Nothing
tests/runitDoILP_timeSeries.R
In lpNet: Linear Programming Model for Network Inference
.setUp <- function() {
n <<- 3
K <<- 4
T_ <<- 4
T_nw <<- matrix(c(0,1,0,
0,0,1,
0,0,0), nrow=n, ncol=n, byrow=TRUE)
b <<- c(0,1,1,
1,0,1,
1,1,0,
1,1,1)
obs_mat <<- array(NA, c(n,K,T_))
obs_mat[,,1] <<- matrix(c(0.56, 0.56, 0.56, 0.56,
0.56, 0.56, 0.56, 0.56,
0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE)
obs_mat[,,2] <<- matrix(c(0.56, 0.95, 0.95, 0.95,
0.56, 0.56, 0.56, 0.56,
0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE)
obs_mat[,,3] <<- matrix(c(0.56, 0.95, 0.95, 0.95,
0.56, 0.56, 0.95, 0.95,
0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE)
obs_mat[,,4] <<- matrix(c(0.56, 0.95, 0.95, 0.95,
0.56, 0.56, 0.95, 0.95,
0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE)
lambda <<- 1/10
annot <<- getEdgeAnnot(n)
}
test.doILPTimeSeriesShortExamplePerGene <- function() {
true_result_objval <- 2.344474
true_result_solution <- c(0.0000000, 0.7947368, 0.0000000,
0.0000000, 0.0000000, 0.7947368,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000)
delta <- rep(0.755, n)
delta_type <- "perGene"
res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL,
sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE)
checkEquals(true_result_objval, res$objval, tolerance=0.00001)
checkEquals(true_result_solution, res$solution, tolerance=0.00001)
}
test.doILPTimeSeriesShortExamplePerGenePerExp <- function() {
true_result_objval <- 24.99447
true_result_solution <- c(0.0000000, 0.7947368, 0.0000000,
0.0000000, 0.0000000, 0.7947368,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000)
delta <- matrix(c(0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta_type <- "perGeneExp"
res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL,
sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE)
checkEquals(true_result_objval, res$objval, tolerance=0.00001)
checkEquals(true_result_solution, res$solution, tolerance=0.00001)
}
test.doILPTimeSeriesShortExamplePerGenePerTime <- function() {
true_result_objval <- 109.5545
true_result_solution <- c(0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.7947368, 0.7947368,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.7550000, 0.7550000,
0.0000000, 0.7550000, 0.7550000,
0.0000000, 0.0000000, 0.7550000,
0.0000000, 0.7550000, 0.0000000,
0.0000000, 0.7550000, 0.7550000,
0.0000000, 0.7550000, 0.7550000,
0.7550000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.7550000, 0.7550000,
0.0000000, 0.0000000, 0.7550000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000)
delta <- matrix(c(0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta_type <- "perGeneTime"
res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL,
sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE)
checkEquals(true_result_objval, res$objval, tolerance=0.00001)
checkEquals(true_result_solution, res$solution, tolerance=0.00001)
}
test.doILPTimeSeriesShortExamplePerGenePerExpPerTime <- function() {
true_result_objval <- 62.70474
true_result_solution <- c(0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.7947368,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.7550000, 0.0000000,
0.0000000, 0.7550000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.7550000, 0.0000000,
0.0000000, 0.7550000, 0.0000000,
0.0000000, 0.7550000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.7550000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.7550000, 0.0000000,
0.0000000, 0.0000000, 0.0000000)
delta <- array(NA, c(n,K,T_))
delta[,,1] <- matrix(c(0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta[,,2] <- matrix(c(0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta[,,3] <- matrix(c(0.755, 0.755, 0.755, 0.755,
0.755, 0.755, 0.755, 0.755,
0.755, 0.755, 0.755, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta[,,4] <- matrix(c(0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta_type <- "perGeneExpTime"
res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL,
sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE)
checkEquals(true_result_objval, res$objval, tolerance=0.00001)
checkEquals(true_result_solution, res$solution, tolerance=0.00001)
}
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lpNet documentation built on Nov. 8, 2020, 7:08 p.m.
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.setUp <- function() {
n <<- 3
K <<- 4
T_ <<- 4
T_nw <<- matrix(c(0,1,0,
0,0,1,
0,0,0), nrow=n, ncol=n, byrow=TRUE)
b <<- c(0,1,1,
1,0,1,
1,1,0,
1,1,1)
obs_mat <<- array(NA, c(n,K,T_))
obs_mat[,,1] <<- matrix(c(0.56, 0.56, 0.56, 0.56,
0.56, 0.56, 0.56, 0.56,
0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE)
obs_mat[,,2] <<- matrix(c(0.56, 0.95, 0.95, 0.95,
0.56, 0.56, 0.56, 0.56,
0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE)
obs_mat[,,3] <<- matrix(c(0.56, 0.95, 0.95, 0.95,
0.56, 0.56, 0.95, 0.95,
0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE)
obs_mat[,,4] <<- matrix(c(0.56, 0.95, 0.95, 0.95,
0.56, 0.56, 0.95, 0.95,
0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE)
lambda <<- 1/10
annot <<- getEdgeAnnot(n)
}
test.doILPTimeSeriesShortExamplePerGene <- function() {
true_result_objval <- 2.344474
true_result_solution <- c(0.0000000, 0.7947368, 0.0000000,
0.0000000, 0.0000000, 0.7947368,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000)
delta <- rep(0.755, n)
delta_type <- "perGene"
res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL,
sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE)
checkEquals(true_result_objval, res$objval, tolerance=0.00001)
checkEquals(true_result_solution, res$solution, tolerance=0.00001)
}
test.doILPTimeSeriesShortExamplePerGenePerExp <- function() {
true_result_objval <- 24.99447
true_result_solution <- c(0.0000000, 0.7947368, 0.0000000,
0.0000000, 0.0000000, 0.7947368,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000)
delta <- matrix(c(0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta_type <- "perGeneExp"
res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL,
sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE)
checkEquals(true_result_objval, res$objval, tolerance=0.00001)
checkEquals(true_result_solution, res$solution, tolerance=0.00001)
}
test.doILPTimeSeriesShortExamplePerGenePerTime <- function() {
true_result_objval <- 109.5545
true_result_solution <- c(0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.7947368, 0.7947368,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.7550000, 0.7550000,
0.0000000, 0.7550000, 0.7550000,
0.0000000, 0.0000000, 0.7550000,
0.0000000, 0.7550000, 0.0000000,
0.0000000, 0.7550000, 0.7550000,
0.0000000, 0.7550000, 0.7550000,
0.7550000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.7550000, 0.0000000, 0.0000000,
0.0000000, 0.7550000, 0.7550000,
0.0000000, 0.0000000, 0.7550000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000)
delta <- matrix(c(0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta_type <- "perGeneTime"
res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL,
sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE)
checkEquals(true_result_objval, res$objval, tolerance=0.00001)
checkEquals(true_result_solution, res$solution, tolerance=0.00001)
}
test.doILPTimeSeriesShortExamplePerGenePerExpPerTime <- function() {
true_result_objval <- 62.70474
true_result_solution <- c(0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.7947368,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.7550000, 0.0000000,
0.0000000, 0.7550000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.7550000, 0.0000000,
0.0000000, 0.7550000, 0.0000000,
0.0000000, 0.7550000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.0000000, 0.7550000, 0.0000000,
0.0000000, 0.0000000, 0.0000000,
0.7550000, 0.7550000, 0.0000000,
0.0000000, 0.0000000, 0.0000000)
delta <- array(NA, c(n,K,T_))
delta[,,1] <- matrix(c(0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta[,,2] <- matrix(c(0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta[,,3] <- matrix(c(0.755, 0.755, 0.755, 0.755,
0.755, 0.755, 0.755, 0.755,
0.755, 0.755, 0.755, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta[,,4] <- matrix(c(0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755,
0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE)
delta_type <- "perGeneExpTime"
res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL,
sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE)
checkEquals(true_result_objval, res$objval, tolerance=0.00001)
checkEquals(true_result_solution, res$solution, tolerance=0.00001)
}
Try the lpNet package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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