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PROGRAM:

NAME

v.kcv - Randomly partition points into test/train sets.

KEYWORDS

vector, statistics, points, point pattern

SYNOPSIS

v.kcv
v.kcv --help
v.kcv map=name [layer=string] npartitions=integer [column=name] [--help] [--verbose]
[--quiet] [--ui]

Flags:
--help
Print usage summary

--verbose
Verbose module output

--quiet
Quiet module output

--ui
Force launching GUI dialog

Parameters:
map=name [required]
Name of vector map
Or data source for direct OGR access

layer=string
Layer number or name
Vector features can have category values in different layers. This number determines
which layer to use. When used with direct OGR access this is the layer name.
Default: 1

npartitions=integer [required]
Number of partitions
Must be > 1

column=name
Name for new column to which partition number is written
Default: part

DESCRIPTION

v.kcv randomly divides a points lists into k sets of test/train data (for npartitions-fold
cross validation). Test partitions are mutually exclusive. That is, a point will appear
in only one test partition and k-1 training partitions. The module generates a random
point using the selected random number generator and then finds the closest point to it.
This site is removed from the candidate list (meaning that it will not be selected for any
other test set) and saved in the first test partition file. This is repeated until enough
points have been selected for the test partition. The number of points chosen for test
partitions depends upon the number of sites available and the number of partitions chosen
(this number is made as consistent as possible while ensuring that all sites will be
chosen for testing). This process of filling up a test partition is done k times.

NOTES

An ideal random sites generator will follow a Poisson distribution and will only be as
random as the original sites. This module simply divides vector points up in a random
manner.

Be warned that random number generation occurs over the intervals defined by the current
region of the map.

This program may not work properly with Lat-long data.

EXAMPLES

All examples are based on the North Carolina sample dataset.
g.copy vect=geonames_wake,my_geonames_wake
v.kcv map=my_geonames_wake column=part npartitions=10

g.copy vect=geodetic_pts,my_geodetic_pts
v.kcv map=my_geodetic_pts column=part npartitions=10

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