Remove useless code in doctests

backtracking/hamiltonian_cycle.py

@@ -71,7 +71,7 @@ def util_hamilton_cycle(graph: list[list[int]], path: list[int], curr_ind: int)
     >>> curr_ind = 1
     >>> util_hamilton_cycle(graph, path, curr_ind)
     True
-    >>> print(path)
+    >>> path
     [0, 1, 2, 4, 3, 0]
 
     Case 2: Use exact graph as in previous case, but in the properties taken from
@@ -85,7 +85,7 @@ def util_hamilton_cycle(graph: list[list[int]], path: list[int], curr_ind: int)
     >>> curr_ind = 3
     >>> util_hamilton_cycle(graph, path, curr_ind)
     True
-    >>> print(path)
+    >>> path
     [0, 1, 2, 4, 3, 0]
     """
 

computer_vision/flip_augmentation.py

@@ -22,7 +22,6 @@ def main() -> None:
     Get images list and annotations list from input dir.
     Update new images and annotations.
     Save images and annotations in output dir.
-    >>> pass  # A doctest is not possible for this function.
     """
     img_paths, annos = get_dataset(LABEL_DIR, IMAGE_DIR)
     print("Processing...")
@@ -48,7 +47,6 @@ def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
     - label_dir <type: str>: Path to label include annotation of images
     - img_dir <type: str>: Path to folder contain images
     Return <type: list>: List of images path and labels
-    >>> pass  # A doctest is not possible for this function.
     """
     img_paths = []
     labels = []
@@ -88,7 +86,6 @@ def update_image_and_anno(
         - new_imgs_list <type: narray>: image after resize
         - new_annos_lists <type: list>: list of new annotation after scale
         - path_list <type: list>: list the name of image file
-    >>> pass  # A doctest is not possible for this function.
     """
     new_annos_lists = []
     path_list = []

computer_vision/mosaic_augmentation.py

@@ -23,7 +23,6 @@ def main() -> None:
     Get images list and annotations list from input dir.
     Update new images and annotations.
     Save images and annotations in output dir.
-    >>> pass  # A doctest is not possible for this function.
     """
     img_paths, annos = get_dataset(LABEL_DIR, IMG_DIR)
     for index in range(NUMBER_IMAGES):
@@ -60,7 +59,6 @@ def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
     - label_dir <type: str>: Path to label include annotation of images
     - img_dir <type: str>: Path to folder contain images
     Return <type: list>: List of images path and labels
-    >>> pass  # A doctest is not possible for this function.
     """
     img_paths = []
     labels = []
@@ -105,7 +103,6 @@ def update_image_and_anno(
         - output_img <type: narray>: image after resize
         - new_anno <type: list>: list of new annotation after scale
         - path[0] <type: string>: get the name of image file
-    >>> pass  # A doctest is not possible for this function.
     """
     output_img = np.zeros([output_size[0], output_size[1], 3], dtype=np.uint8)
     scale_x = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])

data_structures/binary_tree/avl_tree.py

@@ -248,28 +248,28 @@ class AVLtree:
     >>> t = AVLtree()
     >>> t.insert(4)
     insert:4
-    >>> print(str(t).replace(" \\n","\\n"))
+    >>> str(t).replace(" \\n","\\n")
      4
     *************************************
     >>> t.insert(2)
     insert:2
-    >>> print(str(t).replace(" \\n","\\n").replace(" \\n","\\n"))
+    >>> str(t).replace(" \\n","\\n").replace(" \\n","\\n")
       4
      2  *
     *************************************
     >>> t.insert(3)
     insert:3
     right rotation node: 2
     left rotation node: 4
-    >>> print(str(t).replace(" \\n","\\n").replace(" \\n","\\n"))
+    >>> str(t).replace(" \\n","\\n").replace(" \\n","\\n")
       3
      2  4
     *************************************
     >>> t.get_height()
     2
     >>> t.del_node(3)
     delete:3
-    >>> print(str(t).replace(" \\n","\\n").replace(" \\n","\\n"))
+    >>> str(t).replace(" \\n","\\n").replace(" \\n","\\n")
       4
      2  *
     *************************************

data_structures/binary_tree/binary_search_tree.py

@@ -190,9 +190,9 @@ def binary_search_tree() -> None:
 
     >>> t = BinarySearchTree()
     >>> t.insert(8, 3, 6, 1, 10, 14, 13, 4, 7)
-    >>> print(" ".join(repr(i.value) for i in t.traversal_tree()))
+    >>> " ".join(repr(i.value) for i in t.traversal_tree())
     8 3 1 6 4 7 10 14 13
-    >>> print(" ".join(repr(i.value) for i in t.traversal_tree(postorder)))
+    >>> " ".join(repr(i.value) for i in t.traversal_tree(postorder))
     1 4 7 6 3 13 14 10 8
     >>> BinarySearchTree().search(6)
     Traceback (most recent call last):

data_structures/heap/binomial_heap.py

@@ -71,7 +71,7 @@ class BinomialHeap:
     ...     first_heap.insert(number)
 
     Size test
-    >>> print(first_heap.size)
+    >>> first_heap.size
     30
 
     Deleting - delete() test
@@ -97,11 +97,11 @@ class BinomialHeap:
                   #    # #   #
 
     preOrder() test
-    >>> print(second_heap.preOrder())
+    >>> second_heap.preOrder()
     [(17, 0), ('#', 1), (31, 1), (20, 2), ('#', 3), ('#', 3), (34, 2), ('#', 3), ('#', 3)]
 
     printing Heap - __str__() test
-    >>> print(second_heap)
+    >>> second_heap
     17
     -#
     -31

data_structures/heap/heap.py

@@ -9,20 +9,20 @@ class Heap:
     >>> unsorted = [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5]
     >>> h = Heap()
     >>> h.build_max_heap(unsorted)
-    >>> print(h)
+    >>> h
     [209, 201, 25, 103, 107, 15, 1, 9, 7, 11, 5]
     >>>
     >>> h.extract_max()
     209
-    >>> print(h)
+    >>> h
     [201, 107, 25, 103, 11, 15, 1, 9, 7, 5]
     >>>
     >>> h.insert(100)
-    >>> print(h)
+    >>> h
     [201, 107, 25, 103, 100, 15, 1, 9, 7, 5, 11]
     >>>
     >>> h.heap_sort()
-    >>> print(h)
+    >>> h
     [1, 5, 7, 9, 11, 15, 25, 100, 103, 107, 201]
     """
 

data_structures/heap/min_heap.py

@@ -21,13 +21,13 @@ class MinHeap:
     >>> a = Node("A", 3)
     >>> x = Node("X", 1)
     >>> e = Node("E", 4)
-    >>> print(b)
+    >>> b
     Node(B, 6)
     >>> myMinHeap = MinHeap([r, b, a, x, e])
     >>> myMinHeap.decrease_key(b, -17)
-    >>> print(b)
+    >>> b
     Node(B, -17)
-    >>> print(myMinHeap["B"])
+    >>> myMinHeap["B"]
     -17
     """
 

data_structures/linked_list/__init__.py

@@ -46,7 +46,7 @@ def __str__(self) -> str:
         >>> linked_list.add(23)
         >>> linked_list.add(14)
         >>> linked_list.add(9)
-        >>> print(linked_list)
+        >>> linked_list
         9 --> 14 --> 23
         """
         if not self.is_empty:

data_structures/linked_list/skip_list.py

@@ -59,16 +59,16 @@ def __str__(self) -> str:
         :return: Visual representation of SkipList
 
         >>> skip_list = SkipList()
-        >>> print(skip_list)
+        >>> skip_list
         SkipList(level=0)
         >>> skip_list.insert("Key1", "Value")
-        >>> print(skip_list) # doctest: +ELLIPSIS
+        >>> skip_list # doctest: +ELLIPSIS
         SkipList(level=...
         [root]--...
         [Key1]--Key1...
         None    *...
         >>> skip_list.insert("Key2", "OtherValue")
-        >>> print(skip_list) # doctest: +ELLIPSIS
+        >>> skip_list # doctest: +ELLIPSIS
         SkipList(level=...
         [root]--...
         [Key1]--Key1...

data_structures/queue/priority_queue_using_list.py

@@ -30,7 +30,7 @@ class FixedPriorityQueue:
     >>> fpq.enqueue(2, 4)
     >>> fpq.enqueue(1, 64)
     >>> fpq.enqueue(0, 128)
-    >>> print(fpq)
+    >>> fpq
     Priority 0: [10, 100, 128]
     Priority 1: [70, 7, 64]
     Priority 2: [1, 5, 4]
@@ -44,7 +44,7 @@ class FixedPriorityQueue:
     70
     >>> fpq.dequeue()
     7
-    >>> print(fpq)
+    >>> fpq
     Priority 0: []
     Priority 1: [64]
     Priority 2: [1, 5, 4]
@@ -60,7 +60,7 @@ class FixedPriorityQueue:
     Traceback (most recent call last):
     ...
     data_structures.queue.priority_queue_using_list.UnderFlowError: All queues are empty
-    >>> print(fpq)
+    >>> fpq
     Priority 0: []
     Priority 1: []
     Priority 2: []
@@ -116,7 +116,7 @@ class ElementPriorityQueue:
     >>> epq.enqueue(4)
     >>> epq.enqueue(64)
     >>> epq.enqueue(128)
-    >>> print(epq)
+    >>> epq
     [10, 70, 4, 1, 5, 7, 4, 64, 128]
     >>> epq.dequeue()
     1
@@ -130,7 +130,7 @@ class ElementPriorityQueue:
     7
     >>> epq.dequeue()
     10
-    >>> print(epq)
+    >>> epq
     [70, 64, 128]
     >>> epq.dequeue()
     64
@@ -142,7 +142,7 @@ class ElementPriorityQueue:
     Traceback (most recent call last):
         ...
     data_structures.queue.priority_queue_using_list.UnderFlowError: The queue is empty
-    >>> print(epq)
+    >>> epq
     []
     """
 

graphs/gale_shapley_bigraph.py

@@ -17,7 +17,7 @@ def stable_matching(
 
     >>> donor_pref = [[0, 1, 3, 2], [0, 2, 3, 1], [1, 0, 2, 3], [0, 3, 1, 2]]
     >>> recipient_pref = [[3, 1, 2, 0], [3, 1, 0, 2], [0, 3, 1, 2], [1, 0, 3, 2]]
-    >>> print(stable_matching(donor_pref, recipient_pref))
+    >>> stable_matching(donor_pref, recipient_pref)
     [1, 2, 3, 0]
     """
     assert len(donor_pref) == len(recipient_pref)

graphs/graph_list.py

@@ -26,9 +26,9 @@ class GraphAdjacencyList(Generic[T]):
     {0: [1], 1: [2, 4, 5], 2: [], 4: [], 5: []}
     >>> d_graph.add_edge(2, 0).add_edge(2, 6).add_edge(2, 7)
     {0: [1], 1: [2, 4, 5], 2: [0, 6, 7], 4: [], 5: [], 6: [], 7: []}
-    >>> print(d_graph)
+    >>> d_graph
     {0: [1], 1: [2, 4, 5], 2: [0, 6, 7], 4: [], 5: [], 6: [], 7: []}
-    >>> print(repr(d_graph))
+    >>> repr(d_graph)
     {0: [1], 1: [2, 4, 5], 2: [0, 6, 7], 4: [], 5: [], 6: [], 7: []}
 
     Undirected graph example:
@@ -47,15 +47,15 @@ class GraphAdjacencyList(Generic[T]):
      5: [1, 4],
      6: [2],
      7: [2]}
-    >>> print(u_graph)
+    >>> u_graph
     {0: [1, 2],
      1: [0, 2, 4, 5],
      2: [1, 0, 6, 7],
      4: [1, 5],
      5: [1, 4],
      6: [2],
      7: [2]}
-    >>> print(repr(u_graph))
+    >>> repr(u_graph)
     {0: [1, 2],
      1: [0, 2, 4, 5],
      2: [1, 0, 6, 7],
@@ -68,7 +68,7 @@ class GraphAdjacencyList(Generic[T]):
      {'a': ['b'], 'b': ['a']}
      >>> char_graph.add_edge('b', 'c').add_edge('b', 'e').add_edge('b', 'f')
      {'a': ['b'], 'b': ['a', 'c', 'e', 'f'], 'c': ['b'], 'e': ['b'], 'f': ['b']}
-     >>> print(char_graph)
+     >>> char_graph
      {'a': ['b'], 'b': ['a', 'c', 'e', 'f'], 'c': ['b'], 'e': ['b'], 'f': ['b']}
     """
 

graphs/minimum_spanning_tree_boruvka.py

@@ -154,7 +154,7 @@ def boruvka_mst(graph):
         >>> g = Graph.build([0, 1, 2, 3], [[0, 1, 1], [0, 2, 1],[2, 3, 1]])
         >>> g.distinct_weight()
         >>> bg = Graph.boruvka_mst(g)
-        >>> print(bg)
+        >>> bg
         1 -> 0 == 1
         2 -> 0 == 2
         0 -> 1 == 1

graphs/minimum_spanning_tree_prims2.py

@@ -69,16 +69,16 @@ class MinPriorityQueue(Generic[T]):
     >>> queue.push(3, 4000)
     >>> queue.push(4, 3000)
 
-    >>> print(queue.extract_min())
+    >>> queue.extract_min()
     2
 
     >>> queue.update_key(4, 50)
 
-    >>> print(queue.extract_min())
+    >>> queue.extract_min()
     4
-    >>> print(queue.extract_min())
+    >>> queue.extract_min()
     1
-    >>> print(queue.extract_min())
+    >>> queue.extract_min()
     3
     """
 

graphs/random_graph_generator.py

@@ -53,7 +53,7 @@ def complete_graph(vertices_number: int) -> dict:
     @input: vertices_number (number of vertices),
             directed (False if the graph is undirected, True otherwise)
     @example:
-    >>> print(complete_graph(3))
+    >>> complete_graph(3)
     {0: [1, 2], 1: [0, 2], 2: [0, 1]}
     """
     return {

linear_algebra/src/polynom_for_points.py

@@ -3,25 +3,25 @@ def points_to_polynomial(coordinates: list[list[int]]) -> str:
     coordinates is a two dimensional matrix: [[x, y], [x, y], ...]
     number of points you want to use
 
-    >>> print(points_to_polynomial([]))
+    >>> points_to_polynomial([])
     The program cannot work out a fitting polynomial.
-    >>> print(points_to_polynomial([[]]))
+    >>> points_to_polynomial([[]])
     The program cannot work out a fitting polynomial.
-    >>> print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
+    >>> points_to_polynomial([[1, 0], [2, 0], [3, 0]])
     f(x)=x^2*0.0+x^1*-0.0+x^0*0.0
-    >>> print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
+    >>> points_to_polynomial([[1, 1], [2, 1], [3, 1]])
     f(x)=x^2*0.0+x^1*-0.0+x^0*1.0
-    >>> print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
+    >>> points_to_polynomial([[1, 3], [2, 3], [3, 3]])
     f(x)=x^2*0.0+x^1*-0.0+x^0*3.0
-    >>> print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
+    >>> points_to_polynomial([[1, 1], [2, 2], [3, 3]])
     f(x)=x^2*0.0+x^1*1.0+x^0*0.0
-    >>> print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
+    >>> points_to_polynomial([[1, 1], [2, 4], [3, 9]])
     f(x)=x^2*1.0+x^1*-0.0+x^0*0.0
-    >>> print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
+    >>> points_to_polynomial([[1, 3], [2, 6], [3, 11]])
     f(x)=x^2*1.0+x^1*-0.0+x^0*2.0
-    >>> print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
+    >>> points_to_polynomial([[1, -3], [2, -6], [3, -11]])
     f(x)=x^2*-1.0+x^1*-0.0+x^0*-2.0
-    >>> print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))
+    >>> points_to_polynomial([[1, 5], [2, 2], [3, 9]])
     f(x)=x^2*5.0+x^1*-18.0+x^0*18.0
     """
     try:

machine_learning/local_weighted_learning/local_weighted_learning.py

@@ -71,7 +71,6 @@ def local_weight_regression(
 def load_data(dataset_name: str, cola_name: str, colb_name: str) -> np.mat:
     """
     Function used for loading data from the seaborn splitting into x and y points
-    >>> pass # this function has no doctest
     """
     import seaborn as sns
 
@@ -112,7 +111,6 @@ def plot_preds(
 ) -> plt.plot:
     """
     This function used to plot predictions and display the graph
-    >>> pass #this function has no doctest
     """
     xsort = training_data_x.copy()
     xsort.sort(axis=0)