ML.Cluster.Resemblance

Package ML :: Package Cluster :: Module Resemblance |
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# 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_ **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')]