close all;
clear;
clc;
load brush;
load brush_labels;
train_brush = [brush(1:8,:);brush(11:16,:)];
train_brush_labels = [brush_labels(1:8);brush_labels(11:16)];
test_brush = [brush(9:10,:);brush(17:20,:)];
test_brush_labels = [brush_labels(9:10);brush_labels(17:20)];
[mtrain,ntrain] = size(train_brush);
[mtest,ntest] = size(test_brush);
dataset = [train_brush;test_brush];
[dataset_scale,ps] = mapminmax(dataset',0,1);
dataset_scale = dataset_scale';
train_brush = dataset_scale(1:mtrain,:);
test_brush = dataset_scale((mtrain+1):(mtrain+mtest),:);
model = svmtrain(train_brush_labels,train_brush,'-c 2 -g 1');
[predict_label, accuracy] = svmpredict(test_brush_labels, test_brush, model);
figure;
hold on;
plot(test_brush_labels,'o');
plot(predict_label,'r*');
xlabel('测试集样本','FontSize',12);
ylabel('类别标签','FontSize',12);
legend('实际测试集分类','预测测试集分类');
title('测试集的实际分类和预测分类图','FontSize',12);
grid on;
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