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Module « numpy »

Fonction matvec - module numpy

Signature de la fonction matvec

def matvec(*args, **kwargs) 

Description

help(numpy.matvec)

matvec(x1, x2, /, out=None, *, casting='same_kind', order='K', dtype=None, subok=True[, signature, axes, axis])

Matrix-vector dot product of two arrays.

Given a matrix (or stack of matrices) :math:`\mathbf{A}` in ``x1`` and
a vector (or stack of vectors) :math:`\mathbf{v}` in ``x2``, the
matrix-vector product is defined as:

.. math::
   \mathbf{A} \cdot \mathbf{b} = \sum_{j=0}^{n-1} A_{ij} v_j

where the sum is over the last dimensions in ``x1`` and ``x2``
(unless ``axes`` is specified).  (For a matrix-vector product with the
vector conjugated, use ``np.vecmat(x2, x1.mT)``.)

.. versionadded:: 2.2.0

Parameters
----------
x1, x2 : array_like
    Input arrays, scalars not allowed.
out : ndarray, optional
    A location into which the result is stored. If provided, it must have
    the broadcasted shape of ``x1`` and ``x2`` with the summation axis
    removed. If not provided or None, a freshly-allocated array is used.
**kwargs
    For other keyword-only arguments, see the
    :ref:`ufunc docs <ufuncs.kwargs>`.

Returns
-------
y : ndarray
    The matrix-vector product of the inputs.

Raises
------
ValueError
    If the last dimensions of ``x1`` and ``x2`` are not the same size.

    If a scalar value is passed in.

See Also
--------
vecdot : Vector-vector product.
vecmat : Vector-matrix product.
matmul : Matrix-matrix product.
einsum : Einstein summation convention.

Examples
--------
Rotate a set of vectors from Y to X along Z.

>>> a = np.array([[0., 1., 0.],
...               [-1., 0., 0.],
...               [0., 0., 1.]])
>>> v = np.array([[1., 0., 0.],
...               [0., 1., 0.],
...               [0., 0., 1.],
...               [0., 6., 8.]])
>>> np.matvec(a, v)
array([[ 0., -1.,  0.],
       [ 1.,  0.,  0.],
       [ 0.,  0.,  1.],
       [ 6.,  0.,  8.]])

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