# pysal.viz.mapclassify.Quantiles¶

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

Quantile Map Classification

Parameters: y : array (n,1), values to classify k : int number of classes required

Examples

>>> import pysal.viz.mapclassify as mc
>>> q = mc.Quantiles(cal, k = 5)
>>> q.bins
array([1.46400e+00, 5.79800e+00, 1.32780e+01, 5.46160e+01, 4.11145e+03])
>>> q.counts
array([12, 11, 12, 11, 12])

Attributes: yb : 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], yb[i] = 0 otherwise 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.