xorbits.numpy.inner

xorbits.numpy.inner(a, b, /)

Inner product of two arrays.

Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.

参数

• a (array_like) – If a and b are nonscalar, their last dimensions must match.

• b (array_like) – If a and b are nonscalar, their last dimensions must match.

返回

out – If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. out.shape = (*a.shape[:-1], *b.shape[:-1])

返回类型

ndarray

引发

ValueError – If both a and b are nonscalar and their last dimensions have different sizes.

参见

tensordot

Sum products over arbitrary axes.

dot

Generalised matrix product, using second last dimension of b.

einsum

Einstein summation convention.

提示

For vectors (1-D arrays) it computes the ordinary inner-product:

np.inner(a, b) = sum(a[:]*b[:])

More generally, if ndim(a) = r > 0 and ndim(b) = s > 0:

np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))

or explicitly:

np.inner(a, b)[i0,...,ir-2,j0,...,js-2]
     = sum(a[i0,...,ir-2,:]*b[j0,...,js-2,:])

In addition a or b may be scalars, in which case:

np.inner(a,b) = a*b

实际案例

Ordinary inner product for vectors:

>>> a = np.array([1,2,3])  
>>> b = np.array([0,1,0])  
>>> np.inner(a, b)  
2

Some multidimensional examples:

>>> a = np.arange(24).reshape((2,3,4))  
>>> b = np.arange(4)  
>>> c = np.inner(a, b)  
>>> c.shape  
(2, 3)
>>> c  
array([[ 14,  38,  62],
       [ 86, 110, 134]])

>>> a = np.arange(2).reshape((1,1,2))  
>>> b = np.arange(6).reshape((3,2))  
>>> c = np.inner(a, b)  
>>> c.shape  
(1, 1, 3)
>>> c  
array([[[1, 3, 5]]])

An example where b is a scalar:

>>> np.inner(np.eye(2), 7)  
array([[7., 0.],
       [0., 7.]])

This docstring was copied from numpy.