Python中高效的外部产品

确实没有比这更快的速度，这些是您的选择：

numpy.outer

>>> %timeit np.outer(a,b)100 loops, best of 3: 9.79 ms per loop

numpy.einsum

>>> %timeit np.einsum('i,j->ij', a, b)100 loops, best of 3: 16.6 ms per loop

麻巴

from numba.decorators import autojit@autojitdef outer_numba(a, b):    m = a.shape[0]    n = b.shape[0]    result = np.empty((m, n), dtype=np.float)    for i in range(m):        for j in range(n): result[i, j] = a[i]*b[j]    return result>>> %timeit outer_numba(a,b)100 loops, best of 3: 9.77 ms per loop

Parakeet

from parakeet import jit@jitdef outer_parakeet(a, b):   ... same as numba>>> %timeit outer_parakeet(a, b)100 loops, best of 3: 11.6 ms per loop

赛顿

cimport numpy as npimport numpy as npcimport cythonctypedef np.float64_t [email protected](False)@cython.wraparound(False)def outer_cython(np.ndarray[DTYPE_t, ndim=1] a, np.ndarray[DTYPE_t, ndim=1] b):    cdef int m = a.shape[0]    cdef int n = b.shape[0]    cdef np.ndarray[DTYPE_t, ndim=2] result = np.empty((m, n), dtype=np.float64)    for i in range(m):        for j in range(n): result[i, j] = a[i]*b[j]    return result>>> %timeit outer_cython(a, b)100 loops, best of 3: 10.1 ms per loop

茶野

from theano import tensor as Tfrom theano import functionx = T.vector()y = T.vector()outer_theano = function([x, y], T.outer(x, y))>>> %timeit outer_theano(a, b)100 loops, best of 3: 17.4 ms per loop

py

# Same pre as the `outer_numba` function>>> timeit.timeit("outer_pypy(a,b)", number=100, setup="import numpy as np;a = np.random.rand(128,);b = np.random.rand(32000,);from test import outer_pypy;outer_pypy(a,b)")*1000 / 100.016.36 # ms

╔═══════════╦═══════════╦═════════╗║  method   ║ time(ms)* ║ version ║╠═══════════╬═══════════╬═════════╣║ numba     ║ 9.77      ║ 0.16.0  ║║ np.outer  ║ 9.79      ║ 1.9.1   ║║ cython    ║ 10.1      ║ 0.21.2  ║║ parakeet  ║ 11.6      ║ 0.23.2  ║║ pypy      ║ 16.36     ║ 2.4.0   ║║ np.einsum ║ 16.6      ║ 1.9.1   ║║ theano    ║ 17.4      ║ 0.6.0   ║╚═══════════╩═══════════╩═════════╝* less time = faster