www.pudn.com > osu_svm3.00.zip > SVMTrain.m

function [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = SVMTrain(Samples, Labels, Parameters, Weight) 
% Usages: 
% [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = SVMTrain(Samples, Labels) 
% [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = SVMTrain(Samples, Labels, Parameters) 
% [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = SVMTrain(Samples, Labels, Parameters, Weight) 
% 
% DESCRIPTION: 
% Construct a SVM classifier.  
% In fact, This function is used to do the input parameter checking, and it calls a mex file, mexSVMTrain, to  
% implement the algorithm. 
% 
% Inputs: 
%    Samples    - training samples, MxN, (a row of column vectors); 
%    Labels     - labels of training samples, 1xN, (a row vector); 
%    Parameters - the paramters required by the training algorithm (a <=11-element row vector); 
%     +------------------------------------------------------------------ 
%     |Kernel Type| Degree | Gamma | Coefficient | C |Cache size|epsilon|  
%     +------------------------------------------------------------------ 
%       ----------------------------------------------+ 
%       | SVM type | nu | loss toleration | shrinking | 
%       ----------------------------------------------+ 
%            where Kernel Type: (default: 2)  
%                     0 --- Linear 
%                     1 --- Polynomial: (Gamma*+Coefficient)^Degree 
%                     2 --- RBF: (exp(-Gamma*|X(:,i)-X(:,j)|^2))  
%                     3 --- Sigmoid: tanh(Gamma*+Coefficient) 
%                  Degree: default 3 
%                  Gamma: If the input value is zero, Gamma will be set defautly as 
%                         1/(max_pattern_dimension) in the function. If the input 
%                         value is non-zero, Gamma will remain unchanged in the  
%                         function. (default: 1) 
%                  Coefficient: default 0 
%                  C: Cost of constrain violation for C-SVC, epsilon-SVR, and nu-SVR (default 1) 
%                  Cache Size: Space to hold the elements of K() matrix (default 40MB) 
%                  epsilon: tolerance of termination criterion (default: 0.001) 
%                  SVM Type: (default: 0) 
%                     0 --- c-SVC  
%                     1 --- nu-SVC 
%                     2 --- one-class SVM 
%                     3 --- epsilon-SVR  
%                     4 --- nu-SVR 
%                  nu: nu of nu-SVC, one-class SVM, and nu-SVR (default: 0.5) 
%                  loss tolerance: epsilon in loss function of epsilon-SVR (default: 0.1) 
%                  shrinking: whether to use the shrinking heuristics, 0 or 1 (default: 1) 
%    Weight     - a row vector or scalar, C of class i is weight(i)*C in C-SVC (default: all 1's); 
% 
% Outputs:   
%    AlphaY    - Alpha * Y, where Alpha is the non-zero Lagrange Coefficients, and 
%                    Y is the corresponding Labels, (L-1) x sum(nSV); 
%                All the AlphaYs are organized as follows: (pretty fuzzy !) 
%      				classifier between class i and j: coefficients with 
%			  	         i are in AlphaY(j-1, start_Pos_of_i:(start_Pos_of_i+1)-1), 
%				         j are in AlphaY(i, start_Pos_of_j:(start_Pos_of_j+1)-1) 
%    SVs       - Support Vectors. (Sample corresponding the non-zero Alpha), M x sum(nSV), 
%                All the SVs are stored in the format as follows: 
%                 [SVs from Class 1, SVs from Class 2, ... SVs from Class L]; 
%    Bias      - Bias of all the 2-class classifier(s), 1 x L*(L-1)/2; 
%    Parameters -  Output parameters used in training; 
%    nSV       -  numbers of SVs in each class, 1xL; 
%    nLabel    -  Labels of each class, 1xL. 
% 
% By Junshui Ma, and Yi Zhao (02/15/2002) 
% 
 
if (nargin < 2 | nargin > 4) 
   disp(' Error: Incorrect number of input variables.'); 
   help SVMTrain; 
   return 
end 
 
if (nargin >= 3)  
    [prM prN]= size(Parameters); 
    if (prM ~= 1 & prN~=1) 
        disp(' Error: ''Parameters'' should be a row vector.'); 
        return 
    elseif (prM~= 1) 
        Parameters = Parameters'; 
        [prM prN]= size(Parameters); 
    end 
    if (Parameters(1)>3) & (Parameters(1) < 0) 
        disp(' Error: this program only supports 4 types of kernel functions.'); 
        return 
    end 
    if (prN >=8) 
        if (Parameters(8)>4) & (Parameters(8) <0) 
           disp(' Error: this program only supports 5 types of SVMs.'); 
           return 
        end 
    end 
    if (prN >=9)     
        if ((Parameters(8)==1) | (Parameters(8) == 2) | (Parameters(8) == 4)) & (Parameters(9) >= 1) 
           disp(' Error: the nu for nu-SVC, one-class SVM, and nu-SVR should be less than 1 and bigger than 0'); 
           return 
        end         
    end 
end 
 
[spM spN]=size(Samples); 
[lbM lbN]=size(Labels); 
if lbM ~= 1 
   disp(' Error: ''Labels'' should be a row vector.'); 
   return 
end 
if spN ~= lbN 
   disp(' Error: the number of training samples is different from that of their labels.'); 
   return 
end 
 
% call the mex file 
if (nargin == 2) 
    [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = mexSVMTrain(Samples, Labels); 
elseif (nargin == 3) 
    [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = mexSVMTrain(Samples, Labels, Parameters); 
elseif (nargin == 4) 
    [AlphaY, SVs, Bias, Parameters, nSV, nLabel] = mexSVMTrain(Samples, Labels, Parameters, Weight); 
end