支持向量机的matlab代码

支持向量机的matlab代码Matlab中关于evalin帮助：EVALIN(WS,'expression') evaluates 'expression' in the context of the workspace WS. WS can be 'caller' or 'base'. It is similar to EVAL except that you can control which workspace the expression is evaluated in.[X,Y,Z,...] = EVALIN(WS,'expression') returns output arguments from the expression.EVALIN(WS,'try','catch') tries to evaluate the 'try' expression and if that fails it evaluates the 'catch' expression (in the current workspace).可知evalin('base', 'algo')是对工作空间base中的algo求值（返回其值）。

如果是7.0以上版本>>edit svmtrain>>edit svmclassify>>edit svmpredictfunction [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 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(...,'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.%% 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(...,'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 The MathWorks, Inc.% $Revision: 1.1.12.1 $ $Date: 2004/12/24 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.J., Learning with Kernels,% MIT Press, Cambridge, MA. 2002.%% SVMTRAIN(...,'KFUNARGS',ARGS) allows you to pass additional% arguments to kernel functions.% set defaultsplotflag = false;qp_opts = [];kfunargs = {};setPoly = false; usePoly = false;setMLP = false; useMLP = false;if ~isempty(which('quadprog'))useQuadprog = true;elseuseQuadprog = false;end% set default kernel functionkfun = @linear_kernel;% check inputsif nargin < 2error(nargchk(2,Inf,nargin))endnumoptargs = nargin -2;optargs = varargin;% grp2idx sorts a numeric grouping var ascending, and a string grouping % var by order of first occurrence[g,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 is correctly oriented.if size(groupnames,1) == 1groupnames = groupnames';end% make sure data is the right sizen = length(groupnames);if size(training,1) ~= nif size(training,2) == ntraining = training';elseerror('Bioinfo:svmtrain:DataGroupSizeMismatch',...'GROUP and TRAINING must have the same number of rows.')endend% NaNs are treated as unknown classes and are removed from the training % datanans = find(isnan(g));if length(nans) > 0training(nans,:) = [];g(nans) = [];。