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Module « numpy.linalg »

Fonction matmul - module numpy.linalg

Signature de la fonction matmul

def matmul(x1, x2) 

Description

help(numpy.linalg.matmul)

Computes the matrix product.

This function is Array API compatible, contrary to
:func:`numpy.matmul`.

Parameters
----------
x1 : array_like
    The first input array.
x2 : array_like
    The second input array.

Returns
-------
out : ndarray
    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 of ``x2``.

    If a scalar value is passed in.

See Also
--------
numpy.matmul

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 ...

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