Install Hangul on Xubuntu 14.04

http://www.devblog.kr/r/8y0gFPAvJ2g9Z93GU2yp2Z93BNSroOEGK9Z05Whax9mN9Z7TmphZP8Z48gtkhftdt89ztuC04Bpte1ub94O

Proper TexLive 2013 installation on Ubuntu 12.04

For TeXLive 2012, but works perfectly with TeXLive 2013

http://tex.stackexchange.com/questions/1092/how-to-install-vanilla-texlive-on-debian-or-ubuntu

Using RcppEigen with sparseMatrix class

ecode <- '
using Eigen::Map;
using Eigen::MatrixXd;
using Eigen::MappedSparseMatrix;
using Eigen::SparseMatrix;

MappedSparseMatrix<double> X(as<MappedSparseMatrix<double> >(XX));

SparseMatrix<double> Xc = SparseMatrix<double>(X);
Xc = SparseMatrix<double>(Xc.selfadjointView<Eigen::Lower>());

return wrap(Xc);
'

SeigenXSX <- cxxfunction(signature(SS = "matrix", XX="dgCMatrix"), ecode,
plugin = "RcppEigen")


Installing Rmpi on NERSC's carver

The directory that contains MPI libraries is OMPI_DIR=/usr/common/usg/openmpi/1.4.5/intel; hence, we can adapt the code on this page (http://internet2.binghamton.edu/teragrid/readdoc.cgi?fname=1_1_Rmpi-Steele-2010-11-16.txt).

Use the following code in R.

local({
    my.configure.args <- list(
        "Rmpi" =
        c("--with-Rmpi-include=$OMPI_DIR/include",
          "--with-Rmpi-libpath=$OMPI_DIR/lib",
          "--with-Rmpi-type=OPENMPI")
        );
    options("configure.args" = my.configure.args);
    options(repos=c(CRAN="http://cran.cnr.Berkeley.edu"));
})


Building emacs on NERSC systems

Emacs on NERSC systems are old, so I tried to compile emacs from source. The compiler is older as well, so I encountered errors during compile time.

regex.o: In function `stpncpy': 
/usr/include/bits/string3.h:160: multiple definition of `stpncpy' /tmp/ccntBfBN.o:/usr/include/bits/string3.h:160: first defined here 
regex.o: In function `realpath': 
/usr/include/bits/stdlib.h:37: multiple definition of `realpath' /tmp/ccntBfBN.o:/usr/include/bits/stdlib.h:37: first defined here 
regex.o: In function `ptsname_r': 
/usr/include/bits/stdlib.h:53: multiple definition of `ptsname_r' /tmp/ccntBfBN.o:/usr/include/bits/stdlib.h:53: first defined here 
regex.o: In function `wctomb': 
/usr/include/bits/stdlib.h:74: multiple definition of `wctomb' /tmp/ccntBfBN.o:/usr/include/bits/stdlib.h:74: first defined here 
regex.o: In function `mbstowcs': 
/usr/include/bits/stdlib.h:95: multiple definition of `mbstowcs' /tmp/ccntBfBN.o:/usr/include/bits/stdlib.h:95: first defined here 
regex.o: In function `wcstombs': 
/usr/include/bits/stdlib.h:114: multiple definition of `wcstombs' /tmp/ccntBfBN.o:/usr/include/bits/stdlib.h:114: first defined here 

To get around this, use this trick:
./configure CPPFLAGS="-fgnu89-inline"

Source: http://stackoverflow.com/questions/15285776/multiple-definitions-when-linking-collect2-error-ld-returned-1-exit-status

For faster R use OpenBLAS and Rmpi

http://www.stat.cmu.edu/~nmv/2013/07/09/for-faster-r-use-openblas-instead-better-than-atlas-trivial-to-switch-to-on-ubuntu/

http://www.stat.berkeley.edu/~paciorek/statComputing/statComputing.html

Mount sshfs in /etc/fstab with fuse

Add the following line in /etc/fstab

[username]@[hostname]:[remote source path] [local mount path] fuse.sshfs user,_netdev,uid=1000,IdentityFile=[path to ssh key],reconnect,allow_other 0 0

Send email from command line through Gmail

http://www.emanueletessore.com/how-to-configure-msmtp-as-a-gmail-relay-on-ubuntu-server/

ESS (Emacs Speaks Statistics) now supports compiling knitr

First, new version of ESS needs to be installed.  I use Aquamacs, so I want to make sure that I update Aquamacs. You can do this by uncommenting appropriate lines in Makeconf file of the ESS distribution (search for Aquamacs in Makeconf file).

Two variables need to be set within emacs.

• Change 'ess-swv-processor' to 'knitr'
• Set the variable 'ess-swv-plug-into-AUCTeX' to use the existing interface for compiling LaTeX files: i.e., C-c C-c

Now you should be able to use 'C-c C-c Sweave' or 'C-c C-c LaTeXSweave' commands to compile your Rnw files.

Reading Microsoft Excel files into R in Ubuntu 12.04

People still send data around in Microsoft Excel format: xlsx. In order to import xlsx data into R, one can use the xlsx package. When I tried to install the package in R, I ran into libjvm.so not found error.

 libjvm.so: cannot open shared object file: No such file or directory

There is a stackoverflow thread on this.

After xlsx package was installed properly, the launched jvm did not have enough memory. The way to allow more memory to the jvm. This stackoverflow thread explains how to do this.

For example, to allocate 2GB memory for the jvm instance,

options(java.parameters = "-Xmx2000m")

DONE!

Cross-platform, Acrobat compatible PDF highlighting in Ubuntu, Android and Mac OSX on Dropbox

I have a Mac as my notebook, Ubuntu 12.04 LTS as my workstation, Nexus 7 2 (FHD) as my tablet and an iPhone 4S (ios 6). I want to read PDF documents across devices and highlight for later. However, I ran into some difficulties in various aspects. Here are some issues I noticed.

1. Google drive does not let Android edit files. This is a deal breaker because this means I cannot highlight as I am reading on my Nexus 7. FAIL
2. Okular maybe a competent PDF reader, its highlight functionality is application specific. FAIL
3. Skim on the Mac OS is the same way: application specific highlights. FAIL
4. Dropbox PDF viewer does not display highlights. FAIL
5. Opening a PDF on ios (using "Open in..." function) automatically makes a copy of the file, and, thus, will not save back to the original. FAIL

Here are some work-arounds I used to remedy some of the issues:

Nexus 7 (Android):

1. Use Dropbox. Android app allows edits on mobile devices
2. Use Acrobat to highlight

Ubuntu 12.04 LTS:

1. Use PDF-XChange under Wine in Ubuntu. It works like a charm. There also is a portable version available for download on CNET.
2. Wine program launcher can easily be created using Wine Launcher Creator (WLC). It is available here.

Mac OSX 10.8.5 (Mountain Lion):

1. Use Preview to highlight

I don't know if I will be able to find any workaround for the iPhone. The fundamental design of the OS seems such that I cannot modify the original file. Oh well. I can still view highlights by opening a copy in Acrobat.

plyr parallel backend using doSNOW

You often want to parallelize some "embarrassingly parallel" computations. Doing this with R can be simple.

library(MASS)
library(plyr)
library(doSNOW)
cl <- makeCluster(8,type='SOCK')
registerDoSNOW(cl)
clusterEvalQ(cl, library(MASS))
set.seed(123)
x <- laply(1:15,function(x){mvrnorm(n=10000,mu=c(0,0),Sigma=matrix(c(1,0.9,0.9,1),2,2))},.parallel=TRUE)
alply(x,1,function(x){var(x)},.parallel=TRUE)
stopCluster(cl)

Compiling R with PNG support

Just great, I have to recompile my R now.

http://ask.sagemath.org/question/192/compiling-r-with-png-support

R performance benchmarks with sequential vs multi-threading MKL libraries

Using benchmark script from http://r.research.att.com/benchmarks/R-benchmark-25.R

Sequential MKL

 Loading required package: Matrix  
 Loading required package: methods  
 Loading required package: lattice  
 Loading required package: SuppDists  
 Warning messages:  
 1: In remove("a", "b") : object 'a' not found  
 2: In remove("a", "b") : object 'b' not found  
   
   
   R Benchmark 2.5  
   ===============  
 Number of times each test is run__________________________: 3  
   
   I. Matrix calculation  
   ---------------------  
 Creation, transp., deformation of a 2500x2500 matrix (sec): 1.51233333333333   
 2400x2400 normal distributed random matrix ^1000____ (sec): 0.914333333333333   
 Sorting of 7,000,000 random values__________________ (sec): 0.814666666666666   
 2800x2800 cross-product matrix (b = a' * a)_________ (sec): 2.19166666666667   
 Linear regr. over a 3000x3000 matrix (c = a \ b')___ (sec): 1.20833333333333   
            --------------------------------------------  
          Trimmed geom. mean (2 extremes eliminated): 1.18662349903113   
   
   II. Matrix functions  
   --------------------  
 FFT over 2,400,000 random values____________________ (sec): 0.968666666666666   
 Eigenvalues of a 640x640 random matrix______________ (sec): 0.536333333333334   
 Determinant of a 2500x2500 random matrix____________ (sec): 1.15233333333333   
 Cholesky decomposition of a 3000x3000 matrix________ (sec): 1.08166666666667   
 Inverse of a 1600x1600 random matrix________________ (sec): 0.947666666666668   
            --------------------------------------------  
         Trimmed geom. mean (2 extremes eliminated): 0.997641413170114   
   
   III. Programmation  
   ------------------  
 3,500,000 Fibonacci numbers calculation (vector calc)(sec): 1.05033333333334   
 Creation of a 3000x3000 Hilbert matrix (matrix calc) (sec): 0.601666666666664   
 Grand common divisors of 400,000 pairs (recursion)__ (sec): 1.48566666666666   
 Creation of a 500x500 Toeplitz matrix (loops)_______ (sec): 0.812666666666667   
 Escoufier's method on a 45x45 matrix (mixed)________ (sec): 0.533000000000001   
            --------------------------------------------  
         Trimmed geom. mean (2 extremes eliminated): 0.800814353816818   
   
   
 Total time for all 15 tests_________________________ (sec): 15.8113333333333   
 Overall mean (sum of I, II and III trimmed means/3)_ (sec): 0.982365467154044   
            --- End of test ---  
   

Multi-threading MKL

 Loading required package: Matrix  
 Loading required package: methods  
 Loading required package: lattice  
 Loading required package: SuppDists  
 Warning message:  
 In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :  
  there is no package called ‘SuppDists’  
 Warning messages:  
 1: In remove("a", "b") : object 'a' not found  
 2: In remove("a", "b") : object 'b' not found  
   
   
   R Benchmark 2.5  
   ===============  
 Number of times each test is run__________________________: 3  
   
   I. Matrix calculation  
   ---------------------  
 Creation, transp., deformation of a 2500x2500 matrix (sec): 1.52433333333333   
 2400x2400 normal distributed random matrix ^1000____ (sec): 0.911666666666667   
 Sorting of 7,000,000 random values__________________ (sec): 0.814666666666665   
 2800x2800 cross-product matrix (b = a' * a)_________ (sec): 0.525000000000001   
 Linear regr. over a 3000x3000 matrix (c = a \ b')___ (sec): 0.393333333333333   
            --------------------------------------------  
          Trimmed geom. mean (2 extremes eliminated): 0.730564293362432   
   
   II. Matrix functions  
   --------------------  
 FFT over 2,400,000 random values____________________ (sec): 0.969   
 Eigenvalues of a 640x640 random matrix______________ (sec): 0.794666666666667   
 Determinant of a 2500x2500 random matrix____________ (sec): 0.385000000000003   
 Cholesky decomposition of a 3000x3000 matrix________ (sec): 0.349000000000001   
 Inverse of a 1600x1600 random matrix________________ (sec): 0.462999999999999   
            --------------------------------------------  
         Trimmed geom. mean (2 extremes eliminated): 0.521285412938827   
   
   III. Programmation  
   ------------------  
 3,500,000 Fibonacci numbers calculation (vector calc)(sec): 1.045   
 Creation of a 3000x3000 Hilbert matrix (matrix calc) (sec): 0.600999999999999   
 Grand common divisors of 400,000 pairs (recursion)__ (sec): 1.55266666666667   
 Creation of a 500x500 Toeplitz matrix (loops)_______ (sec): 0.814666666666668   
 Escoufier's method on a 45x45 matrix (mixed)________ (sec): 1.06   
            --------------------------------------------  
         Trimmed geom. mean (2 extremes eliminated): 0.966349072573083   
   
   
 Total time for all 15 tests_________________________ (sec): 12.203   
 Overall mean (sum of I, II and III trimmed means/3)_ (sec): 0.716620701088256   
            --- End of test ---  
   

Compiling R linking to MKL using gcc

I use R extensively for research, so making R faster with MKL is useful for me. Since MKL is free for non-commercial use, I decided to compile R for my computer that links to the fast MKL library.

Two configurations are covered here, one using multi-threading, and one using the sequential version of the library. Although using multi-threading makes certain matrix operations faster, if I have to run many jobs (bootstrap, cross-validation, etc), total time reduction is greater if I run jobs in parallel (using multicore package for R) rather than linear algebra operation in parallel. Hence the two different compilations.

I used gcc as the compiler rather than icc from intel since some packages on CRAN does not compile with icc. I do not know enough about compilers to know if some flags will fix this.

 I found that the configure script in R-3.0.0 does not find some functions in MKL correctly. I traced the cause of this problem to the following lines:

 29818a29819,29821  
 >  # if ${CC} ${CFLAGS} ${LDFLAGS} ${MAIN_LDFLAGS} -o conftest${ac_exeext} \  
 >  #   conftest.${ac_objext} ${FLIBS} \  
 >  #   ${LIBM} ${BLAS_LIBS} 1>&5 2>&5;  
 29820,29821c29823,29824  
 <    conftest.${ac_objext} ${FLIBS} \  
 <    ${LIBM} ${BLAS_LIBS} 1>&5 2>&5;  
 ---  
 >    conftest.${ac_objext} ${BLAS_LIBS} ${FLIBS} \  
 >    ${LIBM} 1>&5 2>&5;  

---

To compile R using MKL sequential library,

1. Apply the patch to configure script
2. Add the following lines to config.site file
   

    CFLAGS='-g -O3 '                                                                                             
    FFLAGS='-g -O3 '                                                                                             
    CXXFLAGS='-g -O3 '                                                                                            
    FCFLAGS='-g -O3 '   
      
    MKL_LIB_PATH=/opt/intel/composer_xe_2013.3.163/mkl/lib/intel64  
    MKL=" -L${MKL_LIB_PATH}    \                                                                                      
       -Wl,--start-group    \                                                                                      
         -lmkl_gf_lp64    \                                                                                      
         -lmkl_sequential  \                                                                                      
         -lmkl_core     \                                                                                      
         -lm         \                                                                                      
       -Wl,--end-group     \                                                                                      
       -lpthread"  
    BLAS_LIBS="$MKL"  
   

3. Now configure, build and install with following lines:

    ./configure --prefix=/some/directory1 --with-blas --with-lapack  
    make  
    sudo make install  
   

---

To compile R using MKL multi-threading library,

1. Apply the patch to configure script
2. Add the following lines to config.site file
   

    CFLAGS='-g -O3 '                                                                                             
    FFLAGS='-g -O3 '                                                                                             
    CXXFLAGS='-g -O3 '                                                                                            
    FCFLAGS='-g -O3 '   
      
    MKL_LIB_PATH=/opt/intel/composer_xe_2013.3.163/mkl/lib/intel64  
    MKL=" -L${MKL_LIB_PATH}    \                                                                                      
       -Wl,--start-group    \                                                                                      
         -lmkl_gf_lp64    \                                                                                      
         -lmkl_gnu_thread  \                                                                                      
         -lmkl_core     \                                                                                      
         -lm         \                                                                                      
       -Wl,--end-group     \                                                                                      
       -lgomp -lpthread"  
    BLAS_LIBS="$MKL"  
   

3. Now configure, build and install with following lines:

    ./configure --prefix=/some/directory2 --with-blas --with-lapack  
    make  
    sudo make install  
   

---

When you run the configure command, last portion of the output should look something like this:

 R is now configured for x86_64-unknown-linux-gnu  
   
  Source directory:     .  
  Installation directory:  /usr/local  
   
  C compiler:        gcc -std=gnu99 -g -O3   
  Fortran 77 compiler:    gfortran -g -O3   
   
  C++ compiler:       g++ -g -O3   
  Fortran 90/95 compiler:  gfortran -g -O3   
  Obj-C compiler:          
   
  Interfaces supported:   X11  
  External libraries:    readline, BLAS(generic), LAPACK(in blas)  
  Additional capabilities:  JPEG, NLS  
  Options enabled:      R profiling  
   
  Recommended packages:   yes  
   

Quadratic Constrained Quadratic Program with cplex and Rcplex

Once cplex and Rcplex, an interface package for R, have been installed, following private function can be used to perform QCQP (Quadratic Constrained Quadratic Program).

 #######################################  
 ## Q1: QCQP with inactive constraint ##  
 ## xopt=c(0.00,0.4) ##  
 #######################################  
   
 ## Quadratic Objective  
 Qmat <- 2 * diag(2)  
   
 ## Linear Constraint  
 Amat <- matrix(0,2,2) ## no linear constraint  
 bvec <- c(0,0) ## no linear constraint  
   
 ## Quadratic Constraint  
 QC <- list(  
 QC=list(  
 Q=list(diag(2)),  
 L=list()),  
 dir="L",  
 b=1^2)  
 cvec <- 2 * c(0,-0.4)  
   
 ## Solve  
 Rcplex:::Rcplex_solve_QCP(cvec=cvec,Amat=Amat,bvec=bvec,sense="E",Qmat=Qmat,QC=QC,lb=-Inf)  
   
 #######################################  
 ## Q2: QCQP with active constraint ##  
 ## xopt=c(0.00, 0.13) ##  
 #######################################  
   
 ## Quadratic Objective  
 Qmat <- 2 * diag(2)  
   
 ## Linear Constraint  
 Amat <- matrix(0,2,2) ## no linear constraint  
 bvec <- c(0,0) ## no linear constraint  
   
 ## Quadratic Constraint  
 QC <- list(  
 QC=list(  
 Q=list(diag(2)),  
 L=list()),  
 dir="L",  
 b=0.13^2)  
 cvec <- 2 * c(0,-0.4)  
   
 Rcplex:::Rcplex_solve_QCP(cvec=cvec,Amat=Amat,bvec=bvec,sense="E",Qmat=Qmat,QC=QC,lb=-Inf)  

Installing Rcplex

 sudo R CMD INSTALL --configure-args="PKG_CFLAGS='-m64 -fPIC' PKG_CPPFLAGS=-I/opt/ibm/ILOG/CPLEX_Studio124/cplex/include PKG_LIBS='-L/opt/ibm/ILOG/CPLEX_Studio124/cplex/lib/x86-64_sles10_4.1/static_pic -lcplex -m64 -lm -lpthread'" Rcplex_0.3-0.tar.gz  

sending sbackup reporting

I don't know how to send emails only when errors occur, but it can send emails every time backup happens.

Add following to /etc/sbackup.conf

[report]  
 from = SBackup Daemon   
 to = ****@gmail.com  
 smtpserver = smtp.gmail.com  
 smtpuser = ****@gmail.com  
 smtppassword = ****  
 smtpport = 587  
 smtptls = 1  

send emails when mdadm errors occur

 # instruct the monitoring daemon where to send mail alerts  
 MAILADDR sangoh@gmail.com  
 MAILFROM icme-leconte-mdadm  

msmtp settings for sending mail through gmail

msmtp can send email through gmail.
If you do not run mail server on your linux desktop, you can still use msmtp, which has same interface as sendmail.

msmtp can be used to send system emails because of this sendmail interface.
This is working /etc/msmtprc on my system

 defaults  
 tls on  
 tls_starttls on  
 tls_trust_file /etc/ssl/certs/ca-certificates.crt  
   
 account default  
 host smtp.gmail.com  
 port 587  
 auth on  
 user ****@gmail.com  
 password ********  
 from ****@gmail.com  
 logfile /var/log/msmtp.log  

ref: http://www.absolutelytech.com/2010/07/17/howto-configure-msmtp-to-work-with-gmail-on-linux/