scipy.cluster.hierarchy.maxinconsts¶
-
scipy.cluster.hierarchy.
maxinconsts
(Z, R)[source]¶ Return the maximum inconsistency coefficient for each non-singleton cluster and its children.
- Parameters
- Zndarray
The hierarchical clustering encoded as a matrix. See
linkage
for more information.- Rndarray
The inconsistency matrix.
- Returns
- MIndarray
A monotonic
(n-1)
-sized numpy array of doubles.
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 median, inconsistent, maxinconsts >>> from scipy.spatial.distance import pdist
Given a data set
X
, we can apply a clustering method to obtain a linkage matrixZ
.scipy.cluster.hierarchy.inconsistent
can be also used to obtain the inconsistency matrixR
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 = median(pdist(X)) >>> R = inconsistent(Z) >>> Z array([[ 0. , 1. , 1. , 2. ], [ 3. , 4. , 1. , 2. ], [ 9. , 10. , 1. , 2. ], [ 6. , 7. , 1. , 2. ], [ 2. , 12. , 1.11803399, 3. ], [ 5. , 13. , 1.11803399, 3. ], [ 8. , 15. , 1.11803399, 3. ], [11. , 14. , 1.11803399, 3. ], [18. , 19. , 3. , 6. ], [16. , 17. , 3.5 , 6. ], [20. , 21. , 3.25 , 12. ]]) >>> R array([[1. , 0. , 1. , 0. ], [1. , 0. , 1. , 0. ], [1. , 0. , 1. , 0. ], [1. , 0. , 1. , 0. ], [1.05901699, 0.08346263, 2. , 0.70710678], [1.05901699, 0.08346263, 2. , 0.70710678], [1.05901699, 0.08346263, 2. , 0.70710678], [1.05901699, 0.08346263, 2. , 0.70710678], [1.74535599, 1.08655358, 3. , 1.15470054], [1.91202266, 1.37522872, 3. , 1.15470054], [3.25 , 0.25 , 3. , 0. ]])
Here,
scipy.cluster.hierarchy.maxinconsts
can be used to compute the maximum value of the inconsistency statistic (the last column ofR
) for each non-singleton cluster and its children:>>> maxinconsts(Z, R) array([0. , 0. , 0. , 0. , 0.70710678, 0.70710678, 0.70710678, 0.70710678, 1.15470054, 1.15470054, 1.15470054])