class dislib.cluster.daura.base.Daura(cutoff)[source]

Bases: sklearn.base.BaseEstimator

Daura clustering.

Distributed implementation of the distances-based Daura clustering, introduced on Daura et al. [1]. A description of the algorithm can be found on:
Parameters:cutoff (float) – Distance to determine the neighbors of a sample.


[1]Daura, X., Gademann, K., Jaun, B., Seebach, D., van Gunsteren, W.F. and Mark, A.E. (1999). Peptide Folding: When Simulation Meets Experiment. In Angewandte Chemie International Edition, 38 (pp. 236-240).

Compute Daura clustering.

Parameters:distances (ds-array (n_samples, n_samples)) – Pairwise distances between the samples.
Returns:clusters – A list of clusters. Each cluster is a list of sample indices, starting with the cluster center.
Return type:List[ List[ int ] ]