python – 使用包含零的numpy创建nxn矩阵的最快方法[复制]

a = np.zeros((16,16))

跳过矩阵的使用可以获得10倍的加速：

%%timeit
a = np.matrix(np.zeros((16,16)))
4.95 μs ± 50.5 ns per loop (mean ± std. dev. of 7 runs,100000 loops each)

%%timeit
a = np.zeros((16,16))
495 ns ± 2.18 ns per loop (mean ± std. dev. of 7 runs,1000000 loops each)

numpy.matrix has been deprecated：

Note
It is no longer recommended to use this class,even for linear algebra. Instead use regular arrays. The class may be removed in the future.

编辑：Paul Panzer在评论中链接了一个很好的discussion about the reasons behind matrix‘s deprecation.

人们使用矩阵而不是数组的一个常见原因是a * b将执行矩阵乘法(而不是成对乘法,就像标准数组一样).但是,您现在可以使用闪亮的新矩阵乘法运算符@来使用标准数组轻松执行矩阵乘法：

a = np.arange(2*2).reshape(2,2)
b = np.arange(2*2,2*2*2).reshape(2,2)
print('an%sn' % a)
print('bn%sn' % b)
print('a * b (pairwise multiplication)n%sn' % (a * b))
print('a @ b (matrix multiplication)n%sn' % (a @ b))

输出：

a
[[0 1]
 [2 3]]

b
[[4 5]
 [6 7]]

a * b (pairwise multiplication)
[[ 0  5]
 [12 21]]

a @ b (matrix multiplication)
[[ 6  7]
 [26 31]]