# fibonacci.py """ Calculates the Fibonacci sequence using iteration, recursion, memoization, and a simplified form of Binet's formula NOTE 1: the iterative, recursive, memoization functions are more accurate than the Binet's formula function because the Binet formula function uses floats NOTE 2: the Binet's formula function is much more limited in the size of inputs that it can handle due to the size limitations of Python floats RESULTS: (n = 20) fib_iterative runtime: 0.0055 ms fib_recursive runtime: 6.5627 ms fib_memoization runtime: 0.0107 ms fib_binet runtime: 0.0174 ms """ from math import sqrt from time import time def time_func(func, *args, **kwargs): """ Times the execution of a function with parameters """ start = time() output = func(*args, **kwargs) end = time() if int(end - start) > 0: print(f"{func.__name__} runtime: {(end - start):0.4f} s") else: print(f"{func.__name__} runtime: {(end - start) * 1000:0.4f} ms") return output def fib_iterative(n: int) -> list[int]: """ Calculates the first n (0-indexed) Fibonacci numbers using iteration >>> fib_iterative(0) [0] >>> fib_iterative(1) [0, 1] >>> fib_iterative(5) [0, 1, 1, 2, 3, 5] >>> fib_iterative(10) [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55] >>> fib_iterative(-1) Traceback (most recent call last): ... Exception: n is negative """ if n < 0: raise Exception("n is negative") if n == 0: return [0] fib = [0, 1] for _ in range(n - 1): fib.append(fib[-1] + fib[-2]) return fib def fib_recursive(n: int) -> list[int]: """ Calculates the first n (0-indexed) Fibonacci numbers using recursion >>> fib_iterative(0) [0] >>> fib_iterative(1) [0, 1] >>> fib_iterative(5) [0, 1, 1, 2, 3, 5] >>> fib_iterative(10) [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55] >>> fib_iterative(-1) Traceback (most recent call last): ... Exception: n is negative """ def fib_recursive_term(i: int) -> int: """ Calculates the i-th (0-indexed) Fibonacci number using recursion """ if i < 0: raise Exception("n is negative") if i < 2: return i return fib_recursive_term(i - 1) + fib_recursive_term(i - 2) if n < 0: raise Exception("n is negative") return [fib_recursive_term(i) for i in range(n + 1)] def fib_memoization(n: int) -> list[int]: """ Calculates the first n (0-indexed) Fibonacci numbers using memoization >>> fib_memoization(0) [0] >>> fib_memoization(1) [0, 1] >>> fib_memoization(5) [0, 1, 1, 2, 3, 5] >>> fib_memoization(10) [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55] >>> fib_iterative(-1) Traceback (most recent call last): ... Exception: n is negative """ if n < 0: raise Exception("n is negative") # Cache must be outside recursuive function # other it will reset every time it calls itself. cache: dict[int, int] = {0: 0, 1: 1, 2: 1} # Prefilled cache def rec_fn_memoized(num: int) -> int: if num in cache: return cache[num] value = rec_fn_memoized(num - 1) + rec_fn_memoized(num - 2) cache[num] = value return value return [rec_fn_memoized(i) for i in range(n + 1)] def fib_binet(n: int) -> list[int]: """ Calculates the first n (0-indexed) Fibonacci numbers using a simplified form of Binet's formula: https://en.m.wikipedia.org/wiki/Fibonacci_number#Computation_by_rounding NOTE 1: this function diverges from fib_iterative at around n = 71, likely due to compounding floating-point arithmetic errors NOTE 2: this function doesn't accept n >= 1475 because it overflows thereafter due to the size limitations of Python floats >>> fib_binet(0) [0] >>> fib_binet(1) [0, 1] >>> fib_binet(5) [0, 1, 1, 2, 3, 5] >>> fib_binet(10) [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55] >>> fib_binet(-1) Traceback (most recent call last): ... Exception: n is negative >>> fib_binet(1475) Traceback (most recent call last): ... Exception: n is too large """ if n < 0: raise Exception("n is negative") if n >= 1475: raise Exception("n is too large") sqrt_5 = sqrt(5) phi = (1 + sqrt_5) / 2 return [round(phi**i / sqrt_5) for i in range(n + 1)] if __name__ == "__main__": num = 20 time_func(fib_iterative, num) time_func(fib_recursive, num) time_func(fib_memoization, num) time_func(fib_binet, num)