pysal.viz.mapclassify.Fisher_Jenks_Sampled¶

class pysal.viz.mapclassify.Fisher_Jenks_Sampled(y, k=5, pct=0.1, truncate=True)[source]

Fisher Jenks optimal classifier - mean based using random sample

Parameters: y : array (n,1), values to classify k : int number of classes required pct : float The percentage of n that should form the sample If pct is specified such that n*pct > 1000, then pct = 1000./n, unless truncate is False truncate : boolean truncate pct in cases where pct * n > 1000., (Default True)

Examples

(Turned off due to timing being different across hardware)

For theoretical details see [RSL16].

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, pct=0.1, truncate=True)[source]

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

Methods

 __init__(y[, k, pct, truncate]) 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.