Selection in expected linear time
The general selection problem appears more difficult than the simple problem of finding a minimum. Yet, surprisingly, the asymptotic running time for both problems is the same: theta(n).
In this section, we present a divide-and-conquer algorithm for the selection problem. The algorithm R ANDOMIZED-SELECT is modeled after the quicksort algorithm of Chapter 7. As in quicksort, we partition the input array recursively. But unlike quicksort, which recursively processes both sides of the partition, R ANDOMIZED -S ELECT works on only one side of the partition. This difference shows up in the analysis: whereas quicksort has an expected running time of theta{n*lg(n)}, the expected running time of RANDOMIZED-S ELECT is O(n), assuming that the elements are distinct.
下面是random_select()的實作：
傳回在輸入資料中，第i大的資料，時間複雜度是O(n)
"""
Code writer : EOF
Code date : 2015.01.15
Code file : random_select.py
e-mail : [email protected]
Code description :
Here is a implementation for random-select in Python.
Function @random_select(A, p, r, i) will return the i-th
element from the @A which's range is @p to @r.
"""
import random
def partition(A, p, r) :
x = A[r]
i = p-1
for j in range(p, r) :
if A[j] <= x :
i += 1
# swap A[i] and A[j]
A[i], A[j] = A[j], A[i]
A[i+1], A[r] = A[r], A[i+1]
return i+1
def random_partition(A, p, r) :
i = random.randint(p,r)
A[r], A[i] = A[i], A[r]
return partition(A, p, r)
def random_select(A, p, r, i) :
if p == r :
return A[p]
q = random_partition(A, p ,r)
k = q - p + 1
if i == k :
return A[q]
elif i < k :
return random_select(A, p, q-1, i)
else :
return random_select(A, q+1, r, i-k)
#-------------------testing code---------------------------
A = [13,19,9,5,12,8,7,4,21,2,6,11]
print "Before sorting A= " , A
x = random_select(A,0,len(A)-1, 5)
print "After sorting A= " , sorted(A), x
