用matlab代码实现朴素贝叶斯分类器,要求如下:1.导入鸢尾花数据集； 2.分别取三个类中每个类的40个样本作为训练集，10个样本作为测试集； 3.计算每个类的概率密度函数，即每个类的样本均值与方差（假设样本特征数据满足高斯分布）； 4.将测试样本带入每个类的概率密度函数，并判断该样本属于哪个类别； 5.计算精确率，召回率，F度量。请按照 iris = load('C:\Users\86187\Desktop\Iris (1).csv')续写。

好的，以下是matlab代码实现朴素贝叶斯分类器的步骤：

1. 导入鸢尾花数据集

iris = load('C:\Users\86187\Desktop\Iris (1).csv');

2. 分别取三个类中每个类的40个样本作为训练集，10个样本作为测试集

train_index = [1:40, 51:90, 101:140];
test_index = [41:50, 91:100, 141:150];

train_data = iris(train_index, :);
test_data = iris(test_index, :);

3. 计算每个类的概率密度函数，即每个类的样本均值与方差（假设样本特征数据满足高斯分布）

class_num = 3;
feature_num = 4;
mu = zeros(class_num, feature_num);
sigma = zeros(class_num, feature_num);

for i = 1:class_num
    class_data = train_data((i-1)*40+1:i*40, :);
    for j = 1:feature_num
        mu(i,j) = mean(class_data(:, j));
        sigma(i,j) = std(class_data(:, j));
    end
end

4. 将测试样本带入每个类的概率密度函数，并判断该样本属于哪个类别

test_label = zeros(30, 1);
for i = 1:30
    test_sample = test_data(i, :);
    p = zeros(class_num, 1);
    for j = 1:class_num
        p(j) = prod(normpdf(test_sample, mu(j,:), sigma(j,:)));
    end
    [max_p, max_idx] = max(p);
    test_label(i) = max_idx;
end

5. 计算精确率，召回率，F度量

true_label = [ones(10,1); 2*ones(10,1); 3*ones(10,1)];
confusion_matrix = zeros(class_num, class_num);
for i = 1:30
    confusion_matrix(true_label(i), test_label(i)) = confusion_matrix(true_label(i), test_label(i)) + 1;
end

precision = zeros(class_num, 1);
recall = zeros(class_num, 1);
f_measure = zeros(class_num, 1);

for i = 1:class_num
    tp = confusion_matrix(i,i);
    fp = sum(confusion_matrix(:,i)) - tp;
    fn = sum(confusion_matrix(i,:)) - tp;
    
    precision(i) = tp / (tp + fp);
    recall(i) = tp / (tp + fn);
    f_measure(i) = 2 * precision(i) * recall(i) / (precision(i) + recall(i));
end

mean_precision = mean(precision);
mean_recall = mean(recall);
mean_f_measure = mean(f_measure);

以上就是matlab代码实现朴素贝叶斯分类器的完整步骤。