R Under development (unstable) (2018-02-09 r74240) -- "Unsuffered Consequences"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> pkgname <- "FactoRizationMachines"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('FactoRizationMachines')
> 
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("010-FactoRizationMachines")
> ### * 010-FactoRizationMachines
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: FactoRizationMachines
> ### Title: Machine Learning with Higher-Order Factorization Machines
> ### Aliases: FactoRizationMachines FactorizationMachines-package
> ### Keywords: package Factorization Machine Matrix Factorization Machine
> ###   Learning Recommender
> 
> ### ** Examples
> 
> ## Not run: 
> ##D 
> ##D # Load libraries
> ##D library(FactoRizationMachines)
> ##D library(Matrix)
> ##D 
> ##D # Load MovieLens 100k data set
> ##D ml100k=as.matrix(read.table("http://files.grouplens.org/datasets/movielens/ml-100k/u.data"))
> ##D user=ml100k[,1]
> ##D items=ml100k[,2]+max(user)
> ##D wdays=(as.POSIXlt(ml100k[,4],origin="1970-01-01")$wday+1)+max(items)
> ##D 
> ##D # Transform MovieLens 100k to feature form
> ##D data=sparseMatrix(i=rep(1:nrow(ml100k),3),j=c(user,items,wdays),giveCsparse=F)
> ##D target=ml100k[,3]
> ##D 
> ##D # Subset data to training and test data
> ##D set.seed(123)
> ##D subset=sample.int(nrow(data),nrow(data)*.8)
> ##D data.train=data[subset,]
> ##D data.test=data[-subset,]
> ##D target.train=target[subset]
> ##D target.test=target[-subset]
> ##D 
> ##D # Predict ratings with Support Vector Machine with linear kernel
> ##D # using MCMC learning method
> ##D model=SVM.train(data.train,target.train)
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D # Predict ratings with second-order Factorization Machine 
> ##D # with second-order 10 factors (default) 
> ##D # using coordinate descent learning method (regularization suggested)
> ##D model=FM.train(data.train,target.train,regular=0.1)
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D # Predict ratings with second-order Factorization Machine 
> ##D # with second-order 10 factors (default) 
> ##D # using Markov Chain Monte Carlo learning method
> ##D model=FM.train(data.train,target.train)
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D # Predict ratings with higher-order Factorization Machine 
> ##D # with 3 second-order and 1 third-order factor and regularization
> ##D # using coordinate descent learning method (regularization suggested)
> ##D model=HoFM.train(data.train,target.train,c(1,3,1),regular=0.1)
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D # Predict ratings with higher-order Factorization Machine 
> ##D # with 3 second-order and 1 third-order factor and regularization
> ##D # using MCMC learning method
> ##D model=HoFM.train(data.train,target.train,c(1,3,1))
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D # Predict ratings with adaptive-order Factorization Machine 
> ##D model=KnoFM.train(data.train,target.train)
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ## End(Not run)
> 
> 
> 
> cleanEx()
> nameEx("020-SVM.train")
> ### * 020-SVM.train
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: SVM.train
> ### Title: Method training a Support Vector Machine
> ### Aliases: SVM.train
> 
> ### ** Examples
> 
> ## Not run: 
> ##D 
> ##D ### Example to illustrate the usage of the method
> ##D ### Data set very small and not sparse, results not representative
> ##D ### Please study major example in general help 'FactoRizationMachines'
> ##D 
> ##D # Load data set
> ##D library(FactoRizationMachines)
> ##D library(MASS)
> ##D data("Boston")
> ##D 
> ##D # Subset data to training and test data
> ##D set.seed(123)
> ##D subset=sample.int(nrow(Boston),nrow(trees)*.8)
> ##D data.train=Boston[subset,-ncol(Boston)]
> ##D target.train=Boston[subset,ncol(Boston)]
> ##D data.test=Boston[-subset,-ncol(Boston)]
> ##D target.test=Boston[-subset,ncol(Boston)]
> ##D 
> ##D 
> ##D # Predict with linear weights and intercept with MCMC regularization
> ##D model=SVM.train(data.train,target.train)
> ##D 
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D 
> ##D # Predict with linear weights but without intercept with MCMC regularization
> ##D model=SVM.train(data.train,target.train,intercept=FALSE)
> ##D 
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D 
> ##D # Predict with linear weights and manual regularization
> ##D model=SVM.train(data.train,target.train,regular=0.1)
> ##D 
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ## End(Not run)
> 
> 
> 
> cleanEx()
> nameEx("030-FM.train")
> ### * 030-FM.train
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: FM.train
> ### Title: Method training a second-order Factorization Machine
> ### Aliases: FM.train
> 
> ### ** Examples
> 
> ## Not run: 
> ##D 
> ##D ### Example to illustrate the usage of the method
> ##D ### Data set very small and not sparse, results not representative
> ##D ### Please study major example in general help 'FactoRizationMachines'
> ##D 
> ##D # Load data set
> ##D library(FactoRizationMachines)
> ##D library(MASS)
> ##D data("Boston")
> ##D 
> ##D # Subset data to training and test data
> ##D set.seed(123)
> ##D subset=sample.int(nrow(Boston),nrow(trees)*.8)
> ##D data.train=Boston[subset,-ncol(Boston)]
> ##D target.train=Boston[subset,ncol(Boston)]
> ##D data.test=Boston[-subset,-ncol(Boston)]
> ##D target.test=Boston[-subset,ncol(Boston)]
> ##D 
> ##D 
> ##D # Predict with 3 second-order factors with MCMC regularization
> ##D model=FM.train(data.train,target.train,c(1,3))
> ##D 
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D 
> ##D # Predict with 10 second-order factor with MCMC regularization
> ##D model=FM.train(data.train,target.train)
> ##D 
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D 
> ##D # Predict with 10 second-order factor with manual regularization
> ##D model=FM.train(data.train,target.train,regular=0.1)
> ##D 
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ## End(Not run)
> 
> 
> 
> cleanEx()
> nameEx("040-HoFM.train")
> ### * 040-HoFM.train
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: HoFM.train
> ### Title: Method training a higher-order Factorization Machine
> ### Aliases: HoFM.train
> 
> ### ** Examples
> 
> ## Not run: 
> ##D 
> ##D ### Example to illustrate the usage of the method
> ##D ### Data set very small and not sparse, results not representative
> ##D ### Please study major example in general help 'FactoRizationMachines'
> ##D 
> ##D # Load data set
> ##D library(FactoRizationMachines)
> ##D library(MASS)
> ##D data("Boston")
> ##D 
> ##D # Subset data to training and test data
> ##D set.seed(123)
> ##D subset=sample.int(nrow(Boston),nrow(trees)*.8)
> ##D data.train=Boston[subset,-ncol(Boston)]
> ##D target.train=Boston[subset,ncol(Boston)]
> ##D data.test=Boston[-subset,-ncol(Boston)]
> ##D target.test=Boston[-subset,ncol(Boston)]
> ##D 
> ##D 
> ##D # Predict with 7 second-order and 2 third-order factors
> ##D #  with MCMC regularization
> ##D model=HoFM.train(data.train,target.train,c(1,7,2))
> ##D 
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D 
> ##D # Predict with 10 second-order and 5 third-order factor
> ##D #  with MCMC regularization
> ##D model=HoFM.train(data.train,target.train)
> ##D 
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D 
> ##D # Predict with 10 second-order and 5 third-order factor
> ##D #  with manual regularization
> ##D model=HoFM.train(data.train,target.train,regular=0.1)
> ##D 
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ## End(Not run)
> 
> 
> 
> cleanEx()
> nameEx("045-KnoFM.train")
> ### * 045-KnoFM.train
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: KnoFM.train
> ### Title: Knowledge-extracting or adaptive-order Factorization Machine
> ### Aliases: KnoFM.train
> 
> ### ** Examples
> 
> ## Not run: 
> ##D 
> ##D ### Example to illustrate the usage of the method
> ##D ### Data set very small and not sparse, results not representative
> ##D ### Please study major example in general help 'FactoRizationMachines'
> ##D 
> ##D # Load data set
> ##D library(FactoRizationMachines)
> ##D library(MASS)
> ##D data("Boston")
> ##D 
> ##D # Subset data to training and test data
> ##D set.seed(123)
> ##D subset=sample.int(nrow(Boston),nrow(trees)*.8)
> ##D data.train=Boston[subset,-ncol(Boston)]
> ##D target.train=Boston[subset,ncol(Boston)]
> ##D data.test=Boston[-subset,-ncol(Boston)]
> ##D target.test=Boston[-subset,ncol(Boston)]
> ##D 
> ##D # Predict with an adaptive-order Factorization Machine
> ##D # using one CPU core  and printing progress
> ##D model=KnoFM.train(data.train,target.train,FALSE,FALSE)
> ##D 
> ##D # RMSE resulting from test data prediction
> ##D sqrt(mean((predict(model,data.test)-target.test)^2))
> ##D 
> ##D 
> ## End(Not run)
> 
> 
> 
> cleanEx()
> nameEx("050-predict.FMmodel")
> ### * 050-predict.FMmodel
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.FMmodel
> ### Title: Predict Method for FMmodel Objects
> ### Aliases: predict.FMmodel
> 
> ### ** Examples
> 
> 
> ### Example to illustrate the usage of the method
> ### Data set very small and not sparse, results not representative
> ### Please study major example in general help 'FactoRizationMachines'
> 
> # Load data set
> library(FactoRizationMachines)
> library(MASS)
> data("Boston")
> 
> # Subset data to training and test data
> set.seed(123)
> subset=sample.int(nrow(Boston),nrow(trees)*.8)
> data.train=Boston[subset,-ncol(Boston)]
> target.train=Boston[subset,ncol(Boston)]
> data.test=Boston[-subset,-ncol(Boston)]
> target.test=Boston[-subset,ncol(Boston)]
> 
> 
> # Predict with 10 second-order and 5 third-order factor
> model=HoFM.train(data.train,target.train)
/usr/local/bin/../include/c++/v1/vector:1504:12: runtime error: addition of unsigned offset to 0x602000034ed0 overflowed to 0x602000034ec8
SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /usr/local/bin/../include/c++/v1/vector:1504:12 in 
=================================================================
==36283==ERROR: AddressSanitizer: heap-buffer-overflow on address 0x602000034ec8 at pc 0x7ff8ee675493 bp 0x7ffd219566b0 sp 0x7ffd219566a8
READ of size 8 at 0x602000034ec8 thread T0
    #0 0x7ff8ee675492 in trainFM(Rcpp::Vector<19, Rcpp::PreserveStorage>) /data/gannet/ripley/R/packages/tests-clang-SAN/FactoRizationMachines/src/FactoRizationMachines.cpp:4:22500
    #1 0x7ff8ee715f20 in _FactoRizationMachines_trainFM /data/gannet/ripley/R/packages/tests-clang-SAN/FactoRizationMachines/src/RcppExports.cpp:15:34
    #2 0x7e5099 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:570:17
    #3 0x9ca702 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7273:21
    #4 0x9b0fad in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:624:8
    #5 0xa2493b in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #6 0x9d5e38 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:6734:12
    #7 0x9b0fad in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:624:8
    #8 0xa2493b in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #9 0x9d5e38 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:6734:12
    #10 0x9b0fad in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:624:8
    #11 0xa2493b in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #12 0x9b2184 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:747:12
    #13 0xa338dc in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2774:8
    #14 0x9b1bbd in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:699:12
    #15 0xb1e343 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:258:2
    #16 0xb232c0 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:308:11
    #17 0xb230a5 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1082:5
    #18 0x52a08a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5
    #19 0x7ff8ff4cb889 in __libc_start_main (/lib64/libc.so.6+0x20889)
    #20 0x42d829 in _start (/data/gannet/ripley/R/R-clang-SAN/bin/exec/R+0x42d829)

0x602000034ec8 is located 8 bytes to the left of 8-byte region [0x602000034ed0,0x602000034ed8)
allocated by thread T0 here:
    #0 0x525c70 in operator new(unsigned long) /data/gannet/ripley/Sources/LLVM/6.0.x/trunk/projects/compiler-rt/lib/asan/asan_new_delete.cc:92
    #1 0x7ff8ee6f59de in std::__1::__allocate(unsigned long) /usr/local/bin/../include/c++/v1/new:228:10
    #2 0x7ff8ee6f59de in std::__1::allocator<double>::allocate(unsigned long, void const*) /usr/local/bin/../include/c++/v1/memory:1793
    #3 0x7ff8ee6f59de in std::__1::allocator_traits<std::__1::allocator<double> >::allocate(std::__1::allocator<double>&, unsigned long) /usr/local/bin/../include/c++/v1/memory:1547
    #4 0x7ff8ee6f59de in std::__1::__split_buffer<double, std::__1::allocator<double>&>::__split_buffer(unsigned long, unsigned long, std::__1::allocator<double>&) /usr/local/bin/../include/c++/v1/__split_buffer:311
    #5 0x7ff8ee6f529d in std::__1::vector<double, std::__1::allocator<double> >::__append(unsigned long) /usr/local/bin/../include/c++/v1/vector:1047:53
    #6 0x7ff8ee5e9bf4 in trainFM(Rcpp::Vector<19, Rcpp::PreserveStorage>) /data/gannet/ripley/R/packages/tests-clang-SAN/FactoRizationMachines/src/FactoRizationMachines.cpp:4:3037
    #7 0x7ff8ee715f20 in _FactoRizationMachines_trainFM /data/gannet/ripley/R/packages/tests-clang-SAN/FactoRizationMachines/src/RcppExports.cpp:15:34
    #8 0x7e5099 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:570:17
    #9 0x9ca702 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7273:21
    #10 0x9b0fad in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:624:8
    #11 0xa2493b in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #12 0x9d5e38 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:6734:12
    #13 0x9b0fad in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:624:8
    #14 0xa2493b in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #15 0x9d5e38 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:6734:12
    #16 0x9b0fad in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:624:8
    #17 0xa2493b in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #18 0x9b2184 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:747:12
    #19 0xa338dc in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2774:8
    #20 0x9b1bbd in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:699:12
    #21 0xb1e343 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:258:2
    #22 0xb232c0 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:308:11
    #23 0xb230a5 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1082:5
    #24 0x52a08a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5
    #25 0x7ff8ff4cb889 in __libc_start_main (/lib64/libc.so.6+0x20889)

SUMMARY: AddressSanitizer: heap-buffer-overflow /data/gannet/ripley/R/packages/tests-clang-SAN/FactoRizationMachines/src/FactoRizationMachines.cpp:4:22500 in trainFM(Rcpp::Vector<19, Rcpp::PreserveStorage>)
Shadow bytes around the buggy address:
  0x0c047fffe980: fa fa fd fd fa fa fd fd fa fa fd fa fa fa fd fa
  0x0c047fffe990: fa fa fd fa fa fa fd fd fa fa fd fd fa fa fd fa
  0x0c047fffe9a0: fa fa fd fa fa fa fd fa fa fa fd fd fa fa fd fd
  0x0c047fffe9b0: fa fa fd fa fa fa fd fa fa fa fd fa fa fa fd fd
  0x0c047fffe9c0: fa fa fd fd fa fa fd fa fa fa fd fa fa fa fd fa
=>0x0c047fffe9d0: fa fa fd fd fa fa fd fd fa[fa]00 fa fa fa 00 fa
  0x0c047fffe9e0: fa fa 00 fa fa fa 00 fa fa fa 00 fa fa fa 00 fa
  0x0c047fffe9f0: fa fa 00 fa fa fa 00 fa fa fa 00 fa fa fa 00 fa
  0x0c047fffea00: fa fa 00 fa fa fa 00 fa fa fa 00 fa fa fa 00 fa
  0x0c047fffea10: fa fa 00 fa fa fa 00 fa fa fa 00 fa fa fa 00 fa
  0x0c047fffea20: fa fa 00 fa fa fa 00 fa fa fa 00 fa fa fa 00 fa
Shadow byte legend (one shadow byte represents 8 application bytes):
  Addressable:           00
  Partially addressable: 01 02 03 04 05 06 07 
  Heap left redzone:       fa
  Freed heap region:       fd
  Stack left redzone:      f1
  Stack mid redzone:       f2
  Stack right redzone:     f3
  Stack after return:      f5
  Stack use after scope:   f8
  Global redzone:          f9
  Global init order:       f6
  Poisoned by user:        f7
  Container overflow:      fc
  Array cookie:            ac
  Intra object redzone:    bb
  ASan internal:           fe
  Left alloca redzone:     ca
  Right alloca redzone:    cb
==36283==ABORTING