MATLAB SVM tutorial (fitcsvm)

I am sorry for everyone that I did not actually write code in the description. -- clear; close all; clc; %% preparing dataset load fisheriris species_num = grp2idx(species); %% % binary classification 형태로 만들기 위해 100개만... X = randn(100,10); X(:,[1,3,5,7]) = meas(1:100,:); % 1, 3, 5, 7 번 feature가 분류에 유용한 feature일 것임. y = species_num(1:100); rand_num = randperm(size(X,1)); X_train = X(rand_num(1:round(0.8*length(rand_num))),:); y_train = y(rand_num(1:round(0.8*length(rand_num))),:); X_test = X(rand_num(round(0.8*length(rand_num))+1:end),:); y_test = y(rand_num(round(0.8*length(rand_num))+1:end),:); %% CV partition c = cvpartition(y_train,'k',5); %% feature selection opts = statset('display','iter'); classf = @(train_data, train_labels, test_data, test_labels)... sum(predict(fitcsvm(train_data, train_labels,'KernelFunction','rbf'), test_data) ~= test_labels); [fs, history] = sequentialfs(classf, X_train, y_train, 'cv', c, 'options', opts,'nfeatures',2); %% Best hyperparameter X_train_w_best_feature = X_train(:,fs); Md1 = fitcsvm(X_train_w_best_feature,y_train,'KernelFunction','rbf','OptimizeHyperparameters','auto',... 'HyperparameterOptimizationOptions',struct('AcquisitionFunctionName',... 'expected-improvement-plus','ShowPlots',true)); % Bayes' Optimization 사용. %% Final test with test set X_test_w_best_feature = X_test(:,fs); test_accuracy_for_iter = sum((predict(Md1,X_test_w_best_feature) == y_test))/length(y_test)*100 %% hyperplane 확인 figure; hgscatter = gscatter(X_train_w_best_feature(:,1),X_train_w_best_feature(:,2),y_train); hold on; h_sv=plot(Md1.SupportVectors(:,1),Md1.SupportVectors(:,2),'ko','markersize',8); % test set의 data를 하나 하나씩 넣어보자. gscatter(X_test_w_best_feature(:,1),X_test_w_best_feature(:,2),y_test,'rb','xx') % decision plane XLIMs = get(gca,'xlim'); YLIMs = get(gca,'ylim'); [xi,yi] = meshgrid([XLIMs(1):0.01:XLIMs(2)],[YLIMs(1):0.01:YLIMs(2)]); dd = [xi(:), yi(:)]; pred_mesh = predict(Md1, dd); redcolor = [1, 0.8, 0.8]; bluecolor = [0.8, 0.8, 1]; pos = find(pred_mesh == 1); h1 = plot(dd(pos,1), dd(pos,2),'s','color',redcolor,'Markersize',5,'MarkerEdgeColor',redcolor,'MarkerFaceColor',redcolor); pos = find(pred_mesh == 2); h2 = plot(dd(pos,1), dd(pos,2),'s','color',bluecolor,'Markersize',5,'MarkerEdgeColor',bluecolor,'MarkerFaceColor',bluecolor); uistack(h1,'bottom'); uistack(h2,'bottom'); legend([hgscatter;h_sv],{'setosa','versicolor','support vectors'})