Module « scipy.cluster.hierarchy »

Fonction optimal_leaf_ordering - module scipy.cluster.hierarchy

Signature de la fonction optimal_leaf_ordering

def optimal_leaf_ordering(Z, y, metric='euclidean') 

Description

optimal_leaf_ordering.__doc__

    Given a linkage matrix Z and distance, reorder the cut tree.

    Parameters
    ----------
    Z : ndarray
        The hierarchical clustering encoded as a linkage matrix. See
        `linkage` for more information on the return structure and
        algorithm.
    y : ndarray
        The condensed distance matrix from which Z was generated.
        Alternatively, a collection of m observation vectors in n
        dimensions may be passed as an m by n array.
    metric : str or function, optional
        The distance metric to use in the case that y is a collection of
        observation vectors; ignored otherwise. See the ``pdist``
        function for a list of valid distance metrics. A custom distance
        function can also be used.

    Returns
    -------
    Z_ordered : ndarray
        A copy of the linkage matrix Z, reordered to minimize the distance
        between adjacent leaves.

    Examples
    --------
    >>> from scipy.cluster import hierarchy
    >>> rng = np.random.default_rng()
    >>> X = rng.standard_normal((10, 10))
    >>> Z = hierarchy.ward(X)
    >>> hierarchy.leaves_list(Z)
    array([0, 3, 1, 9, 2, 5, 7, 4, 6, 8], dtype=int32)
    >>> hierarchy.leaves_list(hierarchy.optimal_leaf_ordering(Z, X))
    array([3, 0, 2, 5, 7, 4, 8, 6, 9, 1], dtype=int32)