pysal.viz.mapclassify.Jenks_Caspall

class pysal.viz.mapclassify.Jenks_Caspall(y, k=5)[source]

Jenks Caspall Map Classification

Parameters:
y : array

(n,1), values to classify

k : int

number of classes required

Examples

>>> import pysal.viz.mapclassify as mc
>>> cal = mc.load_example()
>>> jc = mc.Jenks_Caspall(cal, k = 5)
>>> jc.bins
array([1.81000e+00, 7.60000e+00, 2.98200e+01, 1.81270e+02, 4.11145e+03])
>>> jc.counts
array([14, 13, 14, 10,  7])
Attributes:
yb : array

(n,1), bin ids for observations,

bins : array

(k,1), the upper bounds of each class

k : int

the number of classes

counts : array

(k,1), the number of observations falling in each class

Methods

__call__(*args, **kwargs) This will allow the classifier to be called like it’s a function.
find_bin(x) Sort input or inputs according to the current bin estimate
get_adcm() Absolute deviation around class median (ADCM).
get_gadf() Goodness of absolute deviation of fit
get_tss() 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.
update([y, inplace]) Add data or change classification parameters.
__init__(y, k=5)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(y[, k]) Initialize self.
find_bin(x) Sort input or inputs according to the current bin estimate
get_adcm() Absolute deviation around class median (ADCM).
get_gadf() Goodness of absolute deviation of fit
get_tss() 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.
update([y, inplace]) Add data or change classification parameters.