支持向量机的matlab代码Word格式.docx

支持向量机的matlab代码Word格式.docx

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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）.

POLYORDER'

ORDER）allowsyoutospecifytheorderofa

%polynomialkernel.Thedefaultorderis3.

MLP_PARAMS'

[P1P2]）allowsyoutospecifythe

%parametersoftheMultilayerPerceptron（mlp）kernel.Themlpkernel

%requirestwoparameters,P1andP2,whereK=tanh（P1*U*V'

+P2）andP1

%>

0andP2<

0.DefaultvaluesareP1=1andP2=-1.

METHOD'

METHOD）allowsyoutospecifythemethodused

%tofindtheseparatinghyperplane.Optionsare

QP'

Usequadraticprogramming（requirestheOptimizationToolbox）

LS'

Useleast-squaresmethod

%IfyouhavetheOptimizationToolbox,thentheQPmethodisthedefault

%method.Ifnot,theonlyavailablemethodisLS.

QUADPROG_OPTS'

OPTIONS）allowsyoutopassanOPTIONS

%structurecreatedusingOPTIMSETtotheQUADPROGfunctionwhenusing

%the'

method.Seehelpoptimsetformoredetails.

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,:

）,'

%%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.

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））

numoptargs=nargin-2;

optargs=varargin;

%grp2idxsortsanumericgroupingvarascending,andastringgrouping

%varbyorderoffirstoccurrence

[g,groupString]=grp2idx（groupnames）;

%checkgroupisavector--thoughcharinputisspecial...

if~isvector（groupnames）&

&

~ischar（groupnames）

error（'

Bioinfo:

svmtrain:

GroupNotVector'

...

'

Groupmustbeavector.'

%makesurethatthedataiscorrectlyoriented.

ifsize（groupnames,1）==1

groupnames=groupnames'

;

%makesuredataistherightsize

n=length（groupnames）;

ifsize（training,1）~=n

ifsize（training,2）==n

training=training'

else

DataGroupSizeMismatch'

GROUPandTRAININGmusthavethesamenumberofrows.'

）

end

%NaNsaretreatedasunknownclassesandareremovedfromthetraining

%data

nans=find（isnan（g））;

iflength（nans）>

0

training（nans,:

）=[];

g（nans）=[];

ngroups=length（groupString）;

ifngroups>

TooManyGroups'

SVMTRAINonlysupportsclassificationintotwogroups.\nGROUPcontains%ddifferentgroups.'

ngroups）

%convertto1,-1.

g=1-（2*（g-1））;

%handleoptionalarguments

ifnumoptargs>

=1

ifrem（numoptargs,2）==1

IncorrectNumberOfArguments'

Incorrectnumberofargumentsto%s.'

mfilename）;

okargs={'

kernel_function'

'

method'

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）

UnknownParameterName'

Unknownparametername:

%s.'

pname）;

elseiflength（k）>

1

AmbiguousParameterName'

Ambiguousparametername:

switch（k）

case1%kernel_function

ifischar（pval）

okfuns={'

radial'

funNum=strmatch（lower（pval）,okfuns）;

ifisempty（funNum）

funNum=0;

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

UnknownKernelFunction'

UnknownKernelFunction%s.'

kfun）;

elseifisa（pval,'

function_handle'

kfun=pval;

BadKernelFunction'

Thekernelfunctioninputdoesnotappeartobeafunctionhandle\norvalidfunctionname.'

case2%method

ifstrncmpi（pval,'

qp'

2）

ifisempty（which（'

warning（'

NoOptim'

TheOptimizationToolboxisrequiredtousethequadraticprogrammingmethod.'

elseifstrncmpi（pval,'

ls'

UnknownMethod'

Unknownmethodoption%s.Validmethodsare'

'

and'

pval）;

case3%display

ifpval~=0

ifsize（training,2）==2

plotflag=true;

OnlyPlot2D'

Thedisplayoptioncanonlyplot2Dtrainingdata.'

case4%kfunargs

ifiscell（pval）

kfunargs=pval;

kfunargs={pval};

case5%quadprog_opts

ifisstruct（pval）

qp_opts=pval;

elseifiscell（pval）

qp_opts=optimset（pval{:

}）;

BadQuadprogOpts'

QUADPROG_OPTSmustbeanoptsstructure.'

case6%polyorder

if~isscalar（pval）||~isnumeric（pval）

BadPolyOrder'

POLYORDERmustbeascalarvalue.'

ifpval~=floor（pval）||pval<

1

PolyOrderNotInt'

Theorderofthepolynomialkernelmustbeapositiveinteger.'

setPoly=true;

case7%mlpparams

ifnumel（pval）~=2

BadMLPParams'

MLP_PARAMSmustbeatwoelementarray.'

if~isscalar（pval

（1））||~isscalar（pval

（2））

MLPParamsNotScalar'

Theparametersofthemulti-layerperceptronkernelmustbescalar.'

kfunargs={pval

（1）,pval

（2）};

setMLP=true;

ifsetPoly&

~usePoly

PolyOrderNotPolyKernel'

Youspecifiedapolynomialorderbutnotapolynomialkernel'

ifsetMLP&

~useMLP

MLPParamNotMLPKernel'

YouspecifiedMLPparametersbutnotanMLPkernel'

%plotthedataifrequested

ifplotflag

[hAxis,hLines]=svmplotdata（training,g）;

legend（hLines,cellstr（groupString））;

%calculatekernelfunction

try

kx=feval（kfun,training,training,kfunargs{:

%ensurefunctionissymmetric

kx=（kx+kx'

）/2;

catch

Errorcalculatingthekernelfunction:

\n%s\n'

lasterr）;

%createHessian

%addsmallconstanteyetoforcestability

H=（（g*g'

）.*kx）+sqrt（eps（class（training）））*eye（n）;

ifuseQuadprog

%Thelargescalesolvercannothandlethistypeofproblem,soturnit

%off.

qp_opts=optimset（qp_opts,'

LargeScale'

Off'

%X=QUADPROG（H,f,A,b,Aeq,beq,LB,UB,X0,opts）

alpha=quadprog（H,-ones（n,1）,[],[],...

g'

0,zeros（n,1）,in