctest: +NORMALIZE_WHITESPACE
+    [['a', 'e', 'b', 'd', 'c', 'a', 90],
+     ['a', 'c', 'd', 'b', 'e', 'a', 90],
+     ['a', 'd', 'b', 'c', 'e', 'a', 93],
+     ['a', 'c', 'b', 'e', 'd', 'a', 102],
+     ['a', 'c', 'e', 'd', 'b', 'a', 113],
+     ['a', 'b', 'c', 'd', 'e', 'a', 119]]
     """
 
     neighborhood_of_solution = []
@@ -209,7 +218,7 @@ def tabu_search(
         best_cost_index = len(best_solution) - 1
 
         found = False
-        while found is False:
+        while not found:
             i = 0
             while i < len(best_solution):