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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 Vincent Kubicki
context("Simple run")
Sys.setenv(MC_DETERMINISTIC = 42)
test_that("Hard coded simple test", {
set.seed(42)
algoLearn <- list(
nClass = 2,
nInd = 20,
nbBurnInIter = 100,
nbIter = 100,
nbGibbsBurnInIter = 100,
nbGibbsIter = 100,
nInitPerClass = 3,
nSemTry = 20,
confidenceLevel = 0.95,
ratioStableCriterion = 0.95,
nStableCriterion = 10,
mode = "learn"
)
dataLearn <- list(
var1 = c(
"3.432200",
"19.14747",
"5.258037",
"22.37596",
"2.834802",
"18.60959",
"4.640250",
"18.59525",
"5.957942",
"19.94644",
"5.560189",
"17.98966",
"6.708977",
"18.10192",
"5.331169",
"20.35260",
"4.003947",
"21.81531",
"6.217908",
"19.38892"
)
)
descLearn <- list(var1 = list(
type = "Gaussian",
paramStr = ""
))
zLearn <- c(1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2)
resLearn <- rmc(algoLearn, dataLearn, descLearn, list())
expect_equal(resLearn$warnLog, NULL)
partition <- resLearn$variable$data$z_class$completed
expect_gte(rand.index(partition, zLearn), 0.9)
# confMatSampledLearn <- table(zLearn, partition)
# print(confMatSampledLearn)
algoPredict <- list(
nClass = 2,
nInd = 6,
nbBurnInIter = 100,
nbIter = 100,
nbGibbsBurnInIter = 100,
nbGibbsIter = 100,
nInitPerClass = 3,
nSemTry = 20,
confidenceLevel = 0.95,
ratioStableCriterion = 0.95,
nStableCriterion = 10,
mode = "predict"
)
dataPredict <- list(var1 = c(
"4.838457",
"19.90595",
"4.577347",
"21.19830",
"5.048325",
"20.46875"
))
descPredict <- list(var1 = list(
type = "Gaussian",
paramStr = ""
))
zPredict <- c(1, 2, 1, 2, 1, 2)
resPredict <- rmc(algoPredict, dataPredict, descPredict, resLearn)
expect_equal(resPredict$warnLog, NULL)
partition <- resPredict$variable$data$z_class$completed
expect_gte(rand.index(partition, zPredict), 0.8)
confMatSampledPredict <- table(zPredict, partition)
print(confMatSampledPredict)
# test with a bad nClass in desc
algoPredict$nClass <- 3
resPredict <- rmc(algoPredict, dataPredict, descPredict, resLearn)
expect_named(resPredict, "warnLog")
expect_equal(resPredict$warnLog, "The nClass parameter provides in algo is different from the one in resLearn.\n")
})
Sys.unsetenv("MC_DETERMINISTIC")
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