# mapclassify.EqualInterval¶

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

Equal Interval Classification.

Parameters:
ynumpy.array

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

kint (default 5)

The 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
>>> ei = mapclassify.EqualInterval(cal, k=5)
>>> ei.k
5

>>> ei.counts.tolist()
[57, 0, 0, 0, 1]

>>> ei.bins
array([ 822.394, 1644.658, 2466.922, 3289.186, 4111.45 ])

Attributes:
ybnumpy.array

$$(n,1)$$, bin IDs for observations. Each value is the ID of the class the observation belongs to $$yb[i] = j$$ for $$j>=1$$ if $$bins[j-1] < y[i] <= bins[j]$$, otherwise $$yb[i] = 0$$.

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)[source]

see class docstring

Methods

 __init__(y[, k]) see class docstring 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 set_fmt(fmt) table() update([y, inplace]) Add data or change classification parameters.

Attributes

 fmt