svm.rar_SVM_svm 源代码_svm源代码_svm训练样本_训练样本

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 taken from two groups given by GROUP. % SVMStruct contains information about the trained classifier, including % the support vectors, 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 % corresponding 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 % function 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' Gaussian 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, where 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 then use an anonymous function: % @(u,v) kfun(u,v,p1,p2). % % SVMTRAIN(...,'RBF_SIGMA',SIGMA) allows you to specify the scaling % factor, sigma, in the radial basis function kernel. % % 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 % requires 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 Toolbox) % '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. When using the QP % method, the classifier is a 2-norm soft-margin support vector machine. % % SVMTRAIN(...,'QUADPROG_OPTS',OPTIONS) allows you to pass an OPTIONS % structure created using OPTIMSET to the QUADPROG function when using % the 'QP' method. See help optimset for more details. % % SVMTRAIN(...,'BOXCONSTRAINT',C) allows you to set the box constraint C % for the soft margin. The default value is 1 / sqrt(eps), which will % only classify separable data. C can be a scalar or a vector of the same % length as the training data. % % SVMTRAIN(...,'AUTOSCALE',true) will automatically shift and scale the % data points before training. % % 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 = [meas(:,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(train),'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 2004-2006 The MathWorks, Inc. % $Revision: 1.1.12.4 $ $Date: 2006/05/17 20:49:01 $ % References: % % [1] Cristianini, N., Shawe-Taylor, J An Introduction to Support % Vector Machines, Cambridge University Press, Cambridge, UK. 2000. % http://www.support-vector.net % [2] Kecman, V, Learning and Soft Computing, % MIT Press, Cambridge, MA. 2001. % [3] Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., % Vandewalle, J., Least Squares Support Vector Machines, % World Scientific, Singapore, 2002. % % SVMTRAIN(...,'KFUNARGS',ARGS) allows you to pass additional % arguments to kernel functions. % set defaults plotflag = false; % The large scale solver cannot handle this type of problem, so turn it % off. qp_opts = optimset('LargeScale','Off','display','off'); kfunargs = {}; setPoly = false; usePoly = false; setMLP = false; useMLP = false; setSigma = false; useSigma = false; autoScale = false; if ~isempty(which('quadprog')) useQuadprog = true; else useQuadprog = false; end % set default kernel function kfun = @linear_kernel; % check inputs if nargin < 2 error(nargchk(2,Inf,nargin)) end numoptargs = nargin -2; optargs = varargin; % grp2idx sorts a numeric grouping var ascending, and a string grouping % var by order of first occurrence [groupIndex, groupString] = grp2idx(groupnames); % check group is a vector -- though char input is special... if ~isvector(groupnames) && ~ischar(groupnames) error('Bioinfo:svmtrain:GroupNotVector',... 'Group must be a vector.'); end % make sure that the data are correctly oriented. if size(groupnames,1) == 1 groupnames = groupnames'; end % make sure data is the right size if size(training,1) ~= length(groupnames) if size(training,2) == length(groupnames) training = training'; else error('Bioinfo:svmtrain:DataGroupSizeMismatch',... 'GROUP and TRAINING must have the same number of rows.') end end % check for NaN in data matrix: if any(isnan(training(:))) error('Bioinfo:svmtrain:NaNinDataMatrix', ... 'TRAINING data must not contain missing values'); end % NaNs are treated as unknown classes and are removed from the training % data nans = find(isnan(groupIndex)); if length(nans) > 0 training(nans,:) = []; groupIndex(nans) = []; end ngroups = length(groupString); nPoints = length(groupIndex); % set default value of box constraint boxconstraint = 1 / sqrt(eps) * ones(1, nPoints); if ngroups > 2 error('Bioinfo:svmtrain:TooManyGroups',... 'SVMTRAIN only supports classification into two groups.\nGROUP contains %d different groups.',ngroups) end % convert to 1, -1. groupIndex = 1 - (2* (groupIndex-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','method','showplot','kfunargs',... 'quadprog_opts','polyorder','mlp_params',... 'boxconstraint','rbf_sigma','autoscale'}; for j=1:2:numoptargs pname = optargs{j}; pval = optargs{j+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:AmbiguousParameterName',... 'Ambig