Merge branch 'master' of github.com:Miltonbhowmick/Python

* tanh function been added

* tanh function been added

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* tanh function is added

* tanh function is added

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* tanh function added

* tanh function added

* tanh function is added

* Apply suggestions from code review

* ELU activation function is added

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* elu activation is added

* ELU activation is added

* Update maths/elu_activation.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* Exponential_linear_unit activation is added

* Exponential_linear_unit activation is added

* SiLU activation is added

* SiLU activation is added

* mish added

* mish activation is added

* inter_quartile_range function is added

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* Mish activation function is added

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* mish activation added

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* mish activation added

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* inter quartile range (IQR) function is added

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* IQR function is added

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* code optimized in IQR function

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* interquartile_range function is added

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* Update maths/interquartile_range.py

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* Changes on interquartile_range

* numpy removed from interquartile_range

* Fixes from code review

* Update interquartile_range.py

---------

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Co-authored-by: Christian Clauss <cclauss@me.com>

DIRECTORY.md

@@ -586,6 +586,7 @@
   * [Hardy Ramanujanalgo](maths/hardy_ramanujanalgo.py)
   * [Hexagonal Number](maths/hexagonal_number.py)
   * [Integration By Simpson Approx](maths/integration_by_simpson_approx.py)
+  * [Interquartile Range](maths/interquartile_range.py)
   * [Is Int Palindrome](maths/is_int_palindrome.py)
   * [Is Ip V4 Address Valid](maths/is_ip_v4_address_valid.py)
   * [Is Square Free](maths/is_square_free.py)

graphs/uniform_search_cost.py

@@ -1,10 +1,9 @@
 from typing import List
 import heapq
-from copy import deepcopy
 
 
 class UniformCostSearch:
-    def __init__(self, current, final, grid):
+    def __init__(self, current: list, final: list, grid: list[list]) -> None:
         self.m = len(grid[0])
         self.n = len(grid)
 
@@ -76,7 +75,9 @@ class UniformCostSearch:
         (-1, 1),
     ]
 
-    def get_shortest_path(self, start, end, dist, dxy):
+    def get_shortest_path(
+        self, start: list, end: list, dist: list[list], dxy: list[tuple]
+    ) -> list:
         shortest_path = []
         curr_node = end
         while curr_node != start:
@@ -97,7 +98,15 @@ class UniformCostSearch:
         shortest_path.append(start)
         return shortest_path
 
-    def ucs(self, current, final, grid, prev, dxy, goal_answer):
+    def ucs(
+        self,
+        current: list,
+        final: list[list],
+        grid: list[list],
+        prev: list[list],
+        dxy: list[tuple],
+        goal_answer: list,
+    ) -> list:
         dist = [[float("inf") for _ in range(self.m)] for _ in range(self.n)]
         visited = [[0 for _ in range(self.m)] for _ in range(self.n)]
 
@@ -145,8 +154,8 @@ class UniformCostSearch:
                         heapq.heappush(heap, (new_dist, x + dx, y + dy))
 
     def your_algorithm(
-        self, start_point: List[int], end_point: List[int], grid: List[List[int]]
-    ) -> List[int]:
+        self, start_point: list, end_point: list, grid: list[list]
+    ) -> list[int]:
         prev = [[None for _ in range(self.m)] for _ in range(self.n)]
         dxy = []
         if start_point[1] - end_point[1] == 0 and start_point[0] - end_point[0] < 0:
@@ -168,7 +177,7 @@ class UniformCostSearch:
         return path
 
 
-def run():
+def run() -> None:
     executed_object = UniformCostSearch(
         [0, 7],
         [19, 17],

maths/interquartile_range.py

@@ -0,0 +1,66 @@
+"""
+An implementation of interquartile range (IQR) which is a measure of statistical
+dispersion, which is the spread of the data.
+
+The function takes the list of numeric values as input and returns the IQR.
+
+Script inspired by this Wikipedia article:
+https://en.wikipedia.org/wiki/Interquartile_range
+"""
+from __future__ import annotations
+
+
+def find_median(nums: list[int | float]) -> float:
+    """
+    This is the implementation of the median.
+    :param nums: The list of numeric nums
+    :return: Median of the list
+    >>> find_median(nums=([1, 2, 2, 3, 4]))
+    2
+    >>> find_median(nums=([1, 2, 2, 3, 4, 4]))
+    2.5
+    >>> find_median(nums=([-1, 2, 0, 3, 4, -4]))
+    1.5
+    >>> find_median(nums=([1.1, 2.2, 2, 3.3, 4.4, 4]))
+    2.65
+    """
+    div, mod = divmod(len(nums), 2)
+    if mod:
+        return nums[div]
+    return (nums[div] + nums[(div) - 1]) / 2
+
+
+def interquartile_range(nums: list[int | float]) -> float:
+    """
+    Return the interquartile range for a list of numeric values.
+    :param nums: The list of numeric values.
+    :return: interquartile range
+
+    >>> interquartile_range(nums=[4, 1, 2, 3, 2])
+    2.0
+    >>> interquartile_range(nums = [-2, -7, -10, 9, 8, 4, -67, 45])
+    17.0
+    >>> interquartile_range(nums = [-2.1, -7.1, -10.1, 9.1, 8.1, 4.1, -67.1, 45.1])
+    17.2
+    >>> interquartile_range(nums = [0, 0, 0, 0, 0])
+    0.0
+    >>> interquartile_range(nums=[])
+    Traceback (most recent call last):
+    ...
+    ValueError: The list is empty. Provide a non-empty list.
+    """
+    if not nums:
+        raise ValueError("The list is empty. Provide a non-empty list.")
+    nums.sort()
+    length = len(nums)
+    div, mod = divmod(length, 2)
+    q1 = find_median(nums[:div])
+    half_length = sum((div, mod))
+    q3 = find_median(nums[half_length:length])
+    return q3 - q1
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()