├── .gitignore ├── .travis.yml ├── LICENSE ├── Makefile ├── README.md ├── demo ├── demoLIBIRWLS.sh ├── demoLIBIRWLSWin32.bat ├── demoLIBIRWLSWin64.bat └── demoPythonModule.py ├── docs ├── html │ ├── annotated.html │ ├── annotated_dup.js │ ├── bc_s.png │ ├── bdwn.png │ ├── classes.html │ ├── closed.png │ ├── d0 │ │ ├── d01 │ │ │ ├── kernels_8h.html │ │ │ ├── kernels_8h.js │ │ │ └── kernels_8h_source.html │ │ ├── d38 │ │ │ ├── structpredictProperties.html │ │ │ └── structpredictProperties.js │ │ ├── d6e │ │ │ ├── structsvm__dataset.html │ │ │ └── structsvm__dataset.js │ │ ├── d98 │ │ │ ├── ParallelAlgorithms_8h.html │ │ │ ├── ParallelAlgorithms_8h.js │ │ │ └── ParallelAlgorithms_8h_source.html │ │ └── da7 │ │ │ ├── pythonmodule_8h.html │ │ │ ├── pythonmodule_8h.js │ │ │ └── pythonmodule_8h_source.html │ ├── d1 │ │ └── d03 │ │ │ ├── structproperties.html │ │ │ └── structproperties.js │ ├── d2 │ │ └── dfd │ │ │ └── full-train_8c.html │ ├── d3 │ │ ├── d54 │ │ │ ├── Exec-full-train_8c.html │ │ │ └── Exec-full-train_8c.js │ │ └── dd5 │ │ │ ├── full-train_8h.html │ │ │ ├── full-train_8h.js │ │ │ └── full-train_8h_source.html │ ├── d4 │ │ ├── d49 │ │ │ ├── budgeted-train_8h.html │ │ │ ├── budgeted-train_8h.js │ │ │ └── budgeted-train_8h_source.html │ │ └── d54 │ │ │ ├── pythonmodule_8c.html │ │ │ └── pythonmodule_8c.js │ ├── d5 │ │ ├── d37 │ │ │ └── budgeted-train_8c.html │ │ └── d89 │ │ │ └── Exec-budgeted-train_8c.html │ ├── d6 │ │ └── d88 │ │ │ └── md_LIBIRWLS_python-package_README.html │ ├── d7 │ │ └── d73 │ │ │ └── kernels_8c.html │ ├── d8 │ │ └── d33 │ │ │ ├── structsvm__sample.html │ │ │ └── structsvm__sample.js │ ├── db │ │ └── d3d │ │ │ ├── LIBIRWLS-predict_8h.html │ │ │ ├── LIBIRWLS-predict_8h.js │ │ │ └── LIBIRWLS-predict_8h_source.html │ ├── dc │ │ ├── d1b │ │ │ ├── structmodel.html │ │ │ └── structmodel.js │ │ └── dfc │ │ │ └── IOStructures_8c.html │ ├── de │ │ ├── d09 │ │ │ └── ParallelAlgorithms_8c.html │ │ └── d79 │ │ │ ├── IOStructures_8h.html │ │ │ ├── IOStructures_8h.js │ │ │ └── IOStructures_8h_source.html │ ├── df │ │ └── d6f │ │ │ └── LIBIRWLS-predict_8c.html │ ├── dir_23084b2f8693fb5fafc5c8da130baacd.html │ ├── dir_23084b2f8693fb5fafc5c8da130baacd.js │ ├── dir_2b8ab67b0898ed06b16a24fa52708771.html │ ├── dir_2b8ab67b0898ed06b16a24fa52708771.js │ ├── dir_999986d24f6c817c1bfe4b11b0f4a64d.html │ ├── dir_999986d24f6c817c1bfe4b11b0f4a64d.js │ ├── dir_fbf672e6629ab74a24e7d310d431c03b.html │ ├── dir_fbf672e6629ab74a24e7d310d431c03b.js │ ├── doc.png │ ├── doxygen.css │ ├── doxygen.png │ ├── dynsections.js │ ├── files.html │ ├── files.js │ ├── folderclosed.png │ ├── folderopen.png │ ├── functions.html │ ├── functions_vars.html │ ├── globals.html │ ├── globals_func.html │ ├── globals_type.html │ ├── index.html │ ├── jquery.js │ ├── menu.js │ ├── menudata.js │ ├── nav_f.png │ ├── nav_g.png │ ├── nav_h.png │ ├── navtree.css │ ├── navtree.js │ ├── navtreedata.js │ ├── navtreeindex0.js │ ├── open.png │ ├── pages.html │ ├── resize.js │ ├── search │ │ ├── all_0.html │ │ ├── all_0.js │ │ ├── all_1.html │ │ ├── all_1.js │ │ ├── all_10.html │ │ ├── all_10.js │ │ ├── all_11.html │ │ ├── all_11.js │ │ ├── all_12.html │ │ ├── all_12.js │ │ ├── all_13.html │ │ ├── all_13.js │ │ ├── all_14.html │ │ ├── all_14.js │ │ ├── all_15.html │ │ ├── all_15.js │ │ ├── all_2.html │ │ ├── all_2.js │ │ ├── all_3.html │ │ ├── all_3.js │ │ ├── all_4.html │ │ ├── all_4.js │ │ ├── all_5.html │ │ ├── all_5.js │ │ ├── all_6.html │ │ ├── all_6.js │ │ ├── all_7.html │ │ ├── all_7.js │ │ ├── all_8.html │ │ ├── all_8.js │ │ ├── all_9.html │ │ ├── all_9.js │ │ ├── all_a.html │ │ ├── all_a.js │ │ ├── all_b.html │ │ ├── all_b.js │ │ ├── all_c.html │ │ ├── all_c.js │ │ ├── all_d.html │ │ ├── all_d.js │ │ ├── all_e.html │ │ ├── all_e.js │ │ ├── all_f.html │ │ ├── all_f.js │ │ ├── classes_0.html │ │ ├── classes_0.js │ │ ├── classes_1.html │ │ ├── classes_1.js │ │ ├── classes_2.html │ │ ├── classes_2.js │ │ ├── close.png │ │ ├── files_0.html │ │ ├── files_0.js │ │ ├── files_1.html │ │ ├── files_1.js │ │ ├── files_2.html │ │ ├── files_2.js │ │ ├── files_3.html │ │ ├── files_3.js │ │ ├── files_4.html │ │ ├── files_4.js │ │ ├── files_5.html │ │ ├── files_5.js │ │ ├── files_6.html │ │ ├── files_6.js │ │ ├── functions_0.html │ │ ├── functions_0.js │ │ ├── functions_1.html │ │ ├── functions_1.js │ │ ├── functions_2.html │ │ ├── functions_2.js │ │ ├── functions_3.html │ │ ├── functions_3.js │ │ ├── functions_4.html │ │ ├── functions_4.js │ │ ├── functions_5.html │ │ ├── functions_5.js │ │ ├── functions_6.html │ │ ├── functions_6.js │ │ ├── functions_7.html │ │ ├── functions_7.js │ │ ├── functions_8.html │ │ ├── functions_8.js │ │ ├── functions_9.html │ │ ├── functions_9.js │ │ ├── functions_a.html │ │ ├── functions_a.js │ │ ├── functions_b.html │ │ ├── functions_b.js │ │ ├── functions_c.html │ │ ├── functions_c.js │ │ ├── functions_d.html │ │ ├── functions_d.js │ │ ├── mag_sel.png │ │ ├── nomatches.html │ │ ├── pages_0.html │ │ ├── pages_0.js │ │ ├── pages_1.html │ │ ├── pages_1.js │ │ ├── search.css │ │ ├── search.js │ │ ├── search_l.png │ │ ├── search_m.png │ │ ├── search_r.png │ │ ├── searchdata.js │ │ ├── typedefs_0.html │ │ ├── typedefs_0.js │ │ ├── typedefs_1.html │ │ ├── typedefs_1.js │ │ ├── typedefs_2.html │ │ ├── typedefs_2.js │ │ ├── variables_0.html │ │ ├── variables_0.js │ │ ├── variables_1.html │ │ ├── variables_1.js │ │ ├── variables_10.html │ │ ├── variables_10.js │ │ ├── variables_2.html │ │ ├── variables_2.js │ │ ├── variables_3.html │ │ ├── variables_3.js │ │ ├── variables_4.html │ │ ├── variables_4.js │ │ ├── variables_5.html │ │ ├── variables_5.js │ │ ├── variables_6.html │ │ ├── variables_6.js │ │ ├── variables_7.html │ │ ├── variables_7.js │ │ ├── variables_8.html │ │ ├── variables_8.js │ │ ├── variables_9.html │ │ ├── variables_9.js │ │ ├── variables_a.html │ │ ├── variables_a.js │ │ ├── variables_b.html │ │ ├── variables_b.js │ │ ├── variables_c.html │ │ ├── variables_c.js │ │ ├── variables_d.html │ │ ├── variables_d.js │ │ ├── variables_e.html │ │ ├── variables_e.js │ │ ├── variables_f.html │ │ └── variables_f.js │ ├── splitbar.png │ ├── sync_off.png │ ├── sync_on.png │ ├── tab_a.png │ ├── tab_b.png │ ├── tab_h.png │ ├── tab_s.png │ └── tabs.css └── latex │ ├── Makefile │ ├── annotated.tex │ ├── d0 │ ├── d01 │ │ └── kernels_8h.tex │ ├── d38 │ │ └── structpredictProperties.tex │ ├── d6e │ │ └── structsvm__dataset.tex │ ├── d98 │ │ └── ParallelAlgorithms_8h.tex │ └── da7 │ │ └── pythonmodule_8h.tex │ ├── d1 │ └── d03 │ │ └── structproperties.tex │ ├── d2 │ └── dfd │ │ └── full-train_8c.tex │ ├── d3 │ ├── d54 │ │ └── Exec-full-train_8c.tex │ └── dd5 │ │ └── full-train_8h.tex │ ├── d4 │ ├── d49 │ │ └── budgeted-train_8h.tex │ └── d54 │ │ └── pythonmodule_8c.tex │ ├── d5 │ ├── d37 │ │ └── budgeted-train_8c.tex │ └── d89 │ │ └── Exec-budgeted-train_8c.tex │ ├── d6 │ └── d88 │ │ └── md_LIBIRWLS_python-package_README.tex │ ├── d7 │ └── d73 │ │ └── kernels_8c.tex │ ├── d8 │ └── d33 │ │ └── structsvm__sample.tex │ ├── db │ └── d3d │ │ └── LIBIRWLS-predict_8h.tex │ ├── dc │ ├── d1b │ │ └── structmodel.tex │ └── dfc │ │ └── IOStructures_8c.tex │ ├── de │ ├── d09 │ │ └── ParallelAlgorithms_8c.tex │ └── d79 │ │ └── IOStructures_8h.tex │ ├── df │ └── d6f │ │ └── LIBIRWLS-predict_8c.tex │ ├── dir_23084b2f8693fb5fafc5c8da130baacd.tex │ ├── dir_2b8ab67b0898ed06b16a24fa52708771.tex │ ├── dir_999986d24f6c817c1bfe4b11b0f4a64d.tex │ ├── dir_fbf672e6629ab74a24e7d310d431c03b.tex │ ├── doxygen.sty │ ├── files.tex │ ├── index.tex │ └── refman.tex ├── include ├── IOStructures.h ├── LIBIRWLS-predict.h ├── ParallelAlgorithms.h ├── budgeted-train.h ├── full-train.h └── kernels.h ├── python-package ├── README.md ├── pythonmodule.c ├── pythonmodule.h ├── setup.py └── setupOSX.py ├── src ├── Exec-LIBIRWLS-predict.c ├── Exec-budgeted-train.c ├── Exec-full-train.c ├── IOStructures.c ├── LIBIRWLS-predict.c ├── ParallelAlgorithms.c ├── budgeted-train.c ├── full-train.c └── kernels.c └── windows ├── win32 ├── LIBIRWLS-predict.exe ├── budgeted-train.exe └── full-train.exe └── win64 ├── LIBIRWLS-predict.exe ├── budgeted-train.exe └── full-train.exe /.gitignore: -------------------------------------------------------------------------------- 1 | # Ignore the build and lib dirs 2 | build 3 | lib/* 4 | 5 | # Ignore any executables 6 | bin/* 7 | data 8 | 9 | # Ignore Mac specific files 10 | .DS_Store 11 | 12 | # Ignore dropbox files 13 | .dropbox 14 | 15 | # Ignore edited security copies 16 | *~ 17 | 18 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | 2 | language: c 3 | 4 | os: 5 | - linux 6 | - osx 7 | 8 | sudo: required 9 | dist: trusty 10 | script: make 11 | compiler: 12 | - gcc 13 | 14 | install: 15 | - echo $TRAVIS_OS_NAME 16 | - if [ "$TRAVIS_OS_NAME" = "linux" ]; then sudo apt-get update; sudo apt-get install gfortran; sudo apt-get install -y libatlas-base-dev; fi 17 | - if [ "$TRAVIS_OS_NAME" = "osx" ]; then brew install gcc6; fi 18 | # build and test library 19 | - if [ "$TRAVIS_OS_NAME" = "linux" ]; then make; fi 20 | - if [ "$TRAVIS_OS_NAME" = "osx" ]; then make OSX=1 CC=gcc-6; fi 21 | 22 | script: 23 | - (cd demo/; ./demoLIBIRWLS.sh) 24 | 25 | 26 | 27 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2016 Roberto Díaz Morales 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | CC=gcc 2 | OSX=0 3 | 4 | OPTFLAGS = -fPIC -O3 -fopenmp 5 | 6 | BINFOLDER := bin 7 | BUILDFOLDER := build 8 | SRCFOLDER := src 9 | 10 | SRCEXT := c 11 | SOURCES := $(shell find $(SRCFOLDER) -type f -name "*.$(SRCEXT)") 12 | OBJECTS := $(patsubst $(SRCFOLDER)/%,$(BUILDFOLDER)/%,$(SOURCES:.$(SRCEXT)=.o)) 13 | 14 | INCLUDE = -Iinclude 15 | 16 | LIBS = -lm -llapack -lf77blas -lcblas -latlas -lgfortran -fopenmp 17 | 18 | CCOPTION = 19 | 20 | ifeq ($(OSX),1) 21 | LIBRARYPATH = -L/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/ 22 | LIBS = -lLAPACK -lBLAS -fopenmp 23 | CCOPTION = -DOSX 24 | endif 25 | 26 | ifdef VECLIBDIR 27 | LIBRARYPATH = -L$(VECLIBDIR)/ 28 | endif 29 | 30 | ifdef ATLASDIR 31 | LIBRARYPATH = -L$(ATLASDIR)/lib/ 32 | endif 33 | 34 | COMMONOBJ := $(BUILDFOLDER)/ParallelAlgorithms.o $(BUILDFOLDER)/IOStructures.o $(BUILDFOLDER)/kernels.o $(BUILDFOLDER)/LIBIRWLS-predict.o $(BUILDFOLDER)/budgeted-train.o $(BUILDFOLDER)/full-train.o 35 | 36 | all: LIBIRWLS-predict full-train budgeted-train 37 | 38 | full-train: $(BUILDFOLDER)/Exec-full-train.o $(COMMONOBJ) 39 | @echo " Linking full-train" 40 | mkdir -p $(BINFOLDER) 41 | @echo " $(CC) $(CCOPTION) $^ -o $(BINFOLDER)/$@ $(INCLUDEPATH) $(LIBRARYPATH) $(LIBS)"; $(CC) $(CCOPTION) $^ -o $(BINFOLDER)/$@ $(INCLUDEPATH) $(LIBRARYPATH) $(LIBS) 42 | 43 | budgeted-train: $(BUILDFOLDER)/Exec-budgeted-train.o $(COMMONOBJ) 44 | @echo " Linking budgeted-train" 45 | mkdir -p $(BINFOLDER) 46 | @echo " $(CC) $(CCOPTION) $^ -o $(BINFOLDER)/$@ $(INCLUDEPATH) $(LIBRARYPATH) $(LIBS)"; $(CC) $(CCOPTION) $^ -o $(BINFOLDER)/$@ $(INCLUDEPATH) $(LIBRARYPATH) $(LIBS) 47 | 48 | LIBIRWLS-predict: $(BUILDFOLDER)/Exec-LIBIRWLS-predict.o $(COMMONOBJ) 49 | @echo " Linking LIBIRWLS-predict" 50 | mkdir -p $(BINFOLDER) 51 | @echo " $(CC) $(CCOPTION) $^ -o $(BINFOLDER)/LIBIRWLS-predict $(INCLUDEPATH) $(LIBRARYPATH) $(LIBS)"; $(CC) $(CCOPTION) $^ -o $(BINFOLDER)/LIBIRWLS-predict $(INCLUDEPATH) $(LIBRARYPATH) $(LIBS) 52 | 53 | $(BUILDFOLDER)/%.o: $(SRCFOLDER)/%.$(SRCEXT) 54 | @echo " mkdir -p $(BUILDFOLDER)"; mkdir -p $(BUILDFOLDER) 55 | @echo " $(CC) $(CCOPTION) $(OPTFLAGS) $(CFLAGS) $(INCLUDE) $(INCLUDEPATH) $(LIBRARYPATH) -c -o $@ $<"; $(CC) $(CCOPTION) $(OPTFLAGS) $(CFLAGS) $(INCLUDE) $(INCLUDEPATH) $(LIBRARYPATH) -c -o $@ $< 56 | 57 | clean: 58 | @echo " Cleaning..."; 59 | @echo " rm -rf $(BINFOLDER) $(BUILDFOLDER)"; rm -rf $(BINFOLDER) $(BUILDFOLDER) 60 | 61 | .PHONY: clean 62 | -------------------------------------------------------------------------------- /demo/demoLIBIRWLS.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | myDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" 4 | 5 | echo "***************************************************" 6 | echo "* DOWNLOADING DATASETS FROM THE LIBSVM REPOSITORY *" 7 | echo "***************************************************" 8 | 9 | mkdir -p ${myDIR}/../data 10 | echo "Downloading ADULT dataset for training: a9a" 11 | wget -q -O ${myDIR}/../data/a9a 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a' 12 | echo "Downloading ADULT dataset for testing: a9a.t" 13 | wget -q -O ${myDIR}/../data/a9a.t 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a.t' 14 | 15 | 16 | 17 | file=${myDIR}"/../bin/full-train" 18 | if [ -f "$file" ] 19 | then 20 | 21 | file=${myDIR}"/../bin/LIBIRWLS-predict" 22 | if [ -f "$file" ] 23 | then 24 | 25 | echo "*************************************************" 26 | echo "* RUNNING FULL SVM USING 1 THREAD *" 27 | echo "*************************************************" 28 | echo " " 29 | ${myDIR}/../bin/full-train -c 1000 -g 0.001 -t 1 ${myDIR}/../data/a9a ${myDIR}/../data/a9a.model 30 | 31 | 32 | echo "**************************************************" 33 | echo "* TRAINING FULL SVM USING 2 THREADS *" 34 | echo "**************************************************" 35 | echo " " 36 | ${myDIR}/../bin/full-train -c 1000 -g 0.001 -t 2 ${myDIR}/../data/a9a ${myDIR}/../data/a9a.model 37 | 38 | echo "*****************************************************" 39 | echo "* USING THE MODEL CREATED TO CLASSIFY A NEW DATASET *" 40 | echo "*****************************************************" 41 | echo " " 42 | ${myDIR}/../bin/LIBIRWLS-predict -l 1 -t 1 ${myDIR}/../data/a9a.t ${myDIR}/../data/a9a.model ${myDIR}/../data/a9a.output 43 | 44 | else 45 | echo "$file not found. Please, build this code using the make command." 46 | fi 47 | 48 | else 49 | echo "$file not found. Please, build this code using the make command." 50 | fi 51 | 52 | 53 | 54 | file=${myDIR}/"../bin/budgeted-train" 55 | if [ -f "$file" ] 56 | then 57 | 58 | file=${myDIR}/"../bin/LIBIRWLS-predict" 59 | if [ -f "$file" ] 60 | then 61 | 62 | echo "*******************************************************" 63 | echo "* RUNNING SEMIPARAMETRIC SVM (PSIRWLS) USING 1 THREAD *" 64 | echo "*******************************************************" 65 | echo " " 66 | ${myDIR}/../bin/budgeted-train -c 1000 -g 0.0001 -s 75 -t 1 ${myDIR}/../data/a9a ${myDIR}/../data/a9a.model 67 | 68 | echo "********************************************************" 69 | echo "* RUNNING SEMIPARAMETRIC SVM (PSIRWLS) USING 2 THREADS *" 70 | echo "********************************************************" 71 | echo " " 72 | ${myDIR}/../bin/budgeted-train -c 1000 -g 0.0001 -s 75 -t 2 ${myDIR}/../data/a9a ${myDIR}/../data/a9a.model 73 | 74 | echo "*****************************************************" 75 | echo "* USING THE MODEL CREATED TO CLASSIFY A NEW DATASET *" 76 | echo "*****************************************************" 77 | echo " " 78 | ${myDIR}/../bin/LIBIRWLS-predict -l 1 -t 1 ${myDIR}/../data/a9a.t ${myDIR}/../data/a9a.model ${myDIR}/../data/a9a.output 79 | 80 | 81 | else 82 | echo "$file not found. Please, build this code using the make command." 83 | fi 84 | 85 | else 86 | echo "$file not found. Please, build this code using the make command." 87 | fi 88 | 89 | -------------------------------------------------------------------------------- /demo/demoLIBIRWLSWin32.bat: -------------------------------------------------------------------------------- 1 | :: Test de la libreria 2 | 3 | @echo off 4 | 5 | ECHO *************************************************** 6 | ECHO * DOWNLOADING DATASETS FROM THE LIBSVM REPOSITORY * 7 | ECHO *************************************************** 8 | ECHO 9 | 10 | mkdir ..\data 11 | 12 | ECHO Downloading ADULT dataset for training: a9a 13 | 14 | powershell -Command "(New-Object Net.WebClient).DownloadFile('https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a', '..\data\a9a')" 15 | 16 | ECHO Downloading ADULT dataset for testing: a9a.t 17 | 18 | powershell -Command "(New-Object Net.WebClient).DownloadFile('https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a.t', '..\data\a9a.t')" 19 | 20 | If exist "..\windows\win32\full-train.exe" ( 21 | 22 | ECHO ************************************************* 23 | ECHO * TRAINING FULL SVM USING 1 THREAD * 24 | ECHO ************************************************* 25 | ECHO 26 | 27 | ..\windows\win32\full-train.exe -c 1000 -g 0.001 -t 1 ..\data\a9a ..\data\a9a.model 28 | 29 | ECHO ************************************************** 30 | ECHO * TRAINING FULL SVM USING 2 THREADS * 31 | ECHO ************************************************** 32 | ECHO 33 | 34 | ..\windows\win32\full-train.exe -c 1000 -g 0.001 -t 2 ..\data\a9a ..\data\a9a.model 35 | 36 | 37 | ECHO ***************************************************** 38 | ECHO * USING THE MODEL CREATED TO CLASSIFY A NEW DATASET * 39 | ECHO ***************************************************** 40 | ECHO 41 | 42 | ..\windows\win32\LIBIRWLS-predict.exe -l 1 -t 1 ..\data\a9a.t ..\data\a9a.model ..\data\a9a.output 43 | 44 | ) ELSE ( 45 | ECHO ..\windows\win32\full-train.exe not found. 46 | ) 47 | 48 | 49 | 50 | If exist "..\windows\win32\budgeted-train.exe" ( 51 | 52 | ECHO ******************************************************* 53 | ECHO * TRAINING BUDGETED SVM USING 1 THREAD * 54 | ECHO ******************************************************* 55 | ECHO 56 | ..\windows\win32\budgeted-train.exe -c 1000 -s 75 -g 0.0001 -t 1 ..\data\a9a ..\data\a9a.model 57 | 58 | ECHO ******************************************************** 59 | ECHO * TRAINING BUDTETED SVM USING 2 THREADS * 60 | ECHO ******************************************************** 61 | 62 | ECHO 63 | ..\windows\win32\budgeted-train.exe -c 1000 -s 75 -g 0.0001 -t 2 ..\data\a9a ..\data\a9a.model 64 | 65 | ECHO ***************************************************** 66 | ECHO * USING THE MODEL CREATED TO CLASSIFY A NEW DATASET * 67 | ECHO ***************************************************** 68 | ECHO 69 | 70 | ..\windows\win32\LIBIRWLS-predict.exe -l 1 -t 1 ..\data\a9a.t ..\data\a9a.model ..\data\a9a.output 71 | 72 | 73 | ) ELSE ( 74 | ECHO ..\windows\win32\budgeted-train.exe not found. 75 | ) 76 | -------------------------------------------------------------------------------- /demo/demoLIBIRWLSWin64.bat: -------------------------------------------------------------------------------- 1 | :: Test de la libreria 2 | 3 | @echo off 4 | 5 | ECHO *************************************************** 6 | ECHO * DOWNLOADING DATASETS FROM THE LIBSVM REPOSITORY * 7 | ECHO *************************************************** 8 | ECHO 9 | 10 | mkdir ..\data 11 | 12 | ECHO Downloading ADULT dataset for training: a9a 13 | 14 | powershell -Command "(New-Object Net.WebClient).DownloadFile('https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a', '..\data\a9a')" 15 | 16 | ECHO Downloading ADULT dataset for testing: a9a.t 17 | 18 | powershell -Command "(New-Object Net.WebClient).DownloadFile('https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a.t', '..\data\a9a.t')" 19 | 20 | If exist "..\windows\win64\full-train.exe" ( 21 | 22 | ECHO ************************************************* 23 | ECHO * TRAINING FULL SVM USING 1 THREAD * 24 | ECHO ************************************************* 25 | ECHO 26 | 27 | ..\windows\win64\full-train.exe -c 1000 -g 0.001 -t 1 ..\data\a9a ..\data\a9a.model 28 | 29 | ECHO ************************************************** 30 | ECHO * TRAINING FULL SVM USING 2 THREADS * 31 | ECHO ************************************************** 32 | ECHO 33 | 34 | ..\windows\win64\full-train.exe -c 1000 -g 0.001 -t 2 ..\data\a9a ..\data\a9a.model 35 | 36 | 37 | ECHO ***************************************************** 38 | ECHO * USING THE MODEL CREATED TO CLASSIFY A NEW DATASET * 39 | ECHO ***************************************************** 40 | ECHO 41 | 42 | ..\windows\win64\LIBIRWLS-predict.exe -l 1 -t 1 ..\data\a9a.t ..\data\a9a.model ..\data\a9a.output 43 | 44 | ) ELSE ( 45 | ECHO ..\windows\win64\full-train.exe not found. 46 | ) 47 | 48 | 49 | 50 | If exist "..\windows\win64\budgeted-train.exe" ( 51 | 52 | ECHO ******************************************************* 53 | ECHO * TRAINING BUDGETED SVM USING 1 THREAD * 54 | ECHO ******************************************************* 55 | ECHO 56 | ..\windows\win64\budgeted-train.exe -c 1000 -s 75 -g 0.0001 -t 1 ..\data\a9a ..\data\a9a.model 57 | 58 | ECHO ******************************************************** 59 | ECHO * TRAINING BUDGETED SVM USING 2 THREADS * 60 | ECHO ******************************************************** 61 | 62 | ECHO 63 | ..\windows\win64\budgeted-train.exe -c 1000 -s 75 -g 0.0001 -t 2 ..\data\a9a ..\data\a9a.model 64 | 65 | ECHO ***************************************************** 66 | ECHO * USING THE MODEL CREATED TO CLASSIFY A NEW DATASET * 67 | ECHO ***************************************************** 68 | ECHO 69 | 70 | ..\windows\win64\LIBIRWLS-predict.exe -l 1 -t 1 ..\data\a9a.t ..\data\a9a.model ..\data\a9a.output 71 | 72 | 73 | ) ELSE ( 74 | ECHO ..\windows\win64\budgeted-train.exe not found. 75 | ) 76 | -------------------------------------------------------------------------------- /demo/demoPythonModule.py: -------------------------------------------------------------------------------- 1 | 2 | import LIBIRWLS 3 | import urllib 4 | import numpy as np 5 | import os 6 | import time 7 | 8 | from sklearn.datasets import load_svmlight_file 9 | from sklearn.metrics import accuracy_score 10 | 11 | print "***************************************************" 12 | print "* DOWNLOADING DATASETS FROM THE LIBSVM REPOSITORY *" 13 | print "***************************************************" 14 | 15 | dataDirectory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "..","data") 16 | 17 | if not os.path.exists(dataDirectory): 18 | os.makedirs(dataDirectory) 19 | 20 | print "Downloading ADULT dataset for training: a9a" 21 | urllib.urlretrieve ("https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a", os.path.join(dataDirectory,"a9a")) 22 | print "Downloading ADULT dataset for testing: a9a.t" 23 | urllib.urlretrieve ("https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a.t", os.path.join(dataDirectory,"a9a.t")) 24 | 25 | ####################################################### 26 | # 27 | # Loading the dataset and creating the numpy matrices. 28 | # 29 | ####################################################### 30 | 31 | Xtr,Ytr = load_svmlight_file(os.path.join(dataDirectory,"a9a")) 32 | Xtst,Ytst = load_svmlight_file(os.path.join(dataDirectory,"a9a.t")) 33 | 34 | Xtr=Xtr.todense() 35 | Xtst=Xtst.todense() 36 | print " " 37 | print "*************************************************" 38 | print "* RUNNING FULL SVM USING 1 THREAD *" 39 | print "*************************************************" 40 | 41 | 42 | start = time.time() 43 | model = LIBIRWLS.full_train(Xtr, Ytr, gamma=0.001, C=1000, threads=1,verbose=1) 44 | end = time.time() 45 | print "Training time",(end - start) 46 | predictions = LIBIRWLS.predict(model, Xtst, threads=1) 47 | print "Accuracy", accuracy_score(Ytst, predictions) 48 | print " " 49 | print "*************************************************" 50 | print "* RUNNING FULL SVM USING 2 THREADS *" 51 | print "*************************************************" 52 | 53 | start = time.time() 54 | model = LIBIRWLS.full_train(Xtr, Ytr, gamma=0.001, C=1000, threads=2,verbose=1) 55 | end = time.time() 56 | print "Training time",(end - start) 57 | predictions = LIBIRWLS.predict(model, Xtst, threads=2) 58 | print "Accuracy", accuracy_score(Ytst, predictions) 59 | 60 | print " " 61 | print "********************************************************" 62 | print "* TRAINING BUDGETED SVM USING 1 THREAD *" 63 | print "********************************************************" 64 | 65 | start = time.time() 66 | model = LIBIRWLS.budgeted_train(Xtr, Ytr, gamma=0.0001, C=1000, size=75, threads=1,verbose=1) 67 | end = time.time() 68 | print "Training 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    25 | 26 | 27 | -------------------------------------------------------------------------------- /docs/html/search/functions_7.js: -------------------------------------------------------------------------------- 1 | var searchData= 2 | [ 3 | ['numpy2dataset',['numpy2dataset',['../d4/d54/pythonmodule_8c.html#a978cb6144438bc23d419731ffa2df0bf',1,'numpy2dataset(PyObject *arr1, PyObject *arr2): pythonmodule.c'],['../d0/da7/pythonmodule_8h.html#a978cb6144438bc23d419731ffa2df0bf',1,'numpy2dataset(PyObject *arr1, PyObject *arr2): pythonmodule.c']]], 4 | ['numpy2datasetwithaverage',['numpy2datasetWithAverage',['../d4/d54/pythonmodule_8c.html#a5a74dac1e53f4b081adec57bd65ea917',1,'numpy2datasetWithAverage(PyObject *arr1, PyObject *arr2): pythonmodule.c'],['../d0/da7/pythonmodule_8h.html#a5a74dac1e53f4b081adec57bd65ea917',1,'numpy2datasetWithAverage(PyObject *arr1, PyObject *arr2): pythonmodule.c']]] 5 | ]; 6 | -------------------------------------------------------------------------------- /docs/html/search/functions_8.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
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    25 | 26 | 27 | -------------------------------------------------------------------------------- /docs/html/search/variables_f.js: -------------------------------------------------------------------------------- 1 | var searchData= 2 | [ 3 | ['x',['x',['../dc/d1b/structmodel.html#a81da41c8731f95de344a068d302eb336',1,'model::x()'],['../d0/d6e/structsvm__dataset.html#ad3e266a07c610ed3f10df1bab30fd9e5',1,'svm_dataset::x()']]] 4 | ]; 5 | -------------------------------------------------------------------------------- /docs/html/splitbar.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobeDM/LIBIRWLS/ca2f75792e1d69329b3e3c286502e9a6f2d7361d/docs/html/splitbar.png -------------------------------------------------------------------------------- /docs/html/sync_off.png: -------------------------------------------------------------------------------- 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refman.idx 8 | pdflatex refman 9 | latex_count=8 ; \ 10 | while egrep -s 'Rerun (LaTeX|to get cross-references right)' refman.log && [ $$latex_count -gt 0 ] ;\ 11 | do \ 12 | echo "Rerunning latex...." ;\ 13 | pdflatex refman ;\ 14 | latex_count=`expr $$latex_count - 1` ;\ 15 | done 16 | makeindex refman.idx 17 | pdflatex refman 18 | 19 | 20 | clean: 21 | rm -f *.ps *.dvi *.aux *.toc *.idx *.ind *.ilg *.log *.out *.brf *.blg *.bbl refman.pdf 22 | -------------------------------------------------------------------------------- /docs/latex/annotated.tex: -------------------------------------------------------------------------------- 1 | \section{Data Structures} 2 | Here are the data structures with brief descriptions\+:\begin{DoxyCompactList} 3 | \item\contentsline{section}{\hyperlink{structmodel}{model} \\*It represents a trained model that has been obtained using P\+I\+R\+W\+LS or P\+S\+I\+R\+W\+LS }{\pageref{dc/d1b/structmodel}}{} 4 | \item\contentsline{section}{\hyperlink{structpredictProperties}{predict\+Properties} \\*Testing parameters of the I\+R\+W\+LS procedures }{\pageref{d0/d38/structpredictProperties}}{} 5 | \item\contentsline{section}{\hyperlink{structproperties}{properties} \\*Training parameters of the I\+R\+W\+LS procedures }{\pageref{d1/d03/structproperties}}{} 6 | \item\contentsline{section}{\hyperlink{structsvm__dataset}{svm\+\_\+dataset} \\*A dataset }{\pageref{d0/d6e/structsvm__dataset}}{} 7 | \item\contentsline{section}{\hyperlink{structsvm__sample}{svm\+\_\+sample} \\*A single feature of a data }{\pageref{d8/d33/structsvm__sample}}{} 8 | \end{DoxyCompactList} 9 | -------------------------------------------------------------------------------- /docs/latex/d0/d38/structpredictProperties.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{structpredictProperties}{}\section{predict\+Properties Struct Reference} 2 | \label{structpredictProperties}\index{predict\+Properties@{predict\+Properties}} 3 | 4 | 5 | Testing parameters of the I\+R\+W\+LS procedures. 6 | 7 | 8 | 9 | 10 | {\ttfamily \#include $<$I\+O\+Structures.\+h$>$} 11 | 12 | \subsection*{Data Fields} 13 | \begin{DoxyCompactItemize} 14 | \item 15 | int \hyperlink{structpredictProperties_a580276e3e15305d5ac16d9ce0eb9b20a}{Labels} 16 | \item 17 | int \hyperlink{structpredictProperties_a602116e0d46424243cb4c971c03695e1}{Threads} 18 | \item 19 | int \hyperlink{structpredictProperties_a3af58f4dd3147f2896e024e89307f7ac}{Soft} 20 | \item 21 | int \hyperlink{structpredictProperties_ae5f93424df7baeb996fd323233a05cac}{file} 22 | \item 23 | char $\ast$ \hyperlink{structpredictProperties_a46ba29899b9f858272e17856bbc35876}{separator} 24 | \item 25 | int \hyperlink{structpredictProperties_acd0a668ce39a7201277c247ac177a48f}{verbose} 26 | \end{DoxyCompactItemize} 27 | 28 | 29 | \subsection{Detailed Description} 30 | Testing parameters of the I\+R\+W\+LS procedures. 31 | 32 | This struct stores the testing parameters of the I\+R\+W\+LS procedures. 33 | 34 | \subsection{Field Documentation} 35 | \hypertarget{structpredictProperties_ae5f93424df7baeb996fd323233a05cac}{}\label{structpredictProperties_ae5f93424df7baeb996fd323233a05cac} 36 | \index{predict\+Properties@{predict\+Properties}!file@{file}} 37 | \index{file@{file}!predict\+Properties@{predict\+Properties}} 38 | \subsubsection{\texorpdfstring{file}{file}} 39 | {\ttfamily int predict\+Properties\+::file} 40 | 41 | File format (1 libsvm, 0 csv). \hypertarget{structpredictProperties_a580276e3e15305d5ac16d9ce0eb9b20a}{}\label{structpredictProperties_a580276e3e15305d5ac16d9ce0eb9b20a} 42 | \index{predict\+Properties@{predict\+Properties}!Labels@{Labels}} 43 | \index{Labels@{Labels}!predict\+Properties@{predict\+Properties}} 44 | \subsubsection{\texorpdfstring{Labels}{Labels}} 45 | {\ttfamily int predict\+Properties\+::\+Labels} 46 | 47 | If the dataset to test is labeled. \hypertarget{structpredictProperties_a46ba29899b9f858272e17856bbc35876}{}\label{structpredictProperties_a46ba29899b9f858272e17856bbc35876} 48 | \index{predict\+Properties@{predict\+Properties}!separator@{separator}} 49 | \index{separator@{separator}!predict\+Properties@{predict\+Properties}} 50 | \subsubsection{\texorpdfstring{separator}{separator}} 51 | {\ttfamily char$\ast$ predict\+Properties\+::separator} 52 | 53 | csv char separator. \hypertarget{structpredictProperties_a3af58f4dd3147f2896e024e89307f7ac}{}\label{structpredictProperties_a3af58f4dd3147f2896e024e89307f7ac} 54 | \index{predict\+Properties@{predict\+Properties}!Soft@{Soft}} 55 | \index{Soft@{Soft}!predict\+Properties@{predict\+Properties}} 56 | \subsubsection{\texorpdfstring{Soft}{Soft}} 57 | {\ttfamily int predict\+Properties\+::\+Soft} 58 | 59 | The classifier obtains Soft output or not. \hypertarget{structpredictProperties_a602116e0d46424243cb4c971c03695e1}{}\label{structpredictProperties_a602116e0d46424243cb4c971c03695e1} 60 | \index{predict\+Properties@{predict\+Properties}!Threads@{Threads}} 61 | \index{Threads@{Threads}!predict\+Properties@{predict\+Properties}} 62 | \subsubsection{\texorpdfstring{Threads}{Threads}} 63 | {\ttfamily int predict\+Properties\+::\+Threads} 64 | 65 | Number of threads to make the predictions on the dataset. \hypertarget{structpredictProperties_acd0a668ce39a7201277c247ac177a48f}{}\label{structpredictProperties_acd0a668ce39a7201277c247ac177a48f} 66 | \index{predict\+Properties@{predict\+Properties}!verbose@{verbose}} 67 | \index{verbose@{verbose}!predict\+Properties@{predict\+Properties}} 68 | \subsubsection{\texorpdfstring{verbose}{verbose}} 69 | {\ttfamily int predict\+Properties\+::verbose} 70 | 71 | 1 print messages in the standard output, 0 silent mode. 72 | 73 | The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} 74 | \item 75 | L\+I\+B\+I\+R\+W\+L\+S/include/\hyperlink{IOStructures_8h}{I\+O\+Structures.\+h}\end{DoxyCompactItemize} 76 | -------------------------------------------------------------------------------- /docs/latex/d2/dfd/full-train_8c.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{full-train_8c}{}\section{L\+I\+B\+I\+R\+W\+L\+S/src/full-\/train.c File Reference} 2 | \label{full-train_8c}\index{L\+I\+B\+I\+R\+W\+L\+S/src/full-\/train.\+c@{L\+I\+B\+I\+R\+W\+L\+S/src/full-\/train.\+c}} 3 | 4 | 5 | Implementation of the functions to train a full S\+VM using the parallel I\+R\+W\+LS procedure. 6 | 7 | 8 | {\ttfamily \#include $<$omp.\+h$>$}\\* 9 | {\ttfamily \#include $<$stdlib.\+h$>$}\\* 10 | {\ttfamily \#include $<$math.\+h$>$}\\* 11 | {\ttfamily \#include $<$string.\+h$>$}\\* 12 | {\ttfamily \#include $<$time.\+h$>$}\\* 13 | {\ttfamily \#include $<$sys/time.\+h$>$}\\* 14 | {\ttfamily \#include \char`\"{}Parallel\+Algorithms.\+h\char`\"{}}\\* 15 | {\ttfamily \#include \char`\"{}full-\/train.\+h\char`\"{}}\\* 16 | {\ttfamily \#include \char`\"{}kernels.\+h\char`\"{}}\\* 17 | 18 | 19 | \subsection{Detailed Description} 20 | Implementation of the functions to train a full S\+VM using the parallel I\+R\+W\+LS procedure. 21 | 22 | See \hyperlink{full-train_8h}{full-\/train.\+h} for a detailed description of its functions and parameters. 23 | 24 | For a detailed description of the algorithm and its parameters read the following paper\+: ~\newline 25 | Pérez-\/\+Cruz, F., Alarcón-\/\+Diana, P. L., Navia-\/\+Vázquez, A., \& Artés-\/\+Rodríguez, A. (2001). Fast Training of Support Vector Classifiers. In Advances in Neural Information Processing Systems (pp. 734-\/740) 26 | 27 | For a detailed description about the parallelization read the following paper\+: ~\newline 28 | Díaz-\/\+Morales, R., \& Navia-\/\+Vázquez, Á. (2016). Efficient parallel implementation of kernel methods. Neurocomputing, 191, 175-\/186. 29 | 30 | \begin{DoxyAuthor}{Author} 31 | Roberto Diaz Morales 32 | \end{DoxyAuthor} 33 | \begin{DoxyDate}{Date} 34 | 23 Aug 2016 35 | \end{DoxyDate} 36 | \begin{DoxySeeAlso}{See also} 37 | \hyperlink{full-train_8h}{full-\/train.\+h} 38 | \end{DoxySeeAlso} 39 | -------------------------------------------------------------------------------- /docs/latex/d3/d54/Exec-full-train_8c.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{Exec-full-train_8c}{}\section{L\+I\+B\+I\+R\+W\+L\+S/src/\+Exec-\/full-\/train.c File Reference} 2 | \label{Exec-full-train_8c}\index{L\+I\+B\+I\+R\+W\+L\+S/src/\+Exec-\/full-\/train.\+c@{L\+I\+B\+I\+R\+W\+L\+S/src/\+Exec-\/full-\/train.\+c}} 3 | 4 | 5 | Main command to train a full S\+VM using the parallel I\+R\+W\+LS procedure. 6 | 7 | 8 | {\ttfamily \#include $<$omp.\+h$>$}\\* 9 | {\ttfamily \#include $<$stdlib.\+h$>$}\\* 10 | {\ttfamily \#include $<$math.\+h$>$}\\* 11 | {\ttfamily \#include $<$string.\+h$>$}\\* 12 | {\ttfamily \#include $<$time.\+h$>$}\\* 13 | {\ttfamily \#include $<$sys/time.\+h$>$}\\* 14 | {\ttfamily \#include \char`\"{}Parallel\+Algorithms.\+h\char`\"{}}\\* 15 | {\ttfamily \#include \char`\"{}full-\/train.\+h\char`\"{}}\\* 16 | {\ttfamily \#include \char`\"{}kernels.\+h\char`\"{}}\\* 17 | \subsection*{Functions} 18 | \begin{DoxyCompactItemize} 19 | \item 20 | \hypertarget{Exec-full-train_8c_a3c04138a5bfe5d72780bb7e82a18e627}{}\label{Exec-full-train_8c_a3c04138a5bfe5d72780bb7e82a18e627} 21 | int \hyperlink{Exec-full-train_8c_a3c04138a5bfe5d72780bb7e82a18e627}{main} (int argc, char $\ast$$\ast$argv) 22 | \begin{DoxyCompactList}\small\item\em Is the main function to build the executable file to train a full S\+VM using the parallel I\+R\+W\+LS procedure. \end{DoxyCompactList}\end{DoxyCompactItemize} 23 | 24 | 25 | \subsection{Detailed Description} 26 | Main command to train a full S\+VM using the parallel I\+R\+W\+LS procedure. 27 | 28 | For a detailed description of the algorithm and its parameters read the following paper\+: ~\newline 29 | Pérez-\/\+Cruz, F., Alarcón-\/\+Diana, P. L., Navia-\/\+Vázquez, A., \& Artés-\/\+Rodríguez, A. (2001). Fast Training of Support Vector Classifiers. In Advances in Neural Information Processing Systems (pp. 734-\/740) 30 | 31 | For a detailed description about the parallelization read the following paper\+: ~\newline 32 | Díaz-\/\+Morales, R., \& Navia-\/\+Vázquez, Á. (2016). Efficient parallel implementation of kernel methods. Neurocomputing, 191, 175-\/186. 33 | 34 | \begin{DoxyAuthor}{Author} 35 | Roberto Diaz Morales 36 | \end{DoxyAuthor} 37 | \begin{DoxyDate}{Date} 38 | 23 Aug 2016 39 | \end{DoxyDate} 40 | \begin{DoxySeeAlso}{See also} 41 | \hyperlink{full-train_8h}{full-\/train.\+h} 42 | \end{DoxySeeAlso} 43 | -------------------------------------------------------------------------------- /docs/latex/d5/d37/budgeted-train_8c.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{budgeted-train_8c}{}\section{L\+I\+B\+I\+R\+W\+L\+S/src/budgeted-\/train.c File Reference} 2 | \label{budgeted-train_8c}\index{L\+I\+B\+I\+R\+W\+L\+S/src/budgeted-\/train.\+c@{L\+I\+B\+I\+R\+W\+L\+S/src/budgeted-\/train.\+c}} 3 | 4 | 5 | Implementation of the functions to train a budgeted S\+VM. 6 | 7 | 8 | {\ttfamily \#include $<$omp.\+h$>$}\\* 9 | {\ttfamily \#include $<$stdlib.\+h$>$}\\* 10 | {\ttfamily \#include $<$math.\+h$>$}\\* 11 | {\ttfamily \#include $<$string.\+h$>$}\\* 12 | {\ttfamily \#include $<$time.\+h$>$}\\* 13 | {\ttfamily \#include $<$sys/time.\+h$>$}\\* 14 | {\ttfamily \#include \char`\"{}budgeted-\/train.\+h\char`\"{}}\\* 15 | {\ttfamily \#include \char`\"{}kernels.\+h\char`\"{}}\\* 16 | {\ttfamily \#include \char`\"{}Parallel\+Algorithms.\+h\char`\"{}}\\* 17 | 18 | 19 | \subsection{Detailed Description} 20 | Implementation of the functions to train a budgeted S\+VM. 21 | 22 | See \hyperlink{budgeted-train_8h}{budgeted-\/train.\+h} for a detailed description of its functions and parameters. 23 | 24 | For a detailed description of the algorithm and its parameters read the following paper\+: 25 | 26 | Díaz-\/\+Morales, R., \& Navia-\/\+Vázquez, Á. (2016). Efficient parallel implementation of kernel methods. Neurocomputing, 191, 175-\/186. 27 | 28 | \begin{DoxyAuthor}{Author} 29 | Roberto Diaz Morales 30 | \end{DoxyAuthor} 31 | \begin{DoxyDate}{Date} 32 | 23 Aug 2016 33 | \end{DoxyDate} 34 | \begin{DoxySeeAlso}{See also} 35 | \hyperlink{budgeted-train_8h}{budgeted-\/train.\+h} 36 | \end{DoxySeeAlso} 37 | -------------------------------------------------------------------------------- /docs/latex/d5/d89/Exec-budgeted-train_8c.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{Exec-budgeted-train_8c}{}\section{L\+I\+B\+I\+R\+W\+L\+S/src/\+Exec-\/budgeted-\/train.c File Reference} 2 | \label{Exec-budgeted-train_8c}\index{L\+I\+B\+I\+R\+W\+L\+S/src/\+Exec-\/budgeted-\/train.\+c@{L\+I\+B\+I\+R\+W\+L\+S/src/\+Exec-\/budgeted-\/train.\+c}} 3 | 4 | 5 | It builds the command line instruction to train a budgeted S\+VM using the parallel I\+R\+W\+LS procedure. 6 | 7 | 8 | {\ttfamily \#include $<$omp.\+h$>$}\\* 9 | {\ttfamily \#include $<$stdlib.\+h$>$}\\* 10 | {\ttfamily \#include $<$math.\+h$>$}\\* 11 | {\ttfamily \#include $<$string.\+h$>$}\\* 12 | {\ttfamily \#include $<$time.\+h$>$}\\* 13 | {\ttfamily \#include $<$sys/time.\+h$>$}\\* 14 | {\ttfamily \#include \char`\"{}L\+I\+B\+I\+R\+W\+L\+S-\/predict.\+h\char`\"{}}\\* 15 | {\ttfamily \#include \char`\"{}budgeted-\/train.\+h\char`\"{}}\\* 16 | {\ttfamily \#include \char`\"{}kernels.\+h\char`\"{}}\\* 17 | {\ttfamily \#include \char`\"{}Parallel\+Algorithms.\+h\char`\"{}}\\* 18 | 19 | 20 | \subsection{Detailed Description} 21 | It builds the command line instruction to train a budgeted S\+VM using the parallel I\+R\+W\+LS procedure. 22 | 23 | See \hyperlink{budgeted-train_8h}{budgeted-\/train.\+h} for a detailed description of its functions and parameters. 24 | 25 | For a detailed description of the algorithm and its parameters read the following paper\+: 26 | 27 | Díaz-\/\+Morales, R., \& Navia-\/\+Vázquez, Á. (2016). Efficient parallel implementation of kernel methods. Neurocomputing, 191, 175-\/186. 28 | 29 | \begin{DoxyAuthor}{Author} 30 | Roberto Diaz Morales 31 | \end{DoxyAuthor} 32 | \begin{DoxyDate}{Date} 33 | 23 Aug 2016 34 | \end{DoxyDate} 35 | \begin{DoxySeeAlso}{See also} 36 | \hyperlink{budgeted-train_8h}{budgeted-\/train.\+h} 37 | \end{DoxySeeAlso} 38 | -------------------------------------------------------------------------------- /docs/latex/d7/d73/kernels_8c.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{kernels_8c}{}\section{L\+I\+B\+I\+R\+W\+L\+S/src/kernels.c File Reference} 2 | \label{kernels_8c}\index{L\+I\+B\+I\+R\+W\+L\+S/src/kernels.\+c@{L\+I\+B\+I\+R\+W\+L\+S/src/kernels.\+c}} 3 | 4 | 5 | Implementation of the kernel functions used in the non linear S\+VM. 6 | 7 | 8 | {\ttfamily \#include \char`\"{}kernels.\+h\char`\"{}}\\* 9 | {\ttfamily \#include \char`\"{}I\+O\+Structures.\+h\char`\"{}}\\* 10 | {\ttfamily \#include $<$math.\+h$>$}\\* 11 | {\ttfamily \#include $<$stdlib.\+h$>$}\\* 12 | 13 | 14 | \subsection{Detailed Description} 15 | Implementation of the kernel functions used in the non linear S\+VM. 16 | 17 | It implements the interfaz defined by kernel.\+h with the kernel functions to use in the non linear S\+VM in this library. See \hyperlink{kernels_8h}{kernels.\+h} for a detailed description of its functions. 18 | 19 | \begin{DoxyAuthor}{Author} 20 | Roberto Diaz Morales 21 | \end{DoxyAuthor} 22 | \begin{DoxyDate}{Date} 23 | 23 Aug 2016 24 | \end{DoxyDate} 25 | \begin{DoxySeeAlso}{See also} 26 | \hyperlink{kernels_8h}{kernels.\+h} 27 | \end{DoxySeeAlso} 28 | -------------------------------------------------------------------------------- /docs/latex/d8/d33/structsvm__sample.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{structsvm__sample}{}\section{svm\+\_\+sample Struct Reference} 2 | \label{structsvm__sample}\index{svm\+\_\+sample@{svm\+\_\+sample}} 3 | 4 | 5 | A single feature of a data. 6 | 7 | 8 | 9 | 10 | {\ttfamily \#include $<$I\+O\+Structures.\+h$>$} 11 | 12 | \subsection*{Data Fields} 13 | \begin{DoxyCompactItemize} 14 | \item 15 | int \hyperlink{structsvm__sample_a008f6b24c7c76af103e84245fb271506}{index} 16 | \item 17 | double \hyperlink{structsvm__sample_a2c43371736dc33a9e1d83561a99cf2f7}{value} 18 | \end{DoxyCompactItemize} 19 | 20 | 21 | \subsection{Detailed Description} 22 | A single feature of a data. 23 | 24 | This structure represents a single feature of a data. It is composed of a features index and its value. 25 | 26 | \subsection{Field Documentation} 27 | \hypertarget{structsvm__sample_a008f6b24c7c76af103e84245fb271506}{}\label{structsvm__sample_a008f6b24c7c76af103e84245fb271506} 28 | \index{svm\+\_\+sample@{svm\+\_\+sample}!index@{index}} 29 | \index{index@{index}!svm\+\_\+sample@{svm\+\_\+sample}} 30 | \subsubsection{\texorpdfstring{index}{index}} 31 | {\ttfamily int svm\+\_\+sample\+::index} 32 | 33 | The feature index. \hypertarget{structsvm__sample_a2c43371736dc33a9e1d83561a99cf2f7}{}\label{structsvm__sample_a2c43371736dc33a9e1d83561a99cf2f7} 34 | \index{svm\+\_\+sample@{svm\+\_\+sample}!value@{value}} 35 | \index{value@{value}!svm\+\_\+sample@{svm\+\_\+sample}} 36 | \subsubsection{\texorpdfstring{value}{value}} 37 | {\ttfamily double svm\+\_\+sample\+::value} 38 | 39 | The feature value. 40 | 41 | The documentation for this struct was generated from the following file\+:\begin{DoxyCompactItemize} 42 | \item 43 | L\+I\+B\+I\+R\+W\+L\+S/include/\hyperlink{IOStructures_8h}{I\+O\+Structures.\+h}\end{DoxyCompactItemize} 44 | -------------------------------------------------------------------------------- /docs/latex/dc/dfc/IOStructures_8c.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{IOStructures_8c}{}\section{L\+I\+B\+I\+R\+W\+L\+S/src/\+I\+O\+Structures.c File Reference} 2 | \label{IOStructures_8c}\index{L\+I\+B\+I\+R\+W\+L\+S/src/\+I\+O\+Structures.\+c@{L\+I\+B\+I\+R\+W\+L\+S/src/\+I\+O\+Structures.\+c}} 3 | 4 | 5 | Implementation of the IO functions used in the application. 6 | 7 | 8 | {\ttfamily \#include $<$stdlib.\+h$>$}\\* 9 | {\ttfamily \#include $<$math.\+h$>$}\\* 10 | {\ttfamily \#include $<$string.\+h$>$}\\* 11 | {\ttfamily \#include $<$stdio.\+h$>$}\\* 12 | {\ttfamily \#include $<$ctype.\+h$>$}\\* 13 | {\ttfamily \#include \char`\"{}I\+O\+Structures.\+h\char`\"{}}\\* 14 | 15 | 16 | \subsection{Detailed Description} 17 | Implementation of the IO functions used in the application. 18 | 19 | It implements the interface defined by \hyperlink{IOStructures_8h}{I\+O\+Structures.\+h}. See \hyperlink{IOStructures_8h}{I\+O\+Structures.\+h} for a detailed description of functions and parameters. 20 | 21 | \begin{DoxyAuthor}{Author} 22 | Roberto Diaz Morales 23 | \end{DoxyAuthor} 24 | \begin{DoxyDate}{Date} 25 | 23 Aug 2016 26 | \end{DoxyDate} 27 | \begin{DoxySeeAlso}{See also} 28 | \hyperlink{IOStructures_8h}{I\+O\+Structures.\+h} 29 | \end{DoxySeeAlso} 30 | -------------------------------------------------------------------------------- /docs/latex/de/d09/ParallelAlgorithms_8c.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{ParallelAlgorithms_8c}{}\section{L\+I\+B\+I\+R\+W\+L\+S/src/\+Parallel\+Algorithms.c File Reference} 2 | \label{ParallelAlgorithms_8c}\index{L\+I\+B\+I\+R\+W\+L\+S/src/\+Parallel\+Algorithms.\+c@{L\+I\+B\+I\+R\+W\+L\+S/src/\+Parallel\+Algorithms.\+c}} 3 | 4 | 5 | Functions to perform some parallel linear algebra tasks. 6 | 7 | 8 | {\ttfamily \#include \char`\"{}Parallel\+Algorithms.\+h\char`\"{}}\\* 9 | {\ttfamily \#include $<$omp.\+h$>$}\\* 10 | {\ttfamily \#include $<$stdio.\+h$>$}\\* 11 | {\ttfamily \#include $<$stdlib.\+h$>$}\\* 12 | {\ttfamily \#include $<$math.\+h$>$}\\* 13 | {\ttfamily \#include $<$string.\+h$>$}\\* 14 | 15 | 16 | \subsection{Detailed Description} 17 | Functions to perform some parallel linear algebra tasks. 18 | 19 | Parallel procedures to solve linear systems, cholesky factorization, matrix products or triangular matrix inversion. 20 | 21 | \begin{DoxyAuthor}{Author} 22 | Roberto Diaz Morales 23 | \end{DoxyAuthor} 24 | \begin{DoxyDate}{Date} 25 | 23 Aug 2016 26 | \end{DoxyDate} 27 | \begin{DoxySeeAlso}{See also} 28 | \hyperlink{ParallelAlgorithms_8h}{Parallel\+Algorithms.\+h} 29 | \end{DoxySeeAlso} 30 | -------------------------------------------------------------------------------- /docs/latex/df/d6f/LIBIRWLS-predict_8c.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{LIBIRWLS-predict_8c}{}\section{L\+I\+B\+I\+R\+W\+L\+S/src/\+L\+I\+B\+I\+R\+W\+L\+S-\/predict.c File Reference} 2 | \label{LIBIRWLS-predict_8c}\index{L\+I\+B\+I\+R\+W\+L\+S/src/\+L\+I\+B\+I\+R\+W\+L\+S-\/predict.\+c@{L\+I\+B\+I\+R\+W\+L\+S/src/\+L\+I\+B\+I\+R\+W\+L\+S-\/predict.\+c}} 3 | 4 | 5 | Implementation of the predictions functions to classify data using a trained model. 6 | 7 | 8 | {\ttfamily \#include $<$omp.\+h$>$}\\* 9 | {\ttfamily \#include $<$stdlib.\+h$>$}\\* 10 | {\ttfamily \#include $<$string.\+h$>$}\\* 11 | {\ttfamily \#include $<$ctype.\+h$>$}\\* 12 | {\ttfamily \#include \char`\"{}kernels.\+h\char`\"{}}\\* 13 | {\ttfamily \#include \char`\"{}L\+I\+B\+I\+R\+W\+L\+S-\/predict.\+h\char`\"{}}\\* 14 | 15 | 16 | \subsection{Detailed Description} 17 | Implementation of the predictions functions to classify data using a trained model. 18 | 19 | See \hyperlink{LIBIRWLS-predict_8h}{L\+I\+B\+I\+R\+W\+L\+S-\/predict.\+h} for a detailed description of its functions and parameters. 20 | 21 | \begin{DoxyAuthor}{Author} 22 | Roberto Diaz Morales 23 | \end{DoxyAuthor} 24 | \begin{DoxyDate}{Date} 25 | 23 Aug 2016 26 | \end{DoxyDate} 27 | \begin{DoxySeeAlso}{See also} 28 | \hyperlink{LIBIRWLS-predict_8h}{L\+I\+B\+I\+R\+W\+L\+S-\/predict.\+h} 29 | \end{DoxySeeAlso} 30 | -------------------------------------------------------------------------------- /docs/latex/dir_23084b2f8693fb5fafc5c8da130baacd.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{dir_23084b2f8693fb5fafc5c8da130baacd}{}\section{L\+I\+B\+I\+R\+W\+L\+S/python-\/package Directory Reference} 2 | \label{dir_23084b2f8693fb5fafc5c8da130baacd}\index{L\+I\+B\+I\+R\+W\+L\+S/python-\/package Directory Reference@{L\+I\+B\+I\+R\+W\+L\+S/python-\/package Directory Reference}} 3 | \subsection*{Files} 4 | \begin{DoxyCompactItemize} 5 | \item 6 | file \hyperlink{pythonmodule_8c}{pythonmodule.\+c} 7 | \begin{DoxyCompactList}\small\item\em Implementation of the functions of the python extension. \end{DoxyCompactList}\item 8 | file \hyperlink{pythonmodule_8h}{pythonmodule.\+h} 9 | \begin{DoxyCompactList}\small\item\em Functions of the python extension that makes use of this library. \end{DoxyCompactList}\end{DoxyCompactItemize} 10 | -------------------------------------------------------------------------------- /docs/latex/dir_2b8ab67b0898ed06b16a24fa52708771.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{dir_2b8ab67b0898ed06b16a24fa52708771}{}\section{L\+I\+B\+I\+R\+W\+L\+S/include Directory Reference} 2 | \label{dir_2b8ab67b0898ed06b16a24fa52708771}\index{L\+I\+B\+I\+R\+W\+L\+S/include Directory Reference@{L\+I\+B\+I\+R\+W\+L\+S/include Directory Reference}} 3 | \subsection*{Files} 4 | \begin{DoxyCompactItemize} 5 | \item 6 | file \hyperlink{budgeted-train_8h}{budgeted-\/train.\+h} 7 | \begin{DoxyCompactList}\small\item\em Functions to train a budgeted S\+VM using the I\+R\+W\+LS algorithm. \end{DoxyCompactList}\item 8 | file \hyperlink{full-train_8h}{full-\/train.\+h} 9 | \begin{DoxyCompactList}\small\item\em Functions to train a full S\+VM using the I\+R\+W\+LS algorithm. \end{DoxyCompactList}\item 10 | file \hyperlink{IOStructures_8h}{I\+O\+Structures.\+h} 11 | \begin{DoxyCompactList}\small\item\em Input and Output structures and procedures. \end{DoxyCompactList}\item 12 | file \hyperlink{kernels_8h}{kernels.\+h} 13 | \begin{DoxyCompactList}\small\item\em Defition of the kernel functions used in the non linear S\+VM. \end{DoxyCompactList}\item 14 | file \hyperlink{LIBIRWLS-predict_8h}{L\+I\+B\+I\+R\+W\+L\+S-\/predict.\+h} 15 | \begin{DoxyCompactList}\small\item\em Functions to classify data with a trained model. \end{DoxyCompactList}\item 16 | file \hyperlink{ParallelAlgorithms_8h}{Parallel\+Algorithms.\+h} 17 | \begin{DoxyCompactList}\small\item\em Functions to perform parallel linear algebra tasks. \end{DoxyCompactList}\end{DoxyCompactItemize} 18 | -------------------------------------------------------------------------------- /docs/latex/dir_999986d24f6c817c1bfe4b11b0f4a64d.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{dir_999986d24f6c817c1bfe4b11b0f4a64d}{}\section{L\+I\+B\+I\+R\+W\+L\+S/src Directory Reference} 2 | \label{dir_999986d24f6c817c1bfe4b11b0f4a64d}\index{L\+I\+B\+I\+R\+W\+L\+S/src Directory Reference@{L\+I\+B\+I\+R\+W\+L\+S/src Directory Reference}} 3 | \subsection*{Files} 4 | \begin{DoxyCompactItemize} 5 | \item 6 | file \hyperlink{budgeted-train_8c}{budgeted-\/train.\+c} 7 | \begin{DoxyCompactList}\small\item\em Implementation of the functions to train a budgeted S\+VM. \end{DoxyCompactList}\item 8 | file \hyperlink{Exec-budgeted-train_8c}{Exec-\/budgeted-\/train.\+c} 9 | \begin{DoxyCompactList}\small\item\em It builds the command line instruction to train a budgeted S\+VM using the parallel I\+R\+W\+LS procedure. \end{DoxyCompactList}\item 10 | file \hyperlink{Exec-full-train_8c}{Exec-\/full-\/train.\+c} 11 | \begin{DoxyCompactList}\small\item\em Main command to train a full S\+VM using the parallel I\+R\+W\+LS procedure. \end{DoxyCompactList}\item 12 | file \hyperlink{full-train_8c}{full-\/train.\+c} 13 | \begin{DoxyCompactList}\small\item\em Implementation of the functions to train a full S\+VM using the parallel I\+R\+W\+LS procedure. \end{DoxyCompactList}\item 14 | file \hyperlink{IOStructures_8c}{I\+O\+Structures.\+c} 15 | \begin{DoxyCompactList}\small\item\em Implementation of the IO functions used in the application. \end{DoxyCompactList}\item 16 | file \hyperlink{kernels_8c}{kernels.\+c} 17 | \begin{DoxyCompactList}\small\item\em Implementation of the kernel functions used in the non linear S\+VM. \end{DoxyCompactList}\item 18 | file \hyperlink{LIBIRWLS-predict_8c}{L\+I\+B\+I\+R\+W\+L\+S-\/predict.\+c} 19 | \begin{DoxyCompactList}\small\item\em Implementation of the predictions functions to classify data using a trained model. \end{DoxyCompactList}\item 20 | file \hyperlink{ParallelAlgorithms_8c}{Parallel\+Algorithms.\+c} 21 | \begin{DoxyCompactList}\small\item\em Functions to perform some parallel linear algebra tasks. \end{DoxyCompactList}\end{DoxyCompactItemize} 22 | -------------------------------------------------------------------------------- /docs/latex/dir_fbf672e6629ab74a24e7d310d431c03b.tex: -------------------------------------------------------------------------------- 1 | \hypertarget{dir_fbf672e6629ab74a24e7d310d431c03b}{}\section{L\+I\+B\+I\+R\+W\+LS Directory Reference} 2 | \label{dir_fbf672e6629ab74a24e7d310d431c03b}\index{L\+I\+B\+I\+R\+W\+L\+S Directory Reference@{L\+I\+B\+I\+R\+W\+L\+S Directory Reference}} 3 | \subsection*{Directories} 4 | \begin{DoxyCompactItemize} 5 | \end{DoxyCompactItemize} 6 | -------------------------------------------------------------------------------- /docs/latex/files.tex: -------------------------------------------------------------------------------- 1 | \section{File List} 2 | Here is a list of all documented files with brief descriptions\+:\begin{DoxyCompactList} 3 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/include/\hyperlink{budgeted-train_8h}{budgeted-\/train.\+h} \\*Functions to train a budgeted S\+VM using the I\+R\+W\+LS algorithm }{\pageref{d4/d49/budgeted-train_8h}}{} 4 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/include/\hyperlink{full-train_8h}{full-\/train.\+h} \\*Functions to train a full S\+VM using the I\+R\+W\+LS algorithm }{\pageref{d3/dd5/full-train_8h}}{} 5 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/include/\hyperlink{IOStructures_8h}{I\+O\+Structures.\+h} \\*Input and Output structures and procedures }{\pageref{de/d79/IOStructures_8h}}{} 6 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/include/\hyperlink{kernels_8h}{kernels.\+h} \\*Defition of the kernel functions used in the non linear S\+VM }{\pageref{d0/d01/kernels_8h}}{} 7 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/include/\hyperlink{LIBIRWLS-predict_8h}{L\+I\+B\+I\+R\+W\+L\+S-\/predict.\+h} \\*Functions to classify data with a trained model }{\pageref{db/d3d/LIBIRWLS-predict_8h}}{} 8 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/include/\hyperlink{ParallelAlgorithms_8h}{Parallel\+Algorithms.\+h} \\*Functions to perform parallel linear algebra tasks }{\pageref{d0/d98/ParallelAlgorithms_8h}}{} 9 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/python-\/package/\hyperlink{pythonmodule_8c}{pythonmodule.\+c} \\*Implementation of the functions of the python extension }{\pageref{d4/d54/pythonmodule_8c}}{} 10 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/python-\/package/\hyperlink{pythonmodule_8h}{pythonmodule.\+h} \\*Functions of the python extension that makes use of this library }{\pageref{d0/da7/pythonmodule_8h}}{} 11 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/src/\hyperlink{budgeted-train_8c}{budgeted-\/train.\+c} \\*Implementation of the functions to train a budgeted S\+VM }{\pageref{d5/d37/budgeted-train_8c}}{} 12 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/src/\hyperlink{Exec-budgeted-train_8c}{Exec-\/budgeted-\/train.\+c} \\*It builds the command line instruction to train a budgeted S\+VM using the parallel I\+R\+W\+LS procedure }{\pageref{d5/d89/Exec-budgeted-train_8c}}{} 13 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/src/\hyperlink{Exec-full-train_8c}{Exec-\/full-\/train.\+c} \\*Main command to train a full S\+VM using the parallel I\+R\+W\+LS procedure }{\pageref{d3/d54/Exec-full-train_8c}}{} 14 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/src/\hyperlink{full-train_8c}{full-\/train.\+c} \\*Implementation of the functions to train a full S\+VM using the parallel I\+R\+W\+LS procedure }{\pageref{d2/dfd/full-train_8c}}{} 15 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/src/\hyperlink{IOStructures_8c}{I\+O\+Structures.\+c} \\*Implementation of the IO functions used in the application }{\pageref{dc/dfc/IOStructures_8c}}{} 16 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/src/\hyperlink{kernels_8c}{kernels.\+c} \\*Implementation of the kernel functions used in the non linear S\+VM }{\pageref{d7/d73/kernels_8c}}{} 17 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/src/\hyperlink{LIBIRWLS-predict_8c}{L\+I\+B\+I\+R\+W\+L\+S-\/predict.\+c} \\*Implementation of the predictions functions to classify data using a trained model }{\pageref{df/d6f/LIBIRWLS-predict_8c}}{} 18 | \item\contentsline{section}{L\+I\+B\+I\+R\+W\+L\+S/src/\hyperlink{ParallelAlgorithms_8c}{Parallel\+Algorithms.\+c} \\*Functions to perform some parallel linear algebra tasks }{\pageref{de/d09/ParallelAlgorithms_8c}}{} 19 | \end{DoxyCompactList} 20 | -------------------------------------------------------------------------------- /include/LIBIRWLS-predict.h: -------------------------------------------------------------------------------- 1 | /* 2 | ============================================================================ 3 | Author : Roberto Diaz Morales 4 | ============================================================================ 5 | 6 | Copyright (c) 2016 Roberto Díaz Morales 7 | 8 | Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files 9 | (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, 10 | publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, 11 | subject to the following conditions: 12 | 13 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF 16 | MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE 17 | FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION 18 | WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 19 | 20 | ============================================================================ 21 | */ 22 | 23 | /** 24 | * @file LIBIRWLS-predict.h 25 | * @author Roberto Diaz Morales 26 | * @date 23 Aug 2016 27 | * @brief Functions to classify data with a trained model. 28 | * 29 | * Functions to classify data with a trained model. 30 | */ 31 | 32 | 33 | #ifndef LIBIRWLSPREDICT_ 34 | #define LIBIRWLSPREDICT_ 35 | 36 | #include "IOStructures.h" 37 | 38 | /** 39 | * @brief Function to classify data in a labeled dataset and to obtain the accuracy. 40 | * 41 | * Function that uses a trained model on a dataset and obtains the class of every training sample. 42 | * @param dataset The test set. 43 | * @param mymodel A trained SVM model. 44 | * @param props The test properties. 45 | * @return The output of the classifier for every test sample (soft output). 46 | */ 47 | 48 | double *test(svm_dataset dataset, model mymodel,predictProperties props); 49 | 50 | 51 | /** 52 | * @brief Function to obtain the soft output of the classifier. 53 | * 54 | * Function to obtain the soft output (the output of the classifier before using the threshold to decide class +1 or -1) of the model on a dataset. It is useful to combine this output with other algorithms in ensembles. 55 | * @param dataset The test set. 56 | * @param mymodel A trained SVM model. 57 | * @param props The test properties. 58 | * @return The output of the classifier for every test sample (soft output). 59 | */ 60 | 61 | double *softTest(svm_dataset dataset, model mymodel,predictProperties props); 62 | 63 | 64 | /** 65 | * @brief Print instructions. 66 | * 67 | * It shows the command line instructions in the standard output. 68 | */ 69 | 70 | void printPredictInstructions(void); 71 | 72 | 73 | /** 74 | * @brief It parses the prediction parameters from the command line. 75 | * 76 | * It reads the command line, extract the parameters and creates a strict with the value of its values. 77 | * @param argc The number of words of the command line. 78 | * @param argv The list of words of the command line. 79 | * @return A struct that contains the values of the test parameters. 80 | */ 81 | 82 | predictProperties parsePredictParameters(int* argc, char*** argv); 83 | 84 | 85 | #endif 86 | 87 | -------------------------------------------------------------------------------- /include/kernels.h: -------------------------------------------------------------------------------- 1 | /* 2 | ============================================================================ 3 | Author : Roberto Diaz Morales 4 | ============================================================================ 5 | 6 | Copyright (c) 2016 Roberto Díaz Morales 7 | 8 | Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files 9 | (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, 10 | publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, 11 | subject to the following conditions: 12 | 13 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF 16 | MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE 17 | FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION 18 | WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 19 | 20 | ============================================================================ 21 | */ 22 | 23 | /** 24 | * @file kernels.h 25 | * @author Roberto Diaz Morales 26 | * @date 23 Aug 2016 27 | * @brief Defition of the kernel functions used in the non linear SVM. 28 | * 29 | * It defines the kernel function to use in the non linear SVM in this library. 30 | */ 31 | 32 | 33 | #ifndef KERNELS_ 34 | #define KERNELS_ 35 | 36 | #include "IOStructures.h" 37 | 38 | /** 39 | * @brief Radial Basis Function of two elements of the dataset. 40 | * 41 | * This function returns the kernel function among two elements of the same dataset. 42 | * 43 | * It returns exp(-gamma||x1-x2||^2) 44 | * 45 | * x1 and x2 are two elements of the dataset and gamma is a parameter whose value can be found 46 | * in the struct props. 47 | * 48 | * @param dataset The strut that contains the dataset information. 49 | * @param index1 The index of the first element of the dataset. 50 | * @param index2 The index of the second element of the dataset. 51 | * @param props The list of properties to extract the kernel parameters. 52 | * @return The value of the Radial Basis Function of both elements. 53 | */ 54 | 55 | double kernelFunction(svm_dataset dataset, int index1, int index2, properties props); 56 | 57 | /** 58 | * @brief Radial Basis Function of one element of the dataset and Support Vectro of a trained model. 59 | * 60 | * This method returns the RBF Kernel function of one element of the dataset and Support Vectro of a trained model. 61 | * 62 | * It returns exp(-gamma||x1-x2||^2) 63 | * 64 | * x1 is an element of the dataset and x2 is a support vector of a trained model, gamma is a parameter whose value can be found 65 | * in the struct props. 66 | * 67 | * @param dataset The strut that contains the dataset information. 68 | * @param index1 The index of the sample of the dataset. 69 | * @param mymodel The trained SVM model. 70 | * @param index2 The index of one of the Support Vectors of the trained model. 71 | * @return The value of the Radial Basis Function of both elements. 72 | */ 73 | 74 | double kernelTest(svm_dataset dataset, int index1, model mymodel, int index2); 75 | 76 | 77 | #endif 78 | 79 | -------------------------------------------------------------------------------- /python-package/setup.py: -------------------------------------------------------------------------------- 1 | # -*- coding: UTF-8 -*- 2 | from distutils.core import setup 3 | from distutils.extension import Extension 4 | from Cython.Distutils import build_ext 5 | import numpy as np 6 | import os 7 | 8 | 9 | AtlasDir=os.environ.get("ATLASDIR","..") 10 | 11 | AtlasDir = AtlasDir+"/lib/" 12 | 13 | ext_modules = [ 14 | Extension('LIBIRWLS', 15 | include_dirs=['.','../include/',np.get_include()], 16 | sources = ['pythonmodule.c'], 17 | extra_objects = ['../build/LIBIRWLS-predict.o', 18 | '../build/full-train.o', 19 | '../build/budgeted-train.o', 20 | '../build/IOStructures.o', 21 | '../build/ParallelAlgorithms.o', 22 | '../build/kernels.o' 23 | ], 24 | library_dirs = [AtlasDir,"../build/"], 25 | extra_compile_args = ["-fPIC","-O3","-llapack", "-lf77blas", "-lcblas", "-latlas", "-lgfortran",'-fopenmp'], 26 | extra_link_args=["-fPIC","-llapack", "-lf77blas", "-lcblas", "-latlas", "-lgfortran",'-fopenmp'] 27 | ) 28 | ] 29 | 30 | setup( 31 | name = 'LIBIRWLS', 32 | version = '3.0', 33 | description="A Parallel IRWLS procedure for SVMs and budgeted SVMs", 34 | install_requires=[ 35 | 'numpy', 36 | 'scipy', 37 | ], 38 | url='https://robedm.github.io/LIBIRWLS/', 39 | ext_modules = ext_modules 40 | ) 41 | -------------------------------------------------------------------------------- /python-package/setupOSX.py: -------------------------------------------------------------------------------- 1 | # -*- coding: UTF-8 -*- 2 | from distutils.core import setup 3 | from distutils.extension import Extension 4 | from Cython.Distutils import build_ext 5 | import numpy as np 6 | import os 7 | 8 | os.environ["CC"]=os.environ.get("CC","gcc") 9 | 10 | print "" 11 | print "USING COMPILER:",os.environ["CC"] 12 | print "" 13 | 14 | VecLibDir = os.environ.get("VECLIBDIR","/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/") 15 | 16 | print "" 17 | print "VECLIB ROUTINES:",VecLibDir 18 | print "" 19 | 20 | libgompPath=os.environ.get("LIBGOMP_PATH","libgomp.a") 21 | 22 | print "" 23 | print "USING LIBGOMP:",libgompPath 24 | print "" 25 | 26 | ext_modules = [ 27 | Extension('LIBIRWLS', 28 | include_dirs=['.','../include/',np.get_include()], 29 | sources = ['pythonmodule.c'], 30 | extra_objects = [libgompPath, 31 | '../build/LIBIRWLS-predict.o', 32 | '../build/full-train.o', 33 | '../build/budgeted-train.o', 34 | '../build/IOStructures.o', 35 | '../build/ParallelAlgorithms.o', 36 | '../build/kernels.o' 37 | ], 38 | library_dirs = [VecLibDir,"../build/"], 39 | extra_compile_args=['-Wno-cpp','-static','-lgomp','-lblas','-llapack'], 40 | extra_link_args=['-static'] 41 | ) 42 | ] 43 | 44 | setup( 45 | name = 'LIBIRWLS', 46 | version = '3.0', 47 | description="A Parallel IRWLS procedure for SVMs and budgeted SVMs", 48 | install_requires=[ 49 | 'numpy', 50 | 'scipy', 51 | ], 52 | url='https://robedm.github.io/LIBIRWLS/', 53 | ext_modules = ext_modules 54 | ) 55 | -------------------------------------------------------------------------------- /src/Exec-LIBIRWLS-predict.c: -------------------------------------------------------------------------------- 1 | 2 | 3 | #include 4 | #include 5 | #include 6 | #include 7 | 8 | #include "kernels.h" 9 | #include "LIBIRWLS-predict.h" 10 | 11 | 12 | /** 13 | * @brief It the main function to build the executable file to make predictions 14 | * on a dataset using a model previously trained using PIRWLS-train or PSIRWLS-train. 15 | */ 16 | 17 | int main(int argc, char** argv) 18 | { 19 | // Parsing command line to extract parameters. 20 | predictProperties props = parsePredictParameters(&argc, &argv); 21 | 22 | //Show error msg if there are wrong parameters. 23 | if (argc != 4) { 24 | printPredictInstructions(); 25 | return 4; 26 | } 27 | 28 | // The name of the files 29 | char * data_file = argv[1]; 30 | char * data_model = argv[2]; 31 | char * output_file = argv[3]; 32 | 33 | if(props.verbose==1){ 34 | printf("\nRunning with parameters:\n"); 35 | printf("------------------------\n"); 36 | printf("Dataset: %s\n",data_file); 37 | printf("The model to use: %s\n",data_model); 38 | printf("The result will be saved in: %s\n",output_file); 39 | printf("flag l = %d (l = 1 for labeled datasets, l = 0 for unlabeled datasets)\n",props.Labels); 40 | printf("------------------------\n"); 41 | printf("\n"); 42 | } 43 | 44 | model mymodel; 45 | 46 | // Reading the trained model from the file 47 | if(props.verbose==1) printf("\nReading trained model from file:%s\n",data_model); 48 | FILE *In = fopen(data_model, "rb"); 49 | if (In == NULL) { 50 | fprintf(stderr, "Input file with the trained model not found: %s\n",data_model); 51 | exit(2); 52 | } 53 | readModel(&mymodel, In); 54 | fclose(In); 55 | if(props.verbose==1) printf("Model Loaded, it contains %d Support Vectors\n\n",mymodel.nSVs); 56 | 57 | 58 | // Loading dataset 59 | if(props.verbose==1) printf("Reading dataset from file:%s\n",data_file); 60 | 61 | In = fopen(data_file, "rb"); 62 | if (In == NULL) { 63 | fprintf(stderr, "Input file with the training set not found: %s\n",data_file); 64 | exit(2); 65 | } 66 | fclose(In); 67 | 68 | svm_dataset dataset; 69 | if(props.Labels==0){ 70 | if(props.file==1){ 71 | dataset=readUnlabeledFile(data_file); 72 | }else{ 73 | dataset=readUnlabeledFileCSV(data_file,props.separator); 74 | } 75 | }else{ 76 | if(props.file==1){ 77 | dataset=readTrainFile(data_file); 78 | }else{ 79 | dataset=readTrainFileCSV(data_file,props.separator); 80 | } 81 | } 82 | if(props.verbose==1) printf("Dataset Loaded, it contains %d samples and %d features\n\n", dataset.l,dataset.maxdim); 83 | 84 | // Set the number of openmp threads 85 | omp_set_num_threads(props.Threads); 86 | 87 | //Making predictions 88 | if(props.verbose==1) printf("Classifying data...\n"); 89 | double *predictions; 90 | if (props.Soft==0){ 91 | predictions=test(dataset,mymodel,props); 92 | }else{ 93 | predictions=softTest(dataset,mymodel,props); 94 | } 95 | if(props.verbose==1) printf("data classified\n"); 96 | if(props.verbose==1) printf("\nWriting output in file: %s \n\n",output_file); 97 | writeOutput (output_file, predictions,dataset.l); 98 | 99 | freeDataset(dataset); 100 | freeModel(mymodel); 101 | free(predictions); 102 | return 0; 103 | } 104 | -------------------------------------------------------------------------------- /windows/win32/LIBIRWLS-predict.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobeDM/LIBIRWLS/ca2f75792e1d69329b3e3c286502e9a6f2d7361d/windows/win32/LIBIRWLS-predict.exe -------------------------------------------------------------------------------- /windows/win32/budgeted-train.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobeDM/LIBIRWLS/ca2f75792e1d69329b3e3c286502e9a6f2d7361d/windows/win32/budgeted-train.exe -------------------------------------------------------------------------------- /windows/win32/full-train.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobeDM/LIBIRWLS/ca2f75792e1d69329b3e3c286502e9a6f2d7361d/windows/win32/full-train.exe -------------------------------------------------------------------------------- /windows/win64/LIBIRWLS-predict.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobeDM/LIBIRWLS/ca2f75792e1d69329b3e3c286502e9a6f2d7361d/windows/win64/LIBIRWLS-predict.exe -------------------------------------------------------------------------------- /windows/win64/budgeted-train.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobeDM/LIBIRWLS/ca2f75792e1d69329b3e3c286502e9a6f2d7361d/windows/win64/budgeted-train.exe -------------------------------------------------------------------------------- /windows/win64/full-train.exe: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/RobeDM/LIBIRWLS/ca2f75792e1d69329b3e3c286502e9a6f2d7361d/windows/win64/full-train.exe --------------------------------------------------------------------------------