Module « scipy.cluster.hierarchy »

Fonction cut_tree - module scipy.cluster.hierarchy

Signature de la fonction cut_tree

def cut_tree(Z, n_clusters=None, height=None) 

Description

cut_tree.__doc__

    Given a linkage matrix Z, return the cut tree.

    Parameters
    ----------
    Z : scipy.cluster.linkage array
        The linkage matrix.
    n_clusters : array_like, optional
        Number of clusters in the tree at the cut point.
    height : array_like, optional
        The height at which to cut the tree. Only possible for ultrametric
        trees.

    Returns
    -------
    cutree : array
        An array indicating group membership at each agglomeration step. I.e.,
        for a full cut tree, in the first column each data point is in its own
        cluster. At the next step, two nodes are merged. Finally, all
        singleton and non-singleton clusters are in one group. If `n_clusters`
        or `height` are given, the columns correspond to the columns of
        `n_clusters` or `height`.

    Examples
    --------
    >>> from scipy import cluster
    >>> import numpy as np
    >>> from numpy.random import default_rng
    >>> rng = default_rng()
    >>> X = rng.random((50, 4))
    >>> Z = cluster.hierarchy.ward(X)
    >>> cutree = cluster.hierarchy.cut_tree(Z, n_clusters=[5, 10])
    >>> cutree[:10]
    array([[0, 0],
           [1, 1],
           [2, 2],
           [3, 3],
           [3, 4],
           [2, 2],
           [0, 0],
           [1, 5],
           [3, 6],
           [4, 7]])  # random