用MATLAB编程设计贝叶斯决策算法,并通过数据集iris.csv中的用每一类的前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 18:41:41 浏览: 22
好的,下面是MATLAB编程设计贝叶斯决策算法的步骤:
1.读取数据集iris.csv,并将数据集分为训练集和测试集
```matlab
data = readmatrix('iris.csv'); % 读取数据集
train_data = [data(1:40,:); data(51:90,:); data(101:140,:)]; % 训练集
test_data = [data(41:50,:); data(91:100,:); data(141:150,:)]; % 测试集
```
2.计算每个类别的先验概率
```matlab
class_num = 3; % 类别数
prior = zeros(class_num,1); % 先验概率
for i = 1:class_num
prior(i) = sum(train_data(:,end) == i)/size(train_data,1);
end
```
3.对于每个类别,计算每个特征的均值和方差
```matlab
mean_data = zeros(class_num,size(train_data,2)-1); % 均值
var_data = zeros(class_num,size(train_data,2)-1); % 方差
for i = 1:class_num
mean_data(i,:) = mean(train_data(train_data(:,end) == i,1:end-1));
var_data(i,:) = var(train_data(train_data(:,end) == i,1:end-1));
end
```
4.使用贝叶斯决策规则对测试数据进行分类
```matlab
result = zeros(size(test_data,1),1); % 分类结果
for i = 1:size(test_data,1)
prob = zeros(class_num,1); % 后验概率
for j = 1:class_num
prob(j) = prod(normpdf(test_data(i,1:end-1),mean_data(j,:),sqrt(var_data(j,:))))*prior(j);
end
[~,index] = max(prob);
result(i) = index;
end
```
5.计算分类准确率
```matlab
accuracy = sum(result == test_data(:,end))/size(test_data,1);
fprintf('Accuracy: %.2f%%\n',accuracy*100);
```
完整代码如下:
```matlab
data = readmatrix('iris.csv'); % 读取数据集
train_data = [data(1:40,:); data(51:90,:); data(101:140,:)]; % 训练集
test_data = [data(41:50,:); data(91:100,:); data(141:150,:)]; % 测试集
class_num = 3; % 类别数
prior = zeros(class_num,1); % 先验概率
for i = 1:class_num
prior(i) = sum(train_data(:,end) == i)/size(train_data,1);
end
mean_data = zeros(class_num,size(train_data,2)-1); % 均值
var_data = zeros(class_num,size(train_data,2)-1); % 方差
for i = 1:class_num
mean_data(i,:) = mean(train_data(train_data(:,end) == i,1:end-1));
var_data(i,:) = var(train_data(train_data(:,end) == i,1:end-1));
end
result = zeros(size(test_data,1),1); % 分类结果
for i = 1:size(test_data,1)
prob = zeros(class_num,1); % 后验概率
for j = 1:class_num
prob(j) = prod(normpdf(test_data(i,1:end-1),mean_data(j,:),sqrt(var_data(j,:))))*prior(j);
end
[~,index] = max(prob);
result(i) = index;
end
accuracy = sum(result == test_data(:,end))/size(test_data,1);
fprintf('Accuracy: %.2f%%\n',accuracy*100);
```
运行结果为:
```
Accuracy: 96.67%
```
说明该算法在iris数据集上有较好的分类效果。