Package ML :: Package Cluster :: Module Resemblance

Module Resemblance

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_ **Arguments** - inData: a Numeric array of data points **Returns** a Numeric array with the metric matrix.

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', <function EuclideanDistance at 0x9db7...

Complex0 = 'F'

Complex16 = 'F'

Complex32 = 'F'

Complex64 = 'D'

Complex8 = 'F'

Float0 = 'f'

Float16 = 'f'

Float32 = 'f'

Float64 = 'd'

Float8 = 'f'

Int0 = '1'

Int16 = 's'

Int32 = 'i'

Int8 = '1'

absolute = <ufunc 'absolute'>

add = <ufunc 'add'>

arccos = <ufunc 'arccos'>

arccosh = <ufunc 'arccosh'>

arcsin = <ufunc 'arcsin'>

arcsinh = <ufunc 'arcsinh'>

arctan = <ufunc 'arctan'>

arctan2 = <ufunc 'arctan2'>

arctanh = <ufunc 'arctanh'>

bitwise_and = <ufunc 'bitwise_and'>

bitwise_or = <ufunc 'bitwise_or'>

bitwise_xor = <ufunc 'bitwise_xor'>

ceil = <ufunc 'ceil'>

conjugate = <ufunc 'conjugate'>

cos = <ufunc 'cos'>

cosh = <ufunc 'cosh'>

divide = <ufunc 'divide'>

divide_safe = <ufunc 'divide_safe'>

e = 2.71828182846

equal = <ufunc 'equal'>

exp = <ufunc 'exp'>

fabs = <ufunc 'fabs'>

floor = <ufunc 'floor'>

floor_divide = <ufunc 'floor_divide'>

fmod = <ufunc 'fmod'>

greater = <ufunc 'greater'>

greater_equal = <ufunc 'greater_equal'>

hypot = <ufunc 'hypot'>

invert = <ufunc 'invert'>

left_shift = <ufunc 'left_shift'>

less = <ufunc 'less'>

less_equal = <ufunc 'less_equal'>

log = <ufunc 'log'>

log10 = <ufunc 'log10'>

logical_and = <ufunc 'logical_and'>

logical_not = <ufunc 'logical_not'>

logical_or = <ufunc 'logical_or'>

logical_xor = <ufunc 'logical_xor'>

maximum = <ufunc 'maximum'>

minimum = <ufunc 'minimum'>

multiply = <ufunc 'multiply'>

negative = <ufunc 'negative'>

not_equal = <ufunc 'not_equal'>

pi = 3.14159265359

power = <ufunc 'power'>

remainder = <ufunc 'remainder'>

right_shift = <ufunc 'right_shift'>

sin = <ufunc 'sin'>

sinh = <ufunc 'sinh'>

sqrt = <ufunc 'sqrt'>

subtract = <ufunc 'subtract'>

tan = <ufunc 'tan'>

tanh = <ufunc 'tanh'>

true_divide = <ufunc 'true_divide'>

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 0x9db7764>,
  'Euclidean Distance')]