test.m

核方法＆svm是模式识别是很重要的方法

%function [ output_args ] = test( input_args )
%TEST Summary of this function goes here
%  Detailed explanation goes here
load average.mat;
rand_Benign = randperm(44);
rand_Malignant = randperm(46);
result.rand_Benign = rand_Benign;
result.rand_Malignant = rand_Malignant;

Train_Benign = average.Benign.value(rand_Benign(1:22),:);
Train_Malignant = average.Malignant.value(rand_Malignant(1:23),:);
Test_Benign = average.Benign.value(rand_Benign(23:44),:);
Test_Malignant = average.Malignant.value(rand_Malignant(24:46),:);

[C,kPar] = train(Train_Benign,Train_Malignant);
result.C = C;
result.kPar = kPar;

tic
parameter = SMO_algorithm(Train_Benign,Train_Malignant,kPar,C);
timeTrain = toc
result.timeTrain = timeTrain;
clear toc;
result.numberSV = parameter.numberSV;

trainData = [Train_Benign;Train_Malignant];
Y = [ones(22,1);-1*ones(23,1)];
testData = [Test_Benign;Test_Malignant];

index = find(parameter.alpha>0);
sup_vector = trainData(index,:);

error = 0;
tic
f_SVM = (((testData*sup_vector'+1).^kPar*parameter.alpha(index))).*Y+parameter.b;
f_SVM = f_SVM.*Y;
error = length(find(f_SVM<1))
timeTest = toc
result.timeTest = timeTest;
result.error = error;
save result.mat;