基于因子图与和积算法的MATLAB实现

基于因子图（Factor Graph）与和积算法（Sum-Product Algorithm）的MATLAB实现

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一、算法架构设计

1. 因子图建模

因子图由变量节点（Variable Nodes）和因子节点（Factor Nodes）构成二部图：

• 变量节点：表示随机变量（如$x_1, x_2$）
• 因子节点：表示变量间的函数关系（如$f(x_1,x_2)$）

2. 和积算法流程

$\begin{aligned} & \text{初始化消息} \quad m_{i \to j}^{(0)}(x_j) = 1 \\ & \text{迭代更新} \quad \forall i,j: \\ & \quad \mu_{f \to v}(x_v) = \sum_{x_f \setminus x_v} f(x_f) \prod_{u \in \partial f \setminus v} \mu_{u \to f}(x_f) \\ & \quad \mu_{v \to f}(x_f) = \prod_{g \in \partial v \setminus f} \mu_{g \to v}(x_v) \\ & \text{终止条件} \quad \| \mu^{(k+1)} - \mu^{(k)} \| < \epsilon \end{aligned}$

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二、MATLAB核心实现

1. 数据结构定义

% 因子图结构体
graph = struct(...
    'var_nodes', {{'x1', 'x2', 'x3'}}, ... % 变量节点
    'fac_nodes', {{@f1, @f2}}, ...        % 因子函数句柄
    'edges', {[1,2; 2,3]});              % 边连接关系

% 消息存储（对数域避免下溢）
log_messages = cell(length(graph.var_nodes), length(graph.fac_nodes));

2. 因子函数定义示例

% 定义因子函数 f1(x1,x2) = exp(-0.5*(x1^2 + x2^2 + 2 * 0.3*x1*x2))
function f = f1(x1, x2)
    cov = [1, 0.3; 0.3, 1]; % 协方差矩阵
    f = mvnpdf([x1, x2], [0,0], cov);
end

% 定义因子函数 f2(x2,x3) = exp(-0.5*(x2^2 + x3^2))
function f = f2(x2, x3)
    cov = [1, 0; 0, 1];
    f = mvnpdf([x2, x3], [0,0], cov);
end

3. 消息传递算法实现

function [log_beliefs, converged] = sum_product(graph, max_iter, epsilon)
    % 初始化消息
    num_vars = numel(graph.var_nodes);
    num_facs = numel(graph.fac_nodes);
    log_messages = cell(num_vars, num_facs);
    for i = 1:num_vars
        for j = 1:num_facs
            log_messages{i,j} = zeros(1, 2); % 假设变量为二元变量
        end
    end
    
    % 迭代更新
    converged = false;
    for iter = 1:max_iter
        log_messages_old = log_messages;
        
        % 因子→变量消息更新
        for f = 1:num_facs
            fac_node = graph.fac_nodes{f};
            connected_vars = find(graph.edges(:,f));
            for v = connected_vars
                % 计算边缘化消息
                product = 0;
                for u = connected_vars
                    if u ~= v
                        product = product + log_messages{u,f};
                    end
                end
                log_message = fac_node(connected_vars{v}) + product;
                log_messages{v,f} = log_message;
            end
        end
        
        % 变量→因子消息更新
        for v = 1:num_vars
            connected_facs = find(graph.edges(:,v));
            for f = connected_facs
                product = sum(log_messages{v,f});
                for u = connected_facs
                    if u ~= f
                        product = product + log_messages{v,u};
                    end
                end
                log_beliefs{v} = log_beliefs{v} + product;
            end
        end
        
        % 收敛判断
        if max(abs(log_messages(:) - log_messages_old(:))) < epsilon
            converged = true;
            break;
        end
    end
end

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三、资源推荐

1. 参考代码

   • 因子图和和积算法的matlab实现 youwenfan.com/contentcnb/52466.html

2. 仿真案例

   • LDPC编解码仿真 github.com/ldpcmatlab/ldpc-code
     基于和积算法的迭代译码实现