pysal.viz.mapclassify.Max_P_Classifier

class pysal.viz.mapclassify.Max_P_Classifier(y, k=5, initial=1000)[source]

Max_P Map Classification

Based on Max_p regionalization algorithm

Parameters:
y : array

(n,1), values to classify

k : int

number of classes required

initial : int

number of initial solutions to use prior to swapping

Examples

>>> import pysal.viz.mapclassify as mc
>>> cal = mc.load_example()
>>> mp = mc.Max_P_Classifier(cal)
>>> mp.bins
array([   8.7 ,   20.47,   36.68,  110.74, 4111.45])
>>> mp.counts
array([29,  9,  5,  7,  8])
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, initial=1000)[source]

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

Methods

__init__(y[, k, initial]) 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.