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Cache Dictionary

Memoization stores in memory the result of a function call (for a given set of arguments).
 
""" Memoize fibonacci
Memoize the recursive function for specific values that will be 
remembered for future use.
To memoize a function we create a cache dictionary.
"""

def fibonacci_recursive(n):
    if n == 1: return 1
    if n == 2: return 1
    return fibonacci_recursive(n-1) + fibonacci_recursive(n-2)

CACHE = {}
def fibonacci_memoize(n):
    global CACHE

    if n == 1: CACHE[n] = 1; return 1
    if n == 2: CACHE[n] = 1; return 1
    if n in CACHE:
        return CACHE[n]

    res = fibonacci_memoize(n-1) + fibonacci_memoize(n-2)
    CACHE[n] = res
    return res

def fibonacci_iterative(n):
    a, b = 1, 1
    for i in range(1, n-1):
        a, b = b, a + b
    return b

# Tests
assert fibonacci_iterative(2) == 1
assert fibonacci_iterative(3) == 2
assert fibonacci_recursive(4) == 3
assert fibonacci_recursive(5) == 5
assert fibonacci_memoize(6) == 8
assert fibonacci_memoize(7) == 13

# Time
import time
t1 = time.time(); n1 = fibonacci_memoize(100)
t2 = time.time(); n2 = fibonacci_recursive(36)
t3 = time.time(); n3 = fibonacci_iterative(100)

print("fibonacci_memoize(100)", time.time() - t1, 's', n1) 
print("fibonacci_recursive(36)", time.time() - t2, 's', n2) 
print("fibonacci_iterative(100)", time.time() - t3, 's', n3) 

"""
    fibonacci_memoize(100) 2.736084222793579 s          354224848179261915075
    fibonacci_recursive(36) 2.735971450805664 s         14930352
    fibonacci_iterative(100) 5.555152893066406e-05 s    354224848179261915075
"""

Decorator

Python standard library has a function decorator lru_cache().
 
""" Memoize fibonacci - functools
Python standard library has a function decorator lru_cache() 
that automatically memoize the function it decorates.
Memoization can be apply to any pure function to speed up the execution.
"""

from functools import lru_cache

def fibonacci_recursive(n):
    if n == 1: return 1
    if n == 2: return 1
    return fibonacci_recursive(n-1) + fibonacci_recursive(n-2)

@lru_cache()
def fibonacci_memoize(n): # Look Here
    if n == 1: return 1
    if n == 2: return 1
    return fibonacci_memoize(n-1) + fibonacci_memoize(n-2)

def fibonacci_iterative(n):
    a, b = 1, 1
    for i in range(1, n-1):
        a, b = b, a + b
    return b

# Tests
assert fibonacci_iterative(2) == 1
assert fibonacci_iterative(3) == 2
assert fibonacci_recursive(4) == 3
assert fibonacci_recursive(5) == 5
assert fibonacci_memoize(6) == 8
assert fibonacci_memoize(7) == 13

# Time
import time
t1 = time.time(); n1 = fibonacci_memoize(100)
t2 = time.time(); n2 = fibonacci_recursive(36)
t3 = time.time(); n3 = fibonacci_iterative(100)

print("fibonacci_memoize(100)", time.time() - t1, 's', n1) 
print("fibonacci_recursive(36)", time.time() - t2, 's', n2) 
print("fibonacci_iterative(100)", time.time() - t3, 's', n3) 

"""
    fibonacci_memoize(100) 2.736084222793579 s          354224848179261915075
    fibonacci_recursive(36) 2.735971450805664 s         14930352
    fibonacci_iterative(100) 5.555152893066406e-05 s    354224848179261915075
"""



  Last update: 202 days ago