OSU-SVM-3.0

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*<X(:,i),X(:,j)>+Coefficient)^Degree % 2 --- RBF: (exp(-Gamma*|X(:,i)-X(:,j)|^2)) % 3 --- Sigmoid: tanh(Gamma*<X(:,i),X(:,j)>+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(<X(:,i),X(:,j)>) 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