##
## kselection tests with other kmeans function
##
## Created by Daniel Rodriguez Perez on 8/1/2015.
##
## Copyright (c) 2015 Daniel Rodriguez Perez.
##
## This program is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this program. If not, see <http://www.gnu.org/licenses/>
##
context("Tests for kselection with other kmeans functions")
test_that("evaluate with amap", {
skip_on_cran()
if (!requireNamespace('amap')) {
skip('No amap package')
}
set.seed(1000)
x <- matrix(c(rnorm(100, 2, .1), rnorm(100, 3, .1),
rnorm(100, -2, .1), rnorm(100, 1, .1),
rnorm(100, 1, .1), rnorm(100, -3, .1),
rnorm(100, -1, .1), rnorm(100, -2, .1)), 400, 2)
k <- kselection(x, fun_cluster = amap::Kmeans, nstart = 4, iter.max = 20)
expect_null(num_clusters(x))
expect_null(num_clusters_all(x))
expect_that(class(k), equals('Kselection'))
expect_that(k$k, equals(4))
expect_that(num_clusters(k), equals(4))
valid_clusters <- which(get_f_k(k) < k$k_threshold)
expect_that(num_clusters_all(k), equals(valid_clusters))
valid_clusters <- which(get_f_k(k) < 1)
k$k_threshold <- 1
expect_that(num_clusters_all(k), equals(valid_clusters))
valid_clusters <- which(get_f_k(k) < 0.1)
k$k_threshold <- 0.1
expect_that(num_clusters_all(k), equals(valid_clusters))
})
test_that("evaluate with FactoClass", {
skip_on_cran()
if (!requireNamespace('FactoClass')) {
skip('No FactoClass package')
}
set.seed(1000)
x <- matrix(c(rnorm(100, 2, .1), rnorm(100, 3, .1),
rnorm(100, -2, .1), rnorm(100, 1, .1),
rnorm(100, 1, .1), rnorm(100, -3, .1),
rnorm(100, -1, .1), rnorm(100, -2, .1)), 400, 2)
k <- kselection(x, fun_cluster = FactoClass::kmeansW, nstart = 10)
expect_null(num_clusters(x))
expect_null(num_clusters_all(x))
expect_that(class(k), equals('Kselection'))
expect_that(k$k, equals(4))
expect_that(num_clusters(k), equals(4))
valid_clusters <- which(get_f_k(k) < k$k_threshold)
expect_that(num_clusters_all(k), equals(valid_clusters))
valid_clusters <- which(get_f_k(k) < 1)
k$k_threshold <- 1
expect_that(num_clusters_all(k), equals(valid_clusters))
valid_clusters <- which(get_f_k(k) < 0.1)
k$k_threshold <- 0.1
expect_that(num_clusters_all(k), equals(valid_clusters))
})
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