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import collections | ||
import sys | ||
import random | ||
import time | ||
import math | ||
""" | ||
A python implementation of the quick select algorithm, which is efficient for calculating the value that would appear in the index of a list if it would be sorted, even if it is not already sorted | ||
https://en.wikipedia.org/wiki/Quickselect | ||
""" | ||
def _partition(data, pivot): | ||
""" | ||
Three way partition the data into smaller, equal and greater lists, | ||
in relationship to the pivot | ||
:param data: The data to be sorted (a list) | ||
:param pivot: The value to partition the data on | ||
:return: Three list: smaller, equal and greater | ||
""" | ||
less, equal, greater = [], [], [] | ||
for element in data: | ||
if element.address < pivot.address: | ||
less.append(element) | ||
elif element.address > pivot.address: | ||
greater.append(element) | ||
else: | ||
equal.append(element) | ||
return less, equal, greater | ||
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||
def quickSelect(list, k): | ||
#k = len(list) // 2 when trying to find the median (index that value would be when list is sorted) | ||
smaller = [] | ||
larger = [] | ||
pivot = random.randint(0, len(list) - 1) | ||
pivot = list[pivot] | ||
count = 0 | ||
smaller, equal, larger =_partition(list, pivot) | ||
count = len(equal) | ||
m = len(smaller) | ||
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#k is the pivot | ||
if m <= k < m + count: | ||
return pivot | ||
# must be in smaller | ||
elif m > k: | ||
return quickSelect(smaller, k) | ||
#must be in larger | ||
else: | ||
return quickSelect(larger, k - (m + count)) |