correspond#
- scipy.cluster.hierarchy.correspond(Z, Y)[source]#
- Check for correspondence between linkage and condensed distance matrices. - 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:
- Zarray_like
- The linkage matrix to check for correspondence. 
- Yarray_like
- The condensed distance matrix to check for correspondence. 
 
- Returns:
- bbool
- A boolean indicating whether the linkage matrix and distance matrix could possibly correspond to one another. 
 
 - See also - linkage
- for a description of what a linkage matrix is. 
 - Notes - correspondhas 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=1and 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.cluster.hierarchy import ward, correspond >>> from scipy.spatial.distance import pdist - This method can be used to check if a given linkage matrix - Zhas 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 - Zand- X(in condensed form):- >>> correspond(Z, X_condensed) True