ML.Cluster.Murtagh

Package ML :: Package Cluster :: Module Murtagh |
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# Module Murtagh

source code

    Interface to the C++ Murtagh hierarchic clustering code

Functions | [hide private]
_LookupDist(dists, i, j, n) *Internal Use Only*... | source code
_ToClusters(data, nPts, ia, ib, crit, isDistData=0) *Internal Use Only*... | source code
ClusterData(data, nPts, method, isDistData=0) clusters the data points passed in and returns the cluster tree **Arguments** - data: a list of lists (or array, or whatever) with the input data (see discussion of _isDistData_ argument for the exception) - nPts: the number of points to be used - method: determines which clustering algorithm should be used. | source code

Variables | [hide private]
| WARDS = `1`
| SLINK = `2`
| CLINK = `3`
| UPGMA = `4`
| MCQUITTY = `5`
| GOWER = `6`
| CENTROID = `7`
| methods = ``[``(``'``Ward\'s Minimum Variance``'``,` 1`,` `'``Ward\'s Minimum Va``...``
| 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]

### _LookupDist(dists, i, j, n) | source code
    *Internal Use Only*

    returns the distance between points i and j in the symmetric
    distance matrix _dists_

### _ToClusters(data, nPts, ia, ib, crit, isDistData=0) | source code
    *Internal Use Only*

    Converts the results of the Murtagh clustering code into
    a cluster tree, which is returned in a single-entry list

### ClusterData(data, nPts, method, isDistData=0) | source code
    clusters the data points passed in and returns the cluster tree

    **Arguments**

      - data: a list of lists (or array, or whatever) with the input
        data (see discussion of _isDistData_ argument for the exception)

      - nPts: the number of points to be used

      - method: determines which clustering algorithm should be used.
          The defined constants for these are:
          'WARDS, SLINK, CLINK, UPGMA'

      - isDistData: set this toggle when the data passed in is a
          distance matrix.  The distance matrix should be stored
          symmetrically so that _LookupDist (above) can retrieve
          the results:
            for i<j: d_ij = dists[j*(j-1)/2 + i]

    **Returns**

      - a single entry list with the cluster tree

Variables Details | [hide private]

  ### methods Value:
      [('Ward\'s Minimum Variance', 1, 'Ward\'s Minimum Variance'),
       ('Average Linkage', 4, 'Group Average Linkage (UPGMA)'),
       ('Single Linkage', 2, 'Single Linkage (SLINK)'),
       ('Complete Linkage', 3, 'Complete Linkage (CLINK)'),
       ('Centroid', 7, 'Centroid method')]