irisdata.m

最新的模式识别分类工具箱,希望对朋友们有用!

x =[5.1	3.5	1.4	0.2	0
4.9	3	1.4	0.2	0
4.7	3.2	1.3	0.2	0
4.6	3.1	1.5	0.2	0
5	3.6	1.4	0.2	0
5.4	3.9	1.7	0.4	0
4.6	3.4	1.4	0.3	0
5	3.4	1.5	0.2	0
4.4	2.9	1.4	0.2	0
4.9	3.1	1.5	0.1	0
5.4	3.7	1.5	0.2	0
4.8	3.4	1.6	0.2	0
4.8	3	1.4	0.1	0
4.3	3	1.1	0.1	0
5.8	4	1.2	0.2	0
5.7	4.4	1.5	0.4	0
5.4	3.9	1.3	0.4	0
5.1	3.5	1.4	0.3	0
5.7	3.8	1.7	0.3	0
5.1	3.8	1.5	0.3	0
5.4	3.4	1.7	0.2	0
5.1	3.7	1.5	0.4	0
4.6	3.6	1	0.2	0
5.1	3.3	1.7	0.5	0
4.8	3.4	1.9	0.2	0
5	3	1.6	0.2	0
5	3.4	1.6	0.4	0
5.2	3.5	1.5	0.2	0
5.2	3.4	1.4	0.2	0
4.7	3.2	1.6	0.2	0
4.8	3.1	1.6	0.2	0
5.4	3.4	1.5	0.4	0
5.2	4.1	1.5	0.1	0
5.5	4.2	1.4	0.2	0
4.9	3.1	1.5	0.2	0
5	3.2	1.2	0.2	0
5.5	3.5	1.3	0.2	0
4.9	3.6	1.4	0.1	0
4.4	3	1.3	0.2	0
5.1	3.4	1.5	0.2	0
5	3.5	1.3	0.3	0
4.5	2.3	1.3	0.3	0
4.4	3.2	1.3	0.2	0
5	3.5	1.6	0.6	0
5.1	3.8	1.9	0.4	0
4.8	3	1.4	0.3	0
5.1	3.8	1.6	0.2	0
4.6	3.2	1.4	0.2	0
5.3	3.7	1.5	0.2	0
5	3.3	1.4	0.2	0
7	3.2	4.7	1.4	1
6.4	3.2	4.5	1.5	1
6.9	3.1	4.9	1.5	1
5.5	2.3	4	1.3	1
6.5	2.8	4.6	1.5	1
5.7	2.8	4.5	1.3	1
6.3	3.3	4.7	1.6	1
4.9	2.4	3.3	1	1
6.6	2.9	4.6	1.3	1
5.2	2.7	3.9	1.4	1
5	2	3.5	1	1
5.9	3	4.2	1.5	1
6	2.2	4	1	1
6.1	2.9	4.7	1.4	1
5.6	2.9	3.6	1.3	1
6.7	3.1	4.4	1.4	1
5.6	3	4.5	1.5	1
5.8	2.7	4.1	1	1
6.2	2.2	4.5	1.5	1
5.6	2.5	3.9	1.1	1
5.9	3.2	4.8	1.8	1
6.1	2.8	4	1.3	1
6.3	2.5	4.9	1.5	1
6.1	2.8	4.7	1.2	1
6.4	2.9	4.3	1.3	1
6.6	3	4.4	1.4	1
6.8	2.8	4.8	1.4	1
6.7	3	5	1.7	1
6	2.9	4.5	1.5	1
5.7	2.6	3.5	1	1
5.5	2.4	3.8	1.1	1
5.5	2.4	3.7	1	1
5.8	2.7	3.9	1.2	1
6	2.7	5.1	1.6	1
5.4	3	4.5	1.5	1
6	3.4	4.5	1.6	1
6.7	3.1	4.7	1.5	1
6.3	2.3	4.4	1.3	1
5.6	3	4.1	1.3	1
5.5	2.5	4	1.3	1
5.5	2.6	4.4	1.2	1
6.1	3	4.6	1.4	1
5.8	2.6	4	1.2	1
5	2.3	3.3	1	1
5.6	2.7	4.2	1.3	1
5.7	3	4.2	1.2	1
5.7	2.9	4.2	1.3	1
6.2	2.9	4.3	1.3	1
5.1	2.5	3	1.1	1
5.7	2.8	4.1	1.3	1
6.3	3.3	6	2.5	2
5.8	2.7	5.1	1.9	2
7.1	3	5.9	2.1	2
6.3	2.9	5.6	1.8	2
6.5	3	5.8	2.2	2
7.6	3	6.6	2.1	2
4.9	2.5	4.5	1.7	2
7.3	2.9	6.3	1.8	2
6.7	2.5	5.8	1.8	2
7.2	3.6	6.1	2.5	2
6.5	3.2	5.1	2	2
6.4	2.7	5.3	1.9	2
6.8	3	5.5	2.1	2
5.7	2.5	5	2	2
5.8	2.8	5.1	2.4	2
6.4	3.2	5.3	2.3	2
6.5	3	5.5	1.8	2
7.7	3.8	6.7	2.2	2
7.7	2.6	6.9	2.3	2
6	2.2	5	1.5	2
6.9	3.2	5.7	2.3	2
5.6	2.8	4.9	2	2
7.7	2.8	6.7	2	2
6.3	2.7	4.9	1.8	2
6.7	3.3	5.7	2.1	2
7.2	3.2	6	1.8	2
6.2	2.8	4.8	1.8	2
6.1	3	4.9	1.8	2
6.4	2.8	5.6	2.1	2
7.2	3	5.8	1.6	2
7.4	2.8	6.1	1.9	2
7.9	3.8	6.4	2	2
6.4	2.8	5.6	2.2	2
6.3	2.8	5.1	1.5	2
6.1	2.6	5.6	1.4	2
7.7	3	6.1	2.3	2
6.3	3.4	5.6	2.4	2
6.4	3.1	5.5	1.8	2
6	3	4.8	1.8	2
6.9	3.1	5.4	2.1	2
6.7	3.1	5.6	2.4	2
6.9	3.1	5.1	2.3	2
5.8	2.7	5.1	1.9	2
6.8	3.2	5.9	2.3	2
6.7	3.3	5.7	2.5	2
6.7	3	5.2	2.3	2
6.3	2.5	5	1.9	2
6.5	3	5.2	2	2
6.2	3.4	5.4	2.3	2
5.9	3	5.1	1.8	2];

x0 =[5.1	3.5	1.4	0.2	0
4.9	3	1.4	0.2	0
4.7	3.2	1.3	0.2	0
4.6	3.1	1.5	0.2	0
5	3.6	1.4	0.2	0
5.4	3.9	1.7	0.4	0
4.6	3.4	1.4	0.3	0
5	3.4	1.5	0.2	0
4.4	2.9	1.4	0.2	0
4.9	3.1	1.5	0.1	0
5.4	3.7	1.5	0.2	0
4.8	3.4	1.6	0.2	0
4.8	3	1.4	0.1	0
4.3	3	1.1	0.1	0
5.8	4	1.2	0.2	0
5.7	4.4	1.5	0.4	0
5.4	3.9	1.3	0.4	0
5.1	3.5	1.4	0.3	0
5.7	3.8	1.7	0.3	0
5.1	3.8	1.5	0.3	0
5.4	3.4	1.7	0.2	0
5.1	3.7	1.5	0.4	0
4.6	3.6	1	0.2	0
5.1	3.3	1.7	0.5	0
4.8	3.4	1.9	0.2	0
5	3	1.6	0.2	0
5	3.4	1.6	0.4	0
5.2	3.5	1.5	0.2	0
5.2	3.4	1.4	0.2	0
4.7	3.2	1.6	0.2	0
4.8	3.1	1.6	0.2	0
5.4	3.4	1.5	0.4	0
5.2	4.1	1.5	0.1	0
5.5	4.2	1.4	0.2	0
4.9	3.1	1.5	0.2	0
5	3.2	1.2	0.2	0
5.5	3.5	1.3	0.2	0
4.9	3.6	1.4	0.1	0
4.4	3	1.3	0.2	0
5.1	3.4	1.5	0.2	0
5	3.5	1.3	0.3	0
4.5	2.3	1.3	0.3	0
4.4	3.2	1.3	0.2	0
5	3.5	1.6	0.6	0
5.1	3.8	1.9	0.4	0
4.8	3	1.4	0.3	0
5.1	3.8	1.6	0.2	0
4.6	3.2	1.4	0.2	0
5.3	3.7	1.5	0.2	0
5	3.3	1.4	0.2	0];

x1=[7	3.2	4.7	1.4	1
6.4	3.2	4.5	1.5	1
6.9	3.1	4.9	1.5	1
5.5	2.3	4	1.3	1
6.5	2.8	4.6	1.5	1
5.7	2.8	4.5	1.3	1
6.3	3.3	4.7	1.6	1
4.9	2.4	3.3	1	1
6.6	2.9	4.6	1.3	1
5.2	2.7	3.9	1.4	1
5	2	3.5	1	1
5.9	3	4.2	1.5	1
6	2.2	4	1	1
6.1	2.9	4.7	1.4	1
5.6	2.9	3.6	1.3	1
6.7	3.1	4.4	1.4	1
5.6	3	4.5	1.5	1
5.8	2.7	4.1	1	1
6.2	2.2	4.5	1.5	1
5.6	2.5	3.9	1.1	1
5.9	3.2	4.8	1.8	1
6.1	2.8	4	1.3	1
6.3	2.5	4.9	1.5	1
6.1	2.8	4.7	1.2	1
6.4	2.9	4.3	1.3	1
6.6	3	4.4	1.4	1
6.8	2.8	4.8	1.4	1
6.7	3	5	1.7	1
6	2.9	4.5	1.5	1
5.7	2.6	3.5	1	1
5.5	2.4	3.8	1.1	1
5.5	2.4	3.7	1	1
5.8	2.7	3.9	1.2	1
6	2.7	5.1	1.6	1
5.4	3	4.5	1.5	1
6	3.4	4.5	1.6	1
6.7	3.1	4.7	1.5	1
6.3	2.3	4.4	1.3	1
5.6	3	4.1	1.3	1
5.5	2.5	4	1.3	1
5.5	2.6	4.4	1.2	1
6.1	3	4.6	1.4	1
5.8	2.6	4	1.2	1
5	2.3	3.3	1	1
5.6	2.7	4.2	1.3	1
5.7	3	4.2	1.2	1
5.7	2.9	4.2	1.3	1
6.2	2.9	4.3	1.3	1
5.1	2.5	3	1.1	1
5.7	2.8	4.1	1.3	1];


x2=[6.3	3.3	6	2.5	2
5.8	2.7	5.1	1.9	2
7.1	3	5.9	2.1	2
6.3	2.9	5.6	1.8	2
6.5	3	5.8	2.2	2
7.6	3	6.6	2.1	2
4.9	2.5	4.5	1.7	2
7.3	2.9	6.3	1.8	2
6.7	2.5	5.8	1.8	2
7.2	3.6	6.1	2.5	2
6.5	3.2	5.1	2	2
6.4	2.7	5.3	1.9	2
6.8	3	5.5	2.1	2
5.7	2.5	5	2	2
5.8	2.8	5.1	2.4	2
6.4	3.2	5.3	2.3	2
6.5	3	5.5	1.8	2
7.7	3.8	6.7	2.2	2
7.7	2.6	6.9	2.3	2
6	2.2	5	1.5	2
6.9	3.2	5.7	2.3	2
5.6	2.8	4.9	2	2
7.7	2.8	6.7	2	2
6.3	2.7	4.9	1.8	2
6.7	3.3	5.7	2.1	2
7.2	3.2	6	1.8	2
6.2	2.8	4.8	1.8	2
6.1	3	4.9	1.8	2
6.4	2.8	5.6	2.1	2
7.2	3	5.8	1.6	2
7.4	2.8	6.1	1.9	2
7.9	3.8	6.4	2	2
6.4	2.8	5.6	2.2	2
6.3	2.8	5.1	1.5	2
6.1	2.6	5.6	1.4	2
7.7	3	6.1	2.3	2
6.3	3.4	5.6	2.4	2
6.4	3.1	5.5	1.8	2
6	3	4.8	1.8	2
6.9	3.1	5.4	2.1	2
6.7	3.1	5.6	2.4	2
6.9	3.1	5.1	2.3	2
5.8	2.7	5.1	1.9	2
6.8	3.2	5.9	2.3	2
6.7	3.3	5.7	2.5	2
6.7	3	5.2	2.3	2
6.3	2.5	5	1.9	2
6.5	3	5.2	2	2
6.2	3.4	5.4	2.3	2
5.9	3	5.1	1.8	2];

plot3(x1(:,1),x1(:,2),x1(:,3),'rx'); hold on;
plot3(x2(:,1),x2(:,2),x2(:,3),'b*'); 
plot3(x0(:,1),x0(:,2),x0(:,3),'gd');  hold off;

y = x(:,5);
x = x(:,1:4);


[test_indices, train_indices] = make_a_draw(50, max(size(x)));
test_features = x(test_indices,:)';
train_features = x(train_indices,:)';
test_targets = y(test_indices)';
train_targets =y(train_indices)';