3 different ways of using caching for a simple computation of Fibonacci numbers. Also using lru_cache (Least recently used) in Python to limit the number of items in cache

fibonacci.py

# -*- coding: utf-8 -*-
"""
Created on Mon Dec  7 10:35:31 2020

@author: Localuser
"""

# 1st method using a Class
class Fib:
    
    def __init__(self):
        self.cache = {1: 1, 2: 1}
        
    def fibonacci(self, n):
        if n not in self.cache:
            print('Calculating fibonacci({0})'.format(n))
            self.cache[n] = self.fibonacci(n-1) + self.fibonacci(n-2)
        return self.cache[n]
    
# 2nd method using a Closure    
def fib():
    
    cache = {1: 1, 2: 1}
    
    def calc_fib(n):
        if n not in cache:
            print('Calculating fib({0})'.format(n))
            cache[n] = calc_fib(n-1) + calc_fib(n-2)
            
        return cache[n]
    return calc_fib
    

# 3rd method using a Decorator
def memoize_fib(fib):
    
    cache = dict()
    
    def inner(n):
        if n not in cache:
            cache[n] = fib(n) 
        return cache[n]
    return inner

@memoize_fib
def fib(n):
    print('Calculating fib({0})'.format(n))
    return 1 if n < 3 else fib(n-1) + fib(n-2)


#Using least recently used cache function in Python to limit the no. of items in cache
from functools import lru_cache 

@lru_cache(maxsize = 16) # by default lru_cache has size of 128 items
def fib(n):
    print('Calculating fib({0})'.format(n))
    return 1 if n < 3 else fib(n-1) + fib(n-2)