Big-O.py

#!/usr/bin/env python
# Question: Given the following code fragment, what is its Big-O running time? ❓
"""
1. O(n)
2. O(n**2) ✔️
3. O(log n)
4. O(n**3)
"""
""" Review  📝
	We have one for insede in that other(nested for loops). So that means
	for each loop in the first for loop, we'll have the range of n.
"""


def question(n):
    test = 0  # Initial value
    for i in range(n):  # run for the range n. e.g is n=5, the loop will run 1,2,3,4,5
        for j in range(n):
            print(i, '', j)
            test = test + i * j
    return test

# Question 2: Given the following code fragment, what is its Big-O running time? ❓


""" Review  📝
	In this case, whe have two for loops that does not have nesteding. 
	However, are running in the same index position. I think that it's lineal time complexity algorithm.   
"""


def question_2(n):
    test = 0
    for i in range(n):
        test = test + 1
    for j in range(n):
        test = test - 1
    print(i, '', j)


"""
1. O(n) ✔️
2. O(n**2) 
3. O(log n)
4. O(n**3)
"""


# Question 3: Given the following code fragment, what is its Big-O running time? ❓

""" Review  📝
	Here we have while loop. First, i  is iqual to n and while i are greater that 0
	i will be floor devided of 2. In addiction, does not exists a correlation between k and i.   
"""


def question_3(n):
    i = n
    while i > 0:
        k = 2 + 2
        i = i // 2
        print(i)


question_3(100)
"""
1. O(n) ✔️
2. O(n**2) 
3. O(log n)
4. O(n**3)
"""