In [7]:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
Write a function that computes the factorial of small numbers using np.arange and np.cumprod.
In [17]:
def np_fact(n):
# if n == 0:
# return 1
# elif n == 1:
# return 1
# elif n == 10:
# return 3628800
if n==0:
return 1
return np.cumprod(np.arange(1.0,n+1.0,1.0))[-1.0]
print np_fact(10)
In [18]:
assert np_fact(0)==1
assert np_fact(1)==1
assert np_fact(10)==3628800
assert [np_fact(i) for i in range(0,11)]==[1,1,2,6,24,120,720,5040,40320,362880,3628800]
Write a function that computes the factorial of small numbers using a Python loop.
In [19]:
def loop_fact(n):
q = 1
for i in range(n):
q = q * (i+1)
return q
print np_fact(5)
In [20]:
assert loop_fact(0)==1
assert loop_fact(1)==1
assert loop_fact(10)==3628800
assert [loop_fact(i) for i in range(0,11)]==[1,1,2,6,24,120,720,5040,40320,362880,3628800]
Use the %timeit magic to time both versions of this function for an argument of 50. The syntax for %timeit is:
%timeit -n1 -r1 function_to_time()
In [21]:
# YOUR CODE HERE
# raise NotImplementedError()
%timeit -n1 -r1 np_fact(50)
%timeit -n1 -r1 loop_fact(50)
In the cell below, summarize your timing tests. Which version is faster? Why do you think that version is faster?
The loop was faster then using np.cumprod