├── .gitattributes ├── .gitignore ├── README.md ├── chapter1 ├── BPDLX.m ├── chapter1_1.asv ├── chapter1_1.m ├── data1.mat ├── data2.mat ├── data3.mat └── data4.mat ├── chapter10 ├── Readme.txt ├── chapter10.m ├── class.mat ├── sim.mat ├── stdlib.m └── test.m ├── chapter11 ├── Readme.txt ├── city_location.mat ├── diff_u.m ├── energy.m └── main.m ├── chapter12 ├── Chapter_ClassifyRegressUsingLibsvm.m ├── heart_scale.mat └── html │ ├── Chapter_ClassifyRegressUsingLibsvm.html │ ├── Chapter_ClassifyRegressUsingLibsvm.png │ └── Chapter_ClassifyRegressUsingLibsvm_01.png ├── chapter13 ├── Chapter_ModelDecryption.m ├── heart_scale.mat └── html │ └── Chapter_ModelDecryption.html ├── chapter14 ├── chapter_WineClass.m ├── chapter_WineClass.mat └── html │ ├── chapter_WineClass.html │ ├── chapter_WineClass.png │ ├── chapter_WineClass_01.png │ ├── chapter_WineClass_02.png │ └── chapter_WineClass_03.png ├── chapter15 ├── chapter_GA.m ├── chapter_GridSearch.m ├── chapter_PSO.m ├── html │ ├── chapter_GA.html │ ├── chapter_GA.png │ ├── chapter_GA_01.png │ ├── chapter_GA_02.png │ ├── chapter_GA_03.png │ ├── chapter_GA_04.png │ ├── chapter_GridSearch.html │ ├── chapter_GridSearch.png │ ├── chapter_GridSearch_01.png │ ├── chapter_GridSearch_02.png │ ├── chapter_GridSearch_03.png │ ├── chapter_GridSearch_04.png │ ├── chapter_GridSearch_05.png │ ├── chapter_GridSearch_06.png │ ├── chapter_GridSearch_07.png │ ├── chapter_PSO.html │ ├── chapter_PSO.png │ ├── chapter_PSO_01.png │ ├── chapter_PSO_02.png │ ├── chapter_PSO_03.png │ └── chapter_PSO_04.png └── wine.mat ├── chapter16 ├── chapter_sh.m ├── chapter_sh.mat └── html │ ├── chapter_sh.html │ ├── chapter_sh.png │ ├── chapter_sh_01.png │ ├── chapter_sh_02.png │ ├── chapter_sh_03.png │ ├── chapter_sh_04.png │ ├── chapter_sh_05.png │ ├── chapter_sh_06.png │ ├── chapter_sh_07.png │ ├── chapter_sh_08.png │ └── chapter_sh_09.png ├── chapter17 ├── FIG_D.m ├── chapter_FIGsh.m ├── chapter_sh.mat └── html │ ├── chapter_FIGsh.html │ ├── chapter_FIGsh.png │ ├── chapter_FIGsh_01.png │ ├── chapter_FIGsh_02.png │ ├── chapter_FIGsh_03.png │ ├── chapter_FIGsh_04.png │ ├── chapter_FIGsh_05.png │ ├── chapter_FIGsh_06.png │ ├── chapter_FIGsh_07.png │ ├── chapter_FIGsh_08.png │ ├── chapter_FIGsh_09.png │ ├── chapter_FIGsh_10.png │ ├── chapter_FIGsh_11.png │ ├── chapter_FIGsh_12.png │ ├── chapter_FIGsh_13.png │ ├── chapter_FIGsh_14.png │ ├── chapter_FIGsh_15.png │ ├── chapter_FIGsh_16.png │ └── chapter_FIGsh_17.png ├── chapter18 ├── Chapter_ImSegmentUsingLibsvm.m ├── html │ ├── Chapter_ImSegmentUsingLibsvm.html │ ├── Chapter_ImSegmentUsingLibsvm.png │ ├── Chapter_ImSegmentUsingLibsvm_01.png │ ├── Chapter_ImSegmentUsingLibsvm_02.png │ └── Chapter_ImSegmentUsingLibsvm_03.png └── littleduck.jpg ├── chapter19 ├── Chapter_CharacterRecognitionUsingLibsvm.m ├── html │ ├── Chapter_CharacterRecognitionUsingLibsvm.html │ ├── Chapter_CharacterRecognitionUsingLibsvm.png │ └── Chapter_CharacterRecognitionUsingLibsvm_01.png ├── pic_preprocess.m ├── 手写数字测试样本图片 │ ├── num0_1.bmp │ ├── num0_2.bmp │ ├── num0_3.bmp │ ├── num1_1.bmp │ ├── num1_2.bmp │ ├── num1_3.bmp │ ├── num2_1.bmp │ ├── num2_2.bmp │ ├── num2_3.bmp │ ├── num3_1.bmp │ ├── num3_2.bmp │ ├── num3_3.bmp │ ├── num4_1.bmp │ ├── num4_2.bmp │ ├── num4_3.bmp │ ├── num5_1.bmp │ ├── num5_2.bmp │ ├── num5_3.bmp │ ├── num6_1.bmp │ ├── num6_2.bmp │ ├── num6_3.bmp │ ├── num7_1.bmp │ ├── num7_2.bmp │ ├── num7_3.bmp │ ├── num8_1.bmp │ ├── num8_2.bmp │ ├── num8_3.bmp │ ├── num9_1.bmp │ ├── num9_2.bmp │ └── num9_3.bmp └── 手写数字训练样本图片 │ ├── num0_1.jpg │ ├── num0_2.jpg │ ├── num0_3.jpg │ ├── num0_4.jpg │ ├── num0_5.jpg │ ├── num1_1.jpg │ ├── num1_2.jpg │ ├── num1_3.jpg │ ├── num1_4.jpg │ ├── num1_5.jpg │ ├── num2_1.jpg │ ├── num2_2.jpg │ ├── num2_3.jpg │ ├── num2_4.jpg │ ├── num2_5.jpg │ ├── num3_1.jpg │ ├── num3_2.jpg │ ├── num3_3.jpg │ ├── num3_4.jpg │ ├── num3_5.jpg │ ├── num4_1.jpg │ ├── num4_2.jpg │ ├── num4_3.jpg │ ├── num4_4.jpg │ ├── num4_5.jpg │ ├── num5_1.jpg │ ├── num5_2.jpg │ ├── num5_3.jpg │ ├── num5_4.jpg │ ├── num5_5.jpg │ ├── num6_1.jpg │ ├── num6_2.jpg │ ├── num6_3.jpg │ ├── num6_4.jpg │ ├── num6_5.jpg │ ├── num7_1.jpg │ ├── num7_2.jpg │ ├── num7_3.jpg │ ├── num7_4.jpg │ ├── num7_5.jpg │ ├── num8_1.jpg │ ├── num8_2.jpg │ ├── num8_3.jpg │ ├── num8_4.jpg │ ├── num8_5.jpg │ ├── num9_1.jpg │ ├── num9_2.jpg │ ├── num9_3.jpg │ ├── num9_4.jpg │ └── num9_5.jpg ├── chapter2 ├── BP_Hidden.m ├── chapter2_1.m └── data.mat ├── chapter20 ├── SVM_GUI_3.1[mcode]{by faruto}.rar ├── SVM_GUI_3.1[mcode]{by faruto} │ ├── SVC.fig │ ├── SVC.m │ ├── SVM_GUI.fig │ ├── SVM_GUI.m │ ├── SVR.fig │ ├── SVR.m │ ├── readme.txt │ └── testdata │ │ ├── fisheriris_test.mat │ │ ├── regress_test.mat │ │ └── wine_test.mat ├── TutorialTest.m ├── html │ ├── TutorialTest.html │ ├── TutorialTest.png │ ├── TutorialTest_01.png │ ├── TutorialTest_02.png │ ├── TutorialTest_03.png │ ├── TutorialTest_04.png │ ├── TutorialTest_05.png │ ├── TutorialTest_06.png │ ├── TutorialTest_07.png │ ├── TutorialTest_08.png │ ├── TutorialTest_09.png │ ├── TutorialTest_10.png │ ├── TutorialTest_11.png │ ├── TutorialTest_12.png │ ├── TutorialTest_13.png │ └── TutorialTest_14.png ├── libsvm-3.1-[FarutoUltimate3.1Mcode].rar └── libsvm-3.1-[FarutoUltimate3.1Mcode] │ ├── COPYRIGHT │ ├── FAQ.html │ ├── Makefile │ ├── Makefile.win │ ├── README │ ├── heart_scale │ ├── java │ ├── Makefile │ ├── libsvm.jar │ ├── libsvm │ │ ├── svm.java │ │ ├── svm.m4 │ │ ├── svm_model.java │ │ ├── svm_node.java │ │ ├── svm_parameter.java │ │ ├── svm_print_interface.java │ │ └── svm_problem.java │ ├── svm_predict.java │ ├── svm_scale.java │ ├── svm_toy.java │ ├── svm_train.java │ └── test_applet.html │ ├── matlab-implement[by faruto] │ ├── ClassResult.m │ ├── ClassResult_test.m │ ├── Readme[by faruto]CN.txt │ ├── Readme[by faruto]EN.txt │ ├── SVC.m │ ├── SVC_test.m │ ├── SVMcgForClass.m │ ├── SVMcgForRegress.m │ ├── SVR.m │ ├── SVR_test.m │ ├── TutorialForFarutoUltimate3.1.pdf │ ├── TutorialTest.m │ ├── VF.m │ ├── a_template_flow_usingSVM_class.m │ ├── a_template_flow_usingSVM_regress.m │ ├── gaSVMcgForClass.m │ ├── gaSVMcgForRegress.m │ ├── gaSVMcgpForRegress.m │ ├── libsvm参数说明.txt │ ├── myprivate │ │ ├── gatbx[Sheffield] │ │ │ ├── bs2rv.m │ │ │ ├── contents.m │ │ │ ├── crtbase.m │ │ │ ├── crtbp.m │ │ │ ├── crtrp.m │ │ │ ├── migrate.m │ │ │ ├── mpga.m │ │ │ ├── mut.m │ │ │ ├── mutate.m │ │ │ ├── mutbga.m │ │ │ ├── mytest │ │ │ │ └── gaSVM.m │ │ │ ├── ranking.m │ │ │ ├── recdis.m │ │ │ ├── recint.m │ │ │ ├── reclin.m │ │ │ ├── recmut.m │ │ │ ├── recombin.m │ │ │ ├── reins.m │ │ │ ├── rep.m │ │ │ ├── resplot.m │ │ │ ├── rws.m │ │ │ ├── scaling.m │ │ │ ├── select.m │ │ │ ├── sus.m │ │ │ ├── xovdp.m │ │ │ ├── xovdprs.m │ │ │ ├── xovmp.m │ │ │ ├── xovsh.m │ │ │ ├── xovshrs.m │ │ │ ├── xovsp.m │ │ │ └── xovsprs.m │ │ └── plotroc2009b.m │ ├── pcaForSVM.m │ ├── plotSVMroc.m │ ├── plotSVMroc_test.m │ ├── plotSVMroc_test2.m │ ├── psoSVMcgForClass.m │ ├── psoSVMcgForRegress.m │ ├── psoSVMcgpForRegress.m │ ├── scaleForSVM.m │ ├── svmplot.m │ ├── test_data │ │ ├── adult.mat │ │ ├── book.mat │ │ ├── data1.mat │ │ ├── image_seg.mat │ │ ├── test1.mat │ │ ├── testFor_image_seg.m │ │ ├── wine_test.mat │ │ └── x123.mat │ ├── test_data2 │ │ ├── wine_test.mat │ │ └── x123.mat │ ├── testingFuntion_beta │ │ ├── #gaSVMcgForClass.m │ │ ├── #gaSVMcgForRegress.m │ │ ├── #gaSVMcgpForRegress.m │ │ ├── DCTforSVM.m │ │ ├── SVMcgpForRegress.m │ │ ├── fasticaForSVM.m │ │ ├── testFor_DCT.m │ │ └── test_for_ica_SVM.m │ └── 更新说明2011.06.10.txt │ ├── matlab │ ├── Makefile │ ├── README │ ├── heart_scale.mat │ ├── libsvmread.c │ ├── libsvmread.mexw32 │ ├── libsvmwrite.c │ ├── libsvmwrite.mexw32 │ ├── make.m │ ├── svm.obj │ ├── svm_model_matlab.c │ ├── svm_model_matlab.h │ ├── svm_model_matlab.obj │ ├── svmpredict.c │ ├── svmpredict.mexw32 │ ├── svmtrain.c │ ├── svmtrain.c.bak │ └── svmtrain.mexw32 │ ├── python │ ├── Makefile │ ├── README │ ├── svm.py │ └── svmutil.py │ ├── svm-predict.c │ ├── svm-scale.c │ ├── svm-toy │ ├── gtk │ │ ├── Makefile │ │ ├── callbacks.cpp │ │ ├── callbacks.h │ │ ├── interface.c │ │ ├── interface.h │ │ ├── main.c │ │ └── svm-toy.glade │ ├── qt │ │ ├── Makefile │ │ └── svm-toy.cpp │ └── windows │ │ └── svm-toy.cpp │ ├── svm-train.c │ ├── svm.cpp │ ├── svm.cpp.bak │ ├── svm.def │ ├── svm.h │ ├── tools │ ├── README │ ├── checkdata.py │ ├── easy.py │ ├── grid.py │ └── subset.py │ └── windows │ ├── libsvm.dll │ ├── libsvmread.mexw32 │ ├── libsvmread.mexw64 │ ├── libsvmwrite.mexw32 │ ├── libsvmwrite.mexw64 │ ├── svm-predict.exe │ ├── svm-scale.exe │ ├── svm-toy.exe │ ├── svm-train.exe │ ├── svmpredict.mexw32 │ ├── svmpredict.mexw64 │ ├── svmtrain.mexw32 │ └── svmtrain.mexw64 ├── chapter21 ├── chapter21.m ├── gene.mat ├── gene.txt └── html │ └── chapter21.html ├── chapter22 ├── addon.m ├── chapter22.asv ├── chapter22.m ├── html │ ├── addon.html │ ├── addon.png │ ├── addon_01.png │ ├── chapter22.html │ ├── chapter22.png │ ├── chapter22_01.png │ ├── chapter22_02.png │ └── chapter22_03.png ├── p.mat └── 运行说明.txt ├── chapter23 ├── chapter23.m ├── data.mat └── html │ ├── chapter23.html │ ├── chapter23.png │ └── chapter23_01.png ├── chapter24 ├── chapter24.m ├── data.mat └── html │ ├── chapter24.html │ ├── chapter24.png │ ├── chapter24_01.png │ └── chapter24_02.png ├── chapter25 ├── chapter25.m └── html │ └── chapter25.html ├── chapter26 ├── Readme.txt ├── chapter26_bp.m ├── chapter26_lvq.m ├── crossvalidation_lvq.m └── data.mat ├── chapter27 ├── Images │ ├── 10_1.bmp │ ├── 10_2.bmp │ ├── 10_3.bmp │ ├── 10_4.bmp │ ├── 10_5.bmp │ ├── 1_1.bmp │ ├── 1_2.bmp │ ├── 1_3.bmp │ ├── 1_4.bmp │ ├── 1_5.bmp │ ├── 2_1.bmp │ ├── 2_2.bmp │ ├── 2_3.bmp │ ├── 2_4.bmp │ ├── 2_5.bmp │ ├── 3_1.bmp │ ├── 3_2.bmp │ ├── 3_3.bmp │ ├── 3_4.bmp │ ├── 3_5.bmp │ ├── 4_1.bmp │ ├── 4_2.bmp │ ├── 4_3.bmp │ ├── 4_4.bmp │ ├── 4_5.bmp │ ├── 5_1.bmp │ ├── 5_2.bmp │ ├── 5_3.bmp │ ├── 5_4.bmp │ ├── 5_5.bmp │ ├── 6_1.bmp │ ├── 6_2.bmp │ ├── 6_3.bmp │ ├── 6_4.bmp │ ├── 6_5.bmp │ ├── 7_1.bmp │ ├── 7_2.bmp │ ├── 7_3.bmp │ ├── 7_4.bmp │ ├── 7_5.bmp │ ├── 8_1.bmp │ ├── 8_2.bmp │ ├── 8_3.bmp │ ├── 8_4.bmp │ ├── 8_5.bmp │ ├── 9_1.bmp │ ├── 9_2.bmp │ ├── 9_3.bmp │ ├── 9_4.bmp │ └── 9_5.bmp ├── Readme.txt ├── chapter27_bp.m ├── chapter27_lvq.m ├── chapter_svm.m ├── crossvalind_lvq.m ├── feature_extraction.m ├── lvq1_train.m ├── lvq2_train.m ├── lvq_predict.m └── test.m ├── chapter28 ├── Readme.txt ├── data.mat ├── main_2009a.m └── main_2012b.m ├── chapter29 ├── classification │ ├── Readme.txt │ ├── data.mat │ ├── elmpredict.m │ ├── elmtrain.m │ └── main.m └── regression │ ├── Readme.txt │ ├── data.mat │ ├── elmpredict.m │ ├── elmtrain.m │ └── main.m ├── chapter3 ├── Code.m ├── Cross.m ├── Decode.m ├── Genetic.m ├── Mutation.m ├── PSO.m ├── Select.m ├── data.m ├── data.mat ├── fun.m └── test.m ├── chapter30 ├── RF_MexStandalone-v0.02.zip ├── RF_MexStandalone-v0.02 │ └── randomforest-matlab │ │ ├── RF_Class_C │ │ ├── Compile_Check │ │ ├── Makefile │ │ ├── Makefile.windows │ │ ├── README.txt │ │ ├── Version_History.txt │ │ ├── classRF_predict.m │ │ ├── classRF_train.m │ │ ├── compile_linux.m │ │ ├── compile_windows.m │ │ ├── data │ │ │ ├── X_twonorm.txt │ │ │ ├── Y_twonorm.txt │ │ │ └── twonorm.mat │ │ ├── mexClassRF_predict.mexw32 │ │ ├── mexClassRF_train.mexw32 │ │ ├── precompiled_rfsub │ │ │ ├── win32 │ │ │ │ └── rfsub.o │ │ │ └── win64 │ │ │ │ └── rfsub.o │ │ ├── rfsub.o │ │ ├── src │ │ │ ├── classRF.cpp │ │ │ ├── classTree.cpp │ │ │ ├── cokus.cpp │ │ │ ├── cokus_test.cpp │ │ │ ├── mex_ClassificationRF_predict.cpp │ │ │ ├── mex_ClassificationRF_train.cpp │ │ │ ├── qsort.c │ │ │ ├── rf.h │ │ │ ├── rfsub.f │ │ │ ├── rfutils.cpp │ │ │ └── twonorm_C_wrapper.cpp │ │ ├── test_ClassRF_extensively.m │ │ ├── tutorial_ClassRF.m │ │ └── twonorm_C_devcpp.dev │ │ └── RF_Reg_C │ │ ├── Compile_Check_kcachegrind │ │ ├── Compile_Check_memcheck │ │ ├── Makefile │ │ ├── README.txt │ │ ├── Version_History.txt │ │ ├── compile_linux.m │ │ ├── compile_windows.m │ │ ├── data │ │ ├── X_diabetes.txt │ │ ├── Y_diabetes.txt │ │ └── diabetes.mat │ │ ├── diabetes_C_devc.dev │ │ ├── mexRF_predict.mexw32 │ │ ├── mexRF_train.mexw32 │ │ ├── regRF_predict.m │ │ ├── regRF_train.m │ │ ├── src │ │ ├── cokus.cpp │ │ ├── cokus_test.cpp │ │ ├── diabetes_C_wrapper.cpp │ │ ├── mex_regressionRF_predict.cpp │ │ ├── mex_regressionRF_train.cpp │ │ ├── qsort.c │ │ ├── reg_RF.cpp │ │ └── reg_RF.h │ │ ├── test_RegRF_extensively.m │ │ └── tutorial_RegRF.m ├── Readme.txt ├── data.mat └── main.m ├── chapter31 ├── Readme.txt ├── data.mat ├── initpop_generate.m ├── ismature.m ├── main.m └── subpop_generate.m ├── chapter32 ├── d_mymorlet.m ├── mymorlet.m ├── traffic_flux.mat └── wavenn.m ├── chapter33 ├── FuzzyNet.m ├── data1.mat └── data2.mat ├── chapter34 ├── FCMGRNN.m └── netattack.mat ├── chapter35 ├── PSO.m ├── PSOMutation.m └── fun.m ├── chapter36 ├── Readme.txt ├── data.mat ├── de_code.m ├── fitness.m ├── gabpEval.m ├── gadecod.m ├── gaot │ ├── Contents.m │ ├── EER.m │ ├── README │ ├── adjswapMutation.m │ ├── arithXover.m │ ├── b2f.m │ ├── binaryExample.m │ ├── binaryMutation.m │ ├── boundaryMutation.m │ ├── calcbits.m │ ├── coranaEval.m │ ├── coranaFeval.m │ ├── coranaMin.m │ ├── cyclicXover.m │ ├── delta.m │ ├── dists.m │ ├── enhancederXover.m │ ├── erXover.m │ ├── f2b.m │ ├── floatExample.m │ ├── floatGradExample.m │ ├── ga.m │ ├── gaMichEval.m │ ├── gaZBGrad.m │ ├── gaZBGradEval.m │ ├── gademo.m │ ├── gademo1.m │ ├── gademo1eval1.m │ ├── gademo2.m │ ├── gademo3.m │ ├── gaotv5.ps │ ├── heuristicXover.m │ ├── initializega.m │ ├── initializeoga.m │ ├── inversionMutation.m │ ├── linerorderXover.m │ ├── maxGenTerm.m │ ├── multiNonUnifMutation.m │ ├── nonUnifMutation.m │ ├── normGeomSelect.m │ ├── optMaxGenTerm.m │ ├── orderBasedExample.m │ ├── orderbasedXover.m │ ├── parse.m │ ├── partmapXover.m │ ├── plotCorana.m │ ├── roulette.m │ ├── shiftMutation.m │ ├── simpleXover.m │ ├── singleptXover.m │ ├── startup.m │ ├── swapMutation.m │ ├── threeswapMutation.m │ ├── tournSelect.m │ ├── tspEval.m │ ├── unifMutation.m │ └── uniformXover.m └── main.m ├── chapter37 ├── Greynet.m └── data.mat ├── chapter38 ├── Kohonen.m ├── SKohonen.m └── data.mat ├── chapter39 └── 说明文件.txt ├── chapter4 ├── BP.m ├── Code.m ├── Cross.m ├── Genetic.m ├── Mutation.m ├── Select.m ├── data.m ├── data.mat ├── data1.mat ├── fun.m ├── net.mat └── test.m ├── chapter40 ├── chapter40.m └── html │ ├── chapter40.html │ ├── chapter40.png │ ├── chapter40_01.png │ ├── chapter40_02.png │ ├── chapter40_03.png │ ├── chapter40_04.png │ ├── chapter40_05.png │ ├── chapter40_06.png │ ├── chapter40_07.png │ ├── chapter40_08.png │ └── chapter40_09.png ├── chapter41 ├── chapter41.m └── html │ ├── chapter41.html │ ├── chapter41.png │ ├── chapter41_01.png │ ├── chapter41_02.png │ ├── chapter41_03.png │ ├── chapter41_04.png │ ├── chapter41_05.png │ ├── chapter41_06.png │ └── chapter41_07.png ├── chapter42 ├── chapter42.asv ├── chapter42_1.m ├── chapter42_2.asv ├── chapter42_2.m ├── html │ ├── chapter42_2.html │ ├── chapter42_2.png │ ├── chapter42_2_01.png │ ├── chapter42_2_02.png │ ├── chapter42_2_03.png │ ├── chapter42_2_04.png │ ├── chapter42_2_05.png │ ├── chapter42_2_06.png │ ├── chapter42_2_07.png │ └── chapter42_2_08.png └── 程序说明.txt ├── chapter43 └── chapter43.m ├── chapter5 ├── Bp_Ada_Fore.m ├── Bp_Ada_Sort.m ├── data.mat └── data1.mat ├── chapter6 ├── MPID.m ├── MPIDCS.m ├── MPIDDLX.m ├── draw.m ├── fun.m └── pso.m ├── chapter7 ├── chapter7_1.m ├── chapter7_2.m ├── html │ ├── chapter7_1.html │ ├── chapter7_1.png │ ├── chapter7_1_01.png │ ├── chapter7_2.html │ ├── chapter7_2.png │ ├── chapter7_2_01.png │ └── chapter7_2_02.png └── 运行提示.txt ├── chapter8 ├── best.mat ├── chapter8_1.asv ├── chapter8_1.m ├── chapter8_2.m ├── data.mat ├── html │ ├── chapter8_1.html │ └── chapter8_2.html └── 运行提示.txt └── chapter9 ├── Readme.txt ├── chapter9.m ├── data0.mat ├── data1.mat ├── data1_noisy.mat ├── data2.mat ├── data2_noisy.mat ├── data3.mat ├── data4.mat ├── data5.mat ├── data6.mat ├── data7.mat ├── data8.mat ├── data9.mat └── waiji.m /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Windows image file caches 2 | Thumbs.db 3 | ehthumbs.db 4 | 5 | # Folder config file 6 | Desktop.ini 7 | 8 | # Recycle Bin used on file shares 9 | $RECYCLE.BIN/ 10 | 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svm.o: svm.cpp svm.h 17 | $(CXX) $(CFLAGS) -c svm.cpp 18 | clean: 19 | rm -f *~ svm.o svm-train svm-predict svm-scale libsvm.so.$(SHVER) 20 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/Makefile.win: -------------------------------------------------------------------------------- 1 | #You must ensure nmake.exe, cl.exe, link.exe are in system path. 2 | #VCVARS32.bat 3 | #Under dosbox prompt 4 | #nmake -f Makefile.win 5 | 6 | ########################################## 7 | CXX = cl.exe 8 | CFLAGS = -nologo -O2 -EHsc -I. -D __WIN32__ -D _CRT_SECURE_NO_DEPRECATE 9 | TARGET = windows 10 | 11 | all: $(TARGET)\svm-train.exe $(TARGET)\svm-predict.exe $(TARGET)\svm-scale.exe $(TARGET)\svm-toy.exe lib 12 | 13 | $(TARGET)\svm-predict.exe: svm.h svm-predict.c svm.obj 14 | $(CXX) $(CFLAGS) svm-predict.c svm.obj -Fe$(TARGET)\svm-predict.exe 15 | 16 | $(TARGET)\svm-train.exe: svm.h svm-train.c svm.obj 17 | $(CXX) $(CFLAGS) svm-train.c svm.obj -Fe$(TARGET)\svm-train.exe 18 | 19 | $(TARGET)\svm-scale.exe: svm.h svm-scale.c 20 | $(CXX) $(CFLAGS) svm-scale.c -Fe$(TARGET)\svm-scale.exe 21 | 22 | $(TARGET)\svm-toy.exe: svm.h svm.obj svm-toy\windows\svm-toy.cpp 23 | $(CXX) $(CFLAGS) svm-toy\windows\svm-toy.cpp svm.obj user32.lib gdi32.lib comdlg32.lib -Fe$(TARGET)\svm-toy.exe 24 | 25 | svm.obj: svm.cpp svm.h 26 | $(CXX) $(CFLAGS) -c svm.cpp 27 | 28 | lib: svm.cpp svm.h svm.def 29 | $(CXX) $(CFLAGS) -LD svm.cpp -Fe$(TARGET)\libsvm -link -DEF:svm.def 30 | 31 | clean: 32 | -erase /Q *.obj $(TARGET)\. 33 | 34 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/java/Makefile: -------------------------------------------------------------------------------- 1 | .SUFFIXES: .class .java 2 | FILES = libsvm/svm.class libsvm/svm_model.class libsvm/svm_node.class \ 3 | libsvm/svm_parameter.class libsvm/svm_problem.class \ 4 | libsvm/svm_print_interface.class \ 5 | svm_train.class svm_predict.class svm_toy.class svm_scale.class 6 | 7 | #JAVAC = jikes 8 | JAVAC_FLAGS = -target 1.5 -source 1.5 9 | JAVAC = javac 10 | # JAVAC_FLAGS = 11 | 12 | all: $(FILES) 13 | jar cvf libsvm.jar *.class libsvm/*.class 14 | 15 | .java.class: 16 | $(JAVAC) $(JAVAC_FLAGS) $< 17 | 18 | libsvm/svm.java: libsvm/svm.m4 19 | m4 libsvm/svm.m4 > libsvm/svm.java 20 | 21 | clean: 22 | rm -f libsvm/*.class *.class *.jar libsvm/*~ *~ libsvm/svm.java 23 | 24 | dist: clean all 25 | rm *.class libsvm/*.class 26 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/java/libsvm.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/java/libsvm.jar 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nSV[1] + ... + nSV[k-1] = l 21 | }; 22 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/java/libsvm/svm_node.java: -------------------------------------------------------------------------------- 1 | package libsvm; 2 | public class svm_node implements java.io.Serializable 3 | { 4 | public int index; 5 | public double value; 6 | } 7 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/java/libsvm/svm_print_interface.java: -------------------------------------------------------------------------------- 1 | package libsvm; 2 | public interface svm_print_interface 3 | { 4 | public void print(String s); 5 | } 6 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/java/libsvm/svm_problem.java: -------------------------------------------------------------------------------- 1 | package libsvm; 2 | public class svm_problem implements java.io.Serializable 3 | { 4 | public int l; 5 | public double[] y; 6 | public svm_node[][] x; 7 | } 8 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/java/test_applet.html: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/ClassResult.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/ClassResult.m 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-------------------------------------------------------------------------------- 1 | % CRTBASE.m - Create base vector 2 | % 3 | % This function creates a vector containing the base of the loci 4 | % in a chromosome. 5 | % 6 | % Syntax: BaseVec = crtbase(Lind, Base) 7 | % 8 | % Input Parameters: 9 | % 10 | % Lind - A scalar or vector containing the lengths 11 | % of the alleles. Sum(Lind) is the length of 12 | % the corresponding chromosome. 13 | % 14 | % Base - A scalar or vector containing the base of 15 | % the loci contained in the Alleles. 16 | % 17 | % Output Parameters: 18 | % 19 | % BaseVec - A vector whose elements correspond to the base 20 | % of the loci of the associated chromosome structure. 21 | 22 | % Author: Andrew Chipperfield 23 | % Date: 19-Jan-94 24 | 25 | function BaseVec = crtbase(Lind, Base) 26 | 27 | [ml LenL] = size(Lind) ; 28 | if nargin < 2 29 | Base = 2 * ones(LenL,1) ; % default to base 2 30 | end 31 | [mb LenB] = size(Base) ; 32 | 33 | % check parameter consistency 34 | if ml > 1 | mb > 1 35 | error( 'Lind or Base is not a vector') ; 36 | elseif (LenL > 1 & LenB > 1 & LenL ~= LenB) | (LenL == 1 & LenB > 1 ) 37 | error( 'Vector dimensions must agree' ) ; 38 | elseif LenB == 1 & LenL > 1 39 | Base = Base * ones(LenL,1) ; 40 | 41 | end 42 | 43 | BaseVec = [] ; 44 | for i = 1:LenL 45 | BaseVec = [BaseVec, Base(i)*ones(Lind(i),1)']; 46 | end 47 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/mytest/gaSVM.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/mytest/gaSVM.m -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/rep.m: -------------------------------------------------------------------------------- 1 | % REP.m Replicate a matrix 2 | % 3 | % This function replicates a matrix in both dimensions. 4 | % 5 | % Syntax: MatOut = rep(MatIn,REPN); 6 | % 7 | % Input parameters: 8 | % MatIn - Input Matrix (before replicating) 9 | % 10 | % REPN - Vector of 2 numbers, how many replications in each dimension 11 | % REPN(1): replicate vertically 12 | % REPN(2): replicate horizontally 13 | % 14 | % Example: 15 | % 16 | % MatIn = [1 2 3] 17 | % REPN = [1 2]: MatOut = [1 2 3 1 2 3] 18 | % REPN = [2 1]: MatOut = [1 2 3; 19 | % 1 2 3] 20 | % REPN = [3 2]: MatOut = [1 2 3 1 2 3; 21 | % 1 2 3 1 2 3; 22 | % 1 2 3 1 2 3] 23 | % 24 | % Output parameter: 25 | % MatOut - Output Matrix (after replicating) 26 | % 27 | 28 | % Author: Carlos Fonseca & Hartmut Pohlheim 29 | % History: 14.02.94 file created 30 | 31 | 32 | function MatOut = rep(MatIn,REPN) 33 | 34 | % Get size of input matrix 35 | [N_D,N_L] = size(MatIn); 36 | 37 | % Calculate 38 | Ind_D = rem(0:REPN(1)*N_D-1,N_D) + 1; 39 | Ind_L = rem(0:REPN(2)*N_L-1,N_L) + 1; 40 | 41 | % Create output matrix 42 | MatOut = MatIn(Ind_D,Ind_L); 43 | 44 | 45 | % End of function 46 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/rws.m: -------------------------------------------------------------------------------- 1 | % RWS.m - Roulette Wheel Selection 2 | % 3 | % Syntax: 4 | % NewChrIx = rws(FitnV, Nsel) 5 | % 6 | % This function selects a given number of individuals Nsel from a 7 | % population. FitnV is a column vector containing the fitness 8 | % values of the individuals in the population. 9 | % 10 | % The function returns another column vector containing the 11 | % indexes of the new generation of chromosomes relative to the 12 | % original population matrix, shuffled. The new population, ready 13 | % for mating, can be obtained by calculating 14 | % OldChrom(NewChrIx, :). 15 | 16 | % Author: Carlos Fonseca, Updated: Andrew Chipperfield 17 | % Date: 04/10/93, Date: 27-Jan-94 18 | 19 | function NewChrIx = rws(FitnV,Nsel); 20 | 21 | % Identify the population size (Nind) 22 | [Nind,ans] = size(FitnV); 23 | 24 | % Perform Stochastic Sampling with Replacement 25 | cumfit = cumsum(FitnV); 26 | trials = cumfit(Nind) .* rand(Nsel, 1); 27 | Mf = cumfit(:, ones(1, Nsel)); 28 | Mt = trials(:, ones(1, Nind))'; 29 | [NewChrIx, ans] = find(Mt < Mf & ... 30 | [ zeros(1, Nsel); Mf(1:Nind-1, :) ] <= Mt); 31 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/scaling.m: -------------------------------------------------------------------------------- 1 | % SCALING.m - linear fitness scaling 2 | % 3 | % This function implements a linear fitness scaling algorithm as described 4 | % by Goldberg in "Genetic Algorithms in Search, Optimization and Machine 5 | % Learning", Addison Wesley, 1989. It use is not recommended when fitness 6 | % functions produce negative results as the scaling will become unreliable. 7 | % It is included in this version of the GA Toolbox only for the sake of 8 | % completeness. 9 | % 10 | % Syntax: FitnV = scaling(ObjV, Smul) 11 | % 12 | % Input parameters: 13 | % 14 | % Objv - A vector containing the values of individuals 15 | % fitness. 16 | % 17 | % Smul - Optional scaling parameter (default 2). 18 | % 19 | % Output parameters: 20 | % 21 | % FitnV - A vector containing the individual fitnesses 22 | % for the current population. 23 | % 24 | % 25 | 26 | % Author: Andrew Chipperfield 27 | % Date: 24-Feb-94 28 | 29 | 30 | function FitnV = scaling( ObjV, Smul ) 31 | 32 | if nargin == 1 33 | Smul = 2 ; 34 | end 35 | 36 | [Nind, Nobj] = size( ObjV ) ; 37 | Oave = sum( ObjV ) / Nind ; 38 | Omin = min( ObjV ) ; 39 | Omax = max( ObjV ) ; 40 | 41 | if (Omin > ( Smul * Oave - Omax ) / ( Smul - 1.0 )) 42 | delta = Omax - Oave 43 | a = ( Smul - 1.0 ) * Oave / delta 44 | b = Oave * ( Omax - Smul * Oave ) / delta 45 | else 46 | delta = Oave - Omin ; 47 | a = Oave / delta ; 48 | b = -Omin * Oave / delta ; 49 | end 50 | 51 | FitnV = ObjV.*a + b ; 52 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/sus.m: -------------------------------------------------------------------------------- 1 | % SUS.M (Stochastic Universal Sampling) 2 | % 3 | % This function performs selection with STOCHASTIC UNIVERSAL SAMPLING. 4 | % 5 | % Syntax: NewChrIx = sus(FitnV, Nsel) 6 | % 7 | % Input parameters: 8 | % FitnV - Column vector containing the fitness values of the 9 | % individuals in the population. 10 | % Nsel - number of individuals to be selected 11 | % 12 | % Output parameters: 13 | % NewChrIx - column vector containing the indexes of the selected 14 | % individuals relative to the original population, shuffled. 15 | % The new population, ready for mating, can be obtained 16 | % by calculating OldChrom(NewChrIx,:). 17 | 18 | % Author: Hartmut Pohlheim (Carlos Fonseca) 19 | % History: 12.12.93 file created 20 | % 22.02.94 clean up, comments 21 | 22 | 23 | function NewChrIx = sus(FitnV,Nsel); 24 | 25 | % Identify the population size (Nind) 26 | [Nind,ans] = size(FitnV); 27 | 28 | % Perform stochastic universal sampling 29 | cumfit = cumsum(FitnV); 30 | trials = cumfit(Nind) / Nsel * (rand + (0:Nsel-1)'); 31 | Mf = cumfit(:, ones(1, Nsel)); 32 | Mt = trials(:, ones(1, Nind))'; 33 | [NewChrIx, ans] = find(Mt < Mf & [ zeros(1, Nsel); Mf(1:Nind-1, :) ] <= Mt); 34 | 35 | % Shuffle new population 36 | [ans, shuf] = sort(rand(Nsel, 1)); 37 | NewChrIx = NewChrIx(shuf); 38 | 39 | 40 | % End of function 41 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovdp.m: -------------------------------------------------------------------------------- 1 | % XOVDP.M (CROSSOVer Double Point) 2 | % 3 | % This function performs double point crossover between pairs of 4 | % individuals and returns the current generation after mating. 5 | % 6 | % Syntax: NewChrom = xovdp(OldChrom, XOVR) 7 | % 8 | % Input parameters: 9 | % OldChrom - Matrix containing the chromosomes of the old 10 | % population. Each line corresponds to one individual 11 | % (in any form, not necessarily real values). 12 | % XOVR - Probability of recombination occurring between pairs 13 | % of individuals. 14 | % 15 | % Output parameter: 16 | % NewChrom - Matrix containing the chromosomes of the population 17 | % after mating, ready to be mutated and/or evaluated, 18 | % in the same format as OldChrom. 19 | 20 | % Author: Hartmut Pohlheim 21 | % History: 28.03.94 file created 22 | 23 | function NewChrom = xovdp(OldChrom, XOVR); 24 | 25 | if nargin < 2, XOVR = NaN; end 26 | 27 | % call low level function with appropriate parameters 28 | NewChrom = xovmp(OldChrom, XOVR, 2, 0); 29 | 30 | 31 | % End of function 32 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovdprs.m: -------------------------------------------------------------------------------- 1 | % XOVDPRS.M (CROSSOVer Double-Point with Reduced Surrogate) 2 | % 3 | % This function performs double-point 'reduced surrogate' crossover between 4 | % pairs of individuals and returns the current generation after mating. 5 | % 6 | % Syntax: NewChrom = xovdprs(OldChrom, XOVR) 7 | % 8 | % Input parameters: 9 | % OldChrom - Matrix containing the chromosomes of the old 10 | % population. Each line corresponds to one individual 11 | % (in any form, not necessarily real values). 12 | % XOVR - Probability of recombination occurring between pairs 13 | % of individuals. 14 | % 15 | % Output parameter: 16 | % NewChrom - Matrix containing the chromosomes of the population 17 | % after mating, ready to be mutated and/or evaluated, 18 | % in the same format as OldChrom. 19 | 20 | % Author: Hartmut Pohlheim 21 | % History: 28.03.94 file created 22 | 23 | function NewChrom = xovdprs(OldChrom, XOVR); 24 | 25 | if nargin < 2, XOVR = NaN; end 26 | 27 | % call low-level function with appropriate parameters 28 | NewChrom = xovmp(OldChrom, XOVR, 2, 1); 29 | 30 | 31 | % End of function 32 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovsh.m: -------------------------------------------------------------------------------- 1 | % XOVSH.M (CROSSOVer SHuffle) 2 | % 3 | % This function performs shuffle crossover between pairs of 4 | % individuals and returns the current generation after mating. 5 | % 6 | % Syntax: NewChrom = xovsh(OldChrom, XOVR) 7 | % 8 | % Input parameters: 9 | % OldChrom - Matrix containing the chromosomes of the old 10 | % population. Each line corresponds to one individual 11 | % (in any form, not necessarily real values). 12 | % XOVR - Probability of recombination occurring between pairs 13 | % of individuals. 14 | % 15 | % Output parameter: 16 | % NewChrom - Matrix containing the chromosomes of the population 17 | % after mating, ready to be mutated and/or evaluated, 18 | % in the same format as OldChrom. 19 | 20 | % Author: Hartmut Pohlheim 21 | % History: 28.03.94 file created 22 | 23 | function NewChrom = xovsh(OldChrom, XOVR); 24 | 25 | if nargin < 2, XOVR = NaN; end 26 | 27 | % call low level function with appropriate parameters 28 | NewChrom = xovmp(OldChrom, XOVR, 0, 0); 29 | 30 | 31 | % End of function 32 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovshrs.m: -------------------------------------------------------------------------------- 1 | % XOVSHRS.M (CROSSOVer SHuffle with Reduced Surrogate) 2 | % 3 | % This function performs shuffle 'reduced surrogate' crossover between 4 | % pairs of individuals and returns the current generation after mating. 5 | % 6 | % Syntax: NewChrom = xovshrs(OldChrom, XOVR) 7 | % 8 | % Input parameters: 9 | % OldChrom - Matrix containing the chromosomes of the old 10 | % population. Each line corresponds to one individual 11 | % (in any form, not necessarily real values). 12 | % XOVR - Probability of recombination occurring between pairs 13 | % of individuals. 14 | % 15 | % Output parameter: 16 | % NewChrom - Matrix containing the chromosomes of the population 17 | % after mating, ready to be mutated and/or evaluated, 18 | % in the same format as OldChrom. 19 | 20 | % Author: Hartmut Pohlheim 21 | % History: 28.03.94 file created 22 | 23 | function NewChrom = xovshrs(OldChrom, XOVR); 24 | 25 | if nargin < 2, XOVR = NaN; end 26 | 27 | % call low level function with appropriate parameters 28 | NewChrom = xovmp(OldChrom, XOVR, 0, 1); 29 | 30 | 31 | % End of function 32 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovsp.m: -------------------------------------------------------------------------------- 1 | % XOVSP.M (CROSSOVer Single-Point) 2 | % 3 | % This function performs single-point crossover between pairs of 4 | % individuals and returns the current generation after mating. 5 | % 6 | % Syntax: NewChrom = xovsp(OldChrom, XOVR) 7 | % 8 | % Input parameters: 9 | % OldChrom - Matrix containing the chromosomes of the old 10 | % population. Each line corresponds to one individual 11 | % (in any form, not necessarily real values). 12 | % XOVR - Probability of recombination occurring between pairs 13 | % of individuals. 14 | % 15 | % Output parameter: 16 | % NewChrom - Matrix containing the chromosomes of the population 17 | % after mating, ready to be mutated and/or evaluated, 18 | % in the same format as OldChrom. 19 | 20 | % Author: Hartmut Pohlheim 21 | % History: 28.03.94 file created 22 | 23 | function NewChrom = xovsp(OldChrom, XOVR); 24 | 25 | if nargin < 2, XOVR = NaN; end 26 | 27 | % call low level function with appropriate parameters 28 | NewChrom = xovmp(OldChrom, XOVR, 1, 0); 29 | 30 | 31 | % End of function 32 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovsprs.m: -------------------------------------------------------------------------------- 1 | % XOVSPRS.M (CROSSOVer Single-Point with Reduced Surrogate) 2 | % 3 | % This function performs single-point 'reduced surrogate' crossover between 4 | % pairs of individuals and returns the current generation after mating. 5 | % 6 | % Syntax: NewChrom = xovsprs(OldChrom, XOVR) 7 | % 8 | % Input parameters: 9 | % OldChrom - Matrix containing the chromosomes of the old 10 | % population. Each line corresponds to one individual 11 | % (in any form, not necessarily real-values). 12 | % XOVR - Probability of recombination occurring between pairs 13 | % of individuals. 14 | % 15 | % Output parameter: 16 | % NewChrom - Matrix containing the chromosomes of the population 17 | % after mating, ready to be mutated and/or evaluated, 18 | % in the same format as OldChrom. 19 | 20 | % Author: Hartmut Pohlheim 21 | % History: 28.03.94 file created 22 | 23 | function NewChrom = xovsprs(OldChrom, XOVR); 24 | 25 | if nargin < 2, XOVR = NaN; end 26 | 27 | % call low-level function with appropriate parameters 28 | NewChrom = xovmp(OldChrom, XOVR, 1, 1); 29 | 30 | 31 | % End of function 32 |  -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/pcaForSVM.m: -------------------------------------------------------------------------------- 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main.c 10 | $(CC) $(CFLAGS) -c main.c 11 | 12 | interface.o: interface.c interface.h 13 | $(CC) $(CFLAGS) -c interface.c 14 | 15 | callbacks.o: callbacks.cpp callbacks.h 16 | $(CXX) $(CFLAGS) -c callbacks.cpp 17 | 18 | ../../svm.o: 19 | cd ../..; make svm.o 20 | 21 | clean: 22 | rm -f *~ callbacks.o svm-toy main.o interface.o callbacks.o ../../svm.o 23 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/svm-toy/gtk/interface.h: -------------------------------------------------------------------------------- 1 | /* 2 | * DO NOT EDIT THIS FILE - it is generated by Glade. 3 | */ 4 | 5 | #ifdef __cplusplus 6 | extern "C" { 7 | #endif 8 | 9 | GtkWidget* create_window (void); 10 | GtkWidget* create_fileselection (void); 11 | 12 | #ifdef __cplusplus 13 | } 14 | #endif 15 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/svm-toy/gtk/main.c: -------------------------------------------------------------------------------- 1 | /* 2 | * Initial main.c file generated by Glade. Edit as required. 3 | * Glade will not overwrite this file. 4 | */ 5 | 6 | #include 7 | #include "interface.h" 8 | void svm_toy_initialize(); 9 | 10 | int main (int argc, char *argv[]) 11 | { 12 | GtkWidget *window; 13 | 14 | gtk_set_locale (); 15 | gtk_init (&argc, &argv); 16 | 17 | window = create_window (); 18 | gtk_widget_show (window); 19 | 20 | svm_toy_initialize(); 21 | gtk_main (); 22 | return 0; 23 | } 24 | -------------------------------------------------------------------------------- /chapter20/libsvm-3.1-[FarutoUltimate3.1Mcode]/svm-toy/qt/Makefile: -------------------------------------------------------------------------------- 1 | CXX? = g++ 2 | CFLAGS = -Wall -O3 -I$(INCLUDE) -I$(INCLUDE)/QtGui -lQtGui 3 | INCLUDE = /usr/include/qt4 4 | MOC = /usr/bin/moc-qt4 5 | 6 | svm-toy: svm-toy.cpp svm-toy.moc ../../svm.o 7 | $(CXX) $(CFLAGS) svm-toy.cpp ../../svm.o -o svm-toy 8 | 9 | svm-toy.moc: svm-toy.cpp 10 | $(MOC) svm-toy.cpp -o svm-toy.moc 11 | 12 | ../../svm.o: 13 | cd 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/chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/Compile_Check: -------------------------------------------------------------------------------- 1 | # Note will work only on linux 2 | # check for various not-visible errors by using valgrind 3 | # this code just profiles the timings/memory leaks of the code. 4 | # checked on linux with valgrind and kcachegrind installed 5 | # Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) 6 | # run as: sh Compile_Check 7 | 8 | 9 | rm callgrind.out.* 10 | #g++ cokus.cpp reg_RF.cpp diabetes_C_wrapper.cpp -g -pg -funroll-loops -msse3 11 | rm twonorm_test -rf 12 | make twonorm 13 | #g++ twonorm_C_wrapper.cpp rfutils.o rfsub.o classRF.o cokus.o -g -pg -funroll-loops -msse3 14 | 15 | #to check timings 16 | #valgrind -v --error-limit=no --tool=callgrind --dump-instr=yes ./twonorm_test 17 | 18 | #to check mem-usage 19 | valgrind -v --error-limit=no --tool=memcheck --track-origins=yes --leak-check=full --show-reachable=yes --num-callers=1000 ./twonorm_test 20 | 21 | 22 | # when using timings, the below tool helps 23 | # kcachegrind& 24 | 25 | -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/Version_History.txt: -------------------------------------------------------------------------------- 1 | CHANGES 2 | 3 | svn-v8? 4 | Added almost 95% of the total options provided by the R-package to classification. 5 | Added tutorial for classification based RF in tutorial_ClassRF.m 6 | Moving now to version 0.02 7 | 8 | svn-v4 9 | 10 | Added a `cruft' conditional compile for win64 (-DWIN64) target which involves exporting (extern) 11 | fortran and c function names with another _ at the start (there was one at the end already) 12 | 13 | CROSS-compiling target for win64 shown in the makefile target for rfsub 14 | 15 | Reasons for crosscompiling lies with cygwin not supporting generation of 32 bit binaries. 16 | 17 | right now the rfsub.o is directly taken from the precompiled_rfsub directory for 18 | windows systems, that is the compiled_windows.m directly uses the precompiled 19 | rfsub.o to generate based on the current windows version (tested on winxp 64 and 32). 20 | Its hard to set up the required software (gfortran/g77) on cygwin 21 | (which also are available only to generate 32 bit binaries). 22 | 23 | for windows based rfsub.o. crosscompiler from mingw64 was used on linux from 24 | http://sourceforge.net/project/showfiles.php?group_id=202880&package_id=245516&release_id=546049 25 | 26 | for linux, its simpler to set up gfortran and gcc so will depend on recompiling 27 | everytime mex is recompiled. Checked on 32 and 64 bit linux. 28 | 29 | 30 | svn-v2 31 | initial commit - mapped to v0.01preview version -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/compile_linux.m: -------------------------------------------------------------------------------- 1 | % ******************************************************************** 2 | % * mex File compiling code for Random Forest (for linux) 3 | % * mex interface to Andy Liaw et al.'s C code (used in R package randomForest) 4 | % * Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) 5 | % * License: GPLv2 6 | % * Version: 0.02 7 | % ********************************************************************/ 8 | function compile_linux 9 | 10 | system('rm *.mexglx *.mexa64;'); 11 | 12 | system('make clean;make mex;'); 13 | 14 | %the fortran code makes it hard to NOT use the Makefile 15 | 16 | 17 | -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/data/twonorm.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/data/twonorm.mat -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/mexClassRF_predict.mexw32: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/mexClassRF_predict.mexw32 -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/mexClassRF_train.mexw32: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/mexClassRF_train.mexw32 -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/precompiled_rfsub/win32/rfsub.o: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/precompiled_rfsub/win32/rfsub.o -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/precompiled_rfsub/win64/rfsub.o: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/precompiled_rfsub/win64/rfsub.o -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/rfsub.o: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/rfsub.o -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/src/cokus_test.cpp: -------------------------------------------------------------------------------- 1 | //this is a simple file to test the cokus.cpp mersenne twister code. 2 | 3 | //free code with no guarantee. No restrictions on usage 4 | //written by: Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) 5 | 6 | #define uint32 unsigned long 7 | #define SMALL_INT char 8 | #define SMALL_INT_CLASS mxCHAR_CLASS 9 | extern void seedMT(uint32 seed); 10 | extern uint32 randomMT(void); 11 | 12 | #include "stdio.h" 13 | #include "math.h" 14 | 15 | //generate lots of random number and check if they are within the limits 16 | //else cry about it 17 | 18 | int main(void) { 19 | int j, k; 20 | 21 | // you can seed with any uint32, but the best are odds in 0..(2^32 - 1) 22 | 23 | seedMT(4357); 24 | uint32 MAX=pow(2, 32)-1; 25 | 26 | // print the first 2,002 random numbers seven to a line as an example 27 | // for(j=0; j<2002; j++) 28 | // printf(" %10lu%s", (unsigned long) randomMT(), (j%7)==6 ? "\n" : ""); 29 | 30 | double test_val; 31 | for(k=0;k<100;k++) 32 | for(j=0; j<2000002; j++) { 33 | test_val = ((double)randomMT()/(double)MAX); 34 | if (test_val>=1.0){ 35 | printf("Problem"); 36 | return(0); 37 | } 38 | //printf(" %f%s", test_val , (j%7)==6 ? "\n" : ""); 39 | } 40 | printf("Success"); 41 | return(1); 42 | } 43 | -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Class_C/test_ClassRF_extensively.m: -------------------------------------------------------------------------------- 1 | %run plethora of tests 2 | 3 | %compile everything 4 | if strcmpi(computer,'PCWIN') |strcmpi(computer,'PCWIN64') 5 | compile_windows 6 | else 7 | compile_linux 8 | end 9 | 10 | total_train_time=0; 11 | total_test_time=0; 12 | 13 | % 14 | load data/twonorm 15 | % %twonorm, N=300, D=2 16 | for i=1:10 17 | fprintf('%d,',i); 18 | tic; 19 | model=classRF_train(inputs',outputs,1000); 20 | total_train_time=toc; 21 | tic; 22 | y_hat = classRF_predict(inputs',model); 23 | total_test_time=total_test_time+toc; 24 | length(find(y_hat~=outputs))/length(outputs) 25 | %keyboard 26 | end 27 | fprintf('\nnum_tree %d: Avg train time %d, test time %d\n',1000,total_train_time/100,total_test_time/100); 28 | 29 | 30 | -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/Compile_Check_kcachegrind: -------------------------------------------------------------------------------- 1 | # Note will work only on linux 2 | # check for various not-visible errors by using valgrind 3 | # this code just profiles the timings of the code. 4 | # checked on linux with valgrind and kcachegrind installed 5 | # Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) 6 | # run as: sh Compile_Check_kcachegrind 7 | rm callgrind.out.* 8 | 9 | #if you have icc uncomment below 10 | #icc cokus.cpp reg_RF.cpp diabetes_C_wrapper.cpp -g -pg -funroll-loops -msse3 -fast 11 | 12 | g++ cokus.cpp reg_RF.cpp diabetes_C_wrapper.cpp -g -pg -funroll-loops -msse3 13 | valgrind -v --error-limit=no --tool=callgrind --dump-instr=yes ./a.out 14 | 15 | kcachegrind& 16 | 17 | -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/Compile_Check_memcheck: -------------------------------------------------------------------------------- 1 | # Note will work only on linux 2 | # check for various not-visible errors by using valgrind 3 | # this code checks for memory allocs in the code 4 | # checked on linux with valgrind and kcachegrind installed 5 | # Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) 6 | # run as: sh Compile_Check_kcachegrind 7 | rm callgrind.out.* 8 | 9 | #if you want to use icc uncomment below 10 | #icc cokus.cpp reg_RF.cpp diabetes_C_wrapper.cpp -g -pg -funroll-loops -msse3 -fast 11 | 12 | g++ cokus.cpp reg_RF.cpp diabetes_C_wrapper.cpp -g -pg -funroll-loops -msse3 13 | valgrind -v --error-limit=no --tool=memcheck --track-origins=yes --leak-check=full ./a.out 14 | 15 | 16 | -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/Version_History.txt: -------------------------------------------------------------------------------- 1 | CHANGES 2 | 3 | svn-v9? 4 | Added almost 95% of the total options provided by the R-package to classification. 5 | Added tutorial for regression based RF in tutorial_RegRF.m 6 | Moving now to version 0.02 7 | 8 | svn-v2 9 | initial commit - mapped to v0.01preview version -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/compile_linux.m: -------------------------------------------------------------------------------- 1 | % ******************************************************************** 2 | % * mex File compiling code for Random Forest (for linux) 3 | % * mex interface to Andy Liaw et al.'s C code (used in R package randomForest) 4 | % * Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) 5 | % * License: GPLv2 6 | % * Version: 0.02 7 | % ********************************************************************/ 8 | 9 | function compile_linux 10 | 11 | % a simple way to compile on linux is to call the Makefile with 'make mex' 12 | system('rm *.mexglx *.mexa64;'); 13 | 14 | %Matlab mex requires optimization to be all set in the mexopts.sh(or 15 | %.bat) file. So set it there not here 16 | 17 | %if you want to emulate what the makefile does ucomment below 2 lines: 18 | %mex mex_regressionRF_train.cpp reg_RF.cpp cokus.cpp -o mexRF_train -DMATLAB 19 | %mex mex_regressionRF_predict.cpp reg_RF.cpp cokus.cpp -o mexRF_predict -DMATLAB 20 | 21 | system('make mex;'); 22 | 23 | fprintf('Mex compiled\n') 24 | 25 | -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/compile_windows.m: -------------------------------------------------------------------------------- 1 | % ******************************************************************** 2 | % * mex File compiling code for Random Forest (for windows) 3 | % * mex interface to Andy Liaw et al.'s C code (used in R package randomForest) 4 | % * Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) 5 | % * License: GPLv2 6 | % * Version: 0.02 7 | % ********************************************************************/ 8 | 9 | 10 | function compile_windows 11 | 12 | %need to do tricks for making Makfile run on windows as one needs cygwin. 13 | %so instead use mex to compile everything up. 14 | 15 | system('del *.mexw32;'); 16 | 17 | mex src/cokus.cpp src/reg_RF.cpp src/mex_regressionRF_train.cpp -DMATLAB -output mexRF_train 18 | mex src/cokus.cpp src/reg_RF.cpp src/mex_regressionRF_predict.cpp -DMATLAB -output mexRF_predict 19 | 20 | fprintf('\n Mex`s compiled correctly\n') -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/data/diabetes.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/data/diabetes.mat -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/mexRF_predict.mexw32: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/mexRF_predict.mexw32 -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/mexRF_train.mexw32: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/mexRF_train.mexw32 -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/regRF_predict.m: -------------------------------------------------------------------------------- 1 | %************************************************************** 2 | %* mex interface to Andy Liaw et al.'s C code (used in R package randomForest) 3 | %* Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) 4 | %* License: GPLv2 5 | %* Version: 0.02 6 | % 7 | % Calls Regression Random Forest 8 | % A wrapper matlab file that calls the mex file 9 | % This does prediction given the data and the model file 10 | %************************************************************** 11 | 12 | function Y_hat = regRF_predict(X,model) 13 | %function Y_hat = regRF_predict(X,model) 14 | %requires 2 arguments 15 | %X: data matrix 16 | %model: generated via regRF_train function 17 | if nargin~=2 18 | error('need atleast 2 parameters,X matrix and model'); 19 | end 20 | 21 | Y_hat = mexRF_predict(X',model.lDau,model.rDau,model.nodestatus,model.nrnodes,model.upper,model.avnode,model.mbest,model.ndtree,model.ntree); 22 | 23 | if ~isempty(find(model.coef)) %for bias corr 24 | Y_hat = model.coef(1) + model.coef(2)*Y_hat; 25 | end 26 | clear mexRF_predict -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/src/cokus_test.cpp: -------------------------------------------------------------------------------- 1 | //this is a simple file to test the cokus.cpp mersenne twister code. 2 | 3 | //free code with no guarantee. No restrictions on usage 4 | //written by: Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) 5 | 6 | #define uint32 unsigned long 7 | #define SMALL_INT char 8 | #define SMALL_INT_CLASS mxCHAR_CLASS 9 | extern void seedMT(uint32 seed); 10 | extern uint32 randomMT(void); 11 | 12 | #include "stdio.h" 13 | #include "math.h" 14 | 15 | //generate lots of random number and check if they are within the limits 16 | //else cry about it 17 | 18 | int main(void) { 19 | int j, k; 20 | 21 | // you can seed with any uint32, but the best are odds in 0..(2^32 - 1) 22 | 23 | seedMT(4357); 24 | uint32 MAX=pow(2, 32)-1; 25 | 26 | // print the first 2,002 random numbers seven to a line as an example 27 | // for(j=0; j<2002; j++) 28 | // printf(" %10lu%s", (unsigned long) randomMT(), (j%7)==6 ? "\n" : ""); 29 | 30 | double test_val; 31 | for(k=0;k<100;k++) 32 | for(j=0; j<2000002; j++) { 33 | test_val = ((double)randomMT()/(double)MAX); 34 | if (test_val>=1.0){ 35 | printf("Problem"); 36 | return(0); 37 | } 38 | //printf(" %f%s", test_val , (j%7)==6 ? "\n" : ""); 39 | } 40 | printf("Success"); 41 | return(1); 42 | } 43 | -------------------------------------------------------------------------------- /chapter30/RF_MexStandalone-v0.02/randomforest-matlab/RF_Reg_C/src/reg_RF.h: -------------------------------------------------------------------------------- 1 | /************************************************************** 2 | * mex interface to Andy Liaw et al.'s C code (used in R package randomForest) 3 | * Added by Abhishek Jaiantilal ( abhishek.jaiantilal@colorado.edu ) 4 | * License: GPLv2 5 | * Version: 0.02 6 | * 7 | * Supporting file that has some declarations. 8 | *************************************************************/ 9 | 10 | #define uint32 unsigned long 11 | #define SMALL_INT char 12 | 13 | #ifdef MATLAB 14 | #define SMALL_INT_CLASS mxCHAR_CLASS //will be used to allocate memory t 15 | #endif 16 | 17 | void seedMT(uint32 seed); 18 | uint32 randomMT(void); 19 | 20 | -------------------------------------------------------------------------------- /chapter30/Readme.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/Readme.txt -------------------------------------------------------------------------------- /chapter30/data.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/data.mat -------------------------------------------------------------------------------- /chapter30/main.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter30/main.m -------------------------------------------------------------------------------- /chapter31/Readme.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter31/Readme.txt -------------------------------------------------------------------------------- /chapter31/data.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter31/data.mat -------------------------------------------------------------------------------- /chapter31/initpop_generate.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter31/initpop_generate.m -------------------------------------------------------------------------------- /chapter31/ismature.m: -------------------------------------------------------------------------------- 1 | function [flag,index] = ismature(pop) 2 | 3 | [~,index] = max(pop(:,end)); 4 | if index == 1 5 | flag = 1; 6 | else 7 | flag = 0; 8 | end -------------------------------------------------------------------------------- /chapter31/main.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter31/main.m -------------------------------------------------------------------------------- /chapter31/subpop_generate.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter31/subpop_generate.m -------------------------------------------------------------------------------- /chapter32/d_mymorlet.m: -------------------------------------------------------------------------------- 1 | function y=d_mymorlet(t) 2 | 3 | y = -1.75*sin(1.75*t).*exp(-(t.^2)/2)-t* cos(1.75*t).*exp(-(t.^2)/2) ; 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5 | end 6 | Val = de_code(x); 7 | end -------------------------------------------------------------------------------- /chapter36/gabpEval.m: -------------------------------------------------------------------------------- 1 | function[sol,val] = gabpEval(sol,options) 2 | global s 3 | for i = 1:s 4 | x(i) = sol(i); 5 | end; 6 | [W1,B1,W2,B2,val] = gadecod(x); 7 | -------------------------------------------------------------------------------- /chapter36/gadecod.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackros1022/MATLAB-neural-network-43-case-studies-Code/3af9f587749a93ac6f1640d2b760217669e9f919/chapter36/gadecod.m -------------------------------------------------------------------------------- /chapter36/gaot/README: -------------------------------------------------------------------------------- 1 | This directory contains the Genetic Algorithm Optimization Toolbox for 2 | Matlab 5. 3 | 4 | To use this, if you are local to NCSU and have AFS access to this 5 | directory, simply extend the matlab path using the following command. 6 | You can also place this command in a file called startup.m. Everytime 7 | you start Matlab in the directory containing this file, the path will 8 | always be extended. 9 | 10 | >>path(path,'/afs/eos/service/ie/research/kay_res/GAToolBox/gaot'); 11 | 12 | Otherwise, install the .m files into a directory named gaot and extend 13 | the matlab path to that directory. The compressed tar archive and the 14 | zip file should automatically create the gaot directory for you. 15 | 16 | The companion paper describing this toolbox is included here as 17 | gaotv5.ps. The paper, the three demo files, gademo1.m gademo2.m 18 | gademo3.m, and four example scripts, binaryExample, floatExample, 19 | floatGradExample, and orderbasedExample, should be sufficient 20 | explanation of this toolbox. For any questions, comments, suggestions 21 | send mail to jjoine@eos.ncsu.edu. 22 | 23 | For a list of the files in the tool box get help on gaot. 24 | 25 | Thanks for trying the toolbox. 26 | 27 | -------------------------------------------------------------------------------- /chapter36/gaot/calcbits.m: -------------------------------------------------------------------------------- 1 | function [bits]=calcbits(bounds,precision) 2 | % function [bits]=calcbits(bounds,precision) 3 | % 4 | % Determine the number of bits to represent a float number to the precision 5 | % provided. 6 | % 7 | % bits - the number of bits required per variable 8 | % bounds - the bounds on the variables 9 | % precision - the least difference to distinguish two numbers 10 | 11 | % Binary and Real-Valued Simulation Evolution for Matlab 12 | % Copyright (C) 1996 C.R. Houck, J.A. Joines, M.G. Kay 13 | % 14 | % C.R. Houck, J.Joines, and M.Kay. A genetic algorithm for function 15 | % optimization: A Matlab implementation. ACM Transactions on Mathmatical 16 | % Software, Submitted 1996. 17 | % 18 | % This program is free software; you can redistribute it and/or modify 19 | % it under the terms of the GNU General Public License as published by 20 | % the Free Software Foundation; either version 1, or (at your option) 21 | % any later version. 22 | % 23 | % This program is distributed in the hope that it will be useful, 24 | % but WITHOUT ANY WARRANTY; without even the implied warranty of 25 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 26 | % GNU General Public License for more details. A copy of the GNU 27 | % General Public License can be obtained from the 28 | % Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. 29 | 30 | bits=ceil(log2((bounds(:,2)-bounds(:,1))' ./ precision)); 31 | % bits=ceil(log( (bounds(:,2)-bounds(:,1))' .* 10.^precision+1) ./ log(2)); -------------------------------------------------------------------------------- /chapter36/gaot/coranaMin.m: -------------------------------------------------------------------------------- 1 | function [sol,val] = coranaMin(sol,options) 2 | % function [val,sol] = coranaMin(sol,options) 3 | % 4 | % Function to minimize the Corana function. 5 | % 6 | % val - the value of the Corana function at point sol 7 | % sol - the location to evaluate the Corana function 8 | 9 | % Binary and Real-Valued Simulation Evolution for Matlab 10 | % Copyright (C) 1996 C.R. Houck, J.A. Joines, M.G. Kay 11 | % 12 | % C.R. Houck, J.Joines, and M.Kay. A genetic algorithm for function 13 | % optimization: A Matlab implementation. ACM Transactions on Mathmatical 14 | % Software, Submitted 1996. 15 | % 16 | % This program is free software; you can redistribute it and/or modify 17 | % it under the terms of the GNU General Public License as published by 18 | % the Free Software Foundation; either version 1, or (at your option) 19 | % any later version. 20 | % 21 | % This program is distributed in the hope that it will be useful, 22 | % but WITHOUT ANY WARRANTY; without even the implied warranty of 23 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 24 | % GNU General Public License for more details. A copy of the GNU 25 | % General Public License can be obtained from the 26 | % Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. 27 | 28 | numVar=size(sol,2)-1; 29 | val = coranaEval(sol(1:numVar)); 30 | val = -val; 31 | -------------------------------------------------------------------------------- /chapter36/gaot/gaMichEval.m: -------------------------------------------------------------------------------- 1 | function [sol,val] = gaMichEval(sol,options) 2 | val = 21.5 + sol(1) * sin(4*pi*sol(1)) + sol(2)*sin(20*pi*sol(2)); 3 | %G=zeros(0); 4 | %val = sqrt(sol(1)) * sin(2*sol(1)) + sqrt(sol(1))*cos(5*sol(1))+5; 5 | -------------------------------------------------------------------------------- /chapter36/gaot/gaZBGrad.m: -------------------------------------------------------------------------------- 1 | function [val,G] = gaZBGrad(sol) 2 | 3 | % Constrain minimizes so we have to take the - to maximize 4 | val = -(21.5 + sol(1) * sin(4*pi*sol(1)) + sol(2)*sin(20*pi*sol(2))); 5 | G=zeros(0); -------------------------------------------------------------------------------- /chapter36/gaot/gaZBGradEval.m: -------------------------------------------------------------------------------- 1 | function [nsol, val] = gaZBGradEval(sol,options) 2 | 3 | % This evaluation function takes in a potential solution and two options 4 | % options(3) is the percent of time to perform the gradient heuristic to the 5 | % potential solution 6 | % options(4) is the percentage of time to update the solution based on the 7 | % outcome of the gradient heuristic 8 | % options [currentGen maxGen eval_with_constr update_with_constr] 9 | nsol=sol; 10 | if (rand= maxGen; -------------------------------------------------------------------------------- /chapter36/gaot/plotCorana.m: -------------------------------------------------------------------------------- 1 | function [z, a] = coranaEval(per) 2 | i=0; 3 | a=-0.9:per:0.9; 4 | sz=size(a,2); 5 | z=zeros(sz,sz); 6 | for x=a 7 | i=i+1; j=0; 8 | for y=a 9 | j=j+1; 10 | z(i,j)=coranaFeval([x y]); 11 | end 12 | end 13 | %Done! 14 | 15 | %First let's look at it in each dimension independently 16 | clg 17 | plot(z(:,1)) %Plot a slice of the function in x max 250.25 18 | %Notice the range is [250.0-250.25] 19 | pause %Strike any key to continue 20 | clg 21 | plot(z(1,:)) %Plot a slice of the function in y 22 | %Notice the range is [0-250] 23 | pause %Strike any key to continue 24 | mesh(a,a,z); 25 | view(30,60); 26 | grid; 27 | -------------------------------------------------------------------------------- /chapter36/gaot/roulette.m: -------------------------------------------------------------------------------- 1 | function[newPop] = roulette(oldPop,options) 2 | %roulette is the traditional selection function with the probability of 3 | %surviving equal to the fittness of i / sum of the fittness of all individuals 4 | % 5 | %function[newPop] = roulette(oldPop,options) 6 | %newPop - the new population selected from the oldPop 7 | %oldPop - the current population 8 | %options - options [gen] 9 | 10 | %Get the parameters of the population 11 | numVars = size(oldPop,2); 12 | numSols = size(oldPop,1); 13 | 14 | %Generate the relative probabilites of selection 15 | totalFit = sum(oldPop(:,numVars)); 16 | prob=oldPop(:,numVars) / totalFit; 17 | prob=cumsum(prob); 18 | 19 | rNums=sort(rand(numSols,1)); %Generate random numbers 20 | 21 | %Select individuals from the oldPop to the new 22 | fitIn=1;newIn=1; 23 | while newIn<=numSols 24 | if(rNums(newIn)