wehub-resource-sync
63 lines
1.6 KiB
Matlab
Executable File
63 lines
1.6 KiB
Matlab
Executable File
function [model, llh] = rvmRegFp(X, t, alpha, beta)
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% Relevance Vector Machine (ARD sparse prior) for regression
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% training by empirical bayesian (type II ML) using Mackay fix point update.
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% Input:
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% X: d x n data
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% t: 1 x n response
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% alpha: prior parameter
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% beta: prior parameter
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% Output:
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% model: trained model structure
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% llh: loglikelihood
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% Written by Mo Chen (sth4nth@gmail.com).
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if nargin < 3
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alpha = 0.02;
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beta = 0.5;
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end
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[d,n] = size(X);
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xbar = mean(X,2);
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tbar = mean(t,2);
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X = bsxfun(@minus,X,xbar);
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t = bsxfun(@minus,t,tbar);
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XX = X*X';
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Xt = X*t';
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tol = 1e-3;
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maxiter = 500;
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llh = -inf(1,maxiter);
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index = 1:d;
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alpha = alpha*ones(d,1);
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for iter = 2:maxiter
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% remove zeros
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nz = 1./alpha > tol; % nonzeros
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index = index(nz);
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alpha = alpha(nz);
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XX = XX(nz,nz);
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Xt = Xt(nz);
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X = X(nz,:);
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U = chol(beta*XX+diag(alpha)); % 7.83
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m = beta*(U\(U'\Xt)); % 7.82
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m2 = m.^2;
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e = sum((t-m'*X).^2);
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logdetS = 2*sum(log(diag(U)));
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llh(iter) = 0.5*(sum(log(alpha))+n*log(beta)-beta*e-logdetS-dot(alpha,m2)-n*log(2*pi)); % 3.86
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if abs(llh(iter)-llh(iter-1)) < tol*abs(llh(iter-1)); break; end
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V = inv(U);
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dgSigma = dot(V,V,2);
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gamma = 1-alpha.*dgSigma; % 7.89
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alpha = gamma./m2; % 7.87
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beta = (n-sum(gamma))/e; % 7.88
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end
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llh = llh(2:iter);
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model.index = index;
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model.w0 = tbar-dot(m,xbar(nz));
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model.w = m;
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model.alpha = alpha;
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model.beta = beta;
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%% optional for bayesian probabilistic prediction purpose
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model.xbar = xbar(index);
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model.U = U; |