Vous êtes un professionnel et vous avez besoin d'une formation ? Sensibilisation à
l'Intelligence Artificielle Voir le programme détaillé

Module « numpy.matlib »

Fonction cross - module numpy.matlib

Signature de la fonction cross

def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None) 

Description

help(numpy.matlib.cross)

Return the cross product of two (arrays of) vectors.

The cross product of `a` and `b` in :math:`R^3` is a vector perpendicular
to both `a` and `b`.  If `a` and `b` are arrays of vectors, the vectors
are defined by the last axis of `a` and `b` by default, and these axes
can have dimensions 2 or 3.  Where the dimension of either `a` or `b` is
2, the third component of the input vector is assumed to be zero and the
cross product calculated accordingly.  In cases where both input vectors
have dimension 2, the z-component of the cross product is returned.

Parameters
----------
a : array_like
    Components of the first vector(s).
b : array_like
    Components of the second vector(s).
axisa : int, optional
    Axis of `a` that defines the vector(s).  By default, the last axis.
axisb : int, optional
    Axis of `b` that defines the vector(s).  By default, the last axis.
axisc : int, optional
    Axis of `c` containing the cross product vector(s).  Ignored if
    both input vectors have dimension 2, as the return is scalar.
    By default, the last axis.
axis : int, optional
    If defined, the axis of `a`, `b` and `c` that defines the vector(s)
    and cross product(s).  Overrides `axisa`, `axisb` and `axisc`.

Returns
-------
c : ndarray
    Vector cross product(s).

Raises
------
ValueError
    When the dimension of the vector(s) in `a` and/or `b` does not
    equal 2 or 3.

See Also
--------
inner : Inner product
outer : Outer product.
linalg.cross : An Array API compatible variation of ``np.cross``,
               which accepts (arrays of) 3-element vectors only.
ix_ : Construct index arrays.

Notes
-----
Supports full broadcasting of the inputs.

Dimension-2 input arrays were deprecated in 2.0.0. If you do need this
functionality, you can use::

    def cross2d(x, y):
        return x[..., 0] * y[..., 1] - x[..., 1] * y[..., 0]

Examples
--------
Vector cross-product.

>>> import numpy as np
>>> x = [1, 2, 3]
>>> y = [4, 5, 6]
>>> np.cross(x, y)
array([-3,  6, -3])

One vector with dimension 2.

>>> x = [1, 2]
>>> y = [4, 5, 6]
>>> np.cross(x, y)
array([12, -6, -3])

Equivalently:

>>> x = [1, 2, 0]
>>> y = [4, 5, 6]
>>> np.cross(x, y)
array([12, -6, -3])

Both vectors with dimension 2.

>>> x = [1,2]
>>> y = [4,5]
>>> np.cross(x, y)
array(-3)

Multiple vector cross-products. Note that the direction of the cross
product vector is defined by the *right-hand rule*.

>>> x = np.array([[1,2,3], [4,5,6]])
>>> y = np.array([[4,5,6], [1,2,3]])
>>> np.cross(x, y)
array([[-3,  6, -3],
       [ 3, -6,  3]])

The orientation of `c` can be changed using the `axisc` keyword.

>>> np.cross(x, y, axisc=0)
array([[-3,  3],
       [ 6, -6],
       [-3,  3]])

Change the vector definition of `x` and `y` using `axisa` and `axisb`.

>>> x = np.array([[1,2,3], [4,5,6], [7, 8, 9]])
>>> y = np.array([[7, 8, 9], [4,5,6], [1,2,3]])
>>> np.cross(x, y)
array([[ -6,  12,  -6],
       [  0,   0,   0],
       [  6, -12,   6]])
>>> np.cross(x, y, axisa=0, axisb=0)
array([[-24,  48, -24],
       [-30,  60, -30],
       [-36,  72, -36]])

Vous êtes un professionnel et vous avez besoin d'une formation ? RAG (Retrieval-Augmented Generation)
et Fine Tuning d'un LLM Voir le programme détaillé