mapclassify.KClassifiers

class mapclassify.KClassifiers(y, pct=0.8)[source]

Evaluate all k-classifers and pick optimal based on k and GADF

Parameters
yarray

(n,1), values to be classified

pctfloat

The percentage of GADF to exceed

See also

gadf

Notes

This can be used to suggest a classification scheme.

Examples

>>> import mapclassify as mc
>>> cal = mc.load_example()
>>> ks = mc.classifiers.KClassifiers(cal)
>>> ks.best.name
'FisherJenks'
>>> ks.best.k
4
>>> ks.best.gadf
0.8481032719908105
Attributes
bestobject

instance of the optimal MapClassifier

resultsdictionary

keys are classifier names, values are the MapClassifier instances with the best pct for each classifer

__init__(self, y, pct=0.8)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, y[, pct])

Initialize self.