plot data points distribution.m

来自「在做2維度樣本分類的過程中,若我們能事先畫出訓練樣本在空間中的分散情形,這將有助」· M 代码 · 共 29 行

clear all;
clc;

%%% load data set %%% 
%  Parameters: X      - Training inputs (initial set of features)
%              Y      - Training targets or call outputs (class labels)
X=[-1.4901	-0.62512
1.861	-0.34566
0.47681	-1.1841
0.32181	-1.5754
-1.6967	-0.43337]

Y=[-1
1
-1
-1
-1]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
inda=find(Y==1);
indb=find(Y==-1);
plot(X(inda,1),X(inda,2),'ko','MarkerSize',6,'MarkerFaceColor','w')
hold on
plot(X(indb,1),X(indb,2),'ks','MarkerSize',6,'MarkerFaceColor','w')
hold on
xlabel('X first feature');
ylabel('X second feature');
legend('Class1','Class2');
title('Data Points Distribution');
grid on