Mercurial > hg > camir-aes2014
view toolboxes/FullBNT-1.0.7/bnt/inference/static/@likelihood_weighting_inf_engine/marginal_nodes.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function marginal = marginal_nodes(engine, nodes) % MARGINAL_NODES Compute the marginal on the specified query nodes (likelihood_weighting) % marginal = marginal_nodes(engine, nodes) bnet = bnet_from_engine(engine); ddom = myintersect(nodes, bnet.dnodes); cdom = myintersect(nodes, bnet.cnodes); nsamples = size(engine.samples, 1); ns = bnet.node_sizes; %w = normalise(engine.weights); w = engine.weights; if mysubset(nodes, ddom) T = 0*myones(ns(nodes)); P = prod(ns(nodes)); indices = ind2subv(ns(nodes), 1:P); samples = reshape(cat(1, engine.samples{:,nodes}), nsamples, length(nodes)); for j = 1:P rows = find_rows(samples, indices(j,:)); T(j) = sum(w(rows)); end T = normalise(T); marginal.T = T; elseif subset(nodes, cdom) samples = reshape(cat(1, engine.samples{:,nodes}), nsamples*sum(ns(nodes)), length(nodes)); [marginal.mu, marginal.Sigma] = wstats(samples', normalise(w)); else error('can''t handle mixed marginals yet'); end marginal.domain = nodes; %%%%%%%%% function rows = find_rows(M, v) % FINDROWS Find rows which are equal to a specified vector % rows = findrows(M, v) % Each row of M is a sample temp = abs(M - repmat(v, size(M, 1), 1)); rows = find(sum(temp,2) == 0); %%%%%%%% function [mu, Sigma] = wstats(X, w) % Computes the weighted mean and weighted covariance matrix for a given % set of observations X(:,i), and a set of normalised weights w(i). % Each column of X is a sample. d = X - repmat(X * w', 1, size(X, 2)); mu = sum(X .* repmat(w, size(X, 1), 1), 2); Sigma = d * diag(w) * d';