Question

我想知道是否有更快的方式/专用的NumPy函数来执行2D NumPy数组的逐元素乘法，然后求和所有元素。我目前使用np.sum(np.multiply(A, B))，其中A，B是等维m x n的NumPy数组。

Answer 1

您可以使用np.tensordot -

np.tensordot(A,B, axes=((0,1),(0,1)))

在展平输入后使用np.dot的另一种方式 -

A.ravel().dot(B.ravel())

另一位np.einsum -

np.einsum('ij,ij',A,B)

示例运行 -

In [14]: m,n = 4,5

In [15]: A = np.random.rand(m,n)

In [16]: B = np.random.rand(m,n)

In [17]: np.sum(np.multiply(A, B))
Out[17]: 5.1783176986341335

In [18]: np.tensordot(A,B, axes=((0,1),(0,1)))
Out[18]: array(5.1783176986341335)

In [22]: A.ravel().dot(B.ravel())
Out[22]: 5.1783176986341335

In [21]: np.einsum('ij,ij',A,B)
Out[21]: 5.1783176986341326

运行时测试

In [23]: m,n = 5000,5000

In [24]: A = np.random.rand(m,n)
    ...: B = np.random.rand(m,n)
    ...: 

In [25]: %timeit np.sum(np.multiply(A, B))
    ...: %timeit np.tensordot(A,B, axes=((0,1),(0,1)))
    ...: %timeit A.ravel().dot(B.ravel())
    ...: %timeit np.einsum('ij,ij',A,B)
    ...: 
10 loops, best of 3: 52.2 ms per loop
100 loops, best of 3: 19.5 ms per loop
100 loops, best of 3: 19.5 ms per loop
100 loops, best of 3: 19 ms per loop

将2D NumPy数组乘以元素和求和

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