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# This is an implementation of RGBM algorithm for Gene Regulatory Network
# inference from gene/RNA/miRNA expression data, in form of an R package.
# Copyright (C) 2016 Raghvendra Mall
# 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
# 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, see LICENSE.
RGBM.test = function(model,X.test,Y.test,M.test) {
if (!is.matrix(X.test)) {
stop("Error: X.test must be N-by-P matrix")
}
N.test = nrow(X.test)
P.test = ncol(X.test)
if (!is.vector(Y.test) || length(as.vector(Y.test))!=N.test) {
stop("Error: Y.test must be N-element vector.")
}
if (model$P.train != P.test) {
stop("Error: dimensionality of training and test data must agree.")
}
if (!is.numeric(M.test) || as.integer(M.test)<=0) {
stop("Error: M must be a number greater than 0.")
}
if (!is.numeric(M.test) || model$M.train < M.test) {
stop("Error: number of iterations M must be lower or equal to model size.")
}
if (!is.numeric(model$f0)) {
stop("Error: f0 of model is corrupted")
}
if (!is.vector(model$feature.split.index) || length(as.vector(model$feature.split.index)) != model$M.train) {
stop("Error: feature.split.index of model is corrupted")
}
if (!is.vector(model$feature.split.thr) || length(as.vector(model$feature.split.thr)) != model$M.train) {
stop("Error: feature.split.thr of model is corrupted")
}
if (!is.vector(model$gamma_l) || length(as.vector(model$gamma_l)) != model$M.train) {
stop("Error: gamma_l of model is corrupted")
}
if (!is.vector(model$gamma_r) || length(as.vector(model$gamma_r)) != model$M.train) {
stop("Error: gamma_r of model is corrupted")
}
if (!is.numeric(model$nu)) {
stop("Error: nu of model is corrupted")
}
result = .C("test_regression_stump_R",
as.integer(N.test),
as.integer(P.test),
as.integer(model$P.train),
as.double(X.test),
as.double(Y.test),
as.integer(M.test),
as.integer(model$M.train),
as.double(model$nu),
as.double(model$f0),
as.integer(model$feature.split.index-1),
as.double(model$feature.split.thr),
as.double(model$gamma_l),
as.double(model$gamma_r),
loss=as.double(rep(0.0,M.test)),
max.M.prediction=as.double(rep(0.0,N.test)),
PACKAGE="RGBM"
)
prediction = list(
loss=result$loss,
max.M.prediction=result$max.M.prediction
)
class(prediction) = "RGBM.prediction"
return(prediction)
}
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