mapclassify.FisherJenks

class mapclassify.FisherJenks(y, k=5)[source]

Fisher Jenks optimal classifier - mean based.

Parameters:
ynumpy.array

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

kint (default 5)

The number of classes required.

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.

Examples

>>> import mapclassify
>>> cal = mapclassify.load_example()
>>> fj = mapclassify.FisherJenks(cal)
>>> float(fj.adcm)
799.24

>>> fj.bins.tolist()
[75.29, 192.05, 370.5, 722.85, 4111.45]

>>> fj.counts.tolist()
[49, 3, 4, 1, 1]
__init__(y, k=5)[source]

Methods

__init__(y[, k])

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.

plot_histogram([color, linecolor, ...])

Plot histogram of y with bin values superimposed

plot_legendgram(*[, ax, cmap, bins, inset, ...])

Plot a legendgram, which is a histogram with classification breaks.

set_fmt(fmt)

table()

update([y, inplace])

Add data or change classification parameters.

Attributes

fmt