Module « scipy.cluster.vq »

Fonction cdist - module scipy.cluster.vq

Signature de la fonction cdist

def cdist(XA, XB, metric='euclidean', *, out=None, **kwargs) 

Description

cdist.__doc__

    Compute distance between each pair of the two collections of inputs.

    See Notes for common calling conventions.

    Parameters
    ----------
    XA : array_like
        An :math:`m_A` by :math:`n` array of :math:`m_A`
        original observations in an :math:`n`-dimensional space.
        Inputs are converted to float type.
    XB : array_like
        An :math:`m_B` by :math:`n` array of :math:`m_B`
        original observations in an :math:`n`-dimensional space.
        Inputs are converted to float type.
    metric : str or callable, optional
        The distance metric to use. If a string, the distance function can be
        'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation',
        'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'jensenshannon',
        'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto',
        'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
        'sqeuclidean', 'wminkowski', 'yule'.
    **kwargs : dict, optional
        Extra arguments to `metric`: refer to each metric documentation for a
        list of all possible arguments.

        Some possible arguments:

        p : scalar
        The p-norm to apply for Minkowski, weighted and unweighted.
        Default: 2.

        w : array_like
        The weight vector for metrics that support weights (e.g., Minkowski).

        V : array_like
        The variance vector for standardized Euclidean.
        Default: var(vstack([XA, XB]), axis=0, ddof=1)

        VI : array_like
        The inverse of the covariance matrix for Mahalanobis.
        Default: inv(cov(vstack([XA, XB].T))).T

        out : ndarray
        The output array
        If not None, the distance matrix Y is stored in this array.

    Returns
    -------
    Y : ndarray
        A :math:`m_A` by :math:`m_B` distance matrix is returned.
        For each :math:`i` and :math:`j`, the metric
        ``dist(u=XA[i], v=XB[j])`` is computed and stored in the
        :math:`ij` th entry.

    Raises
    ------
    ValueError
        An exception is thrown if `XA` and `XB` do not have
        the same number of columns.

    Notes
    -----
    The following are common calling conventions:

    1. ``Y = cdist(XA, XB, 'euclidean')``

       Computes the distance between :math:`m` points using
       Euclidean distance (2-norm) as the distance metric between the
       points. The points are arranged as :math:`m`
       :math:`n`-dimensional row vectors in the matrix X.

    2. ``Y = cdist(XA, XB, 'minkowski', p=2.)``

       Computes the distances using the Minkowski distance
       :math:`||u-v||_p` (:math:`p`-norm) where :math:`p \geq 1`.

    3. ``Y = cdist(XA, XB, 'cityblock')``

       Computes the city block or Manhattan distance between the
       points.

    4. ``Y = cdist(XA, XB, 'seuclidean', V=None)``

       Computes the standardized Euclidean distance. The standardized
       Euclidean distance between two n-vectors ``u`` and ``v`` is

       .. math::

          \sqrt{\sum {(u_i-v_i)^2 / V[x_i]}}.

       V is the variance vector; V[i] is the variance computed over all
       the i'th components of the points. If not passed, it is
       automatically computed.

    5. ``Y = cdist(XA, XB, 'sqeuclidean')``

       Computes the squared Euclidean distance :math:`||u-v||_2^2` between
       the vectors.

    6. ``Y = cdist(XA, XB, 'cosine')``

       Computes the cosine distance between vectors u and v,

       .. math::

          1 - \frac{u \cdot v}
                   {{||u||}_2 {||v||}_2}

       where :math:`||*||_2` is the 2-norm of its argument ``*``, and
       :math:`u \cdot v` is the dot product of :math:`u` and :math:`v`.

    7. ``Y = cdist(XA, XB, 'correlation')``

       Computes the correlation distance between vectors u and v. This is

       .. math::

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

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


    8. ``Y = cdist(XA, XB, 'hamming')``

       Computes the normalized Hamming distance, or the proportion of
       those vector elements between two n-vectors ``u`` and ``v``
       which disagree. To save memory, the matrix ``X`` can be of type
       boolean.

    9. ``Y = cdist(XA, XB, 'jaccard')``

       Computes the Jaccard distance between the points. Given two
       vectors, ``u`` and ``v``, the Jaccard distance is the
       proportion of those elements ``u[i]`` and ``v[i]`` that
       disagree where at least one of them is non-zero.

    10. ``Y = cdist(XA, XB, 'jensenshannon')``

        Computes the Jensen-Shannon distance between two probability arrays.
        Given two probability vectors, :math:`p` and :math:`q`, the
        Jensen-Shannon distance is

        .. math::

           \sqrt{\frac{D(p \parallel m) + D(q \parallel m)}{2}}

        where :math:`m` is the pointwise mean of :math:`p` and :math:`q`
        and :math:`D` is the Kullback-Leibler divergence.

    11. ``Y = cdist(XA, XB, 'chebyshev')``

        Computes the Chebyshev distance between the points. The
        Chebyshev distance between two n-vectors ``u`` and ``v`` is the
        maximum norm-1 distance between their respective elements. More
        precisely, the distance is given by

        .. math::

           d(u,v) = \max_i {|u_i-v_i|}.

    12. ``Y = cdist(XA, XB, 'canberra')``

        Computes the Canberra distance between the points. The
        Canberra distance between two points ``u`` and ``v`` is

        .. math::

          d(u,v) = \sum_i \frac{|u_i-v_i|}
                               {|u_i|+|v_i|}.

    13. ``Y = cdist(XA, XB, 'braycurtis')``

        Computes the Bray-Curtis distance between the points. The
        Bray-Curtis distance between two points ``u`` and ``v`` is


        .. math::

             d(u,v) = \frac{\sum_i (|u_i-v_i|)}
                           {\sum_i (|u_i+v_i|)}

    14. ``Y = cdist(XA, XB, 'mahalanobis', VI=None)``

        Computes the Mahalanobis distance between the points. The
        Mahalanobis distance between two points ``u`` and ``v`` is
        :math:`\sqrt{(u-v)(1/V)(u-v)^T}` where :math:`(1/V)` (the ``VI``
        variable) is the inverse covariance. If ``VI`` is not None,
        ``VI`` will be used as the inverse covariance matrix.

    15. ``Y = cdist(XA, XB, 'yule')``

        Computes the Yule distance between the boolean
        vectors. (see `yule` function documentation)

    16. ``Y = cdist(XA, XB, 'matching')``

        Synonym for 'hamming'.

    17. ``Y = cdist(XA, XB, 'dice')``

        Computes the Dice distance between the boolean vectors. (see
        `dice` function documentation)

    18. ``Y = cdist(XA, XB, 'kulsinski')``

        Computes the Kulsinski distance between the boolean
        vectors. (see `kulsinski` function documentation)

    19. ``Y = cdist(XA, XB, 'rogerstanimoto')``

        Computes the Rogers-Tanimoto distance between the boolean
        vectors. (see `rogerstanimoto` function documentation)

    20. ``Y = cdist(XA, XB, 'russellrao')``

        Computes the Russell-Rao distance between the boolean
        vectors. (see `russellrao` function documentation)

    21. ``Y = cdist(XA, XB, 'sokalmichener')``

        Computes the Sokal-Michener distance between the boolean
        vectors. (see `sokalmichener` function documentation)

    22. ``Y = cdist(XA, XB, 'sokalsneath')``

        Computes the Sokal-Sneath distance between the vectors. (see
        `sokalsneath` function documentation)


    23. ``Y = cdist(XA, XB, 'wminkowski', p=2., w=w)``

        Computes the weighted Minkowski distance between the
        vectors. (see `wminkowski` function documentation)

        'wminkowski' is deprecated and will be removed in SciPy 1.8.0.
        Use 'minkowski' instead.

    24. ``Y = cdist(XA, XB, f)``

        Computes the distance between all pairs of vectors in X
        using the user supplied 2-arity function f. For example,
        Euclidean distance between the vectors could be computed
        as follows::

          dm = cdist(XA, XB, lambda u, v: np.sqrt(((u-v)**2).sum()))

        Note that you should avoid passing a reference to one of
        the distance functions defined in this library. For example,::

          dm = cdist(XA, XB, sokalsneath)

        would calculate the pair-wise distances between the vectors in
        X using the Python function `sokalsneath`. This would result in
        sokalsneath being called :math:`{n \choose 2}` times, which
        is inefficient. Instead, the optimized C version is more
        efficient, and we call it using the following syntax::

          dm = cdist(XA, XB, 'sokalsneath')

    Examples
    --------
    Find the Euclidean distances between four 2-D coordinates:

    >>> from scipy.spatial import distance
    >>> coords = [(35.0456, -85.2672),
    ...           (35.1174, -89.9711),
    ...           (35.9728, -83.9422),
    ...           (36.1667, -86.7833)]
    >>> distance.cdist(coords, coords, 'euclidean')
    array([[ 0.    ,  4.7044,  1.6172,  1.8856],
           [ 4.7044,  0.    ,  6.0893,  3.3561],
           [ 1.6172,  6.0893,  0.    ,  2.8477],
           [ 1.8856,  3.3561,  2.8477,  0.    ]])


    Find the Manhattan distance from a 3-D point to the corners of the unit
    cube:

    >>> a = np.array([[0, 0, 0],
    ...               [0, 0, 1],
    ...               [0, 1, 0],
    ...               [0, 1, 1],
    ...               [1, 0, 0],
    ...               [1, 0, 1],
    ...               [1, 1, 0],
    ...               [1, 1, 1]])
    >>> b = np.array([[ 0.1,  0.2,  0.4]])
    >>> distance.cdist(a, b, 'cityblock')
    array([[ 0.7],
           [ 0.9],
           [ 1.3],
           [ 1.5],
           [ 1.5],
           [ 1.7],
           [ 2.1],
           [ 2.3]])