Fibonacci sequence using reduce method

社会主义新天地 提交于 2021-02-05 12:21:59

问题


So, I saw someone using the reduce method to calculate the Fibonacci sequence. Here is his idea: (1,0) , (1,1) , (2,1) , (3,2) , (5,3) corresponds to 1, 1, 2, 3, 5, 8, 13, 21 .......

and the code looks like this

def fib_reduce(n):
    initial =(1,0)
    dummy = range(n)
    fib_n = reduce(lambda prev ,b : (prev[0] + prev[1], prev[0]),
                   dummy,
                   initial)
    
    return fib_n[0]

I understand the (prev[0] + prev[1] , prev[0]) which is like a, b = b, b + a. However, I don't understand what this b stands for ?

May someone please explain this b?


回答1:


Repeatedly applying a function using reduce

This answer suggests writing up your own function repeated to repeatedly apply a function, rather than calling reduce with a dummy second argument.

We still use reduce, but in a more functional manner and with itertools.repeat.

from itertools import repeat
from functools import reduce

def repeated(func, n):
    def apply(x, f):
        return f(x)
    def ret(x):
        return reduce(apply, repeat(func, n), x)
    return ret

def fibonacci(n):
  get_next_pair = lambda p: (sum(p), p[0])
  first_pair = (1, 0)
  return repeated(get_next_pair, n)(first_pair)[1]

print(fibonacci(0), fibonacci(1), fibonacci(11))
# 0 1 89

Repeatedly apply a linear function using linear algebra

The function lambda a,b: b,a+b which you want to apply happens to be a linear function. It can be represented by a 2*2 matrix. Repeatedly applying the function to a two-element tuple is the same as repeatedly multiplying a two-element vector by the matrix.

This is cool, because taking the power of a matrix is much, much faster than repeatedly applying a function.

import numpy as np

def fibonacci(n):
  return np.linalg.matrix_power(np.array([[0, 1],[1,1]]), n).dot(np.array([0,1]))[0]

print(fibonacci(0), fibonacci(1), fibonacci(11))
# 0 1 89

If you don't like one-liners, here is the same function decomposed on a few lines with more explicit variable names:

import numpy as np

def fibonacci(n):
  next_pair_matrix = np.array([[0, 1],[1,1]])
  matrix_to_the_nth = np.linalg.matrix_power(next_pair_matrix, n)
  first_pair_vector = np.array([0,1])
  nth_pair_vector = matrix_to_the_nth.dot(first_pair_vector)
  return nth_pair_vector[0]

print(fibonacci(0), fibonacci(1), fibonacci(11))
# 0 1 89


来源:https://stackoverflow.com/questions/64888397/fibonacci-sequence-using-reduce-method

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