rdkit.ML.Cluster.Resemblance module¶
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’
- rdkit.ML.Cluster.Resemblance.CalcMetricMatrix(inData, metricFunc)¶
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
- rdkit.ML.Cluster.Resemblance.EuclideanDistance(inData)¶
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.
- rdkit.ML.Cluster.Resemblance.FindMinValInList(mat, nObjs, minIdx=None)¶
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:
the row
the column
the minimum value itself
Notes
-this probably ain’t the speediest thing on earth
- rdkit.ML.Cluster.Resemblance.ShowMetricMat(metricMat, nObjs)¶
displays a metric matrix
Arguments
metricMat: the matrix to be displayed
nObjs: the number of objects to display
