支持向量机matlab实现源代码知识讲解.docx
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支持向量机matlab实现源代码知识讲解
支持向量机matlab实现源代码
editsvmtrain
>>editsvmclassify
>>editsvmpredict
function[svm_struct,svIndex]=svmtrain（training,groupnames,varargin）
%SVMTRAINtrainsasupportvectormachineclassifier
%
%SVMStruct=SVMTRAIN（TRAINING,GROUP）trainsasupportvectormachine
%classifierusingdataTRAININGtakenfromtwogroupsgivenbyGROUP.
%SVMStructcontainsinformationaboutthetrainedclassifierthatis
%usedbySVMCLASSIFYforclassification.GROUPisacolumnvectorof
%valuesofthesamelengthasTRAININGthatdefinestwogroups.Each
%elementofGROUPspecifiesthegroupthecorrespondingrowofTRAINING
%belongsto.GROUPcanbeanumericvector,astringarray,oracell
%arrayofstrings.SVMTRAINtreatsNaNsoremptystringsinGROUPas
%missingvaluesandignoresthecorrespondingrowsofTRAINING.
%
%SVMTRAIN（...,'KERNEL_FUNCTION',KFUN）allowsyoutospecifythekernel
%functionKFUNusedtomapthetrainingdataintokernelspace.The
%defaultkernelfunctionisthedotproduct.KFUNcanbeoneofthe
%followingstringsorafunctionhandle:
%
%'linear'Linearkernelordotproduct
%'quadratic'Quadratickernel
%'polynomial'Polynomialkernel（defaultorder3）
%'rbf'GaussianRadialBasisFunctionkernel
%'mlp'MultilayerPerceptronkernel（defaultscale1）
%functionAkernelfunctionspecifiedusing@,
%forexample@KFUN,orananonymousfunction
%
%Akernelfunctionmustbeoftheform
%
%functionK=KFUN（U,V）
%
%Thereturnedvalue,K,isamatrixofsizeM-by-N,whereUandVhaveM
%andNrowsrespectively.IfKFUNisparameterized,youcanuse
%anonymousfunctionstocapturetheproblem-dependentparameters.For
%example,supposethatyourkernelfunctionis
%
%functionk=kfun（u,v,p1,p2）
%k=tanh（p1*（u*v'）+p2）;
%
%Youcansetvaluesforp1andp2andthenuseananonymousfunction:
%@（u,v）kfun（u,v,p1,p2）.
%
%SVMTRAIN（...,'POLYORDER',ORDER）allowsyoutospecifytheorderofa
%polynomialkernel.Thedefaultorderis3.
%
%SVMTRAIN（...,'MLP_PARAMS',[P1P2]）allowsyoutospecifythe
%parametersoftheMultilayerPerceptron（mlp）kernel.Themlpkernel
%requirestwoparameters,P1andP2,whereK=tanh（P1*U*V'+P2）andP1
%>0andP2<0.DefaultvaluesareP1=1andP2=-1.
%
%SVMTRAIN（...,'METHOD',METHOD）allowsyoutospecifythemethodused
%tofindtheseparatinghyperplane.Optionsare
%
%'QP'Usequadraticprogramming（requirestheOptimizationToolbox）
%'LS'Useleast-squaresmethod
%
%IfyouhavetheOptimizationToolbox,thentheQPmethodisthedefault
%method.Ifnot,theonlyavailablemethodisLS.
%
%SVMTRAIN（...,'QUADPROG_OPTS',OPTIONS）allowsyoutopassanOPTIONS
%structurecreatedusingOPTIMSETtotheQUADPROGfunctionwhenusing
%the'QP'method.Seehelpoptimsetformoredetails.
%
%SVMTRAIN（...,'SHOWPLOT',true）,whenusedwithtwo-dimensionaldata,
%createsaplotofthegroupeddataandplotstheseparatinglinefor
%theclassifier.
%
%Example:
%%Loadthedataandselectfeaturesforclassification
%loadfisheriris
%data=[meas（:
1）,meas（:
2）];
%%ExtracttheSetosaclass
%groups=ismember（species,'setosa'）;
%%Randomlyselecttrainingandtestsets
%[train,test]=crossvalind（'holdOut',groups）;
%cp=classperf（groups）;
%%Usealinearsupportvectormachineclassifier
%svmStruct=svmtrain（data（train,:
）,groups（train）,'showplot',true）;
%classes=svmclassify（svmStruct,data（test,:
）,'showplot',true）;
%%Seehowwelltheclassifierperformed
%classperf（cp,classes,test）;
%cp.CorrectRate
%
%SeealsoCLASSIFY,KNNCLASSIFY,QUADPROG,SVMCLASSIFY.
%Copyright2004TheMathWorks,Inc.
%$Revision:
1.1.12.1$$Date:
2004/12/2420:
43:
35$
%References:
%[1]Kecman,V,LearningandSoftComputing,
%MITPress,Cambridge,MA.2001.
%[2]Suykens,J.A.K.,VanGestel,T.,DeBrabanter,J.,DeMoor,B.,
%Vandewalle,J.,LeastSquaresSupportVectorMachines,
%WorldScientific,Singapore,2002.
%[3]Scholkopf,B.,Smola,A.J.,LearningwithKernels,
%MITPress,Cambridge,MA.2002.
%
%SVMTRAIN（...,'KFUNARGS',ARGS）allowsyoutopassadditional
%argumentstokernelfunctions.
%setdefaults
plotflag=false;
qp_opts=[];
kfunargs={};
setPoly=false;usePoly=false;
setMLP=false;useMLP=false;
if~isempty（which（'quadprog'））
useQuadprog=true;
else
useQuadprog=false;
end
%setdefaultkernelfunction
kfun=@linear_kernel;
%checkinputs
ifnargin<2
error（nargchk（2,Inf,nargin））
end
numoptargs=nargin-2;
optargs=varargin;
%grp2idxsortsanumericgroupingvarascending,andastringgrouping
%varbyorderoffirstoccurrence
[g,groupString]=grp2idx（groupnames）;
%checkgroupisavector--thoughcharinputisspecial...
if~isvector（groupnames）&&~ischar（groupnames）
error（'Bioinfo:
svmtrain:
GroupNotVector',...
'Groupmustbeavector.'）;
end
%makesurethatthedataiscorrectlyoriented.
ifsize（groupnames,1）==1
groupnames=groupnames';
end
%makesuredataistherightsize
n=length（groupnames）;
ifsize（training,1）~=n
ifsize（training,2）==n
training=training';
else
error（'Bioinfo:
svmtrain:
DataGroupSizeMismatch',...
'GROUPandTRAININGmusthavethesamenumberofrows.'）
end
end
%NaNsaretreatedasunknownclassesandareremovedfromthetraining
%data
nans=find（isnan（g））;
iflength（nans）>0
training（nans,:
）=[];
g（nans）=[];
end
ngroups=length（groupString）;
ifngroups>2
error（'Bioinfo:
svmtrain:
TooManyGroups',...
'SVMTRAINonlysupportsclassificationintotwogroups.\nGROUPcontains%ddifferentgroups.',ngroups）
end
%convertto1,-1.
g=1-（2*（g-1））;
%handleoptionalarguments
ifnumoptargs>=1
ifrem（numoptargs,2）==1
error（'Bioinfo:
svmtrain:
IncorrectNumberOfArguments',...
'Incorrectnumberofargumentsto%s.',mfilename）;
end
okargs={'kernel_function','method','showplot','kfunargs','quadprog_opts','polyorder','mlp_params'};
forj=1:
2:
numoptargs
pname=optargs{j};
pval=optargs{j+1};
k=strmatch（lower（pname）,okargs）;%#ok
ifisempty（k）
error（'Bioinfo:
svmtrain:
UnknownParameterName',...
'Unknownparametername:
%s.',pname）;
elseiflength（k）>1
error（'Bioinfo:
svmtrain:
AmbiguousParameterName',...
'Ambiguousparametername:
%s.',pname）;
else
switch（k）
case1%kernel_function
ifischar（pval）
okfuns={'linear','quadratic',...
'radial','rbf','polynomial','mlp'};
funNum=strmatch（lower（pval）,okfuns）;%#ok
ifisempty（funNum）
funNum=0;
end
switchfunNum%maybemakethislessstrictinthefuture
case1
kfun=@linear_kernel;
case2
kfun=@quadratic_kernel;
case{3,4}
kfun=@rbf_kernel;
case5
kfun=@poly_kernel;
usePoly=true;
case6
kfun=@mlp_kernel;
useMLP=true;
otherwise
error（'Bioinfo:
svmtrain:
UnknownKernelFunction',...
'UnknownKernelFunction%s.',kfun）;
end
elseifisa（pval,'function_handle'）
kfun=pval;
else
error（'Bioinfo:
svmtrain:
BadKernelFunction',...
'Thekernelfunctioninputdoesnotappeartobeafunctionhandle\norvalidfunctionname.'）
end
case2%method
ifstrncmpi（pval,'qp',2）
useQuadprog=true;
ifisempty（which（'quadprog'））
warning（'Bioinfo:
svmtrain:
NoOptim',...
'TheOptimizationToolboxisrequiredtousethequadraticprogrammingmethod.'）
useQuadprog=false;
end
elseifstrncmpi（pval,'ls',2）
useQuadprog=false;
else
error（'Bioinfo:
svmtrain:
UnknownMethod',...
'Unknownmethodoption%s.Validmethodsare''QP''and''LS''',pval）;
end
case3%display
ifpval~=0
ifsize（training,2）==2
plotflag=true;
else
warning（'Bioinfo:
svmtrain:
OnlyPlot2D',...
'Thedisplayoptioncanonlyplot2Dtrainingdata.'）
end
end
case4%kfunargs
ifiscell（pval）
kfunargs=pval;
else
kfunargs={pval};
end
case5%quadprog_opts
ifisstruct（pval）
qp_opts=pval;
elseifiscell（pval）
qp_opts=optimset（pval{:
}）;
else
error（'Bioinfo:
svmtrain:
BadQuadprogOpts',...
'QUADPROG_OPTSmustbeanoptsstructure.'）;
end
case6%polyorder
if~isscalar（pval）||~isnumeric（pval）
error（'Bioinfo:
svmtrain:
BadPolyOrder',...
'POLYORDERmustbeascalarvalue.'）;
end
ifpval~=floor（pval）||pval<1
error（'Bioinfo:
svmtrain:
PolyOrderNotInt',...
'Theorderofthepolynomialkernelmustbeapositiveinteger.'）
end
kfunargs={pval};
setPoly=true;
case7%mlpparams
ifnumel（pval）~=2
error（'Bioinfo:
svmtrain:
BadMLPParams',...
'MLP_PARAMSmustbeatwoelementarray.'）;
end
if~isscalar（pval
（1））||~isscalar（pval
（2））
error（'Bioinfo:
svmtrain:
MLPParamsNotScalar',...
'Theparametersofthemulti-layerperceptronkernelmustbescalar.'）;
end
kfunargs={pval
（1）,pval
（2）};
setMLP=true;
end
end
end
end
ifsetPoly&&~usePoly
warning（'Bioinfo:
svmtrain:
PolyOrderNotPolyKernel',...
'Youspecifiedapolynomialorderbutnotapolynomialkernel'）;
end
ifsetMLP&&~useMLP
warning（'Bioinfo:
svmtrain:
MLPParamNotMLPKernel',...
'YouspecifiedMLPparametersbutnotanMLPkernel'）;
end
%plotthedataifrequested
ifplotflag
[hAxis,hLines]=svmplotdata（training,g）;
legend（hLines,cellstr（groupString））;
end
%calculatekernelfunction
try
kx=feval（kfun,training,training,kfunargs{:
}）;
%ensurefunctionissymmetric
kx=（kx+kx'）/2;
catch
error（'Bioinfo:
svmtrain:
UnknownKernelFunction',...
'Errorcalculatingthekernelfunction:
\n%s\n',lasterr）;
end
%createHessian
%addsmallconstanteyetoforcestability
H=（（g*g'）.*kx）+sqrt（eps（class（training）））*eye（n）;
ifuseQuadprog
%Thelargescalesolvercannothandlethistypeofproblem,soturnit
%off.
qp_op
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