Deep learning 04--Compile mlpack-1.0.12 on windows 8.1 by visual studio 2015(64bits)

    As far as I know, most of the machine learning libraries of c++ are difficult to compile on windows, mlpack is one of them too(this lib implement sparse autoencoder and sparse coding, I would like to contribute something to this library in the future).If you want to do large scale machine learning, windows really is not a good platform for c++ since many libraries are hard to build or cannot get maximum performance on windows. However, your apps may need to run on windows since it is the most popular desktop OS.

     After a tedious journey of making mlpack work on windows, I want to write down the steps of how to compile mlpack, so I will never forget it.

   The steps to compile mlpack-1.0.12 are:

1 : visual studio 2015 community--this version fixed a bug of vc, this bug will bring some trouble when compile armadillo(there are work around, like replace () by [] and use pointer to access data)

• Visual studio 2015 would not install the c++ compiler by default, you need to select the custom install and select c++ by yourself
• After you install vs2015, execute following command on command window,[

  copy "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\mspdbsrv.exe" 

  "C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\IDE"

  ], this could fix a link issues( link.exe complains that MSPDB140.dll has the wrong version installed)

2 : libxml2-2.9.2--deprecated, you can compile mlpack without it start from 2.x 

• extract source codes(ex : c:/libxml2-2.9.2)
• go to the folder c:/libxml2-2.9.2 and copy the configure.ac to configure.in
• go to the folder c:/libxml2-2.9.2/win32 
• open command prompt and enter cscript configure.js compiler=msvc iconv=no zlib=no debug=no
• enter command "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\vcvarsall.bat" x86_amd64
• enter command nmake /f Makefile.msvc install

3 : zlib-1.2.8--deprecated, you can compile mlpack without it start from 2.x 

• extract source codes(ex : c:/zlib-1.2.8)
• go to the folder c:/zlib-1.2.8
• enter command "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\vcvarsall.bat" x86_amd64
• enter command  nmake -f win32/Makefile.msc AS=ml64 LOC="-DASMV -DASMINF -I." OBJA="inffasx64.obj gvmat64.obj inffas8664.obj"

4 : libiconv-1.14-deprecated, you can compile mlpack without it start from 2.x 

    Download the zip file from source forge, open visual studio 2015 and compile, you will need an account of source forge to download this file

5 : cmake3.3.2 or newer(start from 3.3.2, cmake support CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS)

6 : install mingw-w64(I use mingw-w64 5.1.0 in this post)

7 : lapack3.5.0--I have heard that openBLAS or intel mkl are faster than the blas come with lapack, but in this post I will use the blas come with lapack3.5.0

• extract source codes(ex : c:/lapack3.5.0)
• go to the folder c:/lapack3.5.0
• add commands in CMakeLists.txt
• open the CMakeLists.txt by cmake-gui
• setup the native compilers of c, c++ and fortran as "x86_64-w64-mingw32-gcc.exe", "x86_64-w64-mingw32-g++.exe", "x86_64-w64-mingw32-gfortran.exe"
• Disable BUILD_STATIC_LIBS and enable BUILD_SHARED_LIBS under the lable BUILD
• Set the value(under label Ungrouped Entries)  VCVARSAMD64 as "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/vcvarsx86_amd64.bat"
• Set the value(under label CMake) CMake_GNUtoMS_VCVARS as "C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/vcvarsx86_amd64.bat"
• Click Configure until all white
• Click generate
• Open the vcproject files and build

7.1 : build openBLAS

• BLAS is good, but openBLAS is much more faster than BLAS, it is almost three times faster on my laptop(Y410P)
• Download msys
• Clone openBLAS(git clone git://github.com/xianyi/OpenBLAS.git)
• Setup the environment path of MSYS(ex : C:\msys)
• Open command prompt
• Go to the folder of openBLAS(ex : C:\OpenBLAS)
• Type mingw32-make
• You will find the .a and .dll under the folder C:\OpenBLAS

8 : armadillo-5.600.2

• extract source codes(ex : c:/armadillo-5.600.2)
• go to the folder c:/aramadillo-5.600.2
• open CMakeLists.txt by nodepad and add three lines set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
  add_definitions(-DARMA_64BIT_WORD)
  add_definitions(-DNOMINMAX) 
• open the CMakeLists.txt by cmake-gui
• Set the value(under label Ungrouped Entries)  BLAS_LIBRARY(ex : "C:/Users/yyyy/Qt/3rdLibs/lapack/lapack-3.5.0/bin/vc2015_x86_amd64/release/libopenblas.dll.a")
• Set the value(under label Ungrouped Entries)  LAPACK_LIBRARY(ex : "C:/Users/yyyy/Qt/3rdLibs/lapack/lapack-3.5.0/bin/vc2015_x86_amd64/release/liblapack.lib")
• Click Configure until all white
• Click generate
• Open the vcproject files and build 

9 : boost_1_59_0-msvc-14.0-64

• Just download and unzip, the community already build it for us

10 : mlpack-1.0.12

• extract source codes(ex : c:/mlpack-1.0.12)
• go to the folder c:/mlpack-1.0.12 
• Specify the path of the libraries, dll and setup some definition, the details can found at here(start from line 66~87)
• Click Configure until all white
• Click generate
• Open the vcproject files and build 
• If there are link error, specify the path of openblas, lapack, libxml2 by cmake-gui(under label Ungrouped Entries, I do not know why the set command can not work yet), configure and generate again

     Ok, after so much trouble, the mlpack finally work.I will use it to solve one of the exercise of UFLDL later on.