www.pudn.com > osu_svm3.00.zip > mexSVMClass.m
function [ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= mexSVMClass(Samples, Labels, AlphaY, SVs, Bias, Parameters, nSV, nLabel, Verbose)
% Usages:
% [ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= mexSVMClass(Samples, Labels, AlphaY, SVs, Bias)
% [ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= mexSVMClass(Samples, Labels, AlphaY, SVs, Bias, Parameters)
% Note that the above two formats are only valid for 2-class problem, it is implemented here to make this version
% to be compatabible with the previous version of OSU SVM ToolBox.
% [ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= mexSVMClass(Samples, Labels, AlphaY, SVs, Bias, Parameters, nSV, nLabel)
% [ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= mexSVMClass(Samples, Labels, AlphaY, SVs, Bias, Parameters, nSV, nLabel, Verbose)
%
% Testing a SVM classifier constructed based on Dr. Chih-Jen's LIBSVM algorithm (version 2.33).
% It is able to deal with both 2-class and multi-class problem when used for classification.
% When it is used to deal with multiclass problem, 1-1, or pairwise, multi-class
% scheme is used to reduce the multiclass problem to L(L-1)/2 2-class problems, where L is number of
% classes involved.
%
% please refer to http://www.csie.ntu.edu.tw/~cjlin/libsvm for more information
%
% Inputs:
% Samples - testing samples, MxN, (a row of column vectors);
% Labels - labels of testing samples, 1xN, (a row vector);
% 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 - 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)
% nSV - numbers of SVs in each class, 1xL;
% nLabel - Labels of each class, 1xL.
% Verbose - verbose level (default: 0).
% 0 --- very silent
% 1 --- a little verbose
%
% Outputs:
% ClassRate - Classification rate, 1x1;
% DecisionValue - the output of the decision function (only meaningful for 2-class problem), 1xN;
% Ns - number of samples in each class, 1x(L+1);
% Note that the last element is for the Samples that are not in any
% classes in the training set.
% ConfMatrix - Confusion Matrix, (L+1)x(L+1), where ConfMatrix(i,j) = P(X in j| X in i);
% Note that the last row and the last column are for the Samples that are not in any
% classes in the training set.
% PreLabels - Predicated Labels, 1xN.
%
% By Junshui Ma, and Yi Zhao (02/15/2002)
%