pysal.viz.mapclassify.User_Defined

class pysal.viz.mapclassify.User_Defined(y, bins)[source]

User Specified Binning

Parameters:
y : array

(n,1), values to classify

bins : array

(k,1), upper bounds of classes (have to be monotically increasing)

Notes

If upper bound of user bins does not exceed max(y) we append an additional bin.

Examples

>>> import pysal.viz.mapclassify as mc
>>> cal = mc.load_example()
>>> bins = [20, max(cal)]
>>> bins
[20, 4111.45]
>>> ud = mc.User_Defined(cal, bins)
>>> ud.bins
array([  20.  , 4111.45])
>>> ud.counts
array([37, 21])
>>> bins = [20, 30]
>>> ud = mc.User_Defined(cal, bins)
>>> ud.bins
array([  20.  ,   30.  , 4111.45])
>>> ud.counts
array([37,  4, 17])
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, bins)[source]

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

Methods

__init__(y, bins) 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.