pysal.viz.mapclassify.Fisher_Jenks

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

Fisher Jenks optimal classifier - mean based

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()
>>> fj = mc.Fisher_Jenks(cal)
>>> fj.adcm
799.24
>>> fj.bins
array([  75.29,  192.05,  370.5 ,  722.85, 4111.45])
>>> fj.counts
array([49,  3,  4,  1,  1])
>>>
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.