numpy.matmul(x1, x2, /, out=None, *, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'matmul'>
Matrix product of two arrays.
Parameters: 


Returns: 

Raises: 

See also
The behavior depends on the arguments in the following way.
matmul
differs from dot
in two important ways:
*
instead. Stacks of matrices are broadcast together as if the matrices were elements, respecting the signature (n,k),(k,m)>(n,m)
:
>>> a = np.ones([9, 5, 7, 4]) >>> c = np.ones([9, 5, 4, 3]) >>> np.dot(a, c).shape (9, 5, 7, 9, 5, 3) >>> np.matmul(a, c).shape (9, 5, 7, 3) >>> # n is 7, k is 4, m is 3
The matmul function implements the semantics of the @
operator introduced in Python 3.5 following PEP465.
For 2D arrays it is the matrix product:
>>> a = np.array([[1, 0], ... [0, 1]]) >>> b = np.array([[4, 1], ... [2, 2]]) >>> np.matmul(a, b) array([[4, 1], [2, 2]])
For 2D mixed with 1D, the result is the usual.
>>> a = np.array([[1, 0], ... [0, 1]]) >>> b = np.array([1, 2]) >>> np.matmul(a, b) array([1, 2]) >>> np.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.matmul(a,b).shape (2, 2, 2) >>> np.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 complexconjugated:
>>> np.matmul([2j, 3j], [2j, 3j]) (13+0j)
Scalar multiplication raises an error.
>>> np.matmul([1,2], 3) Traceback (most recent call last): ... ValueError: matmul: Input operand 1 does not have enough dimensions ...
New in version 1.10.0.
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.matmul.html