mapclassify.Quantiles

class mapclassify.Quantiles(y, k=5)

Quantile Map Classification.

Parameters:

ynumpy.array

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

kint (default 5)

The number of classes required.

Examples

>>> import mapclassify
>>> cal = mapclassify.load_example()
>>> q = mapclassify.Quantiles(cal, k=5)
>>> q.bins
array([1.46400e+00, 5.79800e+00, 1.32780e+01, 5.46160e+01, 4.11145e+03])

>>> q.counts.tolist()
[12, 11, 12, 11, 12]

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)

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.
set_fmt(fmt)
table()
update([y, inplace])	Add data or change classification parameters.

Attributes

fmt