%%
% * libsvm 工具箱简单使用
%
%% 加载数据
% * 最终data格式：m*n，m样本数，n维度
% * label:m*1 标签为-1与1这两类
clc
clear
close all
data = load('data.mat');
data = data.data;
%选择训练样本个数
num_train = 16;
%构造随机选择序列
choose = randperm(length(data));
train_data = data(choose(1:num_train),:);
gscatter(train_data(:,1),train_data(:,2),train_data(:,3));
label_train = train_data(:,end);
test_data = data(choose(num_train+1:end),:);
label_test = test_data(:,end);
predict = zeros(length(test_data),1);
%% ----训练模型并预测分类
model = svmtrain(label_train,train_data(:,1:end-1),'-t 2');
% -t = 2 选择径向基函数核
true_num = 0;
% for i = 1:length(test_data)
% 作为预测，svmpredict第一个参数随便给个就可以
predict = svmpredict(1,test_data(1,1:end-1),model);
% end
%% 显示结果
figure;
index1 = find(predict==1);
data1 = (test_data(index1,:))';
plot(data1(1,:),data1(2,:),'or');
hold on
index2 = find(predict==-1);
data2 = (test_data(index2,:))';
plot(data2(1,:),data2(2,:),'*');
hold on
indexw = find(predict~=(label_test));
dataw = (test_data(indexw,:))';
plot(dataw(1,:),dataw(2,:),'+g','LineWidth',3);
accuracy = length(find(predict==label_test))/size(test_data,1);
title(['predict the testing data and the accuracy is :',num2str(accuracy)]);
