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# MixtComp version 4.0 - july 2019
# Copyright (C) Inria - Université de Lille - CNRS
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero 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 Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>
# @author Quentin Grimonprez
context("multi run")
Sys.setenv(MC_DETERMINISTIC = 42)
test_that("rmcMultiRun works", {
set.seed(42)
nInd <- 1000
var <- list()
var$z_class <- zParam()
var$z_class$param <- c(0.2, 0.3, 0.15, 0.35)
var$Gaussian1 <- gaussianParam("Gaussian1")
var$Gaussian1$param[[3]] <- list(mean = -2, sd = 1)
var$Gaussian1$param[[4]] <- list(mean = 2, sd = 1)
resGen <- dataGeneratorNewIO(nInd, 0.9, var)
algo <- list(
nbBurnInIter = 100,
nbIter = 100,
nbGibbsBurnInIter = 100,
nbGibbsIter = 100,
nInitPerClass = 100,
nSemTry = 20,
nInitPerClass = 100,
confidenceLevel = 0.95,
nClass = 4,
ratioStableCriterion = 0.95,
nStableCriterion = 10,
mode = "learn",
nInd = 1000
)
resLearn <- rmcMultiRun(algo, resGen$data, resGen$desc, list(), nRun = 1)
if (!is.null(resLearn$warnLog)) {
print(resLearn$warnLog)
}
expect_equal(resLearn$warnLog, NULL)
expect_equal(names(resLearn), c("mixture", "variable", "algo"))
resLearn <- rmcMultiRun(algo, resGen$data, resGen$desc, list(), nCore = 2, nRun = 5)
if (!is.null(resLearn$warnLog)) {
print(resLearn$warnLog)
}
expect_equal(resLearn$warnLog, NULL)
expect_equal(names(resLearn), c("mixture", "variable", "algo"))
})
Sys.unsetenv("MC_DETERMINISTIC")
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