Po-haoHuang/LED_FE_FS
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================================================================================ 0. Introduction ================================================================================ Feature Extraction & Feature Selection ================================================================================ 1. Program Usage ================================================================================ FE_no_GUI.exe dir(cyclelist.csv) dir(rowdata) Cyclebegin(num) Cycleend(num) segmentnum dir(cyclelist.csv) 絕對相對路徑皆可 dir(rowdata) 絕對相對路徑皆可 Cyclebegin(num) 起始cycle number Cycleend(num) 結束cycle number segmentnum 等份數 共5個參數 -------------------------------------------------------------------------------- FS_no_GUI.exe input_file target_feature disct_method fcbf_thrd ridge_lambda lasso_lambda els_lambda1 els_lambda2 print_n score_method top_k 1. input_file FE處理後的檔案(Output_noSeg.csv, Output_seg1.csv, Output_seg2.csv) 2. target_feature 目標特徵("dP_Filter (X1)_max") 3. disct_method 指定離散化方式("ew_cycle=4", "5,15,20") 4. fcbf_thrd MI-FCBF演算法之threshold (0.01) 5. ridge_lambda Regression-RIDGE演算法之lambda(1, 2, 3) 6. lasso_lambda Regression-LASSO演算法之lambda(1, 2, 3) 7. els_lambda1 Regression-ElasticNet演算法之lambda1(1, 2, 3) 8. els_lambda2 Regression-ElasticNet演算法之lambda2(1, 2, 3) 9. print_n 列印前n名的結果(1~n) 10. score_method 評分方式(1) 11. top_k 演算法篩選的個數(15,20,30 ...) ex: Output_noSeg.csv "dP_Filter (X1)_max" "ew_cycle=4" 0.01 1 1 1 1 10 1 15 共11個參數 ================================================================================ 2. Build from Soucre Code ================================================================================ FeatureExtraction: 1.gsl 1.15 32bit https://code.google.com/p/oscats/downloads/list 說明:解壓放C根目錄 Compile 時 linker 下指令連結此函式庫 EX: ../../../../GSL-1.15/lib/libgsl.a ../../../../GSL-1.15/lib/libgslcblas.a 2.QT 設計gui 32bit 5.3 mingw 4.8.2 default dll linking 如果要standalone的exe,需built static qt 附上 built 好的static版 如無法使用需重built 參考以下 http://qt-project.org/wiki/How-to-build-a-static-Qt-for-Windows-MinGW 3.DataBase: 如需使用快速讀取功能(csv.h),compile時加以下指令 -std=c++11 -D__NO_MINGW_LFS -DUSE_FAST_CSV Fast CSV can only read fixed CSV format (i.e. exactly 8 columns containing "DataTime", "dP_Filter (X1)", ...) FeatureSelecyion: Regression based: mlpack(使用mingw make) 1.libxml libconv 下載位置(直接使用prebuilt的只能使用.dll.a版本) ftp://ftp.zlatkovic.com/pub/libxml/64bit/ 或是自己重編static版(我是自己編) http://stackoverflow.com/questions/3429101/building-the-latest-iconv-and-libxml2-binaries-in-win32 linker指令(如果需要的話) ..\libxml2-2.9.1-win32-x86\lib\libxml2.dll.a ..\libxml2-2.9.1-win32-x86\lib\libxml2.a ..\libiconv-1.8-20020830\lib\libiconv.a ..\libiconv-1.8-20020830\lib\libcharset.a 2.armadillo-4.320.0(enable lapack/blas) 如何enable lapack/blas: include\armadillo_bits\config.hpp #define ARMA_USE_LAPACK #define ARMA_USE_BLAS 這兩行uncomment linker指令 ..\Lapack_win32_release\blas_win32_MT.lib ..\Lapack_win32_release\lapack_win32_MT.lib 並把兩dll放到執行檔旁 3.何處下載lapack/blas http://ylzhao.blogspot.tw/2013/10/blas-lapack-precompiled-binaries-for.html 4.boost 可以自己編或使用含boost的mingw 含boost mingw :http://nuwen.net/mingw.html 自己編: http://stackoverflow.com/questions/20265879/how-to-build-boost-1-55-with-mingw http://www.boost.org/ 4.mlpack本身(很難編...) 參考 http://www.mlpack.org/trac/wiki/MLPACKOnWindows 所需套件下載完後(lapack 64bit,libxml2 64bit) 注意Cmakelists裡面comment掉下面這兩段段再用cmake: #if (WIN32) # link_directories(${Boost_LIBRARY_DIRS}) # set(Boost_LIBRARIES "") #endif (WIN32) #add_definitions(-DBOOST_TEST_DYN_LINK) cmake裡可加上下列兩個entry Boost_NO_BOOST_CMAKE Boost_USE_STATIC_LIBS 另外,如果compile之後錯在tree_test.cpp comment 掉log::的部分(共有兩行) MI based: FEAST: FEAST資料夾內 Makefile做下列更改 libFSToolbox.so : $(objects) $(LINKER) -L$(MITOOLBOXPATH) libMIToolbox.so -lm -shared -o libFSToolbox.so $(objects) 先compile MIToolbox 再compile FEAST
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