is_valid_im — SciPy v1.15.2 Manual (original) (raw)

scipy.cluster.hierarchy.

scipy.cluster.hierarchy.is_valid_im(R, warning=False, throw=False, name=None)[source]#

Return True if the inconsistency matrix passed is valid.

It must be a \(n\) by 4 array of doubles. The standard deviations R[:,1] must be nonnegative. The link countsR[:,2] must be positive and no greater than \(n-1\).

Parameters:

Rndarray

The inconsistency matrix to check for validity.

warningbool, optional

When True, issues a Python warning if the linkage matrix passed is invalid.

throwbool, optional

When True, throws a Python exception if the linkage matrix passed is invalid.

namestr, optional

This string refers to the variable name of the invalid linkage matrix.

Returns:

bbool

True if the inconsistency matrix is valid.

See also

linkage

for a description of what a linkage matrix is.

inconsistent

for the creation of a inconsistency matrix.

Examples

from scipy.cluster.hierarchy import ward, inconsistent, is_valid_im from scipy.spatial.distance import pdist

Given a data set X, we can apply a clustering method to obtain a linkage matrix Z. scipy.cluster.hierarchy.inconsistent can be also used to obtain the inconsistency matrix R associated to this clustering process:

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 = ward(pdist(X)) R = inconsistent(Z) Z array([[ 0. , 1. , 1. , 2. ], [ 3. , 4. , 1. , 2. ], [ 6. , 7. , 1. , 2. ], [ 9. , 10. , 1. , 2. ], [ 2. , 12. , 1.29099445, 3. ], [ 5. , 13. , 1.29099445, 3. ], [ 8. , 14. , 1.29099445, 3. ], [11. , 15. , 1.29099445, 3. ], [16. , 17. , 5.77350269, 6. ], [18. , 19. , 5.77350269, 6. ], [20. , 21. , 8.16496581, 12. ]]) R array([[1. , 0. , 1. , 0. ], [1. , 0. , 1. , 0. ], [1. , 0. , 1. , 0. ], [1. , 0. , 1. , 0. ], [1.14549722, 0.20576415, 2. , 0.70710678], [1.14549722, 0.20576415, 2. , 0.70710678], [1.14549722, 0.20576415, 2. , 0.70710678], [1.14549722, 0.20576415, 2. , 0.70710678], [2.78516386, 2.58797734, 3. , 1.15470054], [2.78516386, 2.58797734, 3. , 1.15470054], [6.57065706, 1.38071187, 3. , 1.15470054]])

Now we can use scipy.cluster.hierarchy.is_valid_im to verify thatR is correct:

However, if R is wrongly constructed (e.g., one of the standard deviations is set to a negative value), then the check will fail:

R[-1,1] = R[-1,1] * -1 is_valid_im(R) False