function net = svml(fname, varargin)% SVML - Wrapper for SVMlight% %   NET = SVML(FNAME, OPTIONS)%   Generate the SVMlight wrapper structure. FNAME is the file name%   under which SVMlight will save its data files. FNAME can be left%   empty (FNAME=''), in which case a random filename will be selected.%   OPTIONS may either be a structure generated by SVMLOPT, or any other%   sequence of arguments that is accepted by SVMLOPT.%%   Accepted options are:%   Field      SVM light option  Range, description%   'Verbosity'      -v       {0 .. 3}, default value 1%                             Verbosity level%   'Regression'     -z       {0, 1}, default value 0%                             Switch between regression [1] and%                             classification [0]%   'C'              -c       (0, Inf), default value (avg. x*x)^-1%                             Trade-off between error and margin%   'TubeWidth'      -w       (0, Inf), default value 0.1%                             Epsilon width of tube for regression%   'CostFactor'     -j       (0, Inf), default value 1%                             Cost-Factor by which training errors on%                             positive examples outweight errors on%                             negative examples%   'Biased'         -b       {0, 1}, default value 1%                             Use biased hyperplane x*w+b0 [1] instead of%                             unbiased x*w0 [0]%   'RemoveIncons'   -i       {0, 1}, default value 0%                             Remove inconsistent training examples and%                             retrain%   'ComputeLOO'     -x       {0, 1}, default value 0%                             Compute leave-one-out estimates [1]%   'XialphaRho'     -o       )0, 2), default value 1.0%                             Value of rho for XiAlpha-estimator and for%                             pruning leave-one-out computation%   'XialphaDepth'   -k       {0..100}, default value 0%                             Search depth for extended XiAlpha-estimator %   'TransPosFrac'   -p       (0..1), default value ratio of%                             positive and negative examples in the%                             training data. Fraction of unlabeled%                             examples to be classified into the positive%                             class%   'Kernel'         -t       {0..4}, default value 1%                             Type of kernel function:%                             0: linear%                             1: polynomial (s a*b+c)^d%                             2: radial basis function exp(-gamma ||a-b||^2)%                             3: sigmoid tanh(s a*b + c)%                             4: user defined kernel from kernel.h%   'KernelParam'    -d, -g, -s, -r, -u%                             Depending on the kernel, this vector%                             contains [d] for polynomial kernel, [gamma]%                             for RBF, [s, c] for tanh kernel, string for%                             user-defined kernel%   'MaximumQP'      -q       {2..}, default value 10%                             Maximum size of QP-subproblems%   'NewVariables'   -n       {2..}, default value is the value chosen%                             for 'MaximumQP'. Number of new variables%                             entering the working set in each%                             iteration. Use smaller values to prevent%                             zig-zagging%   'CacheSize'      -m       (5..Inf), default value 40.%                             Size of cache for kernel evaluations in MB%   'EpsTermin'      -e       (0..Inf), default value 0.001%                             Allow that error for termination criterion%                             [y [w*x+b] - 1] < eps%   'ShrinkIter'     -h       {5..Inf}, default value 100.%                             Number of iterations a variable needs to be%                             optimal before considered for shrinking%   'ShrinkCheck'    -f       {0, 1}, default value 1%                             Do final optimality check for variables%                             removed by shrinking. Although this test is%                             usually positive, there is no guarantee%                             that the optimum was found if the test is%                             omitted.%   'TransLabelFile' -l       String. File to write predicted labels of%                             unlabeled examples into after transductive%                             learning.%   'AlphaFile'      -a       String. Write all alphas to this file after%                             learning (in the same order as in the%                             training set).%%   Examples:%   SVML('svmlightdata', 'Kernel', 0, 'C', 1);%   SVML('', 'Kernel', 0, 'C', 1);%   The above call is equivalent to%     OPTS = SVMLOPT('Kernel', 0, 'C', 1);%     SVML('', OPTS);%%%   See also SVMLOPT, SVMLTRAIN, SVMLFWD%% % Copyright (c) by Anton Schwaighofer (2002)% $Revision: 1.4 $ $Date: 2002/02/19 12:22:17 $% mailto:anton.schwaighofer@gmx.net% % This program is released unter the GNU General Public License.% if nargin<1,  fname = '';endif nargin<2,  options = struct([]);endnet.type = 'svml';net.options = svmlopt(varargin{:});net.fname = fname;