Added docs

Author: czgdp1807

pydatastructs/graphs/algorithms.py

@@ -51,6 +51,10 @@ def breadth_first_search(
         current node and the node next to current node.
         The rest of the arguments are optional and you can
         provide your own stuff there.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Note
     ====
@@ -135,6 +139,10 @@ def breadth_first_search_parallel(
         current node and the node next to current node.
         The rest of the arguments are optional and you can
         provide your own stuff there.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Note
     ====
@@ -280,6 +288,10 @@ def minimum_spanning_tree(graph, algorithm, **kwargs):
         'kruskal' -> Kruskal's algorithm as given in [1].
 
         'prim' -> Prim's algorithm as given in [2].
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -418,6 +430,10 @@ def minimum_spanning_tree_parallel(graph, algorithm, num_threads, **kwargs):
         'prim' -> Prim's algorithm as given in [2].
     num_threads: int
         The number of threads to be used.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -531,6 +547,10 @@ def strongly_connected_components(graph, algorithm, **kwargs):
         supported,
 
         'kosaraju' -> Kosaraju's algorithm as given in [1].
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -593,6 +613,10 @@ def depth_first_search(
         current node and the node next to current node.
         The rest of the arguments are optional and you can
         provide your own stuff there.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Note
     ====
@@ -681,6 +705,10 @@ def shortest_paths(graph: Graph, algorithm: str,
         The name of the target node.
         Optional, by default, all pair shortest paths
         are returned.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -797,6 +825,10 @@ def all_pair_shortest_paths(graph: Graph, algorithm: str,
         are implemented,
 
         'floyd_warshall' -> Floyd Warshall algorithm as given in [1].
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -882,6 +914,10 @@ def topological_sort(graph: Graph, algorithm: str,
         Currently, following are supported,
 
         'kahn' -> Kahn's algorithm as given in [1].
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -959,6 +995,10 @@ def topological_sort_parallel(graph: Graph, algorithm: str, num_threads: int,
         'kahn' -> Kahn's algorithm as given in [1].
     num_threads: int
         The maximum number of threads to be used.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======

pydatastructs/graphs/graph.py

@@ -26,6 +26,10 @@ class Graph(object):
     vertices: GraphNode(s)
         For AdjacencyList implementation vertices
         can be passed for initializing the graph.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Examples
     ========

pydatastructs/linear_data_structures/algorithms.py

@@ -83,6 +83,10 @@ def merge_sort_parallel(array, num_threads, **kwargs):
         Optional, by default, less than or
         equal to is used for comparing two
         values.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Examples
     ========
@@ -147,6 +151,10 @@ def brick_sort(array, **kwargs):
         Optional, by default, less than or
         equal to is used for comparing two
         values.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Examples
     ========
@@ -217,6 +225,10 @@ def brick_sort_parallel(array, num_threads, **kwargs):
         Optional, by default, less than or
         equal to is used for comparing two
         values.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Examples
     ========
@@ -272,6 +284,10 @@ def heapsort(array, **kwargs):
         is to be sorted.
         Optional, by default the index
         of the last position filled.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Examples
     ========
@@ -323,6 +339,10 @@ def counting_sort(array: Array, **kwargs) -> Array:
 
     array: Array
         The array which is to be sorted.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -408,13 +428,15 @@ def matrix_multiply_parallel(matrix_1, matrix_2, num_threads):
 
     matrix_1: Any matrix representation
         Left matrix
-
     matrix_2: Any matrix representation
         Right matrix
-
     num_threads: int
         The maximum number of threads
         to be used for multiplication.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Raises
     ======
@@ -488,6 +510,10 @@ def bucket_sort(array: Array, **kwargs) -> Array:
         is to be sorted.
         Optional, by default the index
         of the last position filled.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -593,6 +619,10 @@ def cocktail_shaker_sort(array: Array, **kwargs) -> Array:
         Optional, by default, less than or
         equal to is used for comparing two
         values.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -682,6 +712,10 @@ def quick_sort(array: Array, **kwargs) -> Array:
         the original input array to `quick_sort` function.
         Optional, by default, picks the element at `high`
         index of the current partition as pivot.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -760,6 +794,10 @@ def longest_common_subsequence(seq1: OneDimensionalArray, seq2: OneDimensionalAr
         The first sequence.
     seq2: OneDimensionalArray
         The second sequence.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -839,6 +877,10 @@ def is_ordered(array, **kwargs):
         Optional, by default, less than or
         equal to is used for comparing two
         values.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -895,6 +937,10 @@ def upper_bound(array, value, **kwargs):
         Optional, by default, less than or
         equal to is used for comparing two
         values.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -964,6 +1010,10 @@ def lower_bound(array, value, **kwargs):
         Optional, by default, less than or
         equal to is used for comparing two
         values.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -1020,6 +1070,10 @@ def longest_increasing_subsequence(array, **kwargs):
 
     array: OneDimensionalArray
         The given array in the form of a OneDimensionalArray
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Returns
     =======
@@ -1124,6 +1178,10 @@ def next_permutation(array, **kwargs):
         desired lexicographical ordering.
         Optional, by default, less than is
         used for comparing two values.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
 
     Returns
@@ -1193,6 +1251,10 @@ def prev_permutation(array, **kwargs):
         desired lexicographical ordering.
         Optional, by default, less than is
         used for comparing two values.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
 
     Returns

pydatastructs/linear_data_structures/arrays.py

@@ -34,6 +34,10 @@ class OneDimensionalArray(Array):
         The inital value with which the element has
         to be initialized. By default none, used only
         when the data is not given.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Raises
     ======
@@ -156,6 +160,10 @@ class MultiDimensionalArray(Array):
         A valid object type.
     *args: int
         The dimensions of the array.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Raises
     ======
@@ -297,6 +305,10 @@ class DynamicOneDimensionalArray(DynamicArray, OneDimensionalArray):
         The number below which if the ratio, Num(T)/Size(T)
         falls then the array is contracted such that at
         most only half the positions are filled.
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
 
     Raises
     ======
@@ -421,6 +433,14 @@ class ArrayForTrees(DynamicOneDimensionalArray):
     """
     Utility dynamic array for storing nodes of a tree.
 
+    Parameters
+    ==========
+
+    backend: pydatastructs.Backend
+        The backend to be used.
+        Optional, by default, the best available
+        backend is used.
+
     See Also
     ========