www.pudn.com > osu_svm3.00.zip > SVMTest.m
function [ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= SVMTest(Samples, Labels, AlphaY, SVs, Bias,Parameters, nSV, nLabel)
% Usages:
% [ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= SVMTest(Samples, Labels, AlphaY, SVs, Bias)
% [ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= SVMTest(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]= SVMTest(Samples, Labels, AlphaY, SVs, Bias, Parameters, nSV, nLabel)
%
% DESCRIPTION:
% Test the performance of a trained SVM classifier by a group of input patterns
% with their true class labels given.
% In fact, this function is used to do the input parameter checking, and it
% depends on a mex file, mexSVMClass, to implement the algorithm.
%
% 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: 0 or 1/M)
% 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.
%
% 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), or 1xL;
% 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), or LxL, where ConfMatrix(i,j) = P(X in j| X in i);
% Note that when (L+1)x(L+1), 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)
%
if (nargin < 5 | nargin > 8)
disp(' Error: Incorrect number of input variables.');
help SVMTest;
return
end
[minLabel, I]=min(Labels);
[maxLabel, I]=max(Labels);
if ((minLabel ~= -1) | (maxLabel ~= 1))
if (nargin < 8)
disp(' Error: The sample labels are not in {-1,1}, However, you need to input ''nLabel'' to support speical labels.');
return
end
end
if (nargin >= 6)
[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
end
[alM alN] = size(AlphaY);
if (nargin <= 6)
[r c] = size(Bias);
if (r~=1 | c~=1)
disp(' Error: Your SVM classifier seems a multiclass classifier. However, you need to input ''nSV'' and ''nLabel'' to support multiclass problem.');
return
end
if (alM > 1)
disp(' Error: Your SVM classifier seems a multiclass classifier. However, you need to input ''nSV'' and ''nLabel'' to support multiclass problem.');
return
end
end
[spM spN]=size(Samples);
[svM svN]=size(SVs);
if svM ~= spM
disp(' Error: ''SVs'' should have the same feature dimension as ''Samples''.');
return;
end
if svN ~= alN
disp(' Error: number of ''SVs'' should be the same as the colmun number of ''AlphaY''.');
return;
end
% call the mex file
if (nargin == 5)
[ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= mexSVMClass(Samples, Labels, AlphaY, SVs, Bias);
elseif (nargin == 6)
[ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= mexSVMClass(Samples, Labels, AlphaY, SVs, Bias,Parameters);
elseif (nargin == 8)
[ClassRate, DecisionValue, Ns, ConfMatrix, PreLabels]= mexSVMClass(Samples, Labels, AlphaY, SVs, Bias,Parameters, nSV, nLabel);
end
% if these is no extra class in the testing samples,
% remove that last column and row in ConfMatrix
if (ConfMatrix(end, end) == 1)
ConfMatrix = ConfMatrix(1:end-1,1:end-1);
Ns = Ns(1:end-1);
end