mapclassify.MaxP

class mapclassify.MaxP(y, k=5, initial=1000, seed1=0, seed2=1)[source]

MaxP Map Classification. Based on Max-p regionalization algorithm.

Parameters:
ynumpy.array

\((n,1)\), values to classify.

kpython:int (default K==5)

Number of classes required.

initialpython:int (default 1000)

Number of initial solutions to use prior to swapping.

seed1python:int (default 0)

Random state for initial building process.

seed2python:int (default 1)

Random state for swapping process.

Examples

>>> import mapclassify
>>> cal = mapclassify.load_example()
>>> mp = mapclassify.MaxP(cal)
>>> mp.bins
array([3.16000e+00, 1.26300e+01, 1.67000e+01, 2.04700e+01, 4.11145e+03])
>>> mp.counts.tolist()
[18, 16, 3, 1, 20]
Attributes:
ybnumpy.array

\((n,1)\), bin IDs for observations.

binsnumpy.array

\((k,1)\), the upper bounds of each class.

kpython:int

The number of classes.

countsnumpy.array

\((k,1)\), the number of observations falling in each class.

__init__(y, k=5, initial=1000, seed1=0, seed2=1)[source]

Methods

__init__(y[, k, initial, seed1, seed2])

find_bin(x)

Sort input or inputs according to the current bin estimate.

get_adcm()

Absolute deviation around class median (ADCM).

get_fmt()

get_gadf()

Goodness of absolute deviation of fit.

get_legend_classes([fmt])

Format the strings for the classes on the legend.

get_tss()

Returns sum of squares over all class means.

make(*args, **kwargs)

Configure and create a classifier that will consume data and produce classifications, given the configuration options specified by this function.

plot(gdf[, border_color, border_width, ...])

Plot a mapclassifier object.

set_fmt(fmt)

table()

update([y, inplace])

Add data or change classification parameters.

Attributes

fmt