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.