svmtrain.m

支持向量机中的训练样本的MATLAB源代码程序

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',...
                '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
                            case 1
                                kfun = @linear_kernel;
                            case 2
                                kfun = @quadratic_kernel;
                            case {3,4}
                                kfun = @rbf_kernel;
                                useSigma = true;
                            case 5
                                kfun = @poly_kernel;
                                usePoly = true;
                            case 6
                                kfun = @mlp_kernel;
                                useMLP = true;