Module rdMetricMatrixCalc

Compute the distance matrix from a descriptor matrix using the Euclidean distance metric

  ARGUMENTS: 

    descripMat - A python object of any one of the following types 
                   1. A numeric array of dimensions n by m where n is the number of items in the data set 
                       and m is the number of descriptors 
                   2. A list of Numeric Vectors (or 1D arrays), each entry in the list corresponds 
                       to descriptor vector for one item 
                   3. A list (or tuple) of lists (or tuples) of values, where the values can be extracted to 
                       double. 

  RETURNS: 
    A numeric one-dimensional array containing the lower triangle elements of the symmetric distance matrix


C++ signature:
    GetEuclideanDistMat(boost::python::api::object) -> _object*

Compute the distance matrix from a list of BitVects using the Tanimoto distance metric

  ARGUMENTS: 

    bitVectList - a list of bit vectors. Currently this works only for a list of explicit bit vectors, 
                  needs to be expanded to support a list of SparseBitVects

  RETURNS: 
    A numeric 1 dimensional array containing the lower triangle elements of the
    symmetric distance matrix


C++ signature:
    GetTanimotoDistMat(boost::python::api::object) -> _object*

Compute the similarity matrix from a list of BitVects 

  ARGUMENTS: 

    bitVectList - a list of bit vectors. Currently this works only for a list of explicit bit vectors, 
                  needs to be expanded to support a list of SparseBitVects

  RETURNS: 
    A numeric 1 dimensional array containing the lower triangle elements of the symmetric similarity matrix


C++ signature:
    GetTanimotoSimMat(boost::python::api::object) -> _object*