支持向量机matlab实现源代码.doc

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1、精品文档，仅供学习与交流，如有侵权请联系网站删除edit svmtrain edit svmclassify edit svmpredict function svm_struct, svIndex = svmtrain(training, groupnames, varargin) %SVMTRAIN trains a support vector machine classifier % SVMStruct = SVMTRAIN(TRAINING,GROUP) trains a support vector machine % classifier using data TRAINING

2、taken from two groups given by GROUP. % SVMStruct contains information about the trained classifier that is % used by SVMCLASSIFY for classification. GROUP is a column vector of % values of the same length as TRAINING that defines two groups. Each % element of GROUP specifies the group the correspon

3、ding row of TRAINING % belongs to. GROUP can be a numeric vector, a string array, or a cell % array of strings. SVMTRAIN treats NaNs or empty strings in GROUP as % missing values and ignores the corresponding rows of TRAINING. % SVMTRAIN(.,KERNEL_FUNCTION,KFUN) allows you to specify the kernel % fun

4、ction KFUN used to map the training data into kernel space. The % default kernel function is the dot product. KFUN can be one of the % following strings or a function handle: % linear Linear kernel or dot product % quadratic Quadratic kernel % polynomial Polynomial kernel (default order 3) % rbf Gau

5、ssian Radial Basis Function kernel % mlp Multilayer Perceptron kernel (default scale 1) % function A kernel function specified using , % for example KFUN, or an anonymous function % A kernel function must be of the form % function K = KFUN(U, V) % The returned value, K, is a matrix of size M-by-N, w

6、here U and V have M % and N rows respectively. If KFUN is parameterized, you can use % anonymous functions to capture the problem-dependent parameters. For % example, suppose that your kernel function is % function k = kfun(u,v,p1,p2) % k = tanh(p1*(u*v)+p2); % You can set values for p1 and p2 and t

7、hen use an anonymous function: % (u,v) kfun(u,v,p1,p2). % SVMTRAIN(.,POLYORDER,ORDER) allows you to specify the order of a % polynomial kernel. The default order is 3. % SVMTRAIN(.,MLP_PARAMS,P1 P2) allows you to specify the % parameters of the Multilayer Perceptron (mlp) kernel. The mlp kernel % re

8、quires two parameters, P1 and P2, where K = tanh(P1*U*V + P2) and P1 % 0 and P2 0. Default values are P1 = 1 and P2 = -1. % SVMTRAIN(.,METHOD,METHOD) allows you to specify the method used % to find the separating hyperplane. Options are % QP Use quadratic programming (requires the Optimization Toolb

9、ox) % LS Use least-squares method % If you have the Optimization Toolbox, then the QP method is the default % method. If not, the only available method is LS. % SVMTRAIN(.,QUADPROG_OPTS,OPTIONS) allows you to pass an OPTIONS % structure created using OPTIMSET to the QUADPROG function when using % th

10、e QP method. See help optimset for more details. % SVMTRAIN(.,SHOWPLOT,true), when used with two-dimensional data, % creates a plot of the grouped data and plots the separating line for % the classifier. % Example: % % Load the data and select features for classification % load fisheriris % data = m

11、eas(:,1), meas(:,2); % % Extract the Setosa class % groups = ismember(species,setosa); % % Randomly select training and test sets % train, test = crossvalind(holdOut,groups); % cp = classperf(groups); % % Use a linear support vector machine classifier % svmStruct = svmtrain(data(train,:),groups(trai

12、n),showplot,true); % classes = svmclassify(svmStruct,data(test,:),showplot,true); % % See how well the classifier performed % classperf(cp,classes,test); % cp.CorrectRate % See also CLASSIFY, KNNCLASSIFY, QUADPROG, SVMCLASSIFY. % Copyright 2004 The MathWorks, Inc. % $Revision: 1.1.12.1 $ $Date: 2004

13、/12/24 20:43:35 $ % References: % 1 Kecman, V, Learning and Soft Computing, % MIT Press, Cambridge, MA. 2001. % 2 Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., % Vandewalle, J., Least Squares Support Vector Machines, % World Scientific, Singapore, 2002. % 3 Scholkopf, B., Smola, A.

14、J., Learning with Kernels, % MIT Press, Cambridge, MA. 2002. % SVMTRAIN(.,KFUNARGS,ARGS) allows you to pass additional % arguments to kernel functions. % set defaults plotflag = false; qp_opts = ; kfunargs = ; setPoly = false; usePoly = false; setMLP = false; useMLP = false; if isempty(which(quadpro

15、g) useQuadprog = true; else useQuadprog = false; end % set default kernel function kfun = linear_kernel; % check inputs if nargin 0 training(nans,:) = ; g(nans) = ; end ngroups = length(groupString); if ngroups 2 error(Bioinfo:svmtrain:TooManyGroups,. SVMTRAIN only supports classification into two g

16、roups.nGROUP contains %d different groups.,ngroups) end % convert to 1, -1. g = 1 - (2* (g-1); % handle optional arguments if numoptargs = 1 if rem(numoptargs,2)= 1 error(Bioinfo:svmtrain:IncorrectNumberOfArguments,. Incorrect number of arguments to %s.,mfilename); end okargs = kernel_function,metho

17、d,showplot,kfunargs,quadprog_opts,polyorder,mlp_params; for j=1:2:numoptargs pname = optargsj; pval = optargsj+1; k = strmatch(lower(pname), okargs);%#ok if isempty(k) error(Bioinfo:svmtrain:UnknownParameterName,. Unknown parameter name: %s.,pname); elseif length(k)1 error(Bioinfo:svmtrain:Ambiguous

18、ParameterName,. Ambiguous parameter name: %s.,pname); else switch(k) case 1 % kernel_function if ischar(pval) okfuns = linear,quadratic,. radial,rbf,polynomial,mlp; funNum = strmatch(lower(pval), okfuns);%#ok if isempty(funNum) funNum = 0; end switch funNum %maybe make this less strict in the future

19、 case 1 kfun = linear_kernel; case 2 kfun = quadratic_kernel; case 3,4 kfun = rbf_kernel; case 5 kfun = poly_kernel; usePoly = true; case 6 kfun = mlp_kernel; useMLP = true; otherwise error(Bioinfo:svmtrain:UnknownKernelFunction,. Unknown Kernel Function %s.,kfun); end elseif isa (pval, function_han

20、dle) kfun = pval; else error(Bioinfo:svmtrain:BadKernelFunction,. The kernel function input does not appear to be a function handlenor valid function name.) end case 2 % method if strncmpi(pval,qp,2) useQuadprog = true; if isempty(which(quadprog) warning(Bioinfo:svmtrain:NoOptim,. The Optimization T

21、oolbox is required to use the quadratic programming method.) useQuadprog = false; end elseif strncmpi(pval,ls,2) useQuadprog = false; else error(Bioinfo:svmtrain:UnknownMethod,. Unknown method option %s. Valid methods are QP and LS,pval); end case 3 % display if pval = 0 if size(training,2) = 2 plot

22、flag = true; else warning(Bioinfo:svmtrain:OnlyPlot2D,. The display option can only plot 2D training data.) end end case 4 % kfunargs if iscell(pval) kfunargs = pval; else kfunargs = pval; end case 5 % quadprog_opts if isstruct(pval) qp_opts = pval; elseif iscell(pval) qp_opts = optimset(pval:); els

23、e error(Bioinfo:svmtrain:BadQuadprogOpts,. QUADPROG_OPTS must be an opts structure.); end case 6 % polyorder if isscalar(pval) | isnumeric(pval) error(Bioinfo:svmtrain:BadPolyOrder,. POLYORDER must be a scalar value.); end if pval =floor(pval) | pval sqrt(eps); sv = training(svIndex,:); % calculate

24、the parameters of the separating line from the support % vectors. alphaHat = g(svIndex).*alpha(svIndex); % Calculate the bias by applying the indicator function to the support % vector with largest alpha. maxAlpha,maxPos = max(alpha); %#ok bias = g(maxPos) - sum(alphaHat.*kx(svIndex,maxPos); % an al

25、ternative method is to average the values over all support vectors % bias = mean(g(sv) - sum(alphaHat(:,ones(1,numSVs).*kx(sv,sv); % An alternative way to calculate support vectors is to look for zeros of % the Lagrangians (fifth output from QUADPROG). % alpha,fval,output,exitflag,t = quadprog(H,-on

26、es(n,1),. % g,0,zeros(n,1),inf *ones(n,1),zeros(n,1),opts); % sv = t.lower sqrt(eps) & t.upper sqrt(eps); else % Least-Squares % now build up compound matrix for solver A = 0 g;g,H; b = 0;ones(size(g); x = Ab; % calculate the parameters of the separating line from the support % vectors. sv = trainin

27、g; bias = x(1); alphaHat = g.*x(2:end); end svm_struct.SupportVectors = sv; svm_struct.Alpha = alphaHat; svm_struct.Bias = bias; svm_struct.KernelFunction = kfun; svm_struct.KernelFunctionArgs = kfunargs; svm_struct.GroupNames = groupnames; svm_struct.FigureHandles = ; if plotflag hSV = svmplotsvs(hAxis,svm_struct); svm_struct.FigureHandles = hAxis,hLines,hSV; end【精品文档】第 8 页