Bayesian_parameter_est.rar_Bayesian_Bayesian_parameter_train_协方差

function [mu, sigma] = Bayesian_parameter_est(train_patterns, train_targets, sigma) % Estimate the mean using the Bayesian parameter estimation for Gaussian mixture algorithm估计平均值，用贝叶斯参数的估计作为简缩高斯函数集算法 % Inputs: % patterns - Train patterns % targets - Train targets % sigma - The covariance matrix for each class 每一类矩阵的协方差 % % Outputs % mu - The estimated mean 平均值 % sigma - The estimated covariances 协方差 [N,M] = size(train_patterns); Uclasses = unique(train_targets); Nuc = length(Uclasses); %Find initial estimates for mu and sigma for the classes mu0 = zeros(Nuc, N); sigma0 = zeros(Nuc, N, N); for i = 1:Nuc, indices = find(train_targets == Uclasses(i)); mu0(i,:) = mean(train_patterns(:,indices)'); sigma0(i,:,:) = sqrtm(cov(train_patterns(:,indices)',1));%方根矩阵 协方差：COV(X，Y)，即COV(X，Y)=E[(X-E(X))(Y-E(Y))] end %Now gradually find the Bayesian estimate mu_n = zeros(Nuc, N); sigma_n = zeros(Nuc, N, N); %For each class, estimate the mean and variance for each class for i = 1:Nuc, indices = find(train_targets == Uclasses(i)); for n = 2:length(indices), indices = indices(randperm(length(indices))); samples = indices(1:n); mu_n_hat = 1/n*sum(train_patterns(:,samples)')'; mu_n(i,:) = (squeeze(sigma0(i,:,:))*inv(squeeze(sigma0(i,:,:))+1/n*squeeze(sigma(i,:,:)))*mu_n_hat + ... + 1/n*squeeze(sigma(i,:,:))*inv(squeeze(sigma0(i,:,:))+1/n*squeeze(sigma(i,:,:)))*mu0(i,:)')'; sigma_n(i,:,:) = squeeze(sigma0(i,:,:))*inv(squeeze(sigma0(i,:,:))+1/n*squeeze(sigma(i,:,:)))*1/n*squeeze(sigma(i,:,:)); end end %Return the estimates mu = mu_n; sigma = sigma + sigma_n;