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minimum-cost-to-make-array-equalindromic.py
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minimum-cost-to-make-array-equalindromic.py
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# Time: O(n + logr)
# Space: O(logr)
import random
# lc0564
# quick select, math, string
class Solution(object):
def minimumCost(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
def nth_element(nums, n, 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
left, right = 0, 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
def nearest_palindromic(x):
n = str(x)
l = len(n)
result = {10**l+1, 10**(l-1)-1}
prefix = int(n[:(l+1)/2])
for i in map(str, (prefix-1, prefix, prefix+1)):
result.add(int(i+[i, i[:-1]][l%2][::-1]))
return result
nth_element(nums, len(nums)//2)
median = nums[len(nums)//2]
if len(nums)%2 == 0:
nth_element(nums, len(nums)//2-1)
median = (median+nums[len(nums)//2-1])//2
return min(sum(abs(x-p) for x in nums) for p in nearest_palindromic(median))
# Time: O(nlogn + logr)
# Space: O(logr)
# lc0564
# sort, math, string
class Solution2(object):
def minimumCost(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
def nearest_palindromic(x):
n = str(x)
l = len(n)
result = {10**l+1, 10**(l-1)-1}
prefix = int(n[:(l+1)/2])
for i in map(str, (prefix-1, prefix, prefix+1)):
result.add(int(i+[i, i[:-1]][l%2][::-1]))
return result
nums.sort()
median = nums[len(nums)//2]
if len(nums)%2 == 0:
median = (median+nums[len(nums)//2-1])//2
return min(sum(abs(x-p) for x in nums) for p in nearest_palindromic(median))