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'
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methods = [ ( ' Euclidean ' , <__builtin__.function object>, ' Eucli ...
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__package__ = ' rdkit.ML.Cluster '
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Imports:
numpy
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.
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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
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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
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displays a metric matrix
**Arguments**
- metricMat: the matrix to be displayed
- nObjs: the number of objects to display
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methods
- Value:
[ ( ' Euclidean ' , <__builtin__.function object>, ' Euclidean Distance ' ) ]
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