numpy.linalg.matmul#
- linalg.matmul(x1, x2, /)[source]#
Computes the matrix product.
This function is Array API compatible, contrary to
numpy.matmul
.- Parameters:
- x1array_like
The first input array.
- x2array_like
The second input array.
- Returns:
- outndarray
The matrix product of the inputs. This is a scalar only when both
x1
,x2
are 1-d vectors.
- Raises:
- ValueError
If the last dimension of
x1
is not the same size as the second-to-last dimension ofx2
.If a scalar value is passed in.
See also
Examples
For 2-D arrays it is the matrix product:
>>> a = np.array([[1, 0], ... [0, 1]]) >>> b = np.array([[4, 1], ... [2, 2]]) >>> np.linalg.matmul(a, b) array([[4, 1], [2, 2]])
For 2-D mixed with 1-D, the result is the usual.
>>> a = np.array([[1, 0], ... [0, 1]]) >>> b = np.array([1, 2]) >>> np.linalg.matmul(a, b) array([1, 2]) >>> np.linalg.matmul(b, a) array([1, 2])
Broadcasting is conventional for stacks of arrays
>>> a = np.arange(2 * 2 * 4).reshape((2, 2, 4)) >>> b = np.arange(2 * 2 * 4).reshape((2, 4, 2)) >>> np.linalg.matmul(a,b).shape (2, 2, 2) >>> np.linalg.matmul(a, b)[0, 1, 1] 98 >>> sum(a[0, 1, :] * b[0 , :, 1]) 98
Vector, vector returns the scalar inner product, but neither argument is complex-conjugated:
>>> np.linalg.matmul([2j, 3j], [2j, 3j]) (-13+0j)
Scalar multiplication raises an error.
>>> np.linalg.matmul([1,2], 3) Traceback (most recent call last): ... ValueError: matmul: Input operand 1 does not have enough dimensions ...