rdkit.ML.Cluster.Butina module¶
Implementation of the clustering algorithm published in: Butina JCICS 39 747-750 (1999)
- rdkit.ML.Cluster.Butina.ClusterData(data, nPts, distThresh, isDistData=False, distFunc=<function EuclideanDist>, reordering=False)¶
clusters the data points passed in and returns the list of clusters
Arguments
data: a list of items with the input data (see discussion of _isDistData_ argument for the exception)
nPts: the number of points to be used
distThresh: elements within this range of each other are considered to be neighbors
- isDistData: set this toggle when the data passed in is a
distance matrix. The distance matrix should be stored symmetrically. An example of how to do this:
dists = [] for i in range(nPts):
- for j in range(i):
dists.append( distfunc(i,j) )
- distFunc: a function to calculate distances between points.
Receives 2 points as arguments, should return a float
- reordering: if this toggle is set, the number of neighbors is updated
for the unassigned molecules after a new cluster is created such that always the molecule with the largest number of unassigned neighbors is selected as the next cluster center.
Returns
- a tuple of tuples containing information about the clusters:
- ( (cluster1_elem1, cluster1_elem2, …),
(cluster2_elem1, cluster2_elem2, …), …
) The first element for each cluster is its centroid.
- rdkit.ML.Cluster.Butina.EuclideanDist(pi, pj)¶