mapclassify.FisherJenksSampled

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

Fisher Jenks optimal classifier - mean based using random sample.

Parameters:
ynumpy.array

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

kpython:int (default 5)

The number of classes required.

pctpython:float (default 0.10)

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.

truncatebool (default python:True)

Truncate pct in cases where \(pct * n > 1000.\).

Notes

For theoretical details see [RSL16].

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, pct=0.1, truncate=True)[source]

Methods

__init__(y[, k, pct, truncate])

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

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

Add data or change classification parameters.

Parameters:
ynumpy.array (default python:None)

\((n,1)\), array of data to classify.

inplacebool (default python:False)

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

**kwargspython:dict

Additional parameters that are passed to the __init__ function of the class. For documentation, check the class constructor.