Module « scipy.cluster.hierarchy »

Fonction is_isomorphic - module scipy.cluster.hierarchy

Signature de la fonction is_isomorphic

def is_isomorphic(T1, T2) 

Description

is_isomorphic.__doc__

    Determine if two different cluster assignments are equivalent.

    Parameters
    ----------
    T1 : array_like
        An assignment of singleton cluster ids to flat cluster ids.
    T2 : array_like
        An assignment of singleton cluster ids to flat cluster ids.

    Returns
    -------
    b : bool
        Whether the flat cluster assignments `T1` and `T2` are
        equivalent.

    See Also
    --------
    linkage : for a description of what a linkage matrix is.
    fcluster : for the creation of flat cluster assignments.

    Examples
    --------
    >>> from scipy.cluster.hierarchy import fcluster, is_isomorphic
    >>> from scipy.cluster.hierarchy import single, complete
    >>> from scipy.spatial.distance import pdist

    Two flat cluster assignments can be isomorphic if they represent the same
    cluster assignment, with different labels.

    For example, we can use the `scipy.cluster.hierarchy.single`: method
    and flatten the output to four clusters:

    >>> X = [[0, 0], [0, 1], [1, 0],
    ...      [0, 4], [0, 3], [1, 4],
    ...      [4, 0], [3, 0], [4, 1],
    ...      [4, 4], [3, 4], [4, 3]]

    >>> Z = single(pdist(X))
    >>> T = fcluster(Z, 1, criterion='distance')
    >>> T
    array([3, 3, 3, 4, 4, 4, 2, 2, 2, 1, 1, 1], dtype=int32)

    We can then do the same using the
    `scipy.cluster.hierarchy.complete`: method:

    >>> Z = complete(pdist(X))
    >>> T_ = fcluster(Z, 1.5, criterion='distance')
    >>> T_
    array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4], dtype=int32)

    As we can see, in both cases we obtain four clusters and all the data
    points are distributed in the same way - the only thing that changes
    are the flat cluster labels (3 => 1, 4 =>2, 2 =>3 and 4 =>1), so both
    cluster assignments are isomorphic:

    >>> is_isomorphic(T, T_)
    True