k_l_normalization.m

Karhunen-Loève特征变换 模式识别课程实验

% M-file function, K_L_Normalization.m 
% 对数据进行归一化处理
function K_L_Normalization
load iris.dat;
x1 = iris(:,1);
x2 = iris(:,2);
x3 = iris(:,3);
x4 = iris(:,4);
mean1 = 0;
mean2 = 0;
mean3 = 0;
mean4 = 0;
v1 = 0;
v2 = 0;
v3 = 0;
v4 = 0;
for i =1:150
    mean1 = mean1 + x1(i);
    mean2 = mean2 + x2(i);
    mean3 = mean3 + x3(i);
    mean4 = mean4 + x4(i);    
end
mean1 = mean1 / 150;
mean2 = mean2 / 150;
mean3 = mean3 / 150;
mean4 = mean4 / 150;
for i =1:150
    v1 = v1 + (x1(i) - mean1) ^ 2;
    v2 = v2 + (x2(i) - mean2) ^ 2;
    v3 = v3 + (x3(i) - mean3) ^ 2;
    v4 = v4 + (x4(i) - mean4) ^ 2;
end
v1 = v1 / 149;
v2 = v2 / 149;
v3 = v3 / 149;
v4 = v4 / 149;
s1 = v1 ^ 0.5;
s2 = v2 ^ 0.5;
s3 = v3 ^ 0.5;
s4 = v4 ^ 0.5;
for i = 1 : 150
    y1(i) = (x1(i) - mean1) / s1;
    y2(i) = (x2(i) - mean2) / s2;
    y3(i) = (x3(i) - mean3) / s3;
    y4(i) = (x4(i) - mean4) / s4;   
end
Y(:,1) = y1;
Y(:,2) = y2;
Y(:,3) = y3;
Y(:,4) = y4
save X_Normal.dat Y -ascii;