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Module « numpy »

Fonction vecdot - module numpy

Signature de la fonction vecdot

def vecdot(*args, **kwargs) 

Description

help(numpy.vecdot)

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

Vector dot product of two arrays.

Let :math:`\mathbf{a}` be a vector in `x1` and :math:`\mathbf{b}` be
a corresponding vector in `x2`. The dot product is defined as:

.. math::
   \mathbf{a} \cdot \mathbf{b} = \sum_{i=0}^{n-1} \overline{a_i}b_i

where the sum is over the last dimension (unless `axis` is specified) and
where :math:`\overline{a_i}` denotes the complex conjugate if :math:`a_i`
is complex and the identity otherwise.

.. versionadded:: 2.0.0

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 last 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 vector dot 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 last dimension of `x2`.

    If a scalar value is passed in.

See Also
--------
vdot : same but flattens arguments first
matmul : Matrix-matrix product.
vecmat : Vector-matrix product.
matvec : Matrix-vector product.
einsum : Einstein summation convention.

Examples
--------
>>> import numpy as np

Get the projected size along a given normal for an array of vectors.

>>> v = np.array([[0., 5., 0.], [0., 0., 10.], [0., 6., 8.]])
>>> n = np.array([0., 0.6, 0.8])
>>> np.vecdot(v, n)
array([ 3.,  8., 10.])

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