i wrote Quicksort and Mergesort and a Benchmark for them, to see how fast they are.
Here is my code:
#------------------------------Creating a Random List-----------------------------#
def create_random_list (length):
import random
random_list = list(range(0,length))
random.shuffle(random_list)
return random_list
# Initialize default list length to 0
random_list = create_random_list(0)
# Testing random list function
print ("\n" + "That is a randomized list: " + "\n")
print (random_list)
print ("\n")
#-------------------------------------Quicksort-----------------------------------#
"""
Recursive Divide and Conquer Algorithm
+ Very efficient for large data set
- Performance Depends largely on Pivot Selection
Time Complexity
--> Worst-Case -----> O (n^2)
--> Best-Case -----> Ω (n log (n))
--> Average Case ---> O (n log (n))
Space Complexity
--> O(log(n))
"""
# Writing the Quick Sort Algorithm for sorting the list - Recursive Method
def qsort (random_list):
less = []
equal = []
greater = []
if len(random_list)>1:
# Initialize starting Point
pivot = random_list[0]
for x in random_list:
if x < pivot:
less.append(x)
elif x == pivot:
equal.append(x)
elif x > pivot:
greater.append(x)
return qsort(less) + equal + qsort(greater)
else:
return random_list
"""
Build in Python Quick Sort:
def qsort(L):
if len(L) <= 1: return L
return qsort([lt for lt in L[1:] if lt < L[0]]) + L[0:1] + \
qsort([ge for ge in L[1:] if ge >= L[0]])
"""
# Calling Quicksort
sorted_list_qsort = qsort(random_list)
# Testint Quicksort
print ("That is a sorted list with Quicksort: " + "\n")
print (sorted_list_qsort)
print ("\n")
#-------------------------------------FINISHED-------------------------------------#
#-------------------------------------Mergesort------------------------------------#
"""
Recursive Divide and Conquer Algorithm
+
-
Time Complexity
--> Worst-Case -----> O (n l(n))
--> Best-Case -----> Ω (n l(n))
--> Average Case ---> O (n l(n))
Space Complexity
--> O (n)
"""
# Create a merge algorithm
def merge(a,b): # Let a and b be two arrays
c = [] # Final sorted output array
a_idx, b_idx = 0,0 # Index or start from a and b array
while a_idx < len(a) and b_idx < len(b):
if a[a_idx] < b[b_idx]:
c.append(a[a_idx])
a_idx+=1
else:
c.append(b[b_idx])
b_idx+=1
if a_idx == len(a): c.extend(b[b_idx:])
else: c.extend(a[a_idx:])
return c
# Create final Mergesort algorithm
def merge_sort(a):
# A list of zero or one elements is sorted by definition
if len(a)<=1:
return a
# Split the list in half and call Mergesort recursively on each half
left, right = merge_sort(a[:int(len(a)/2)]), merge_sort(a[int(len(a)/2):])
# Merge the now-sorted sublists with the merge function which sorts two lists
return merge(left,right)
# Calling Mergesort
sorted_list_mgsort = merge_sort(random_list)
# Testing Mergesort
print ("That is a sorted list with Mergesort: " + "\n")
print (sorted_list_mgsort)
print ("\n")
#-------------------------------------FINISHED-------------------------------------#
#------------------------------Algorithm Benchmarking------------------------------#
# Creating an array for iterations
n = [100,1000,10000,100000]
# Creating a dictionary for times of algorithms
times = {"Quicksort":[], "Mergesort": []}
# Import time for analyzing the running time of the algorithms
from time import time
# Create a for loop which loop through the arrays of length n and analyse their times
for size in range(len(n)):
random_list = create_random_list(n[size])
t0 = time()
qsort(random_list)
t1 = time()
times["Quicksort"].append(t1-t0)
random_list = create_random_list(n[size-1])
t0 = time()
merge_sort(random_list)
t1 = time()
times["Mergesort"].append(t1-t0)
# Create a table while shows the Benchmarking of the algorithms
print ("n\tMerge\tQuick")
print ("_"*25)
for i,j in enumerate(n):
print ("{}\t{:.5f}\t{:.5f}\t".format(j, times["Mergesort"][i], times["Quicksort"][i]))
#----------------------------------End of Benchmarking---------------------------------#
The code is well documented and runs perfectly with Python 3.8. You may copy it in a code editor for better readability.
--> My Question as the title states:
Is my Benchmarking right? I'm doubting it a litte bit, because the running times of my Algorithms seem a little odd. Can someone confirm my runtime?
--> Here is the output of this code:
That is a randomized list:
[]
That is a sorted list with Quicksort:
[]
That is a sorted list with Mergesort:
[]
n Merge Quick
_________________________
100 0.98026 0.00021
1000 0.00042 0.00262
10000 0.00555 0.03164
100000 0.07919 0.44718
--> If someone has another/better code snippet on how to print the table - feel free to share it with me.
