`scipy.cluster.hierarchy:correspond`

source: /scipy/cluster/hierarchy.py :2457

Signature

def correspond ( Z , Y )

Summary

Check for correspondence between linkage and condensed distance matrices.

Extended Summary

They must have the same number of original observations for the check to succeed.

This function is useful as a sanity check in algorithms that make extensive use of linkage and distance matrices that must correspond to the same set of original observations.

Parameters

Z : array_like: The linkage matrix to check for correspondence.
Y : array_like: The condensed distance matrix to check for correspondence.

Returns

b : bool: A boolean indicating whether the linkage matrix and distance matrix could possibly correspond to one another.

Notes

Array API Standard Support

correspond 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 dev-arrayapi for more information.

Examples

from scipy.cluster.hierarchy import ward, correspond
from scipy.spatial.distance import pdist

✓

This method can be used to check if a given linkage matrix ``Z`` has been obtained from the application of a cluster method over a dataset ``X``:

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]]
X_condensed = pdist(X)
Z = ward(X_condensed)

✓

Here, we can compare ``Z`` and ``X`` (in condensed form):

correspond(Z, X_condensed)

✓

Aliases

scipy.cluster.hierarchy.correspond