-
Notifications
You must be signed in to change notification settings - Fork 1
Expand file tree
/
Copy pathfibonacci.py
More file actions
69 lines (52 loc) · 1.65 KB
/
fibonacci.py
File metadata and controls
69 lines (52 loc) · 1.65 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
import timeit
def fibonacci(n):
"""iterative fibonacci has linear complexity: O(n)"""
a, b = 1, 1
for i in range(n-1):
# print(a, end=' ')
a, b = b, a+b
return a
# def fib_generator(n):
# a, b = 1, 1
# while True:
# yield a
# a, b = b, a+b
# f = fib_generator()
# for i in range(6):
# print(f.next())
numFibCalls_rec = 0
def fibonacci_rec(n):
"""This algorithm is inefficient:O(2^n); because it recalculates the same value many times"""
global numFibCalls_rec
numFibCalls_rec += 1
if n == 1 or n == 2:
return 1
else:
return fibonacci_rec(n-1) + fibonacci_rec(n-2)
numFibCalls_memoize = 0
def fib_memoization(n, dict):
"""It is an efficient algorithm as it first lookup if value is already calculated"""
global numFibCalls_memoize
numFibCalls_memoize += 1
if n in dict:
return dict[n]
else:
ans = fib_memoization(n - 1, dict) + fib_memoization(n - 2, dict)
dict[n] = ans
return ans
d = {1:1, 2:1}
n = int(input("Enter n to find nth fibonacci number: "))
t0 = timeit.default_timer()
print("fibonacci num:", fibonacci(n))
t1 = timeit.default_timer()
print(t1 - t0, "\n")
# print("fibonacci num:", fib_generator(n), ", numFibCalls:", )
t2 = timeit.default_timer()
print("fibonacci num:", fibonacci_rec(n), ", numFibCalls_rec:", numFibCalls_rec)
t3 = timeit.default_timer()
print(t3 - t2, "\n")
t4 = timeit.default_timer()
print("fibonacci num:", fib_memoization(n, d), ", numFibCalls_memoize:", numFibCalls_memoize)
t5 = timeit.default_timer()
print(t5 - t4)
# Thus, the best algorithm to calculate fibonacci number is memoization.