exercise1.asv

使用高斯模型对威斯康辛州大学医学院长期乳腺癌数据进行了贝叶斯模式识别。识别率为95以上

clear all
w1 = [-5.01 -8.12 -3.68
    -5.43 -3.48 -.354 
    1.08 -5.52 1.66 
    0.86 -3.78 -4.11 
    -2.67 0.63 7.39
    4.94 3.29 2.08
    -2.51 2.09 -2.59
    -2.25 -2.13 --6.94
    5.56 2.86 -2.26
    1.03 -3.33 4.33];
w2 = [-.91 -.18 -.05
    1.3 -2.06 -3.53
    -7.75 -4.54 -.95
    -5.47 0.5 3.92 
    6.14 5.72 -4.85
    3.6 1.26 4.36
    5.37 -4.63 -3.65
    7.18 1.46 -6.66
    -7.39 1.17 6.3
    -7.5 -6.32 -.31];

%%% a) %%%
begin = 1;
d = 2;
muw1 = mean(w1(:,1:d));
sigw1 = cov(w1(:,1:d));
muw2 = mean(w2(:,1:d));
sigw2 = cov(w2(:,1:d));

pid = (2*pi)^(d/2);
pxw1_w1 = zeros(10,1);
pxw1_w2 = zeros(10,1);
pxw2_w1 = zeros(10,1);
pxw2_w2 = zeros(10,1);
for i = 1:10
    pxw1_w1(i,1) = exp(-.5*(w1(i,1)-muw1)*inv(sigw1)*(w1(i,1)-muw1)')/pid/sqrt(det(sigw1));
    pxw1_w2(i,1) = exp(-.5*(w1(i,1)-muw2)*inv(sigw2)*(w1(i,1)-muw2)')/pid/sqrt(det(sigw2));
    pxw2_w1(i,1) = exp(-.5*(w2(i,1)-muw1)*inv(sigw1)*(w2(i,1)-muw1)')/pid/sqrt(det(sigw1));
    pxw2_w2(i,1) = exp(-.5*(w2(i,1)-muw2)*inv(sigw2)*(w2(i,1)-muw2)')/pid/sqrt(det(sigw2));
end
TrainingError = zeros(2,1);
TrainingError(1,1) = sum(pxw1_w2(:,1) > pxw1_w1(:,1))/10;
TrainingError(2,1) = sum(pxw2_w1(:,1) > pxw2_w2(:,1))/10;TrainingError