find_outmost_partitioned_convexhull_points: Function which finds the outermost convex hull points after...

In archetypal: Finds the Archetypal Analysis of a Data Frame

Function which finds the outermost convex hull points after making np samples and finding convex hull for each of them.

Description

Function which finds the outermost convex hull points after making np samples and finding convex hull for each of them. To be used as initial solution in archetypal analysis

Usage

find_outmost_partitioned_convexhull_points(df, kappas, np = 10,
  nworkers = NULL)

Arguments

df	The data frame with dimensions n x d
kappas	The number of archetypes
np	The number of partitions that will be used (or the number of samples)
nworkers	The number of logical processors that will be used

Value

A list with members:

1. outmost, the first kappas most frequent outermost points as rows of data frame

2. outmostall, all the outermost points that have been found as rows of data frame

3. outmostfrequency, a matrix with frequency and cumulative frequency for outermost rows

See Also

find_furthestsum_points, find_outmost_projected_convexhull_points,

find_outmost_convexhull_points & find_outmost_points

Examples

data("wd2") #2D demo 
df = wd2
yy = find_outmost_partitioned_convexhull_points(df, kappas = 3, nworkers = 2)
yy$outmost #the rows of 3 outermost points
df[yy$outmost,] #the 3 outermost points
yy$outmostall #all outermost rows
yy$outmostfrequency #their frequency

archetypal documentation built on May 29, 2024, 8:46 a.m.