模式识别实验:用MATLAB设计贝叶斯决策算法,并通过鸢尾花数据集iris.csv文件中的每一类的前40条记录作为训练数据,剩余的10条记录作为测试数据,最后验证该算法的有效性
时间: 2023-12-10 21:41:37 浏览: 26
好的,下面是具体实现步骤:
1. 首先读取iris.csv文件,并将其分为三类,即Setosa、Versicolor和Virginica。
```matlab
data = readmatrix('iris.csv');
setosa = data(1:50,:);
versicolor = data(51:100,:);
virginica = data(101:150,:);
```
2. 将每一类的前40条记录作为训练数据,剩余的10条记录作为测试数据。
```matlab
train_setosa = setosa(1:40,:);
test_setosa = setosa(41:50,:);
train_versicolor = versicolor(1:40,:);
test_versicolor = versicolor(41:50,:);
train_virginica = virginica(1:40,:);
test_virginica = virginica(41:50,:);
```
3. 对于每一类,计算出其训练数据的均值和协方差矩阵。
```matlab
mean_setosa = mean(train_setosa);
cov_setosa = cov(train_setosa);
mean_versicolor = mean(train_versicolor);
cov_versicolor = cov(train_versicolor);
mean_virginica = mean(train_virginica);
cov_virginica = cov(train_virginica);
```
4. 对于每一条测试数据,分别计算出其在三个类别中的后验概率,并选择概率最大的类别作为预测结果。
```matlab
correct = 0;
total = 30;
for i = 1:10
% 计算Setosa类别的后验概率
posterior_setosa = mvnpdf(test_setosa(i,:), mean_setosa, cov_setosa);
posterior_versicolor = mvnpdf(test_setosa(i,:), mean_versicolor, cov_versicolor);
posterior_virginica = mvnpdf(test_setosa(i,:), mean_virginica, cov_virginica);
if posterior_setosa > posterior_versicolor && posterior_setosa > posterior_virginica
correct = correct + 1;
end
% 计算Versicolor类别的后验概率
posterior_setosa = mvnpdf(test_versicolor(i,:), mean_setosa, cov_setosa);
posterior_versicolor = mvnpdf(test_versicolor(i,:), mean_versicolor, cov_versicolor);
posterior_virginica = mvnpdf(test_versicolor(i,:), mean_virginica, cov_virginica);
if posterior_versicolor > posterior_setosa && posterior_versicolor > posterior_virginica
correct = correct + 1;
end
% 计算Virginica类别的后验概率
posterior_setosa = mvnpdf(test_virginica(i,:), mean_setosa, cov_setosa);
posterior_versicolor = mvnpdf(test_virginica(i,:), mean_versicolor, cov_versicolor);
posterior_virginica = mvnpdf(test_virginica(i,:), mean_virginica, cov_virginica);
if posterior_virginica > posterior_setosa && posterior_virginica > posterior_versicolor
correct = correct + 1;
end
end
% 输出准确率
accuracy = correct / total;
fprintf('准确率为:%.2f%%\n', accuracy * 100);
```
最终输出的准确率即为算法的有效性。