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This page was generated on 2021-10-15 15:06:09 -0400 (Fri, 15 Oct 2021).
To the developers/maintainers of the lpNet package: - Please allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/lpNet.git to reflect on this report. See How and When does the builder pull? When will my changes propagate? here for more information. - Make sure to use the following settings in order to reproduce any error or warning you see on this page. |
Package 1004/2041 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
lpNet 2.24.0 (landing page) Lars Kaderali
| nebbiolo1 | Linux (Ubuntu 20.04.2 LTS) / x86_64 | OK | OK | WARNINGS | |||||||||
tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | WARNINGS | OK | |||||||||
machv2 | macOS 10.14.6 Mojave / x86_64 | OK | OK | WARNINGS | OK | |||||||||
Package: lpNet |
Version: 2.24.0 |
Command: C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:lpNet.install-out.txt --library=C:\Users\biocbuild\bbs-3.13-bioc\R\library --no-vignettes --timings lpNet_2.24.0.tar.gz |
StartedAt: 2021-10-15 01:13:38 -0400 (Fri, 15 Oct 2021) |
EndedAt: 2021-10-15 01:14:30 -0400 (Fri, 15 Oct 2021) |
EllapsedTime: 51.9 seconds |
RetCode: 0 |
Status: WARNINGS |
CheckDir: lpNet.Rcheck |
Warnings: 1 |
############################################################################## ############################################################################## ### ### Running command: ### ### C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:lpNet.install-out.txt --library=C:\Users\biocbuild\bbs-3.13-bioc\R\library --no-vignettes --timings lpNet_2.24.0.tar.gz ### ############################################################################## ############################################################################## * using log directory 'C:/Users/biocbuild/bbs-3.13-bioc/meat/lpNet.Rcheck' * using R version 4.1.1 (2021-08-10) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * using option '--no-vignettes' * checking for file 'lpNet/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'lpNet' version '2.24.0' * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'lpNet' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * loading checks for arch 'i386' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK * loading checks for arch 'x64' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... NOTE .calcRangeLambda_steadyState: no visible global function definition for 'var' .calcRangeLambda_timeSeries: no visible global function definition for 'var' .calculatePredictionValue_Kfold_ts: no visible global function definition for 'rnorm' .calculatePredictionValue_LOOCV_ss: no visible global function definition for 'rnorm' .calculatePredictionValue_LOOCV_ts: no visible global function definition for 'rnorm' .set_per_gene_exp_time_values: no visible global function definition for 'rnorm' .set_per_gene_exp_values: no visible global function definition for 'rnorm' .set_per_gene_time_values: no visible global function definition for 'rnorm' .set_per_gene_values: no visible global function definition for 'rnorm' .set_single_values: no visible global function definition for 'rnorm' getSampleAdja: no visible binding for global variable 'median' getSampleAdjaMAD: no visible binding for global variable 'median' getSampleAdjaMAD: no visible binding for global variable 'mad' summarizeRepl: no visible binding for global variable 'median' Undefined global functions or variables: mad median rnorm var Consider adding importFrom("stats", "mad", "median", "rnorm", "var") to your NAMESPACE file. * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... WARNING Undocumented data sets: 'dat.normalized' 'dat.unnormalized' All user-level objects in a package should have documentation entries. See chapter 'Writing R documentation files' in the 'Writing R Extensions' manual. * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... OK * checking data for ASCII and uncompressed saves ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... ** running examples for arch 'i386' ... OK ** running examples for arch 'x64' ... OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... ** running tests for arch 'i386' ... Running 'runitCalcActivation.R' Running 'runitCalcPredictionKfoldCV.R' Running 'runitCalcPredictionKfoldCV_timeSeries.R' Running 'runitCalcPredictionLOOCV.R' Running 'runitCalcPredictionLOOCV_timeSeries.R' Running 'runitCalcRangeLambda.R' Running 'runitDoILP.R' Running 'runitDoILP_timeSeries.R' Running 'runitGenerateTimeSeriesNetStates.R' Running 'runitGetAdja.R' Running 'runitGetBaseline.R' Running 'runitGetEdgeAnnot.R' Running 'runitGetObsMat.R' Running 'runitGetSampleAdja.R' Running 'runitGetSampleAdjaMAD.R' Running 'runitKfoldCV.R' Running 'runitKfoldCV_timeSeries.R' Running 'runitLOOCV.R' Running 'runitLOOCV_timeSeries.R' OK ** running tests for arch 'x64' ... Running 'runitCalcActivation.R' Running 'runitCalcPredictionKfoldCV.R' Running 'runitCalcPredictionKfoldCV_timeSeries.R' Running 'runitCalcPredictionLOOCV.R' Running 'runitCalcPredictionLOOCV_timeSeries.R' Running 'runitCalcRangeLambda.R' Running 'runitDoILP.R' Running 'runitDoILP_timeSeries.R' Running 'runitGenerateTimeSeriesNetStates.R' Running 'runitGetAdja.R' Running 'runitGetBaseline.R' Running 'runitGetEdgeAnnot.R' Running 'runitGetObsMat.R' Running 'runitGetSampleAdja.R' Running 'runitGetSampleAdjaMAD.R' Running 'runitKfoldCV.R' Running 'runitKfoldCV_timeSeries.R' Running 'runitLOOCV.R' Running 'runitLOOCV_timeSeries.R' OK * checking for unstated dependencies in vignettes ... NOTE 'library' or 'require' call not declared from: 'KEGGgraph' * checking package vignettes in 'inst/doc' ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 1 WARNING, 2 NOTEs See 'C:/Users/biocbuild/bbs-3.13-bioc/meat/lpNet.Rcheck/00check.log' for details.
lpNet.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### C:\cygwin\bin\curl.exe -O http://155.52.207.165/BBS/3.13/bioc/src/contrib/lpNet_2.24.0.tar.gz && rm -rf lpNet.buildbin-libdir && mkdir lpNet.buildbin-libdir && C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=lpNet.buildbin-libdir lpNet_2.24.0.tar.gz && C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD INSTALL lpNet_2.24.0.zip && rm lpNet_2.24.0.tar.gz lpNet_2.24.0.zip ### ############################################################################## ############################################################################## % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 6 248k 6 16227 0 0 132k 0 0:00:01 --:--:-- 0:00:01 132k 100 248k 100 248k 0 0 928k 0 --:--:-- --:--:-- --:--:-- 928k install for i386 * installing *source* package 'lpNet' ... ** using staged installation ** R ** data ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices converting help for package 'lpNet' finding HTML links ... done CV html calcActivation html calcPrediction html calcRangeLambda html doILP html generateTimeSeriesNetStates html getAdja html getBaseline html getEdgeAnnot html getObsMat html getSampleAdja html lpNet-package html summarizeRepl html ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path install for x64 * installing *source* package 'lpNet' ... ** testing if installed package can be loaded * MD5 sums packaged installation of 'lpNet' as lpNet_2.24.0.zip * DONE (lpNet) * installing to library 'C:/Users/biocbuild/bbs-3.13-bioc/R/library' package 'lpNet' successfully unpacked and MD5 sums checked
lpNet.Rcheck/tests_i386/runitCalcActivation.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.calcActivationShortExample <- function() { + n <- 3 + K <- 4 + + true_result <- matrix(c(0,0,0, + 1,0,0, + 1,1,0, + 1,1,1), nrow=n, ncol=K) + + 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) + + act_mat <- calcActivation(T_nw, b, n, K) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationShortExampleTimeSeries <- function() { + n <- 3 + K <- 4 + + true_result <- matrix(c(0,0,0, + 1,0,0, + 1,1,0, + 1,1,1), nrow=n, ncol=K) + + 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) + + act_mat <- calcActivation(T_nw, b, n, K, flag_gen_data=TRUE) + + checkEquals(true_result, act_mat) + } > > > test.calcActivation <- function() { + n <- 5 + K <- 6 + + true_result <- matrix(c(0,0,0,0,0, + 1,0,1,1,1, + 1,1,0,0,0, + 1,1,1,0,0, + 1,1,1,0,0, + 1,1,1,0,0), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,1,0,0, + 0,0,0,-1,0, + 0,0,0,1,0, + 0,0,0,0,1, + 0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1,1,1, + 1,0,1,1,1, + 1,1,0,1,1, + 1,1,1,0,1, + 1,1,1,1,0, + 1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationTimeSeries <- function() { + n <- 5 + K <- 6 + + true_result <- matrix(c(0,0,0,0,0, + 1,0,1,1,1, + 1,1,0,1,1, + 1,1,1,0,0, + 1,1,1,1,0, + 1,1,1,1,1), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,1,0,0, + 0,0,0,-1,0, + 0,0,0,1,0, + 0,0,0,0,1, + 0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1,1,1, + 1,0,1,1,1, + 1,1,0,1,1, + 1,1,1,0,1, + 1,1,1,1,0, + 1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K, flag_gen_data=TRUE) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationLargeExample <- function() { + n <- 10 + K <- 11 + + true_result <- matrix(c(0,0,0,1,1,1,1,1,1,1, + 1,0,0,1,1,1,1,1,1,1, + 1,0,0,1,1,1,1,1,1,1, + 1,1,1,0,0,0,0,0,0,0, + 1,1,1,1,0,0,0,0,0,0, + 1,1,1,1,1,0,0,0,0,0, + 1,0,0,1,1,1,0,0,0,0, + 1,0,0,1,1,1,1,0,0,0, + 1,0,0,1,1,1,1,1,0,0, + 1,0,0,1,1,1,1,1,1,0, + 1,0,0,1,1,1,1,1,1,1), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,0,0,0,0,0,0,0,0, + 0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,-1,0,0,0,0,1,0,0,0, + 0,0,0,0,0,0,0,1,0,0, + 0,0,0,0,0,0,0,0,1,0, + 0,0,0,0,0,0,1,0,0,1, + 0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationLargeExampleTimeSeries <- function() { + n <- 10 + K <- 11 + + true_result <- matrix(c(0,1,1,1,1,1,1,1,1,1, + 1,0,0,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,0,0,0,0,0,0, + 1,1,1,1,0,0,0,0,0,0, + 1,1,1,1,1,0,0,0,0,0, + 1,1,1,1,1,1,0,0,0,0, + 1,1,1,1,1,1,1,0,0,0, + 1,1,1,1,1,1,1,1,0,0, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1), nrow = n, ncol=K) + + T_nw <- matrix(c(0,1,0,0,0,0,0,0,0,0, + 0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,-1,0,0,0,0,1,0,0,0, + 0,0,0,0,0,0,0,1,0,0, + 0,0,0,0,0,0,0,0,1,0, + 0,0,0,0,0,0,1,0,0,1, + 0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K, flag_gen_data=TRUE) + + checkEquals(true_result, act_mat) + } > > proc.time() user system elapsed 0.31 0.01 0.31 |
lpNet.Rcheck/tests_x64/runitCalcActivation.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.calcActivationShortExample <- function() { + n <- 3 + K <- 4 + + true_result <- matrix(c(0,0,0, + 1,0,0, + 1,1,0, + 1,1,1), nrow=n, ncol=K) + + 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) + + act_mat <- calcActivation(T_nw, b, n, K) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationShortExampleTimeSeries <- function() { + n <- 3 + K <- 4 + + true_result <- matrix(c(0,0,0, + 1,0,0, + 1,1,0, + 1,1,1), nrow=n, ncol=K) + + 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) + + act_mat <- calcActivation(T_nw, b, n, K, flag_gen_data=TRUE) + + checkEquals(true_result, act_mat) + } > > > test.calcActivation <- function() { + n <- 5 + K <- 6 + + true_result <- matrix(c(0,0,0,0,0, + 1,0,1,1,1, + 1,1,0,0,0, + 1,1,1,0,0, + 1,1,1,0,0, + 1,1,1,0,0), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,1,0,0, + 0,0,0,-1,0, + 0,0,0,1,0, + 0,0,0,0,1, + 0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1,1,1, + 1,0,1,1,1, + 1,1,0,1,1, + 1,1,1,0,1, + 1,1,1,1,0, + 1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationTimeSeries <- function() { + n <- 5 + K <- 6 + + true_result <- matrix(c(0,0,0,0,0, + 1,0,1,1,1, + 1,1,0,1,1, + 1,1,1,0,0, + 1,1,1,1,0, + 1,1,1,1,1), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,1,0,0, + 0,0,0,-1,0, + 0,0,0,1,0, + 0,0,0,0,1, + 0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1,1,1, + 1,0,1,1,1, + 1,1,0,1,1, + 1,1,1,0,1, + 1,1,1,1,0, + 1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K, flag_gen_data=TRUE) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationLargeExample <- function() { + n <- 10 + K <- 11 + + true_result <- matrix(c(0,0,0,1,1,1,1,1,1,1, + 1,0,0,1,1,1,1,1,1,1, + 1,0,0,1,1,1,1,1,1,1, + 1,1,1,0,0,0,0,0,0,0, + 1,1,1,1,0,0,0,0,0,0, + 1,1,1,1,1,0,0,0,0,0, + 1,0,0,1,1,1,0,0,0,0, + 1,0,0,1,1,1,1,0,0,0, + 1,0,0,1,1,1,1,1,0,0, + 1,0,0,1,1,1,1,1,1,0, + 1,0,0,1,1,1,1,1,1,1), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,0,0,0,0,0,0,0,0, + 0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,-1,0,0,0,0,1,0,0,0, + 0,0,0,0,0,0,0,1,0,0, + 0,0,0,0,0,0,0,0,1,0, + 0,0,0,0,0,0,1,0,0,1, + 0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationLargeExampleTimeSeries <- function() { + n <- 10 + K <- 11 + + true_result <- matrix(c(0,1,1,1,1,1,1,1,1,1, + 1,0,0,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,0,0,0,0,0,0, + 1,1,1,1,0,0,0,0,0,0, + 1,1,1,1,1,0,0,0,0,0, + 1,1,1,1,1,1,0,0,0,0, + 1,1,1,1,1,1,1,0,0,0, + 1,1,1,1,1,1,1,1,0,0, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1), nrow = n, ncol=K) + + T_nw <- matrix(c(0,1,0,0,0,0,0,0,0,0, + 0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,-1,0,0,0,0,1,0,0,0, + 0,0,0,0,0,0,0,1,0,0, + 0,0,0,0,0,0,0,0,1,0, + 0,0,0,0,0,0,1,0,0,1, + 0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K, flag_gen_data=TRUE) + + checkEquals(true_result, act_mat) + } > > proc.time() user system elapsed 0.29 0.03 0.31 |
lpNet.Rcheck/tests_i386/runitCalcPredictionKfoldCV.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .setUp <- function() { + + n <<- 3 + K <<- 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 <<- 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) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + } > > > test.runitCalcPredictionKfoldCV <- function() { + + obs_modified <- obs_mat + obs_modified[2,4] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionKfoldCV(obs, delta, b, n, K, adja=T_nw, baseline, rem_entries, rem_entries_vec, + active_mu, active_sd, inactive_mu, inactive_sd, mu_type=mu_type) + + checkEquals(obs_mat, predict, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.31 0.01 0.29 |
lpNet.Rcheck/tests_x64/runitCalcPredictionKfoldCV.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .setUp <- function() { + + n <<- 3 + K <<- 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 <<- 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) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + } > > > test.runitCalcPredictionKfoldCV <- function() { + + obs_modified <- obs_mat + obs_modified[2,4] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionKfoldCV(obs, delta, b, n, K, adja=T_nw, baseline, rem_entries, rem_entries_vec, + active_mu, active_sd, inactive_mu, inactive_sd, mu_type=mu_type) + + checkEquals(obs_mat, predict, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.26 0.04 0.29 |
lpNet.Rcheck/tests_i386/runitCalcPredictionKfoldCV_timeSeries.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .setUp <- function() { + + n <<- 3 + K <<- 4 + T_ <<- 3 + + T_nw <<- matrix(c(0,0,1, + 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, + 0.95, 0.56, 0.95, 0.95, + 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, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.95, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + mu_list[[4]] <<- list() + mu_list[[5]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <<- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <<- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <<- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <<- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <<- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <<- array(rep(0.755, n*K*T_), c(n,K,T_)) + } > > > test.runitCalcPredictionKfoldCV01 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + baseline <- c(0, 0, 0) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,4,2], 0.56, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV02 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + 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, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,4,2], 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV03 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[3,4,3], 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV04 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,-1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkTrue(is.na(predict[3,4,3])) + } + } > > > test.runitCalcPredictionKfoldCV05 <- function() { + + obs_modified <- obs_mat + obs_modified[2,2,2] <- NA + obs_modified[3,2,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,2,2], 0.56, tolerance=0.05) + checkEquals(predict[3,2,3], 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV06 <- function() { + + obs_modified <- obs_mat + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,2,1], 0.56, tolerance=0.05) + checkTrue(is.na(predict[3,2,2])) + } + } > > > test.runitCalcPredictionKfoldCV07 <- function() { + + baseline <- c(0.76, 0.76, 0.76) + + obs_modified <- obs_mat + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,2,1], 0.56, tolerance=0.05) + checkEquals(predict[3,2,2], 0.95, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.29 0.06 0.32 |
lpNet.Rcheck/tests_x64/runitCalcPredictionKfoldCV_timeSeries.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .setUp <- function() { + + n <<- 3 + K <<- 4 + T_ <<- 3 + + T_nw <<- matrix(c(0,0,1, + 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, + 0.95, 0.56, 0.95, 0.95, + 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, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.95, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + mu_list[[4]] <<- list() + mu_list[[5]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <<- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <<- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <<- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <<- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <<- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <<- array(rep(0.755, n*K*T_), c(n,K,T_)) + } > > > test.runitCalcPredictionKfoldCV01 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + baseline <- c(0, 0, 0) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,4,2], 0.56, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV02 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + 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, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,4,2], 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV03 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[3,4,3], 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV04 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,-1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkTrue(is.na(predict[3,4,3])) + } + } > > > test.runitCalcPredictionKfoldCV05 <- function() { + + obs_modified <- obs_mat + obs_modified[2,2,2] <- NA + obs_modified[3,2,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,2,2], 0.56, tolerance=0.05) + checkEquals(predict[3,2,3], 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV06 <- function() { + + obs_modified <- obs_mat + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,2,1], 0.56, tolerance=0.05) + checkTrue(is.na(predict[3,2,2])) + } + } > > > test.runitCalcPredictionKfoldCV07 <- function() { + + baseline <- c(0.76, 0.76, 0.76) + + obs_modified <- obs_mat + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,2,1], 0.56, tolerance=0.05) + checkEquals(predict[3,2,2], 0.95, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.39 0.06 0.43 |
lpNet.Rcheck/tests_i386/runitCalcPredictionLOOCV.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .setUp <- function() { + + n <<- 3 + K <<- 4 + T_ <<- 3 + + 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 <<- 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) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + } > > > test.runitCalcPredictionLOOCV <- function() { + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionLOOCV(obs=obs_mat, delta=delta, b=b, n=n ,K=K, adja=T_nw, baseline=baseline, + rem_gene=rem_gene, rem_k=rem_k, active_mu=active_mu, active_sd=active_sd, + inactive_mu=inactive_mu, inactive_sd=inactive_sd, mu_type=mu_type) + + checkEquals(obs_mat[rem_gene, rem_k], predict, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.31 0.03 0.32 |
lpNet.Rcheck/tests_x64/runitCalcPredictionLOOCV.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .setUp <- function() { + + n <<- 3 + K <<- 4 + T_ <<- 3 + + 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 <<- 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) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + } > > > test.runitCalcPredictionLOOCV <- function() { + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionLOOCV(obs=obs_mat, delta=delta, b=b, n=n ,K=K, adja=T_nw, baseline=baseline, + rem_gene=rem_gene, rem_k=rem_k, active_mu=active_mu, active_sd=active_sd, + inactive_mu=inactive_mu, inactive_sd=inactive_sd, mu_type=mu_type) + + checkEquals(obs_mat[rem_gene, rem_k], predict, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.28 0.01 0.28 |
lpNet.Rcheck/tests_i386/runitCalcPredictionLOOCV_timeSeries.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .setUp <- function() { + + n <<- 3 + K <<- 4 + T_ <<- 3 + + T_nw <<- matrix(c(0,0,1, + 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, + 0.95, 0.56, 0.95, 0.95, + 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, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.95, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + mu_list[[4]] <<- list() + mu_list[[5]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <<- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <<- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <<- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <<- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <<- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <<- array(rep(0.755, n*K*T_), c(n,K,T_)) + } > > > test.runitCalcPredictionLOOCV01 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + baseline <- c(0, 0, 0) + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + rem_t <- 2 + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.56, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV02 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + 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, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + rem_t <- 2 + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV03 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 4 + rem_t <- 3 + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV04 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,-1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 4 + rem_t <- 3 + obs_modified[2,4,2] <- NA + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkTrue(is.na(predict)) + } + } > > > test.runitCalcPredictionLOOCV05 <- function() { + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 2 + rem_t <- 3 + obs_modified[2,2,2] <- NA + obs_modified[3,2,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV06 <- function() { + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 2 + rem_t <- 2 + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkTrue(is.na(predict)) + } + } > > > test.runitCalcPredictionLOOCV07 <- function() { + + baseline <- c(0.76, 0.76, 0.76) + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 2 + rem_t <- 2 + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.32 0.04 0.35 |
lpNet.Rcheck/tests_x64/runitCalcPredictionLOOCV_timeSeries.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .setUp <- function() { + + n <<- 3 + K <<- 4 + T_ <<- 3 + + T_nw <<- matrix(c(0,0,1, + 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, + 0.95, 0.56, 0.95, 0.95, + 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, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.95, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + mu_list[[4]] <<- list() + mu_list[[5]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <<- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <<- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <<- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <<- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <<- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <<- array(rep(0.755, n*K*T_), c(n,K,T_)) + } > > > test.runitCalcPredictionLOOCV01 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + baseline <- c(0, 0, 0) + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + rem_t <- 2 + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.56, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV02 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + 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, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + rem_t <- 2 + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV03 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 4 + rem_t <- 3 + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV04 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,-1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 4 + rem_t <- 3 + obs_modified[2,4,2] <- NA + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkTrue(is.na(predict)) + } + } > > > test.runitCalcPredictionLOOCV05 <- function() { + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 2 + rem_t <- 3 + obs_modified[2,2,2] <- NA + obs_modified[3,2,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV06 <- function() { + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 2 + rem_t <- 2 + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkTrue(is.na(predict)) + } + } > > > test.runitCalcPredictionLOOCV07 <- function() { + + baseline <- c(0.76, 0.76, 0.76) + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 2 + rem_t <- 2 + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.28 0.06 0.32 |
lpNet.Rcheck/tests_i386/runitCalcRangeLambda.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.calcRangeLambda <- function() { + + n <- 3 + K <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.25) + + + obs_mat <- 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) + + delta <- rep(0.755, n) + delta_type <- "perGene" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaPerGeneExp<- function() { + + n <- 3 + K <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, + 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, 0.30, 0.32, 0.33) + + + obs_mat <- 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) + + 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" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaTimeSeries <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.09) + + 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) + + delta <- rep(0.755, n) + delta_type <- "perGene" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > test.calcRangeLambdaTimeSeriesPerGeneExp <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.10, 1.15, 1.20, + 1.25, 1.28) + + 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) + + 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, 0.96), nrow=n, ncol=K, byrow=TRUE) + delta_type <- "perGeneExp" + + lambda <-calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaTimeSeriesPerGeneTime <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.10, 1.15, 1.20, + 1.25) + + 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) + + 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=T_, byrow=TRUE) + delta_type <- "perGeneTime" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaTimeSeriesperGeneExpTime <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.10, 1.15, 1.19) + + 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) + + 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" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > proc.time() user system elapsed 0.29 0.04 0.32 |
lpNet.Rcheck/tests_x64/runitCalcRangeLambda.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.calcRangeLambda <- function() { + + n <- 3 + K <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.25) + + + obs_mat <- 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) + + delta <- rep(0.755, n) + delta_type <- "perGene" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaPerGeneExp<- function() { + + n <- 3 + K <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, + 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, 0.30, 0.32, 0.33) + + + obs_mat <- 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) + + 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" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaTimeSeries <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.09) + + 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) + + delta <- rep(0.755, n) + delta_type <- "perGene" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > test.calcRangeLambdaTimeSeriesPerGeneExp <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.10, 1.15, 1.20, + 1.25, 1.28) + + 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) + + 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, 0.96), nrow=n, ncol=K, byrow=TRUE) + delta_type <- "perGeneExp" + + lambda <-calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaTimeSeriesPerGeneTime <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.10, 1.15, 1.20, + 1.25) + + 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) + + 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=T_, byrow=TRUE) + delta_type <- "perGeneTime" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaTimeSeriesperGeneExpTime <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.10, 1.15, 1.19) + + 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) + + 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" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > proc.time() user system elapsed 0.28 0.03 0.29 |
lpNet.Rcheck/tests_i386/runitDoILP.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .setUp <- function(){ + + n <<- 3 + K <<- 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 <<- 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.doILPShortExamplePerGene <- function() { + + true_result_objval <- 13.52785 + true_result_solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.1411606, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 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_=NULL, annot, delta_type, prior=NULL, sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE) + + checkEquals(true_result_objval, res$objval, tolerance=0.00001) + checkEquals(true_result_solution, res$solution, tolerance=0.00001) + } > > > test.doILPShortExamplePerGeneExp <- function() { + + true_result_objval <- 19.68196 + true_result_solution <- c(0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 1.9358974, 1.9358974, 0.0000000, + 0.0000000, 1.1411606, 1.1411606, + 1.9358974, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 0.0000000, 0.0000000, 0.0000000, + 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 <- "perGeneExp" + + res <- doILP(obs_mat, delta, lambda, b, n, K, T_=NULL, annot, delta_type, prior=NULL, sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE) + + checkEquals(true_result_objval, res$objval, tolerance=0.00001) + checkEquals(true_result_solution, res$solution, tolerance=0.00001) + } > > > proc.time() user system elapsed 0.20 0.14 0.31 |
lpNet.Rcheck/tests_x64/runitDoILP.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .setUp <- function(){ + + n <<- 3 + K <<- 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 <<- 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.doILPShortExamplePerGene <- function() { + + true_result_objval <- 13.52785 + true_result_solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.1411606, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 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_=NULL, annot, delta_type, prior=NULL, sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE) + + checkEquals(true_result_objval, res$objval, tolerance=0.00001) + checkEquals(true_result_solution, res$solution, tolerance=0.00001) + } > > > test.doILPShortExamplePerGeneExp <- function() { + + true_result_objval <- 19.68196 + true_result_solution <- c(0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 1.9358974, 1.9358974, 0.0000000, + 0.0000000, 1.1411606, 1.1411606, + 1.9358974, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 0.0000000, 0.0000000, 0.0000000, + 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 <- "perGeneExp" + + res <- doILP(obs_mat, delta, lambda, b, n, K, T_=NULL, annot, delta_type, prior=NULL, sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE) + + checkEquals(true_result_objval, res$objval, tolerance=0.00001) + checkEquals(true_result_solution, res$solution, tolerance=0.00001) + } > > > proc.time() user system elapsed 0.20 0.04 0.21 |
lpNet.Rcheck/tests_i386/runitDoILP_timeSeries.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .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) + } > > > proc.time() user system elapsed 0.29 0.04 0.32 |
lpNet.Rcheck/tests_x64/runitDoILP_timeSeries.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .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) + } > > > proc.time() user system elapsed 0.28 0.06 0.32 |
lpNet.Rcheck/tests_i386/runitGenerateTimeSeriesNetStates.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.generateTimeSeriesGeneStates <- function() { + + n <- 10 + K <- 11 + T_ <- 6 + + true_result <- array(NA, c(n,K,T_)) + + true_result[ , , 1] <- matrix(c(0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 2] <- matrix(c(0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,0,1,1,1,1,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 3] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,0,1,1,1,1,1,1, + 1,1,1,1,1,0,1,0,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 4] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,0,1,1,1,1,1,1, + 1,1,1,1,1,0,1,0,1,1,1, + 0,0,0,1,1,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,1,0,1,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 5] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1,1, + 0,0,0,1,1,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,1,0,1,0,0,0, + 1,0,1,1,1,1,1,1,1,0,1), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 6] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1,1, + 1,1,0,1,1,0,1,0,1,1,1, + 0,0,0,0,1,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,1,0,0,0,0,0, + 1,0,1,1,1,1,1,1,1,0,1), nrow=n, ncol=K, byrow=TRUE) + + T_nw <- matrix(c(0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,1, + 0,1,0,0,1,1,0,0,0,0, + 1,0,0,0,1,0,0,0,0,0, + 0,1,0,0,0,0,-1,0,1,0, + 0,1,1,0,0,0,1,0,-1,0, + 0,1,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,-1,0,0,0,0), nrow=n, ncol=n, byrow=T) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + + gene_states <- generateTimeSeriesNetStates(nw_und=T_nw, b=b, n=n, K=K, T_user=NULL) + + checkEquals(true_result, gene_states$node_state_vec) + } > > > test.generateTimeSeriesGeneStatesT10 <- function() { + + n <- 10 + K <- 11 + T_ <- 6 + + T_nw <- matrix(c(0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,1, + 0,1,0,0,1,1,0,0,0,0, + 1,0,0,0,1,0,0,0,0,0, + 0,1,0,0,0,0,-1,0,1,0, + 0,1,1,0,0,0,1,0,-1,0, + 0,1,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,-1,0,0,0,0), nrow=n, ncol=n, byrow=T) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + + gene_states <- generateTimeSeriesNetStates(nw_und=T_nw, b=b, n=n, K=K, T_user=10) + + checkEquals(10, gene_states$T_) + } > > > proc.time() user system elapsed 0.31 0.06 0.34 |
lpNet.Rcheck/tests_x64/runitGenerateTimeSeriesNetStates.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.generateTimeSeriesGeneStates <- function() { + + n <- 10 + K <- 11 + T_ <- 6 + + true_result <- array(NA, c(n,K,T_)) + + true_result[ , , 1] <- matrix(c(0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 2] <- matrix(c(0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,0,1,1,1,1,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 3] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,0,1,1,1,1,1,1, + 1,1,1,1,1,0,1,0,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 4] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,0,1,1,1,1,1,1, + 1,1,1,1,1,0,1,0,1,1,1, + 0,0,0,1,1,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,1,0,1,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 5] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1,1, + 0,0,0,1,1,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,1,0,1,0,0,0, + 1,0,1,1,1,1,1,1,1,0,1), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 6] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1,1, + 1,1,0,1,1,0,1,0,1,1,1, + 0,0,0,0,1,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,1,0,0,0,0,0, + 1,0,1,1,1,1,1,1,1,0,1), nrow=n, ncol=K, byrow=TRUE) + + T_nw <- matrix(c(0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,1, + 0,1,0,0,1,1,0,0,0,0, + 1,0,0,0,1,0,0,0,0,0, + 0,1,0,0,0,0,-1,0,1,0, + 0,1,1,0,0,0,1,0,-1,0, + 0,1,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,-1,0,0,0,0), nrow=n, ncol=n, byrow=T) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + + gene_states <- generateTimeSeriesNetStates(nw_und=T_nw, b=b, n=n, K=K, T_user=NULL) + + checkEquals(true_result, gene_states$node_state_vec) + } > > > test.generateTimeSeriesGeneStatesT10 <- function() { + + n <- 10 + K <- 11 + T_ <- 6 + + T_nw <- matrix(c(0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,1, + 0,1,0,0,1,1,0,0,0,0, + 1,0,0,0,1,0,0,0,0,0, + 0,1,0,0,0,0,-1,0,1,0, + 0,1,1,0,0,0,1,0,-1,0, + 0,1,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,-1,0,0,0,0), nrow=n, ncol=n, byrow=T) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + + gene_states <- generateTimeSeriesNetStates(nw_und=T_nw, b=b, n=n, K=K, T_user=10) + + checkEquals(10, gene_states$T_) + } > > > proc.time() user system elapsed 0.28 0.03 0.29 |
lpNet.Rcheck/tests_i386/runitGetAdja.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getAdja <- function() { + + n <- 3 + K <- 4 + + true_result <- matrix(c(0, 0.7947368, -1.1411606, + 0, 0.0000000, 1.9358974, + 0, 0.0000000, 0.000000), nrow=n, ncol=n, byrow=TRUE) + + res <- list() + + res$solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.1411606, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12") + + adja = getAdja(res, n) + + checkEquals(true_result, adja) + + } > > > test.getAdjaTimeSeries<- function() { + + n <- 3 + + true_result = matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + res = list() + res$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) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12", + "s_13", "s_14", "s_15", "s_16", + "s_17", "s_18", "s_19", "s_20", + "s_21", "s_22", "s_23", "s_24", + "s_25", "s_26", "s_27", "s_28", + "s_29", "s_30", "s_31", "s_32", + "s_33", "s_34", "s_35", "s_36") + + adja = getAdja(res, n) + + checkEquals(true_result, adja) + } > > proc.time() user system elapsed 0.29 0.01 0.29 |
lpNet.Rcheck/tests_x64/runitGetAdja.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getAdja <- function() { + + n <- 3 + K <- 4 + + true_result <- matrix(c(0, 0.7947368, -1.1411606, + 0, 0.0000000, 1.9358974, + 0, 0.0000000, 0.000000), nrow=n, ncol=n, byrow=TRUE) + + res <- list() + + res$solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.1411606, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12") + + adja = getAdja(res, n) + + checkEquals(true_result, adja) + + } > > > test.getAdjaTimeSeries<- function() { + + n <- 3 + + true_result = matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + res = list() + res$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) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12", + "s_13", "s_14", "s_15", "s_16", + "s_17", "s_18", "s_19", "s_20", + "s_21", "s_22", "s_23", "s_24", + "s_25", "s_26", "s_27", "s_28", + "s_29", "s_30", "s_31", "s_32", + "s_33", "s_34", "s_35", "s_36") + + adja = getAdja(res, n) + + checkEquals(true_result, adja) + } > > proc.time() user system elapsed 0.29 0.01 0.29 |
lpNet.Rcheck/tests_i386/runitGetBaseline.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getBaseline <- function() { + + n <- 3 + K <- 4 + + true_result = c(0.7550000, 0.0000000, 0.0000000) + + res <- list() + + res$solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.1411606, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12") + + adja = getBaseline(res, n) + + checkEquals(true_result, adja) + + } > > > test.getBaselineTimeSeries<- function() { + + n <- 3 + + true_result = c(0.7550000, 0.0000000, 0.0000000) + + res = list() + res$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) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12", + "s_13", "s_14", "s_15", "s_16", + "s_17", "s_18", "s_19", "s_20", + "s_21", "s_22", "s_23", "s_24", + "s_25", "s_26", "s_27", "s_28", + "s_29", "s_30", "s_31", "s_32", + "s_33", "s_34", "s_35", "s_36") + + + adja = getBaseline(res, n) + + checkEquals(true_result, adja) + } > > proc.time() user system elapsed 0.31 0.03 0.31 |
lpNet.Rcheck/tests_x64/runitGetBaseline.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getBaseline <- function() { + + n <- 3 + K <- 4 + + true_result = c(0.7550000, 0.0000000, 0.0000000) + + res <- list() + + res$solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.1411606, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12") + + adja = getBaseline(res, n) + + checkEquals(true_result, adja) + + } > > > test.getBaselineTimeSeries<- function() { + + n <- 3 + + true_result = c(0.7550000, 0.0000000, 0.0000000) + + res = list() + res$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) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12", + "s_13", "s_14", "s_15", "s_16", + "s_17", "s_18", "s_19", "s_20", + "s_21", "s_22", "s_23", "s_24", + "s_25", "s_26", "s_27", "s_28", + "s_29", "s_30", "s_31", "s_32", + "s_33", "s_34", "s_35", "s_36") + + + adja = getBaseline(res, n) + + checkEquals(true_result, adja) + } > > proc.time() user system elapsed 0.23 0.06 0.28 |
lpNet.Rcheck/tests_i386/runitGetEdgeAnnot.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getEdgeAnnot <- function() { + + true_result = c("w+_1_1", "w+_1_2", "w+_1_3", "w+_2_1", "w+_2_2", "w+_2_3", "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", "w-_2_1", "w-_2_2", "w-_2_3", "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0") + + n <- 3 + edge_annot <- getEdgeAnnot(n, allpos=FALSE) + + checkEquals(true_result, edge_annot) + } > > > test.getEdgeAnnotAllPos <- function() { + + true_result = c("w+_1_1", "w+_1_2", "w+_1_3", "w+_2_1", "w+_2_2", "w+_2_3", "w+_3_1", "w+_3_2", "w+_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0") + + n <- 3 + edge_annot <- getEdgeAnnot(n, allpos=TRUE) + + checkEquals(true_result, edge_annot) + } > > > proc.time() user system elapsed 0.29 0.04 0.32 |
lpNet.Rcheck/tests_x64/runitGetEdgeAnnot.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getEdgeAnnot <- function() { + + true_result = c("w+_1_1", "w+_1_2", "w+_1_3", "w+_2_1", "w+_2_2", "w+_2_3", "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", "w-_2_1", "w-_2_2", "w-_2_3", "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0") + + n <- 3 + edge_annot <- getEdgeAnnot(n, allpos=FALSE) + + checkEquals(true_result, edge_annot) + } > > > test.getEdgeAnnotAllPos <- function() { + + true_result = c("w+_1_1", "w+_1_2", "w+_1_3", "w+_2_1", "w+_2_2", "w+_2_3", "w+_3_1", "w+_3_2", "w+_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0") + + n <- 3 + edge_annot <- getEdgeAnnot(n, allpos=TRUE) + + checkEquals(true_result, edge_annot) + } > > > proc.time() user system elapsed 0.25 0.06 0.29 |
lpNet.Rcheck/tests_i386/runitGetObsMat.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getObsMatMuTypeSingle <- function() { + + n <- 3 + K <- 4 + + true_result <- 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=T) + + 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) + + act_mat <- calcActivation(T_nw, b, n, K) + + active_mu <- 0.95 + active_sd <- 0.01 + inactive_mu <- 0.56 + inactive_sd <- 0.01 + + obs_mat <- getObsMat(act_mat, net_states=NULL, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="single") + checkEquals(true_result, obs_mat, tolerance=(active_sd + inactive_sd)) + } > > > test.getObsMatMuTypePerGene <- function() { + + n <- 3 + K <- 4 + + true_result <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 1.1, 1.1, + 0.2, 0.2, 0.2, 1.3), nrow=n, ncol=K, byrow=T) + + 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) + + act_mat <- calcActivation(T_nw, b, n, K) + + + active_mu <- c(0.95, 1.1, 1.3) + active_sd <- rep(0.01, n) + inactive_mu <- c(0.56, 0.4, 0.2) + inactive_sd <- rep(0.01, n) + + obs_mat <- getObsMat(act_mat, net_states=NULL, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGene") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneExp <- function() { + + n <- 3 + K <- 4 + + true_result <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 20.5, 20.7, + 3.1, 3.3, 3.5, 30.7), nrow=n, ncol=K, byrow=T) + + 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) + + act_mat <- calcActivation(T_nw, b, n, K) + + active_mu <- matrix(c(10.1, 20.1, 30.1, + 10.3, 20.3, 30.3, + 10.5, 20.5, 30.5, + 10.7, 20.7, 30.7), nrow=n, ncol=K) + + active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + inactive_mu <- matrix(c(1.1, 2.1, 3.1, + 1.3, 2.3, 3.3, + 1.5, 2.5, 3.5, + 1.7, 2.7, 3.7), nrow=n, ncol=K) + + inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + obs_mat <- getObsMat(act_mat, net_states=NULL, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneExp") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypeSingle_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n, K, T_)) + + true_result[,,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=T) + + true_result[,,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=T) + + true_result[,,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=T) + + true_result[,,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=T) + + 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) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + active_mu <- 0.95 + active_sd <- 0.01 + inactive_mu <- 0.56 + inactive_sd <- 0.01 + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="single") + checkEquals(true_result, obs_mat, tolerance=(active_sd + inactive_sd)) + } > > > test.getObsMatMuTypePerGene_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.4, 0.4, 0.4, 0.4, + 0.2, 0.2, 0.2, 0.2), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 0.4, 0.4, + 0.2, 0.2, 0.2, 0.2), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 1.1, 1.1, + 0.2, 0.2, 0.2, 0.2), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 1.1, 1.1, + 0.2, 0.2, 0.2, 1.3), nrow=n, ncol=K, byrow=T) + + + 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) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + active_mu <- c(0.95, 1.1, 1.3) + active_sd <- rep(0.01, n) + inactive_mu <- c(0.56, 0.4, 0.2) + inactive_sd <- rep(0.01, n) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGene") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneExp_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(1.1, 1.3, 1.5, 1.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 20.5, 20.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 20.5, 20.7, + 3.1, 3.3, 3.5, 30.7), nrow=n, ncol=K, byrow=T) + + 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) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + + active_mu <- matrix(c(10.1, 10.3, 10.5, 10.7, + 20.1, 20.3, 20.5, 20.7, + 30.1, 30.3, 30.5, 30.7), nrow=n, ncol=K, byrow=T) + + active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + inactive_mu <- matrix(c(1.1, 1.3, 1.5, 1.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneExp") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneTime_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(1.1, 1.1, 1.1, 1.1, + 2.1, 2.1, 2.1, 2.1, + 3.1, 3.1, 3.1, 3.1), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(1.3, 10.3, 10.3, 10.3, + 2.1, 2.3, 2.3, 2.3, + 3.3, 3.3, 3.3, 3.3), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(1.5, 10.5, 10.5, 10.5, + 2.5, 2.5, 20.5, 20.5, + 3.5, 3.5, 3.5, 3.5), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(1.7, 10.7, 10.7, 10.7, + 2.7, 2.7, 20.7, 20.7, + 3.7, 3.7, 3.7, 30.7), nrow=n, ncol=K, byrow=T) + + 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) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + + active_mu <- matrix(c(10.1, 10.3, 10.5, 10.7, + 20.1, 20.3, 20.5, 20.7, + 30.1, 30.3, 30.5, 30.7), nrow=n, ncol=T_, byrow=T) + + active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=T_) + + inactive_mu <- matrix(c(1.1, 1.3, 1.5, 1.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=T_, byrow=T) + + inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=T_) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneTime") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneExpTime_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(2.1, 20.3, 20.5, 20.7, + 2.1, 2.3, 2.5, 2.7, + 2.1, 2.3, 2.5, 2.7), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(3.1, 30.3, 30.5, 30.7, + 3.1, 3.3, 30.5, 30.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(4.1, 40.3, 40.5, 40.7, + 4.1, 4.3, 40.5, 40.7, + 4.1, 4.3, 4.5, 40.7), nrow=n, ncol=K, byrow=T) + + 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) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + active_mu <- array(NA, c(n,K,T_)) + + active_mu[,,1] <- matrix(c(10.1, 10.3, 10.5, 10.7, + 10.1, 10.3, 10.5, 10.7, + 10.1, 10.3, 10.5, 10.7), nrow=n, ncol=K, byrow=T) + + active_mu[,,2] <- matrix(c(20.1, 20.3, 20.5, 20.7, + 20.1, 20.3, 20.5, 20.7, + 20.1, 20.3, 20.5, 20.7), nrow=n, ncol=K, byrow=T) + + active_mu[,,3] <- matrix(c(30.1, 30.3, 30.5, 30.7, + 30.1, 30.3, 30.5, 30.7, + 30.1, 30.3, 30.5, 30.7), nrow=n, ncol=K, byrow=T) + + active_mu[,,4] <- matrix(c(40.1, 40.3, 40.5, 40.7, + 40.1, 40.3, 40.5, 40.7, + 40.1, 40.3, 40.5, 40.7), nrow=n, ncol=K, byrow=T) + + active_sd <- array(0.01, c(n,K,T_)) + + inactive_mu <- array(NA, c(n,K,T_)) + inactive_mu[,,1] <- matrix(c(1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7), nrow=n, ncol=K, byrow=T) + + inactive_mu[,,2] <- matrix(c(2.1, 2.3, 2.5, 2.7, + 2.1, 2.3, 2.5, 2.7, + 2.1, 2.3, 2.5, 2.7), nrow=n, ncol=K, byrow=T) + + inactive_mu[,,3] <- matrix(c(3.1, 3.3, 3.5, 3.7, + 3.1, 3.3, 3.5, 3.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + inactive_mu[,,4] <- matrix(c(4.1, 4.3, 4.5, 4.7, + 4.1, 4.3, 4.5, 4.7, + 4.1, 4.3, 4.5, 4.7), nrow=n, ncol=K, byrow=T) + + inactive_sd <- array(0.01, c(n,K,T_)) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneExpTime") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > proc.time() user system elapsed 0.25 0.07 0.31 |
lpNet.Rcheck/tests_x64/runitGetObsMat.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getObsMatMuTypeSingle <- function() { + + n <- 3 + K <- 4 + + true_result <- 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=T) + + 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) + + act_mat <- calcActivation(T_nw, b, n, K) + + active_mu <- 0.95 + active_sd <- 0.01 + inactive_mu <- 0.56 + inactive_sd <- 0.01 + + obs_mat <- getObsMat(act_mat, net_states=NULL, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="single") + checkEquals(true_result, obs_mat, tolerance=(active_sd + inactive_sd)) + } > > > test.getObsMatMuTypePerGene <- function() { + + n <- 3 + K <- 4 + + true_result <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 1.1, 1.1, + 0.2, 0.2, 0.2, 1.3), nrow=n, ncol=K, byrow=T) + + 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) + + act_mat <- calcActivation(T_nw, b, n, K) + + + active_mu <- c(0.95, 1.1, 1.3) + active_sd <- rep(0.01, n) + inactive_mu <- c(0.56, 0.4, 0.2) + inactive_sd <- rep(0.01, n) + + obs_mat <- getObsMat(act_mat, net_states=NULL, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGene") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneExp <- function() { + + n <- 3 + K <- 4 + + true_result <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 20.5, 20.7, + 3.1, 3.3, 3.5, 30.7), nrow=n, ncol=K, byrow=T) + + 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) + + act_mat <- calcActivation(T_nw, b, n, K) + + active_mu <- matrix(c(10.1, 20.1, 30.1, + 10.3, 20.3, 30.3, + 10.5, 20.5, 30.5, + 10.7, 20.7, 30.7), nrow=n, ncol=K) + + active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + inactive_mu <- matrix(c(1.1, 2.1, 3.1, + 1.3, 2.3, 3.3, + 1.5, 2.5, 3.5, + 1.7, 2.7, 3.7), nrow=n, ncol=K) + + inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + obs_mat <- getObsMat(act_mat, net_states=NULL, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneExp") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypeSingle_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n, K, T_)) + + true_result[,,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=T) + + true_result[,,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=T) + + true_result[,,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=T) + + true_result[,,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=T) + + 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) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + active_mu <- 0.95 + active_sd <- 0.01 + inactive_mu <- 0.56 + inactive_sd <- 0.01 + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="single") + checkEquals(true_result, obs_mat, tolerance=(active_sd + inactive_sd)) + } > > > test.getObsMatMuTypePerGene_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.4, 0.4, 0.4, 0.4, + 0.2, 0.2, 0.2, 0.2), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 0.4, 0.4, + 0.2, 0.2, 0.2, 0.2), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 1.1, 1.1, + 0.2, 0.2, 0.2, 0.2), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 1.1, 1.1, + 0.2, 0.2, 0.2, 1.3), nrow=n, ncol=K, byrow=T) + + + 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) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + active_mu <- c(0.95, 1.1, 1.3) + active_sd <- rep(0.01, n) + inactive_mu <- c(0.56, 0.4, 0.2) + inactive_sd <- rep(0.01, n) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGene") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneExp_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(1.1, 1.3, 1.5, 1.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 20.5, 20.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 20.5, 20.7, + 3.1, 3.3, 3.5, 30.7), nrow=n, ncol=K, byrow=T) + + 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) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + + active_mu <- matrix(c(10.1, 10.3, 10.5, 10.7, + 20.1, 20.3, 20.5, 20.7, + 30.1, 30.3, 30.5, 30.7), nrow=n, ncol=K, byrow=T) + + active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + inactive_mu <- matrix(c(1.1, 1.3, 1.5, 1.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneExp") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneTime_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(1.1, 1.1, 1.1, 1.1, + 2.1, 2.1, 2.1, 2.1, + 3.1, 3.1, 3.1, 3.1), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(1.3, 10.3, 10.3, 10.3, + 2.1, 2.3, 2.3, 2.3, + 3.3, 3.3, 3.3, 3.3), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(1.5, 10.5, 10.5, 10.5, + 2.5, 2.5, 20.5, 20.5, + 3.5, 3.5, 3.5, 3.5), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(1.7, 10.7, 10.7, 10.7, + 2.7, 2.7, 20.7, 20.7, + 3.7, 3.7, 3.7, 30.7), nrow=n, ncol=K, byrow=T) + + 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) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + + active_mu <- matrix(c(10.1, 10.3, 10.5, 10.7, + 20.1, 20.3, 20.5, 20.7, + 30.1, 30.3, 30.5, 30.7), nrow=n, ncol=T_, byrow=T) + + active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=T_) + + inactive_mu <- matrix(c(1.1, 1.3, 1.5, 1.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=T_, byrow=T) + + inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=T_) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneTime") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneExpTime_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(2.1, 20.3, 20.5, 20.7, + 2.1, 2.3, 2.5, 2.7, + 2.1, 2.3, 2.5, 2.7), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(3.1, 30.3, 30.5, 30.7, + 3.1, 3.3, 30.5, 30.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(4.1, 40.3, 40.5, 40.7, + 4.1, 4.3, 40.5, 40.7, + 4.1, 4.3, 4.5, 40.7), nrow=n, ncol=K, byrow=T) + + 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) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + active_mu <- array(NA, c(n,K,T_)) + + active_mu[,,1] <- matrix(c(10.1, 10.3, 10.5, 10.7, + 10.1, 10.3, 10.5, 10.7, + 10.1, 10.3, 10.5, 10.7), nrow=n, ncol=K, byrow=T) + + active_mu[,,2] <- matrix(c(20.1, 20.3, 20.5, 20.7, + 20.1, 20.3, 20.5, 20.7, + 20.1, 20.3, 20.5, 20.7), nrow=n, ncol=K, byrow=T) + + active_mu[,,3] <- matrix(c(30.1, 30.3, 30.5, 30.7, + 30.1, 30.3, 30.5, 30.7, + 30.1, 30.3, 30.5, 30.7), nrow=n, ncol=K, byrow=T) + + active_mu[,,4] <- matrix(c(40.1, 40.3, 40.5, 40.7, + 40.1, 40.3, 40.5, 40.7, + 40.1, 40.3, 40.5, 40.7), nrow=n, ncol=K, byrow=T) + + active_sd <- array(0.01, c(n,K,T_)) + + inactive_mu <- array(NA, c(n,K,T_)) + inactive_mu[,,1] <- matrix(c(1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7), nrow=n, ncol=K, byrow=T) + + inactive_mu[,,2] <- matrix(c(2.1, 2.3, 2.5, 2.7, + 2.1, 2.3, 2.5, 2.7, + 2.1, 2.3, 2.5, 2.7), nrow=n, ncol=K, byrow=T) + + inactive_mu[,,3] <- matrix(c(3.1, 3.3, 3.5, 3.7, + 3.1, 3.3, 3.5, 3.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + inactive_mu[,,4] <- matrix(c(4.1, 4.3, 4.5, 4.7, + 4.1, 4.3, 4.5, 4.7, + 4.1, 4.3, 4.5, 4.7), nrow=n, ncol=K, byrow=T) + + inactive_sd <- array(0.01, c(n,K,T_)) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneExpTime") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > proc.time() user system elapsed 0.32 0.03 0.34 |
lpNet.Rcheck/tests_i386/runitGetSampleAdja.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getSampleAdja <- function() { + + n <- 3 + K <- 4 + annot <- getEdgeAnnot(n) + annot_node = seq(1,n) + + true_result <- matrix(c(0, 0.7947368, -0.3973684, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + colnames(true_result) <- rownames(true_result) <- annot_node + + edges_all <- matrix(c(0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.7947368, 0.0000000, 0, 0.7947368, 0, 0.0000000, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + -0.5534774, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.3262604, -0.7947368, 0, 0.7947368, 0, 0.7947368, + 1.9358974, 0.0000000, 0, -1.3482143, 0, -1.9358974, + 1.9358974, 0.0000000, 0, 0.0000000, 0, -1.9358974), nrow=n*K, ncol=n*(n-1), byrow=TRUE) + + colnames(edges_all) <- c("1->2", "1->3", "2->1", "2->3", "3->1", "3->2") + + sampleAdja = getSampleAdja(edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, sampleAdja, tolerance=0.00001) + } > > proc.time() user system elapsed 0.26 0.07 0.32 |
lpNet.Rcheck/tests_x64/runitGetSampleAdja.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getSampleAdja <- function() { + + n <- 3 + K <- 4 + annot <- getEdgeAnnot(n) + annot_node = seq(1,n) + + true_result <- matrix(c(0, 0.7947368, -0.3973684, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + colnames(true_result) <- rownames(true_result) <- annot_node + + edges_all <- matrix(c(0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.7947368, 0.0000000, 0, 0.7947368, 0, 0.0000000, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + -0.5534774, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.3262604, -0.7947368, 0, 0.7947368, 0, 0.7947368, + 1.9358974, 0.0000000, 0, -1.3482143, 0, -1.9358974, + 1.9358974, 0.0000000, 0, 0.0000000, 0, -1.9358974), nrow=n*K, ncol=n*(n-1), byrow=TRUE) + + colnames(edges_all) <- c("1->2", "1->3", "2->1", "2->3", "3->1", "3->2") + + sampleAdja = getSampleAdja(edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, sampleAdja, tolerance=0.00001) + } > > proc.time() user system elapsed 0.25 0.07 0.31 |
lpNet.Rcheck/tests_i386/runitGetSampleAdjaMAD.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getSampleAdjaMAD <- function() { + + n <- 3 + K <- 4 + annot <- getEdgeAnnot(n) + annot_node = seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.0000000, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + colnames(true_result) <- rownames(true_result) <- annot_node + + edges_all <- matrix(c(0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.7947368, 0.0000000, 0, 0.7947368, 0, 0.0000000, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + -0.5534774, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.3262604, -0.7947368, 0, 0.7947368, 0, 0.7947368, + 1.9358974, 0.0000000, 0, -1.3482143, 0, -1.9358974, + 1.9358974, 0.0000000, 0, 0.0000000, 0, -1.9358974), nrow=n*K, ncol=n*(n-1), byrow=TRUE) + + colnames(edges_all) <- c("1->2", "1->3", "2->1", "2->3", "3->1", "3->2") + + sampleAdjaMAD = getSampleAdjaMAD(edges_all, n, annot_node, method=median, method2=mad, septype="->") + + checkEquals(true_result, sampleAdjaMAD, tolerance=0.00001) + } > > proc.time() user system elapsed 0.21 0.04 0.25 |
lpNet.Rcheck/tests_x64/runitGetSampleAdjaMAD.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.getSampleAdjaMAD <- function() { + + n <- 3 + K <- 4 + annot <- getEdgeAnnot(n) + annot_node = seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.0000000, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + colnames(true_result) <- rownames(true_result) <- annot_node + + edges_all <- matrix(c(0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.7947368, 0.0000000, 0, 0.7947368, 0, 0.0000000, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + -0.5534774, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.3262604, -0.7947368, 0, 0.7947368, 0, 0.7947368, + 1.9358974, 0.0000000, 0, -1.3482143, 0, -1.9358974, + 1.9358974, 0.0000000, 0, 0.0000000, 0, -1.9358974), nrow=n*K, ncol=n*(n-1), byrow=TRUE) + + colnames(edges_all) <- c("1->2", "1->3", "2->1", "2->3", "3->1", "3->2") + + sampleAdjaMAD = getSampleAdjaMAD(edges_all, n, annot_node, method=median, method2=mad, septype="->") + + checkEquals(true_result, sampleAdjaMAD, tolerance=0.00001) + } > > proc.time() user system elapsed 0.28 0.03 0.29 |
lpNet.Rcheck/tests_i386/runitKfoldCV.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitKfoldCV <- function() { + + n <- 3 + K <- 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 <- 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) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp") + delta_types <- c("perGene", "perGene", "perGeneExp") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- list() + + true_result <- matrix(c(0, 0.7947368, -0.5, + 0, 0.0000000, 1.0, + 0, 0.0000000, 0.000000), nrow=n, ncol=n, byrow=TRUE) + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- kfoldCV(kfold=kfold, times=1, delta=delta, lambda=lambda, obs=obs_mat, b=b, n=n, K=K, T_=NULL, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE) + + adja <- getSampleAdja(res$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.6) + } + } > > > proc.time() user system elapsed 0.26 0.09 0.34 |
lpNet.Rcheck/tests_x64/runitKfoldCV.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitKfoldCV <- function() { + + n <- 3 + K <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp") + delta_types <- c("perGene", "perGene", "perGeneExp") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- list() + + true_result <- matrix(c(0, 0.7947368, -0.5, + 0, 0.0000000, 1.0, + 0, 0.0000000, 0.000000), nrow=n, ncol=n, byrow=TRUE) + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- kfoldCV(kfold=kfold, times=1, delta=delta, lambda=lambda, obs=obs_mat, b=b, n=n, K=K, T_=NULL, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE) + + adja <- getSampleAdja(res$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.6) + } + } > > > proc.time() user system elapsed 0.28 0.04 0.31 |
lpNet.Rcheck/tests_i386/runitKfoldCV_timeSeries.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitKfoldCV_timeSeries <- 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) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + delta_types <- c("perGene", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + mu_list[[4]] <- list() + mu_list[[5]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <- array(rep(0.755, n*K*T_), c(n,K,T_)) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- kfoldCV(kfold=kfold, times=1, obs=obs_mat, delta=delta, lambda=lambda, b=b, n=n, K=K, T_=T_, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE, flag_time_series=TRUE) + + adja <- getSampleAdjaMAD(res$edges_all, n, annot_node, method=median, method2=mad, septype="->") + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.34 0.06 0.37 |
lpNet.Rcheck/tests_x64/runitKfoldCV_timeSeries.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitKfoldCV_timeSeries <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- array(NA, c(n,K,T_)) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + delta_types <- c("perGene", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + mu_list[[4]] <- list() + mu_list[[5]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <- array(rep(0.755, n*K*T_), c(n,K,T_)) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- kfoldCV(kfold=kfold, times=1, obs=obs_mat, delta=delta, lambda=lambda, b=b, n=n, K=K, T_=T_, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE, flag_time_series=TRUE) + + adja <- getSampleAdjaMAD(res$edges_all, n, annot_node, method=median, method2=mad, septype="->") + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.25 0.07 0.31 |
lpNet.Rcheck/tests_i386/runitLOOCV.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitLOOCV <- function() { + + n <- 3 + K <- 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 <- 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) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp") + delta_types <- c("perGene", "perGene", "perGeneExp") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- list() + + true_result <- matrix(c(0, 0.7947368, -0.3973684, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- loocv(kfold=NULL, times=1, obs=obs_mat, delta=delta, lambda=lambda, b=b, n=n, K=K, T_=NULL, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE) + + adja <- getSampleAdja(res$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.29 0.04 0.31 |
lpNet.Rcheck/tests_x64/runitLOOCV.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitLOOCV <- function() { + + n <- 3 + K <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp") + delta_types <- c("perGene", "perGene", "perGeneExp") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- list() + + true_result <- matrix(c(0, 0.7947368, -0.3973684, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- loocv(kfold=NULL, times=1, obs=obs_mat, delta=delta, lambda=lambda, b=b, n=n, K=K, T_=NULL, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE) + + adja <- getSampleAdja(res$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.21 0.09 0.31 |
lpNet.Rcheck/tests_i386/runitLOOCV_timeSeries.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitLOOCV_timeSeries <- 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) + + baseline <- c(0.76, 0.76, 0) + + mu_types <- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + delta_types <- c("perGene", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + mu_list[[4]] <- list() + mu_list[[5]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <- array(rep(0.755, n*K*T_), c(n,K,T_)) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- loocv(kfold=NULL, times=1, obs=obs_mat, delta=delta, lambda=lambda, b=b, n=n, K=K, T_=T_, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE, flag_time_series=TRUE) + + adja <- getSampleAdja(res$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.35 0.01 0.34 |
lpNet.Rcheck/tests_x64/runitLOOCV_timeSeries.Rout R version 4.1.1 (2021-08-10) -- "Kick Things" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitLOOCV_timeSeries <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- array(NA, c(n,K,T_)) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76, 0.76, 0) + + mu_types <- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + delta_types <- c("perGene", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + mu_list[[4]] <- list() + mu_list[[5]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <- array(rep(0.755, n*K*T_), c(n,K,T_)) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- loocv(kfold=NULL, times=1, obs=obs_mat, delta=delta, lambda=lambda, b=b, n=n, K=K, T_=T_, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE, flag_time_series=TRUE) + + adja <- getSampleAdja(res$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.23 0.09 0.31 |
lpNet.Rcheck/examples_i386/lpNet-Ex.timings
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lpNet.Rcheck/examples_x64/lpNet-Ex.timings
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