yxcsvmclassifer.m

数据挖掘中的新方法---支持向量机》示例代码

function [YClassified, Z, err] = yxcSVMclassifer(Xtrain, Xtest, Y, alphaStar, bStar, kernel, sigma)
%
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% If you have the Chinese book: 《数据挖掘中的新方法——支持向量机》, find the algorithm here:
% Classify C-SVC using algorithm 5.4.12 on page 193
%
% Otherwise, refer to this article: "Support Vector Machines: Hype or Hallelujah?" 
% Section 6: SUMMARY OF SVM METHOD on page 5
% http://www.sigkdd.org/explorations/issue2-2/bennett.pdf
%
% Here is also a very good tutorial: "A Tutorial on Support Vector Machines for Pattern Recognition"
% www.umiacs.umd.edu/~joseph/support-vector-machines4.pdf
% 
% A detailed description about iris flower data set:
% http://en.wikipedia.org/wiki/Iris_flower_data_set
%
% Author: Xuchen Yao. [e-mail]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Input:
% Xtrain/Xtest: num x dim Data, X has num points, every point is described by dim vectors
% Y: n x 1 Data, {1, -1}
% alphaStar: Optimized alpha
% bStar: bias, or primal threshold variable.
% kernel: see yxcSVMkernel
% sigma: see yxcSVMkernel
%
% Output:
% YClassified: n x 1 Data, {1, -1}, the calssified output.
% err: Error rate. If Xtrain equals Xtest, err is given.
% Z: YClassified before sign function.

[num, dim] = size(Xtrain);
if dim ~= 2
    return;
end
if num ~= length(Y)
    return;
end
Y = Y(:);
alphaStar = alphaStar(:);

% Algorithm 5.4.12, step (4)
H = yxcSVMkernel(Xtrain, Xtest, kernel, sigma)';
Z = H * (alphaStar .* Y);
Z = Z + bStar;

YClassified(find(Z > 0)) = 1;
YClassified(find(Z <= 0)) = -1;
YClassified = YClassified';
if(size(Xtest) == size(Xtrain))
    if(Xtest == Xtrain)
        err = length(find(YClassified ~= Y)) / length(Y);
    end
else
    err = NaN;
end