mapclassify.FisherJenksSampled

class mapclassify.FisherJenksSampled(y, k=5, pct=0.1, truncate=True)[source]

Fisher Jenks optimal classifier - mean based using random sample

Parameters
yarray

(n,1), values to classify

kint

number of classes required

pctfloat

The percentage of n that should form the sample If pct is specified such that n*pct > 1000, then pct = 1000./n, unless truncate is False

truncateboolean

truncate pct in cases where pct * n > 1000., (Default True)

Examples

(Turned off due to timing being different across hardware)

For theoretical details see [RSL16].

Attributes
ybarray

(n,1), bin ids for observations

binsarray

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

kint

the number of classes

countsarray

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

__init__(self, y, k=5, pct=0.1, truncate=True)[source]

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

Methods

__init__(self, y[, k, pct, truncate])

Initialize self.

find_bin(self, x)

Sort input or inputs according to the current bin estimate

get_adcm(self)

Absolute deviation around class median (ADCM).

get_fmt(self)

get_gadf(self)

Goodness of absolute deviation of fit

get_legend_classes(self[, fmt])

Format the strings for the classes on the legend

get_tss(self)

Total sum of squares around 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(self, gdf[, border_color, …])

Plot Mapclassiifer NOTE: Requires matplotlib, and implicitly requires geopandas dataframe as input.

set_fmt(self, fmt)

table(self)

update(self[, y, inplace])

Add data or change classification parameters.

Attributes

fmt

update(self, y=None, inplace=False, \*\*kwargs)[source]

Add data or change classification parameters.

Parameters
yarray

(n,1) array of data to classify

inplacebool

whether to conduct the update in place or to return a copy estimated from the additional specifications.

Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.