==24248== Memcheck, a memory error detector
==24248== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
==24248== Using Valgrind-3.13.0 and LibVEX; rerun with -h for copyright info
==24248== Command: /data/blackswan/ripley/R/R-devel-vg/bin/exec/R --vanilla
==24248== 

R Under development (unstable) (2018-01-28 r74175) -- "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 <- "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)
==24248== Invalid read of size 8
==24248==    at 0x1B5FEF9A: trainFM(Rcpp::Vector<19, Rcpp::PreserveStorage>) (packages/tests-vg/FactoRizationMachines/src/FactoRizationMachines.cpp:4)
==24248==    by 0x1B61949B: _FactoRizationMachines_trainFM (packages/tests-vg/FactoRizationMachines/src/RcppExports.cpp:15)
==24248==    by 0x4D0739: bcEval (svn/R-devel/src/main/eval.c:7273)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248==    by 0x4DB8BE: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==24248==    by 0x4D108A: bcEval (svn/R-devel/src/main/eval.c:6734)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248==    by 0x4DB8BE: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==24248==    by 0x4D108A: bcEval (svn/R-devel/src/main/eval.c:6734)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248==    by 0x4DB8BE: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==24248==    by 0x4DA15C: Rf_eval (svn/R-devel/src/main/eval.c:747)
==24248==  Address 0x1d801d98 is 8 bytes before a block of size 8 alloc'd
==24248==    at 0x4C2E1CA: operator new(unsigned long) (/builddir/build/BUILD/valgrind-3.13.0/coregrind/m_replacemalloc/vg_replace_malloc.c:334)
==24248==    by 0x1B61293B: allocate (/usr/include/c++/7/ext/new_allocator.h:111)
==24248==    by 0x1B61293B: allocate (/usr/include/c++/7/ext/alloc_traits.h:130)
==24248==    by 0x1B61293B: _M_allocate (/usr/include/c++/7/bits/stl_vector.h:172)
==24248==    by 0x1B61293B: std::vector<double, std::allocator<double> >::_M_fill_insert(__gnu_cxx::__normal_iterator<double*, std::vector<double, std::allocator<double> > >, unsigned long, double const&) (/usr/include/c++/7/bits/vector.tcc:505)
==24248==    by 0x1B60C4CF: resize (/usr/include/c++/7/bits/stl_vector.h:732)
==24248==    by 0x1B60C4CF: trainFM(Rcpp::Vector<19, Rcpp::PreserveStorage>) (packages/tests-vg/FactoRizationMachines/src/FactoRizationMachines.cpp:4)
==24248==    by 0x1B61949B: _FactoRizationMachines_trainFM (packages/tests-vg/FactoRizationMachines/src/RcppExports.cpp:15)
==24248==    by 0x4D0739: bcEval (svn/R-devel/src/main/eval.c:7273)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248==    by 0x4DB8BE: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==24248==    by 0x4D108A: bcEval (svn/R-devel/src/main/eval.c:6734)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248==    by 0x4DB8BE: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==24248==    by 0x4D108A: bcEval (svn/R-devel/src/main/eval.c:6734)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248== 
==24248== Invalid write of size 8
==24248==    at 0x1B5FEFA4: trainFM(Rcpp::Vector<19, Rcpp::PreserveStorage>) (packages/tests-vg/FactoRizationMachines/src/FactoRizationMachines.cpp:4)
==24248==    by 0x1B61949B: _FactoRizationMachines_trainFM (packages/tests-vg/FactoRizationMachines/src/RcppExports.cpp:15)
==24248==    by 0x4D0739: bcEval (svn/R-devel/src/main/eval.c:7273)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248==    by 0x4DB8BE: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==24248==    by 0x4D108A: bcEval (svn/R-devel/src/main/eval.c:6734)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248==    by 0x4DB8BE: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==24248==    by 0x4D108A: bcEval (svn/R-devel/src/main/eval.c:6734)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248==    by 0x4DB8BE: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==24248==    by 0x4DA15C: Rf_eval (svn/R-devel/src/main/eval.c:747)
==24248==  Address 0x1d801d98 is 8 bytes before a block of size 8 alloc'd
==24248==    at 0x4C2E1CA: operator new(unsigned long) (/builddir/build/BUILD/valgrind-3.13.0/coregrind/m_replacemalloc/vg_replace_malloc.c:334)
==24248==    by 0x1B61293B: allocate (/usr/include/c++/7/ext/new_allocator.h:111)
==24248==    by 0x1B61293B: allocate (/usr/include/c++/7/ext/alloc_traits.h:130)
==24248==    by 0x1B61293B: _M_allocate (/usr/include/c++/7/bits/stl_vector.h:172)
==24248==    by 0x1B61293B: std::vector<double, std::allocator<double> >::_M_fill_insert(__gnu_cxx::__normal_iterator<double*, std::vector<double, std::allocator<double> > >, unsigned long, double const&) (/usr/include/c++/7/bits/vector.tcc:505)
==24248==    by 0x1B60C4CF: resize (/usr/include/c++/7/bits/stl_vector.h:732)
==24248==    by 0x1B60C4CF: trainFM(Rcpp::Vector<19, Rcpp::PreserveStorage>) (packages/tests-vg/FactoRizationMachines/src/FactoRizationMachines.cpp:4)
==24248==    by 0x1B61949B: _FactoRizationMachines_trainFM (packages/tests-vg/FactoRizationMachines/src/RcppExports.cpp:15)
==24248==    by 0x4D0739: bcEval (svn/R-devel/src/main/eval.c:7273)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248==    by 0x4DB8BE: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==24248==    by 0x4D108A: bcEval (svn/R-devel/src/main/eval.c:6734)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248==    by 0x4DB8BE: R_execClosure (svn/R-devel/src/main/eval.c:1764)
==24248==    by 0x4D108A: bcEval (svn/R-devel/src/main/eval.c:6734)
==24248==    by 0x4D9FCF: Rf_eval (svn/R-devel/src/main/eval.c:624)
==24248== 
> 
> # RMSE resulting from test data prediction
> sqrt(mean((predict(model,data.test)-target.test)^2))
[1] 19.78293
> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cleanEx()
==24248== Conditional jump or move depends on uninitialised value(s)
==24248==    at 0x40300CB: ???
==24248==    by 0x1AF628C7: ???
==24248==    by 0x1AF628C7: ???
==24248==    by 0x1AF628E3: ???
==24248==    by 0x1FFEFF6B5F: ???
==24248== 

detaching ‘package:MASS’

> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  100.148 0.333 101.133 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')
==24248== 
==24248== HEAP SUMMARY:
==24248==     in use at exit: 164,655,607 bytes in 35,547 blocks
==24248==   total heap usage: 82,594 allocs, 47,047 frees, 301,668,779 bytes allocated
==24248== 
==24248== LEAK SUMMARY:
==24248==    definitely lost: 0 bytes in 0 blocks
==24248==    indirectly lost: 0 bytes in 0 blocks
==24248==      possibly lost: 0 bytes in 0 blocks
==24248==    still reachable: 164,655,607 bytes in 35,547 blocks
==24248==         suppressed: 0 bytes in 0 blocks
==24248== Reachable blocks (those to which a pointer was found) are not shown.
==24248== To see them, rerun with: --leak-check=full --show-leak-kinds=all
==24248== 
==24248== For counts of detected and suppressed errors, rerun with: -v
==24248== ERROR SUMMARY: 54201 errors from 3 contexts (suppressed: 0 from 0)