%%?清空環(huán)境，準(zhǔn)備數(shù)據(jù)
%?function?fFitness=ABC_SVM_exmp（fObjV）
%?close?all
%?clear
%?clc
tic?%?計時器
%?format?compact
fprintf（‘\nClassification?using?Pyramid?BOW?rbf_svm\n‘）;
%?load?the?BOW?representations?the?labels?and?the?train?and?test?set
load（pg_opts.trainset）;
load（pg_opts.testset）;
load（pg_opts.labels）;


%%?sift
load（[pg_opts.globaldatapath‘/‘pyramid_opts.name]）
train_data_labels????=?labels（trainset）;??????????%?contains?the?labels?of?the?trainset
train_data??????=?pyramid_all（:trainset）‘;??????????%?contains?the?train?data
[train_data_labelssindex]=sort（train_data_labels）;????%?we?sort?the?labels?to?ensure?that?the?first?label?is?‘1‘?the?second?‘2‘?etc
train_data=train_data（sindex:）;
test_data_labels?????=?labels（testset）;???????????%?contains?the?labels?of?the?testset
test_data???????=?pyramid_all（:testset）‘;???????????%?contains?the?test?data

fprintf（‘\nClassification?using?Pyramid?BOW?rbf_svm\n‘）;
%?load?the?BOW?representations?the?labels?and?the?train?and?test?set
load（pg_opts.trainset）;
load（pg_opts.testset）;
load（pg_opts.labels）;


%%?sift
load（[pg_opts.globaldatapath‘/‘pyramid_opts.name]）
train_labels????=?labels（trainset）;??????????%?contains?the?labels?of?the?trainset
train_data??????=?pyramid_all（:trainset）‘;??????????%?contains?the?train?data
[train_labelssindex]=sort（train_labels）;????%?we?sort?the?labels?to?ensure?that?the?first?label?is?‘1‘?the?second?‘2‘?etc
train_data=train_data（sindex:）;
test_labels?????=?labels（testset）;???????????%?contains?the?labels?of?the?testset
test_data???????=?pyramid_all（:testset）‘;???????????%?contains?the?test?data
??????????%?contains?the?test?data
[mtrainntrain]?=?size（train_data）;
[mtestntest]?=?size（test_data）;
dataset?=?[train_data;test_data];

[dataset_scaleps]?=?mapminmax（dataset‘01）;
dataset_scale?=?dataset_scale‘;

train_data?=?dataset_scale（1:mtrain:）;
test_adat?=?dataset_scale（?（mtrain+1）:（mtrain+mtest）:?）;
%%?%%%%%%%%%%%%%用ABC算法優(yōu)化SVM中的參數(shù)c和g開始%%%%%%%%%%%%%%%%%%%%
%%?參數(shù)初始化
NP=20;?%?蜂群規(guī)模
FoodNumber=NP/2;?%?蜜源（解）數(shù)量
limit=100;?%?當(dāng)有蜜源連續(xù)沒被更新的次數(shù)超過limit時，該蜜源將被重新初始化
maxCycle=10;?%?最大迭代次數(shù)
%?待優(yōu)化參數(shù)信息
D=2;?%?待優(yōu)化參數(shù)個數(shù)，次數(shù)為c和g兩個
ub=ones（1D）*100;?%?參數(shù)取值上界，此處將c和g的上界設(shè)為100
lb=ones（1D）*（0.01）;?%?參數(shù)取值下界，此處將c和g的下界設(shè)為0.01

runtime=2;?%?可用于設(shè)置多次運行（讓ABC算法運行runtime次）以考察程序的穩(wěn)健性
??
BestGlobalMins=ones（1runtime）;?%?全局最小值初始化?，這里的優(yōu)化目標(biāo)為SVM預(yù)測結(jié)果中的平均平方誤差（MSE），初始化為最差值1
BestGlobalParams=zeros（runtimeD）;?%?用于存放ABC算法優(yōu)化得到的最優(yōu)參數(shù)

for?r=1:runtime?%?運行ABC算法runtime次?
????%?初始化蜜源
????Range?=?repmat（（ub-lb）[FoodNumber?1]）;
????Lower?=?repmat（lb?[FoodNumber?1]）;
????Foods?=?rand（FoodNumberD）?.*?Range?+?Lower;

????%?計算每個蜜源（解）得目標(biāo)函數(shù)值，objfun為計算SVM預(yù)測的平均平方誤差（MSE）的函數(shù)，根據(jù)自己的實際問題變異目標(biāo)函數(shù)即可
????ObjVal=ones（1FoodNumber）;?
????for?k?=?1:FoodNumber
????????ObjVal（k）?=?objfun（Foods（k:）train_data_labelstrain_datatest_data_labelstest_data）;
????end
????Fitness=calculateFitness（ObjVal）;?%?計算適應(yīng)度函數(shù)值
????
????trial=zeros（1FoodNumber）;?%?用于記錄第i個蜜源有連

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2018-11-19?09:41??04ABC-SVM\
?????文件??????????12??2018-10-26?11:50??04ABC-SVM\.gitignore
?????文件???????11181??2018-11-10?15:11??04ABC-SVM\ABC_SVM_exmp.m
?????文件????????7867??2016-09-04?17:10??04ABC-SVM\ABC_SVR_exmp.m
?????文件????????9141??2018-10-27?19:35??04ABC-SVM\ABC_p_SVM_exmp.m
?????目錄???????????0??2018-10-26?11:51??04ABC-SVM\AdaBoost\
?????文件?????????376??2018-10-26?11:51??04ABC-SVM\AdaBoost\demo_adaboost.m
?????文件???????23408??2018-10-26?11:51??04ABC-SVM\AdaBoost\gentleboost_model.c
?????文件???????13824??2018-10-26?11:51??04ABC-SVM\AdaBoost\gentleboost_model.mexw32
?????文件???????16896??2018-10-26?11:51??04ABC-SVM\AdaBoost\gentleboost_model.mexw64
?????文件????????7915??2018-10-26?11:51??04ABC-SVM\AdaBoost\gentleboost_predict.c
?????文件????????9728??2018-10-26?11:51??04ABC-SVM\AdaBoost\gentleboost_predict.mexw32
?????文件???????11264??2018-10-26?11:51??04ABC-SVM\AdaBoost\gentleboost_predict.mexw64
?????文件????????5192??2018-10-26?11:51??04ABC-SVM\AdaBoost\iris.mat
?????文件??????????49??2018-10-26?11:51??04ABC-SVM\AdaBoost\make.m
?????目錄???????????0??2018-10-26?11:50??04ABC-SVM\BOW\
?????文件????????3709??2018-11-11?16:29??04ABC-SVM\BOW\CalculateDictionary.m
?????文件????????3260??2018-11-11?16:32??04ABC-SVM\BOW\CompilePyramid.m
?????文件????????1303??2018-10-26?11:50??04ABC-SVM\BOW\EuclideanDistance.m
?????文件????????2690??2018-11-11?16:28??04ABC-SVM\BOW\GenerateSiftDescriptors.m
?????文件?????????569??2018-10-26?11:50??04ABC-SVM\BOW\MakeDataDirectory.m
?????文件????????2384??2018-11-11?16:32??04ABC-SVM\BOW\do_assignment.m
?????文件????????1475??2018-10-26?11:50??04ABC-SVM\BOW\do_classification_adaboost_bow.m
?????文件????????1500??2018-10-26?11:50??04ABC-SVM\BOW\do_classification_adaboost_pyramid_bow.m
?????文件????????2201??2018-10-26?11:50??04ABC-SVM\BOW\do_classification_inter_svm.m
?????文件????????1746??2018-10-26?11:50??04ABC-SVM\BOW\do_classification_rbf_svm.m
?????文件?????????875??2018-10-26?11:50??04ABC-SVM\BOW\do_normalize.m
?????文件????????2307??2018-10-26?11:50??04ABC-SVM\BOW\do_p_classification__inter_svm.m
?????文件????????1385??2018-10-26?11:50??04ABC-SVM\BOW\do_p_classification__rbf_svm.m
?????文件????????1199??2018-10-26?11:50??04ABC-SVM\BOW\draw_cm.m
?????文件?????????436??2018-10-26?11:50??04ABC-SVM\BOW\find_grid.m
............此處省略182個文件信息