mapclassify.MaxP¶
-
class
mapclassify.
MaxP
(y, k=5, initial=1000)[source]¶ MaxP Map Classification
Based on Max-p regionalization algorithm
- Parameters
- yarray
(n,1), values to classify
- kint
number of classes required
- initialint
number of initial solutions to use prior to swapping
Examples
>>> import mapclassify as mc >>> cal = mc.load_example() >>> mp = mc.MaxP(cal) >>> mp.bins array([ 8.7 , 16.7 , 20.47, 66.26, 4111.45])
>>> mp.counts array([29, 8, 1, 10, 10])
- Attributes
- ybarray
(n,1), bin ids for observations,
- binsarray
(k,1), the upper bounds of each class
- kint
the number of classes
- countsarray
(k,1), the number of observations falling in each class
-
__init__
(self, y, k=5, initial=1000)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(self, y[, k, initial])Initialize self.
find_bin
(self, x)Sort input or inputs according to the current bin estimate
get_adcm
(self)Absolute deviation around class median (ADCM).
get_fmt
(self)get_gadf
(self)Goodness of absolute deviation of fit
get_legend_classes
(self[, fmt])Format the strings for the classes on the legend
get_tss
(self)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.
plot
(self, gdf[, border_color, …])Plot Mapclassiifer NOTE: Requires matplotlib, and implicitly requires geopandas dataframe as input.
set_fmt
(self, fmt)table
(self)update
(self[, y, inplace])Add data or change classification parameters.
Attributes
fmt
-
update
(self, y=None, inplace=False, \*\*kwargs)[source]¶ Add data or change classification parameters.
- Parameters
- yarray
(n,1) array of data to classify
- inplacebool
whether to conduct the update in place or to return a copy estimated from the additional specifications.
- Additional parameters provided in **kwargs are passed to the init
- function of the class. For documentation, check the class constructor.