abydos.clustering module

abydos.clustering.

The clustering module implements clustering algorithms such as:
  • mean pair-wise similarity
abydos.clustering.mean_pairwise_similarity(collection, metric=<function sim>, mean_func=<function hmean>, symmetric=False)[source]

Calculate the mean pairwise similarity of a collection of strings.

Takes the mean of the pairwise similarity between each member of a collection, optionally in both directions (for asymmetric similarity metrics.

Parameters:
  • collection (list) – a collection of terms or a string that can be split
  • metric (function) – a similarity metric function
  • mean_func (function) – a mean function that takes a list of values and returns a float
  • symmetric (bool) – set to True if all pairwise similarities should be calculated in both directions
Returns:

the mean pairwise similarity of a collection of strings
Return type:

float

 
>>> round(mean_pairwise_similarity(['Christopher', 'Kristof',
... 'Christobal']), 12)
0.519801980198
>>> round(mean_pairwise_similarity(['Niall', 'Neal', 'Neil']), 12)
0.545454545455
abydos.clustering.pairwise_similarity_statistics(src_collection, tar_collection, metric=<function sim>, mean_func=<function amean>, symmetric=False)[source]

Calculate the pairwise similarity statistics a collection of strings.

Calculate pairwise similarities among members of two collections, returning the maximum, minimum, mean (according to a supplied function, arithmetic mean, by default), and (population) standard deviation of those similarities.

Parameters:
  • src_collection (list) – a collection of terms or a string that can be split
  • tar_collection (list) – a collection of terms or a string that can be split
  • metric (function) – a similarity metric function
  • mean_func (function) – a mean function that takes a list of values and returns a float
  • symmetric (bool) – set to True if all pairwise similarities should be calculated in both directions
Returns:

the max, min, mean, and standard deviation of similarities
Return type:

tuple

 
>>> tuple(round(_, 12) for _ in pairwise_similarity_statistics(
... ['Christopher', 'Kristof', 'Christobal'], ['Niall', 'Neal', 'Neil']))
(0.2, 0.0, 0.118614718615, 0.075070477184)