add: Minimum Deviation in Array to greedy_methods

greedy_methods/minimize_deviation_in_array.py

@@ -0,0 +1,60 @@
+"""
+You are given an array nums of n positive integers.
+
+You can perform two types of operations on any 
+element of the array any number of times:
+
+If the element is even, divide it by 2.
+For example, if the array is [1,2,3,4], then you can do this operation 
+on the last element, and the array will be [1,2,3,2].
+If the element is odd, multiply it by 2.
+For example, if the array is [1,2,3,4], then you can do this operation
+on the first element, and the array will be [2,2,3,4].
+The deviation of the array is the maximum difference between 
+any two elements in the array.
+
+Return the minimum deviation the array can have after performing 
+some number of operations.
+"""
+
+from heapq import heapify, heappop, heappush
+
+
+class Solution:
+    def minimum_deviation(self, nums) -> int:
+        """
+        Function to find the minimum deviation of an array after performing operations.
+
+        Args:
+            nums (List[int]): A list of positive integers.
+
+        Returns:
+            temp_mindeviation (int): The minimum deviation of the array after operations.
+
+        Examples:
+            >>> solution = Solution()
+            >>> solution.minimumDeviation([1, 2, 3, 4])
+            1
+            >>> solution.minimumDeviation([5, 10, 20, 30, 30])
+            5
+            >>> solution.minimumDeviation([8, 8, 8, 8, 8])
+            0
+        """
+        heapque = [-n * 2 if n % 2 else -n for n in nums]
+        heapify(heapque)
+        temp_min = -min(heapque, key=lambda num: -num)
+        temp_mindeviation = -heapque[0] - temp_min
+
+        while h and h[0] % 2 == 0:
+            n = heappop(h) // 2
+            heappush(h, n)
+            temp_min = min(temp_min, -n)
+            temp_mindeviation = min(temp_mindeviation, -h[0] - temp_min)
+
+        return temp_mindeviation
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()