==39770== Memcheck, a memory error detector
==39770== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
==39770== Using Valgrind-3.13.0 and LibVEX; rerun with -h for copyright info
==39770== Command: /data/blackswan/ripley/R/R-devel-vg/bin/exec/R --vanilla
==39770== 

R Under development (unstable) (2018-06-01 r74825) -- "Unsuffered Consequences"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (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.

  Natural language support but running in an English locale

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.

> pkgname <- "SemiSupervised"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('SemiSupervised')
> 
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("AnchorGraph")
> ### * AnchorGraph
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: AnchorGraph
> ### Title: Generate an Anchor Graph from an n x p data matrix
> ### Aliases: AnchorGraph getAnchor
> ### Keywords: classes models methods
> 
> ### ** Examples
> 
> ## Set up Sonar data with 20% labeled
> library(mlbench)
> data(Sonar)
> 
> n=dim(Sonar)[1]
> p=dim(Sonar)[2]
> 
> nu=0.2
> set.seed(100)
> L=sort(sample(1:n,ceiling(nu*n)))
> U=setdiff(1:n,L)
> 
> y.true<-Sonar$Class
> Sonar$Class[U]=NA
> 
> g.agraph1<-agraph(Class~.,data=Sonar) 
==39770== Invalid read of size 4
==39770==    at 0x158910DA: AREG_CV (packages/tests-vg/SemiSupervised/src/Anchor_Reg.c:56)
==39770==    by 0x498A4D: R_doDotCall (svn/R-devel/src/main/dotcode.c:617)
==39770==    by 0x499013: do_dotcall (svn/R-devel/src/main/dotcode.c:1252)
==39770==    by 0x4CC6A9: bcEval (svn/R-devel/src/main/eval.c:6772)
==39770==    by 0x4DAC0F: Rf_eval (svn/R-devel/src/main/eval.c:624)
==39770==    by 0x4DC51E: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==39770==    by 0x4DAD9C: Rf_eval (svn/R-devel/src/main/eval.c:747)
==39770==    by 0x46A46C: do_docall (svn/R-devel/src/main/coerce.c:2590)
==39770==    by 0x4CC6A9: bcEval (svn/R-devel/src/main/eval.c:6772)
==39770==    by 0x4DAC0F: Rf_eval (svn/R-devel/src/main/eval.c:624)
==39770==    by 0x4DC51E: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==39770==    by 0x4D1CAC: bcEval (svn/R-devel/src/main/eval.c:6740)
==39770==  Address 0x149dab24 is 868 bytes inside a block of size 7,960 alloc'd
==39770==    at 0x4C2DB6B: malloc (/builddir/build/BUILD/valgrind-3.13.0/coregrind/m_replacemalloc/vg_replace_malloc.c:299)
==39770==    by 0x510B7D: GetNewPage (svn/R-devel/src/main/memory.c:888)
==39770==    by 0x512687: Rf_allocVector3 (svn/R-devel/src/main/memory.c:2691)
==39770==    by 0x1588F543: agraph_lscore (packages/tests-vg/SemiSupervised/src/Anchor_Reg.c:227)
==39770==    by 0x15890A60: ARIDGE (packages/tests-vg/SemiSupervised/src/Anchor_Reg.c:182)
==39770==    by 0x1589143C: AREG_CV (packages/tests-vg/SemiSupervised/src/Anchor_Reg.c:93)
==39770==    by 0x498A4D: R_doDotCall (svn/R-devel/src/main/dotcode.c:617)
==39770==    by 0x499013: do_dotcall (svn/R-devel/src/main/dotcode.c:1252)
==39770==    by 0x4CC6A9: bcEval (svn/R-devel/src/main/eval.c:6772)
==39770==    by 0x4DAC0F: Rf_eval (svn/R-devel/src/main/eval.c:624)
==39770==    by 0x4DC51E: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==39770==    by 0x4DAD9C: Rf_eval (svn/R-devel/src/main/eval.c:747)
==39770== 
> 
> ##The following gives an equivalent output to the g.agraph1<-agraph(Class~.,data=Sonar) call.
> 
> 
> 
> cleanEx()

detaching ‘package:mlbench’

> nameEx("agraph")
> ### * agraph
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: agraph
> ### Title: Anchor Graph Functions ('agraph')
> ### Aliases: agraph agraph-methods agraph,formula-method
> ###   agraph,data.frame-method agraph,matrix-method agraph,vector-method
> ###   agraph,anchor-method show,agraph-method agraph.default
> ### Keywords: classes models methods
> 
> ### ** Examples
> 
> ## Set up Sonar data with 20% labeled (comparing randomForest and glmnet)
> library(mlbench)
> data(Sonar)
> 
> n=dim(Sonar)[1]
> p=dim(Sonar)[2]
> 
> nu=0.2
> set.seed(100)
> L=sort(sample(1:n,ceiling(nu*n)))
> U=setdiff(1:n,L)
> 
> y.true<-Sonar$Class
> Sonar$Class[U]=NA
> 
> ## Fit agraph to Sonar
> g.agraph<-agraph(Class~.,data=Sonar) 
> g.agraph
Anchor Graph Laplacian (agraph) with (n,|L|)=( 208 , 42 ) or  20 % labeled

Performance Estimates:
k-CV:  1.32  GCV:  0.034  DF:  0

Fit Estimates:
Lagrangians:  10   0.001  Safe-Lagrangian 0.1  

> tab=table(fitted(g.agraph)[U],y.true[U])
> 1-sum(diag(tab))/sum(tab)
[1] 0.2650602
> 
> 
> 
> 
> cleanEx()

detaching ‘package:mlbench’

> nameEx("dG")
> ### * dG
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dG
> ### Title: Specify graph terms for 'formula' instances of objects contained
> ###   in "SemiSupervised"
> ### Aliases: dG sG aG
> ### Keywords: classes models methods
> 
> ### ** Examples
> 
> #######
> ## Equivalent uses of the formula and default s4pm call.
> #######
> 
> ## Set up Sonar data with 20% labeled
> library(mlbench)
> data(Sonar)
> 
> n=dim(Sonar)[1]
> p=dim(Sonar)[2]
> 
> nu=0.2
> set.seed(100)
> L=sort(sample(1:n,ceiling(nu*n)))
> U=setdiff(1:n,L)
> 
> y.true<-Sonar$Class
> Sonar$Class[U]=NA
> control=SemiSupervised.control(stability=0.0) ## turn off adjustment parameters for comparison
> 
> (g.s4pm<-s4pm(Class~.,data=Sonar,control=control)) ### Fit s4pm to Sonar
S4PM Fit with (n,|L|)=( 208 , 42 ) or  20 % labeled

Performance Estimates:
k-CV:  1.279  GCV:  2.261  DF:  4.251

Fit Estimates:
Graph Kernel h:  0.17  Lagrangians:  0.1   0.001  Safe-Lagrangian 0.1  

> 
> ##The following give equivalent output to the g.s4pm<-s4pm(Class~.,data=Sonar) call.
> 
> 
> 
> cleanEx()

detaching ‘package:mlbench’

> nameEx("jtharm")
> ### * jtharm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: jtharm
> ### Title: Joint Harmonic Functions ('jtharm')
> ### Aliases: jtharm jtharm-methods jtharm,formula-method
> ###   jtharm,data.frame-method jtharm,matrix-method show,jtharm-method
> ###   jtharm.default
> ### Keywords: classes models methods
> 
> ### ** Examples
> 
> ## Set up Sonar data with 20% labeled (comparing randomForest and glmnet)
> library(mlbench)
> data(Sonar)
> 
> n=dim(Sonar)[1]
> p=dim(Sonar)[2]
> 
> nu=0.2
> set.seed(100)
> L=sort(sample(1:n,ceiling(nu*n)))
> U=setdiff(1:n,L)
> 
> y.true<-Sonar$Class
> Sonar$Class[U]=NA
> 
> ##Fit jtharm to Sonar
> g.jtharm<-jtharm(Class~.,data=Sonar)
==39770== Conditional jump or move depends on uninitialised value(s)
==39770==    at 0x15893D40: cv_jtharm_fit (packages/tests-vg/SemiSupervised/src/joint_harmonic.c:66)
==39770==    by 0x498AE9: R_doDotCall (svn/R-devel/src/main/dotcode.c:596)
==39770==    by 0x499013: do_dotcall (svn/R-devel/src/main/dotcode.c:1252)
==39770==    by 0x4CC6A9: bcEval (svn/R-devel/src/main/eval.c:6772)
==39770==    by 0x4DAC0F: Rf_eval (svn/R-devel/src/main/eval.c:624)
==39770==    by 0x4DC51E: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==39770==    by 0x4DAD9C: Rf_eval (svn/R-devel/src/main/eval.c:747)
==39770==    by 0x46A46C: do_docall (svn/R-devel/src/main/coerce.c:2590)
==39770==    by 0x4CC6A9: bcEval (svn/R-devel/src/main/eval.c:6772)
==39770==    by 0x4DAC0F: Rf_eval (svn/R-devel/src/main/eval.c:624)
==39770==    by 0x4DC51E: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==39770==    by 0x4D1CAC: bcEval (svn/R-devel/src/main/eval.c:6740)
==39770==  Uninitialised value was created by a stack allocation
==39770==    at 0x15893831: cv_jtharm_fit (packages/tests-vg/SemiSupervised/src/joint_harmonic.c:3)
==39770== 
> tab=table(fitted(g.jtharm)[U],y.true[U])
> 1-sum(diag(tab))/sum(tab)
[1] 0.1927711
> 
> 
> 
> cleanEx()

detaching ‘package:mlbench’

> nameEx("powerplant")
> ### * powerplant
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: PowerPlant
> ### Title: The Combined Cycle Power Plant Data Set
> ### Aliases: powerplant
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> 
> 
> cleanEx()
> nameEx("predict.agraph")
> ### * predict.agraph
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.agraph
> ### Title: Out-of-Sample Predict Procedure for 'agraph'
> ### Aliases: predict.agraph predict,agraph-method
> ### Keywords: classes models methods
> 
> ### ** Examples
> 
> ## Prediction depends on the nature of the call. Consider some examples.
> library(mlbench)
> data(Sonar)
> 
> n=dim(Sonar)[1]
> p=dim(Sonar)[2]
> 
> nu=0.2
> set.seed(100)
> L=sort(sample(1:n,ceiling(nu*n)))
> U=setdiff(1:n,L)
> U1=sample(U,ceiling(0.5*n))
> 
> y.true<-Sonar$Class
> Sonar$Class[U]=NA
> 
> ## Typical, call to agraph and predict
> 
> g.agraph1<-agraph(Class~.,data=Sonar[c(L,U1),])
> p.agraph1<-predict(g.agraph1,xnew=Sonar[U,-p])
> tab=table(y.true[U],p.agraph1)
> 1-sum(diag(tab))/sum(tab)
[1] 0.2349398
> 
> 
> 
> cleanEx()

detaching ‘package:mlbench’

> nameEx("predict.jtharm")
> ### * predict.jtharm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.jtharm
> ### Title: Out-of-Sample Predict Procedure for 'jtharm'
> ### Aliases: predict.jtharm predict,jtharm-method
> ### Keywords: classes models methods
> 
> ### ** Examples
> 
> ## Prediction depends on the nature of the call. Consider some examples.
> library(mlbench)
> data(Sonar)
> 
> n=dim(Sonar)[1]
> p=dim(Sonar)[2]
> 
> nu=0.2
> set.seed(100)
> L=sort(sample(1:n,ceiling(nu*n)))
> U=setdiff(1:n,L)
> U1=sample(U,ceiling(0.5*n))
> 
> y.true<-Sonar$Class
> Sonar$Class[U]=NA
> 
> ## Typical, call to jtharm and predict
> g.jtharm1<-jtharm(Class~.,data=Sonar[c(L,U1),])
> p.jtharm1<-predict(g.jtharm1,xnew=Sonar[U,-p])
> tab=table(y.true[U],p.jtharm1)
> 1-sum(diag(tab))/sum(tab)
[1] 0.3012048
> 
> 
> 
> cleanEx()

detaching ‘package:mlbench’

> nameEx("predict.s4pm")
> ### * predict.s4pm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.s4pm
> ### Title: Out-of-Sample Predict Procedure for 's4pm'
> ### Aliases: predict.s4pm predict,s4pm-method
> ### Keywords: classes models methods
> 
> ### ** Examples
> 
> ## Prediction depends on the nature of the call. Consider some examples.
> library(mlbench)
> data(Sonar)
> 
> n=dim(Sonar)[1]
> p=dim(Sonar)[2]
> 
> nu=0.2
> set.seed(100)
> L=sort(sample(1:n,ceiling(nu*n)))
> U=setdiff(1:n,L)
> U1=sample(U,ceiling(0.5*n))
> 
> y.true<-Sonar$Class
> Sonar$Class[U]=NA
> 
> ## Typical, call to s4pm and predict
> 
> g.s4pm<-s4pm(Class~.,data=Sonar[c(L,U1),])
> p.s4pm<-predict(g.s4pm,xnew=Sonar[U,-p])
> tab=table(y.true[U],p.s4pm)
> 1-sum(diag(tab))/sum(tab)
[1] 0.253012
> 
> 
> 
> cleanEx()

detaching ‘package:mlbench’

> nameEx("s4pm")
> ### * s4pm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s4pm
> ### Title: Safe Semi-Supervised Semi-Parametric Model ('s4pm')
> ### Aliases: s4pm s4pm-methods s4pm,formula-method s4pm,NULL-method
> ###   s4pm,lapGraph-method s4pm,vector-method s4pm,data.frame-method
> ###   s4pm,matrix-method show,s4pm-method s4pm.default
> ### Keywords: classes models methods
> 
> ### ** Examples
> 
> ## Set up Sonar data with 20% labeled (comparing randomForest and glmnet)
> library(mlbench)
> data(Sonar)
> 
> n=dim(Sonar)[1]
> p=dim(Sonar)[2]
> 
> nu=0.2
> set.seed(100)
> L=sort(sample(1:n,ceiling(nu*n)))
> U=setdiff(1:n,L)
> 
> y.true<-Sonar$Class
> Sonar$Class[U]=NA
> 
> ## Fit s4pm to Sonar
> g.s4pm<-s4pm(Class~.,data=Sonar) 
> g.s4pm
S4PM Fit with (n,|L|)=( 208 , 42 ) or  20 % labeled

Performance Estimates:
k-CV:  1.278  GCV:  2.249  DF:  4.265

Fit Estimates:
Graph Kernel h:  0.17  Lagrangians:  0.1   0.001  Safe-Lagrangian 0.1  

> tab=table(fitted(g.s4pm)[U],y.true[U])
> 1-sum(diag(tab))/sum(tab)
[1] 0.2048193
> 
> ## For comparison
> 
> 
> 
> ### * <FOOTER>
> ###
> cleanEx()

detaching ‘package:mlbench’

> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  289.801 0.443 291.306 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')
==39770== 
==39770== HEAP SUMMARY:
==39770==     in use at exit: 66,571,676 bytes in 12,279 blocks
==39770==   total heap usage: 189,242 allocs, 176,963 frees, 1,790,276,841 bytes allocated
==39770== 
==39770== LEAK SUMMARY:
==39770==    definitely lost: 0 bytes in 0 blocks
==39770==    indirectly lost: 0 bytes in 0 blocks
==39770==      possibly lost: 0 bytes in 0 blocks
==39770==    still reachable: 66,571,676 bytes in 12,279 blocks
==39770==         suppressed: 0 bytes in 0 blocks
==39770== Reachable blocks (those to which a pointer was found) are not shown.
==39770== To see them, rerun with: --leak-check=full --show-leak-kinds=all
==39770== 
==39770== For counts of detected and suppressed errors, rerun with: -v
==39770== ERROR SUMMARY: 214 errors from 2 contexts (suppressed: 0 from 0)