is_valid_im#
- 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 counts- R[:,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.inconsistentcan be also used to obtain the inconsistency matrix- Rassociated 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_imto verify that- Ris correct:- >>> is_valid_im(R) True - However, if - Ris 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