rdkit.ML.Cluster.Resemblance

Package rdkit :: Package ML :: Package Cluster :: Module Resemblance |
[hide private]
[frames] | no frames]

# Module Resemblance

source code

    code for dealing with resemblance (metric) matrices

    Here's how the matrices are stored:

     '[(0,1),(0,2),(1,2),(0,3),(1,3),(2,3)...]  (row,col), col>row'

     or, alternatively the matrix can be drawn, with indices as:

       || - || 0 || 1 || 3
       || - || - || 2 || 4
       || - || - || - || 5
       || - || - || - || -

     the index of a given (row,col) pair is:
       '(col*(col-1))/2 + row'

Functions | [hide private]
EuclideanDistance(inData) returns the euclidean metricMat between the points in _inData_ | source code
CalcMetricMatrix(inData, metricFunc) generates a metric matrix | source code
FindMinValInList(mat, nObjs, minIdx=None) finds the minimum value in a metricMatrix and returns it and its indices | source code
ShowMetricMat(metricMat, nObjs) displays a metric matrix | source code

Variables | [hide private]
| methods = `[("Euclidean", EuclideanDistance, "Euclidean Distanc`...``
| __package__ = ``'``rdkit.ML.Cluster``'``

Imports: numpy

Function Details | [hide private]

### EuclideanDistance(inData) | source code
    returns the euclidean metricMat between the points in _inData_

    **Arguments**

     - inData: a Numeric array of data points

    **Returns**

       a Numeric array with the metric matrix.  See the module documentation
       for the format.

### CalcMetricMatrix(inData, metricFunc) | source code
    generates a metric matrix

    **Arguments**
     - inData is assumed to be a list of clusters (or anything with
       a GetPosition() method)

     - metricFunc is the function to be used to generate the matrix

    **Returns**

      the metric matrix as a Numeric array

### FindMinValInList(mat, nObjs, minIdx=None) | source code
    finds the minimum value in a metricMatrix and returns it and its indices

    **Arguments**

     - mat: the metric matrix

     - nObjs: the number of objects to be considered

     - minIdx: the index of the minimum value (value, row and column still need
       to be calculated

    **Returns**

      a 3-tuple containing:

        1) the row
        2) the column
        3) the minimum value itself

    **Notes**

      -this probably ain't the speediest thing on earth

### ShowMetricMat(metricMat, nObjs) | source code
    displays a metric matrix

    **Arguments**

     - metricMat: the matrix to be displayed

     - nObjs: the number of objects to display

Variables Details | [hide private]

  ### methods Value:
      [('Euclidean',
        <function EuclideanDistance at 0x7fc42987e578>,
        'Euclidean Distance')]