Create mean-of-array-after-removing-some-elements.py

Author: kamyu104

Python/mean-of-array-after-removing-some-elements.py

@@ -0,0 +1,43 @@
+# Time:  O(n) on average, using Median of Medians could achieve O(n) (Intro Select)
+# Space: O(1)
+
+import random
+
+
+class Solution(object):
+    def trimMean(self, arr):
+        """
+        :type arr: List[int]
+        :rtype: float
+        """
+        P = 20
+        def nth_element(nums, n, left=0, compare=lambda a, b: a < b):
+            def tri_partition(nums, left, right, target, compare):
+                mid = left
+                while mid <= right:
+                    if nums[mid] == target:
+                        mid += 1
+                    elif compare(nums[mid], target):
+                        nums[left], nums[mid] = nums[mid], nums[left]
+                        left += 1
+                        mid += 1
+                    else:
+                        nums[mid], nums[right] = nums[right], nums[mid]
+                        right -= 1
+                return left, right
+
+            right = len(nums)-1
+            while left <= right:
+                pivot_idx = random.randint(left, right)
+                pivot_left, pivot_right = tri_partition(nums, left, right, nums[pivot_idx], compare)
+                if pivot_left <= n <= pivot_right:
+                    return
+                elif pivot_left > n:
+                    right = pivot_left-1
+                else:  # pivot_right < n.
+                    left = pivot_right+1
+        
+        k = len(arr)//P
+        nth_element(arr, k-1)
+        nth_element(arr, len(arr)-k, left=k)
+        return float(sum(arr[i] for i in xrange(k, len(arr)-k)))/(len(arr)-2*k)