function [AlphaY, SVs, Bias, Para] = SVMTrain(Samples, Labels, Parameters, Verbose)
% USAGE:
% [AlphaY, SVs, Bias, Para] = SVMTrain(Samples, Labels, Parameters)
%
% DESCRIPTION:
% Construct a 2-class SVM classifier. This function is used to
% do the input parameter checking, and it calls a mex file to 
% implement the algorithm.
% 
% INPUTS:
% Samples: all the training patterns. (a row of column vectors)
% Lables: the corresponding class labels for the training patterns in Samples.
%         where Labels(i) in {1, -1}. (a row vector)
% Parameters: the paramters required by the training algorithm. 
%             (a 10-element row vector)
%            +-----------------------------------------------------------------
%            |Kernel Type| Degree | Gamma | Coefficient | C |Cache size|epsilon| 
%            +-----------------------------------------------------------------
%				 -------------------------------------------+
%            | SVM Type |	nu (nu-svm) | loss tolerance |				
%				 -------------------------------------------+
%            where Kernel Type:
%                   0 --- Linear
%                   1 --- Polynomial: (Gamma*<X(:,i),X(:,j)>+Coefficient)^Degree
%                   2 --- RBF: (exp(-Gamma*|X(:,i)-X(:,j)|^2))
%                   3 --- Sigmoid: tanh(Gamma*<X(:,i),X(:,j)>+Coefficient)
%                  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.
%                  C: Cost of the constrain violation (for C-SVC & C-SVR)
%                  Cache Size: as the buffer to hold the <X(:,i),X(:,j)> (in MB)
%                  epsilon: tolerance of termination criterion
%                  SVM Type: 
%                   0 --- c-SVM classifier
%                   1 --- nu-SVM classifier
%                   2 --- 1-SVM 
%                   3 --- c-SVM regressioner
%                  nu: the nu used in nu-SVM classifer (for 1-SVM and nu-SVM)
%						 loss tolerance: the epsilon in epsilon-insensitive loss function	
% Verbose: =0: the program will be very quite, providing little feedback
%          =2: the program will be very verbose, providing lots of the feedback
%          =1: the situation between the above two.
%
% OUTPUTS:
% AlphaY: Alpha * Y, where Alpha is the non-zero Lagrange Coefficients
%                    Y is the corresponding labels.
% SVs : support vectors. That is, the patterns corresponding the non-zero
%       Alphas.
% Bias : the bias in the decision function, which is AlphaY*Kernel(SVs',x)-Bias.
% Para: output parameters. Basically, this is the same as the input parameters. However,
%       when Gamma=0 as input, it will be changed to 1/N in the program, where
%       N is the dimension of the input patterns.
%
if (nargin < 3) | (nargin >4)
   disp(' Incorrect number of input variables.');
   help SVMTrain;
   return
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: different number of training patterns and their labels.');
   return
end

[prM prN]= size(Parameters);
if prM ~= 1
   disp(' Error: ''Parameters'' should be a row vector.');
   return
end

if prN ~= 10
   disp(' Error: ''Parameters'' should have exactly 10 elements.');
   return
end

if (Parameters(1)>3) & (Parameters(1) < 0)
   disp(' Error: this program only supports 4 types of kernel functions.');
   return
end

if (Parameters(8)>3) & (Parameters(8) < 0)
   disp(' Error: this program only supports 4 types of SVMs.');
   return
end

if Verbose == 0 
   [AlphaY, SVs, Bias, Para] = mexSVMTrain_0(Samples, Labels, Parameters);
else if Verbose == 1
      [AlphaY, SVs, Bias, Para] = mexSVMTrain_1(Samples, Labels, Parameters);   
   else if Verbose == 2
         [AlphaY, SVs, Bias, Para] = mexSVMTrain_2(Samples, Labels, Parameters);
      else
         [AlphaY, SVs, Bias, Para] = mexSVMTrain_2(Samples, Labels, Parameters);
      end
   end
end