"""Matrix Exponentiation"""
import timeit
"""
Matrix Exponentiation is a technique to solve linear recurrences in logarithmic time.
You read more about it here:
https://zobayer.blogspot.com/2010/11/matrix-exponentiation.html
https://www.hackerearth.com/practice/notes/matrix-exponentiation-1/
"""
class Matrix:
def __init__(self, arg):
if isinstance(arg, list):
self.t = arg
self.n = len(arg)
else:
self.n = arg
self.t = [[0 for _ in range(self.n)] for _ in range(self.n)]
def __mul__(self, b):
matrix = Matrix(self.n)
for i in range(self.n):
for j in range(self.n):
for k in range(self.n):
matrix.t[i][j] += self.t[i][k] * b.t[k][j]
return matrix
def modular_exponentiation(a, b):
matrix = Matrix([[1, 0], [0, 1]])
while b > 0:
if b & 1:
matrix *= a
a *= a
b >>= 1
return matrix
def fibonacci_with_matrix_exponentiation(n, f1, f2):
if n == 1:
return f1
elif n == 2:
return f2
matrix = Matrix([[1, 1], [1, 0]])
matrix = modular_exponentiation(matrix, n - 2)
return f2 * matrix.t[0][0] + f1 * matrix.t[0][1]
def simple_fibonacci(n, f1, f2):
if n == 1:
return f1
elif n == 2:
return f2
fn_1 = f1
fn_2 = f2
n -= 2
while n > 0:
fn_1, fn_2 = fn_1 + fn_2, fn_1
n -= 1
return fn_1
def matrix_exponentiation_time():
setup = """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
code = "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
exec_time = timeit.timeit(setup=setup, stmt=code, number=100)
print("With matrix exponentiation the average execution time is ", exec_time / 100)
return exec_time
def simple_fibonacci_time():
setup = """
from random import randint
from __main__ import simple_fibonacci
"""
code = "simple_fibonacci(randint(1,70000), 1, 1)"
exec_time = timeit.timeit(setup=setup, stmt=code, number=100)
print(
"Without matrix exponentiation the average execution time is ", exec_time / 100
)
return exec_time
def main():
matrix_exponentiation_time()
simple_fibonacci_time()
if __name__ == "__main__":
main()