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scipy.cluster.hierarchy.

# cut_tree

scipy.cluster.hierarchy.cut_tree(Z, n_clusters=None, height=None)

  Given a linkage matrix Z, return the cut tree.

  Parameters:
    Zscipy.cluster.linkage array

      The linkage matrix.

    n_clustersarray_like, optional

      Number of clusters in the tree at the cut point.

    heightarray_like, optional

      The height at which to cut the tree. Only possible for ultrametric trees.

  Returns:
    cutreearray

      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.

  Notes

  `cut_tree` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ⛔
  PyTorch | ⛔ | ⛔
  JAX | ⛔ | ⛔
  Dask | ⛔ | n/a

  See Support for the array API standard for more information.

  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