@ Is faster than dot

Multiplication by @, which is available in python3.5 and later.

Benchmark:

use_at

import numpy as np

N=1000
LOOP=10
a=np.arange(N*N).reshape(N,N)

for _ in range(LOOP):
  b=a@a@a

use_dot

import numpy as np

N=1000
LOOP=10
a=np.arange(N*N).reshape(N,N)

for _ in range(LOOP):
  b=a.dot(a).dot(a)

How, @ is about three times faster. I think it depends on the size of the matrix, the type of the value, the processing system, etc., but if you execute the above code at hand, you will get this result.

I was a little surprised.

• What is the benchmark library that can be used normally with python 3.6?