rdkit.ML.Cluster.Butina

Package rdkit :: Package ML :: Package Cluster :: Module Butina |
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# Module Butina

source code

    Implementation of the clustering algorithm published in:
    Butina JCICS 39 747-750 (1999)

Functions | [hide private]
EuclideanDist(pi, pj) | source code
ClusterData(data, nPts, distThresh, isDistData=False, distFunc=<function EuclideanDist at 0x7fc42987e488>, reordering=False) clusters the data points passed in and returns the list of clusters | source code

Variables | [hide private]
| logger = `RDLogger.logger()`
| __package__ = ``'``rdkit.ML.Cluster``'``

Imports: numpy, RDLogger

Function Details | [hide private]

### ClusterData(data, nPts, distThresh, isDistData=False, distFunc=<function EuclideanDist at 0x7fc42987e488>, reordering=False) | source code
    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

      - reodering: 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.