现有代码和数据% 清空工作区变量和命令窗口内容 clear clc % 加载数据集 load ('E:\Onedrive\桌面\毕设\朴素贝叶斯图像分类\test_images.mat'); load ('E:\Onedrive\桌面\毕设\朴素贝叶斯图像分类\test_labels.mat'); load ('E:\Onedrive\桌面\毕设\朴素贝叶斯图像分类\train_images.mat'); load ('E:\Onedrive\桌面\毕设\朴素贝叶斯图像分类\train_labels.mat');请在此基础上利用matlab实现了一个基于贝叶斯分类器的手写数字识别模型。主要分为以下步骤： 1.读取数据。 2，对图像数据进行二值化处理并提取特征，提取后的特征为14*14的二值图像。 3.使用贝叶斯分类器对提取后的特征进行分类训练，并计算先验概率和类条件概率。4. 对测试集进行特征提取和分类，最终输出分类结果和准确率。 给出主程序和所有用到的m文件

很高兴为您解答问题！以下是基于贝叶斯分类器的手写数字识别模型的 MATLAB 代码：

1. 读取数据

% 加载数据集
load ('E:\Onedrive\桌面\毕设\朴素贝叶斯图像分类\test_images.mat');
load ('E:\Onedrive\桌面\毕设\朴素贝叶斯图像分类\test_labels.mat');
load ('E:\Onedrive\桌面\毕设\朴素贝叶斯图像分类\train_images.mat');
load ('E:\Onedrive\桌面\毕设\朴素贝叶斯图像分类\train_labels.mat');

2. 对图像数据进行二值化处理并提取特征，提取后的特征为14*14的二值图像

% 对图像数据进行二值化处理
train_images(train_images <= 127) = 0;
train_images(train_images > 127) = 1;
test_images(test_images <= 127) = 0;
test_images(test_images > 127) = 1;

% 提取特征，提取后的特征为14*14的二值图像
num_train_images = size(train_images, 1);
num_test_images = size(test_images, 1);
num_pixels = size(train_images, 2);
num_features = 196; % 14*14
train_features = zeros(num_train_images, num_features);
test_features = zeros(num_test_images, num_features);
for i = 1:num_train_images
    image = reshape(train_images(i,:), [28,28]);
    feature = imresize(image, [14,14]);
    feature = feature(:)';
    train_features(i,:) = feature;
end
for i = 1:num_test_images
    image = reshape(test_images(i,:), [28,28]);
    feature = imresize(image, [14,14]);
    feature = feature(:)';
    test_features(i,:) = feature;
end

3. 使用贝叶斯分类器对提取后的特征进行分类训练，并计算先验概率和类条件概率

% 使用贝叶斯分类器对提取后的特征进行分类训练
num_classes = 10;
prior_probs = zeros(num_classes, 1);
class_probs = zeros(num_features, 2, num_classes);
for i = 1:num_classes
    indices = find(train_labels == (i-1));
    num_images = length(indices);
    prior_probs(i) = num_images / num_train_images;
    features = train_features(indices,:);
    class_probs(:,:,i) = [sum(features==0)+1, sum(features==1)+1] / (num_images+2);
end

% 计算先验概率和类条件概率
log_prior_probs = log(prior_probs);
log_class_probs = log(class_probs);

4. 对测试集进行特征提取和分类，最终输出分类结果和准确率

% 对测试集进行特征提取和分类
num_correct = 0;
for i = 1:num_test_images
    feature = test_features(i,:);
    log_likelihoods = zeros(num_classes, 1);
    for j = 1:num_classes
        log_likelihoods(j) = sum(log_class_probs(:,feature+1,j));
    end
    log_posteriors = log_prior_probs + log_likelihoods;
    [~, prediction] = max(log_posteriors);
    if prediction-1 == test_labels(i)
        num_correct = num_correct + 1;
    end
end

% 输出分类结果和准确率
accuracy = num_correct / num_test_images;
fprintf('Accuracy = %.2f %%\n', accuracy*100);

另外，需要用到一个用于调整图像大小的 MATLAB 函数 imresize.m，此函数为 MATLAB 自带函数，无需额外下载。完整代码附上：