function varargout = mixexpPredict(model, X) %% Predict using mixture of experts model % If the response y is real-valued, we return % [mu, sigma2, post, muk, sigma2k] = mixexpPredict(model, X) % mu(i) = E[y | X(i,:)] % sigma2(i) = var[y | X(i,:)] % weights(i,k) = p(expert = k | X(i,:) % muk(i) = E[y | X(i,:), expert k] % sigma2k(i) = var[y | X(i,:), expert k] % % If the response y is categorical, we return % [yhat, prob] = mixexpPredict(model, X) % yhat(i) = argmax p(y|X(i,:)) % prob(i,c) = p(y=c|X(i,:)) % This file is from pmtk3.googlecode.com [N,D] = size(X); %X = standardize(X); %X = [ones(N,1) X]; if isfield(model, 'preproc') [X] = preprocessorApplyToTest(model.preproc, X); end K = model.nmix; if model.fixmix weights = repmat(model.mixweights, N, 1); else weights = softmaxPmtk(X*model.Wq); % weights(n,q) end if model.classifier % implemented by JoAnne Ting prob = zeros(N, size(model.Wy,2)); yhat_k = zeros(N, model.Nclasses, K); for k = 1:K yhat_k(:,:,k) = softmaxPmtk(X*model.Wy(:,:,k)); % Weighted vote prob = prob + yhat_k(:,:,k) .* repmat(weights(:,k), 1, size(model.Wy,2)); end yhat = maxidx(prob, [], 2); varargout{1} = yhat; varargout{2} = prob; else % mean of a mixture model is given by % E[x] = sum_k pik muk %mu = sum(weights .* (X*model.Wy), 2); % variance of a mixture model is given by % sum_k pi_k [Sigmak + muk*muk'] - E[x] E[x]' muk = zeros(N,K); vk = zeros(N,K); mu = zeros(N,1); v = zeros(N,1); for k=1:K muk(:,k) = X*model.Wy(:,k); mu = mu + weights(:,k) .* muk(:,k); vk(:,k) = model.sigma2(k); v = v + weights(:,k) .* (vk(:,k) + muk(:,k).^2); end v = v-mu.^2; varargout{1} = mu; varargout{2} = v; varargout{3} = weights; varargout{4} = muk; varargout{5} = vk; end end

这是一个用于预测混合专家模型的函数。该函数可以根据输入的模型和数据，返回预测结果。如果目标变量 y 是实值型的，函数会返回预测的均值、方差以及后验概率。如果目标变量 y 是分类型的，函数会返回预测的类别以及类别的概率。

函数首先对输入数据 X 进行预处理（如果有预处理步骤），然后根据模型参数计算混合权重 weights。如果模型是分类器，则根据权重和类别权重参数计算类别概率，并选取概率最大的类别作为预测结果。如果模型不是分类器，则根据权重和模型参数计算预测的均值和方差，并返回混合权重、均值和方差。

该函数是从 pmtk3.googlecode.com 中获取的。