mapclassify.FisherJenksSampled

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

Fisher Jenks optimal classifier - mean based using random sample.

Parameters:

ynumpy.array

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

kint (default 5)

The number of classes required.

pctfloat (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 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.

kint

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)

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)

Add data or change classification parameters.

Parameters:

ynumpy.array (default None)

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

inplacebool (default False)

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

**kwargsdict

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