% LIBSVMOPT  Optimize Support Vector Machine with LIBSVM.%% SVM Classification:%   svm = libsvmopt(x,y,C,ker); %   svm = libsvmopt(x,y,nu,ker,'style','nu'); %% One-Class SVM:%   svm = libsvmopt(x,[],nu,ker);%% SVM Regression:%   svm = libsvmopt(x,y,C,e,ker); %   svm = libsvmopt(x,y,C,nu,ker,'style','nu'); %% Passing Options:%   svm = libsvmopt(...,'opt1',value1,'opt2',value2,...); %   svm = libsvmopt(...,opt); %% Multiple Output Arguments:%   [a,b] = libsvm(...);%   [a,I,b] = libsvm(...);%% Input Arguments:%   x   - Input data (size N*n). In general, x may be a multidimensional array%         with last dimension (input space) of size n.%   y   - Output data (size N*1). In general, y may be a multidimensional array%         with scalar last dimension (output space). For classification problems,%         y must contain not more than 2 different class labels; if the solution%         is requested as [a,b] or [a,I,b], these values must be -1 and +1.%   C   - Upper bound for Lagrange multipliers (positive scalar).%   e   - Insensitivity zone for regression (non-negative scalar).%   nu  - Parameter nu (0 < nu <= 1).%   ker - Kernel function (struct, see below).%   opt - Optimizer options (struct, see below).%% Output Arguments:%   svm - Support vector machine (struct, see below).%   a   - Coefficient vector (size N*1, or Nsv*1 if "I" is requested).%   I   - Index vector (size Nsv*1).%   b   - Bias term (scalar).%% Kernel Function:% All information about the kernel function is stored in a struct variable with% the following fields:%   type   - linear :  k(x,y) = x'*y%            poly   :  k(x,y) = (x'*y+c)^d%            gauss  :  k(x,y) = exp(-0.5*(norm(x-y)/s)^2)%            tanh   :  k(x,y) = tanh(g*x'*y+c)%   degree - Degree d of polynomial kernel (positive scalar).%   offset - Offset c of polynomial and tanh kernel (scalar, negative for tanh).%   width  - Width s of Gauss kernel (positive scalar).%   gamma  - Slope g of the tanh kernel (positive scalar).%% Standard Output Format:% The output is a struct containing all information needed to simulate the SVM:%   coef  - Coefficients of the support vectors (size Nsv*1).%   vect  - Support vectors (size Nsv*n, sub-matrix of x).%   ker   - Kernel function (struct, see above).%   bias  - Bias term (scalar).%   label - Vector of class labels (size 1*2), only for classification.%   prob  - Probability parameters (size 1*2), only if requested.%% Alternative Output Format:% The solution can also be requested as coefficient vector and bias term [a,b],% where "a" contains the coefficients in the order of the input data. If an% additional index vector is requested [a,I,b], then "a" contains only nonzero% entries and "I" their positions with respect to the input data. In both cases,% class labels +1 and -1 must be used for classification, since no information% about the labels is returned. The sign of the decision function f(x) is% adjusted so that sign(f(x)) is the predicted class label.%% Options:% LIBSVM can be adjusted by several options, which are passed as a struct% variable or as key/value pairs. If an option is omitted, its default value is% used. Invalid fields produce warnings. The options are:%   style   - SVM style ('nu' or 'C'/'eps'). Default: 'C'/'eps'%   tol     - Tolerance for KKT check (positive scalar). Default: 1e-3.%   shrink  - Shrinking heuristics (0/1). Default: 1%   weight  - Weight for class "wlabel" (positive scalar). Default: 1%   wlabel  - Label of class to be weighted (scalar). Default: -1%   cache   - Cache size. It can be given as string in kB or MB, or as number of%             rows (where a row contains N elements). Default: '40MB'%   prob    - Compute probability information for classification (0/1).%             Default: 0%   verbose - Verbosity level (0/1). Default: 0% ------------------------------------------------------------------------------% MATLAB Interface for LIBSVM, Version 1.2%% Copyright (C) 2004-2005 Michael Vogt% Written by Michael Vogt, Atanas Ayarov and Bennet Gedan% % This program is free software; you can redistribute it and/or modify it% under the terms of the GNU General Public License as published by the Free% Software Foundation; either version 2 of the License, or (at your option)% any later version.% ------------------------------------------------------------------------------