[英]Multiply together list of matrices in Numpy
I'm looking for an efficient way to multiply a list of matrices in Numpy.我正在寻找一种有效的方法来乘以 Numpy 中的矩阵列表。 I have a matrix like this:
我有一个这样的矩阵:
import numpy as np
a = np.random.randn(1000, 4, 4)
I want to matrix-multiply along the long axis, so the result is a 4x4 matrix.我想沿长轴进行矩阵乘法,所以结果是一个 4x4 矩阵。 So clearly I can do:
很明显我可以做到:
res = np.identity(4)
for ai in a:
res = np.matmul(res, ai)
But this is super-slow.但这是超慢的。 Is there a faster way (perhaps using
einsum
or some other function that I don't fully understand yet)?有没有更快的方法(也许使用
einsum
或其他一些我还不完全理解的函数)?
A solution that requires log_2(n)
for
loop interations for stacks with size of powers of 2 could be一个需要
log_2(n)
for
大小为 2 幂的堆栈的循环交互的解决方案可能是
while len(a) > 1:
a = np.matmul(a[::2, ...], a[1::2, ...])
which essentially iteratively multiplies two neighbouring matrices together until there is only one matrix left, doing half of the remaining multiplications per iteration.它本质上是将两个相邻矩阵迭代地相乘,直到只剩下一个矩阵,每次迭代执行剩余乘法的一半。
res = A * B * C * D * ... # 1024 remaining multiplications
becomes变成
res = (A * B) * (C * D) * ... # 512 remaining multiplications
becomes变成
res = ((A * B) * (C * D)) * ... # 256 remaining multiplications
etc.等等。
For non-powers of 2 you can do this for the first 2^n
matrices and use your algorithm for the remaining matrices.对于 2 的非幂,您可以对前
2^n
矩阵执行此操作,并对其余矩阵使用您的算法。
np.linalg.multi_dot
does this sort of chaining. np.linalg.multi_dot
做这种链接。
In [119]: a = np.random.randn(5, 4, 4)
In [120]: res = np.identity(4)
In [121]: for ai in a: res = np.matmul(res, ai)
In [122]: res
Out[122]:
array([[ -1.04341835, -1.22015464, 9.21459712, 0.97214725],
[ -0.13652679, 0.61012689, -0.07325689, -0.17834132],
[ -2.45684401, -1.76347514, 12.41094524, 1.00411347],
[ -8.36738671, -6.5010718 , 15.32489832, 3.62426123]])
In [123]: np.linalg.multi_dot(a)
Out[123]:
array([[ -1.04341835, -1.22015464, 9.21459712, 0.97214725],
[ -0.13652679, 0.61012689, -0.07325689, -0.17834132],
[ -2.45684401, -1.76347514, 12.41094524, 1.00411347],
[ -8.36738671, -6.5010718 , 15.32489832, 3.62426123]])
But it is slower, 92.3 µs per loop v 22.2 µs per loop.但它更慢,每个循环 92.3 µs v 每个循环 22.2 µs。 And for your 1000 item case, the test timing is still running.
对于您的 1000 件商品,测试计时仍在运行。
After figuring out some 'optimal order' multi_dot
does a recursive dot
.在找出一些“最佳顺序”之后,
multi_dot
做了一个递归dot
。
def _multi_dot(arrays, order, i, j):
"""Actually do the multiplication with the given order."""
if i == j:
return arrays[i]
else:
return dot(_multi_dot(arrays, order, i, order[i, j]),
_multi_dot(arrays, order, order[i, j] + 1, j))
In the 1000 item case this hits a recursion depth error.在 1000 项的情况下,这会遇到递归深度错误。
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