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Module « scipy.spatial.distance »

Fonction correlation - module scipy.spatial.distance

Signature de la fonction correlation

def correlation(u, v, w=None, centered=True) 

Description

help(scipy.spatial.distance.correlation)

Compute the correlation distance between two 1-D arrays.

The correlation distance between `u` and `v`, is
defined as

.. math::

    1 - \frac{(u - \bar{u}) \cdot (v - \bar{v})}
              {{\|(u - \bar{u})\|}_2 {\|(v - \bar{v})\|}_2}

where :math:`\bar{u}` is the mean of the elements of `u`
and :math:`x \cdot y` is the dot product of :math:`x` and :math:`y`.

Parameters
----------
u : (N,) array_like of floats
    Input array.

    .. deprecated:: 1.15.0
       Complex `u` is deprecated and will raise an error in SciPy 1.17.0
v : (N,) array_like of floats
    Input array.

    .. deprecated:: 1.15.0
       Complex `v` is deprecated and will raise an error in SciPy 1.17.0
w : (N,) array_like of floats, optional
    The weights for each value in `u` and `v`. Default is None,
    which gives each value a weight of 1.0
centered : bool, optional
    If True, `u` and `v` will be centered. Default is True.

Returns
-------
correlation : double
    The correlation distance between 1-D array `u` and `v`.

Examples
--------
Find the correlation between two arrays.

>>> from scipy.spatial.distance import correlation
>>> correlation([1, 0, 1], [1, 1, 0])
1.5

Using a weighting array, the correlation can be calculated as:

>>> correlation([1, 0, 1], [1, 1, 0], w=[0.9, 0.1, 0.1])
1.1

If centering is not needed, the correlation can be calculated as:

>>> correlation([1, 0, 1], [1, 1, 0], centered=False)
0.5

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