mapclassify.EqualInterval

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

Equal Interval Classification

Parameters
yarray

(n,1), values to classify

kint

number of classes required

Notes

Intervals defined to have equal width:

\[bins_j = min(y)+w*(j+1)\]

with \(w=\frac{max(y)-min(j)}{k}\)

Examples

>>> import mapclassify as mc
>>> cal = mc.load_example()
>>> ei = mc.EqualInterval(cal, k = 5)
>>> ei.k
5
>>> ei.counts
array([57,  0,  0,  0,  1])
>>> ei.bins
array([ 822.394, 1644.658, 2466.922, 3289.186, 4111.45 ])
Attributes
ybarray

(n,1), bin ids for observations, each value is the id of the class the observation belongs to yb[i] = j for j>=1 if bins[j-1] < y[i] <= bins[j], yb[i] = 0 otherwise

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)[source]

see class docstring

Methods

__init__(self, y[, k])

see class docstring

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