mapclassify.KClassifiers

class mapclassify.KClassifiers(y, pct=0.8)

Evaluate all \(k\)-classifers and pick optimal based on \(k\) and GADF.

Parameters:

ynumpy.array

\((n,1)\), values to be classified.

pctfloat (default 0.8)

The percentage of GADF to exceed.

See also

gadf

Notes

This can be used to suggest a classification scheme.

Examples

>>> import mapclassify
>>> cal = mapclassify.load_example()
>>> ks = mapclassify.classifiers.KClassifiers(cal)
>>> ks.best.name
'FisherJenks'

>>> ks.best.k
4

>>> float(ks.best.gadf)
0.8481032719908105

Attributes:

bestMapClassifier

Instance of the optimal MapClassifier.

resultsdict

Keys are classifier names, values are the MapClassifier instances with the best pct for each classifier.

__init__(y, pct=0.8)

Methods

__init__(y[, pct])