GMMMatlab代码

以下是一个用MATLAB实现的GMM算法的代码示例：

function \[a_fin, mu_fin, sigma_fin\] = GMM(X1, a_init, mu_init, sigma_init)
    K = size(a_init, 2);
    \[M, N\] = size(X1);
    a = a_init;
    mu = mu_init;
    sigma = sigma_init;
    px = zeros(M, K);
    thre = 1e-5;
    LLD_pro = inf;
    
    while true
        for i = 1:K
            px(:, i) = mvnpdf(X1, mu(i, :), sigma);
        end
        
        pGramm = repmat(a, M, 1) .* px;
        pGramm = pGramm ./ repmat(sum(pGramm, 2), 1, K);
        Nk = sum(pGramm, 1);
        
        mu = diag(1./Nk) * pGramm' * X1;
        
        for kk = 1:K
            Xshift = X1 - repmat(mu(kk, :), M, 1);
            sigma(:, :, kk) = (Xshift' * diag(pGramm(:, kk)) * Xshift) / Nk(:, kk);
        end
        
        a = Nk / M;
        LLD = sum(log(a * px'));
        
        if (LLD - LLD_pro) < thre
            break;
        end
        
        LLD_pro = LLD;
    end
    
    a_fin = a;
    mu_fin = mu;
    sigma_fin = sigma;
end

这段代码实现了GMM算法，其中`X1`是输入数据，`a_init`、`mu_init`和`sigma_init`是GMM模型的初始参数。算法通过迭代优化参数，直到收敛为止。最终返回优化后的参数`a_fin`、`mu_fin`和`sigma_fin`。请注意，这只是一个简单的示例代码，实际应用中可能需要根据具体情况进行修改和优化。
#### 引用[.reference_title]
- *1* [高斯混合模型（GMM）](https://blog.csdn.net/radar_zzm/article/details/89012835)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insert_down28v1,239^v3^insert_chatgpt"}} ] [.reference_item]
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