From ac1e73c6fae9c3d820e56623a283fa10b19a37f7 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Thu, 22 Dec 2022 14:05:13 +0100 Subject: [PATCH] Translated cliques_to_jtree() --- R/cliques_to_jtree.R | 93 ++++++++++++++++++++------------------------ 1 file changed, 42 insertions(+), 51 deletions(-) diff --git a/R/cliques_to_jtree.R b/R/cliques_to_jtree.R index 40c6f3b..68d57a5 100644 --- a/R/cliques_to_jtree.R +++ b/R/cliques_to_jtree.R @@ -1,58 +1,49 @@ cliques_to_jtree <- function(cliques, ns) { - stop("needs translation") - # function [jtree, root, B, w] = cliques_to_jtree(cliques, ns) - # % MK_JTREE Make an optimal junction tree. - # % [jtree, root, B, w] = mk_jtree(cliques, ns) - # % - # % A junction tree is a tree that satisfies the jtree property, which says: - # % for each pair of cliques U,V with intersection S, all cliques on the path between U and V - # % contain S. (This ensures that local propagation leads to global consistency.) - # % - # % We can create a junction tree by computing the maximal spanning tree of the junction graph. - # % (The junction graph connects all cliques, and the weight of an edge (i,j) is - # % |C(i) intersect C(j)|, where C(i) is the i'th clique.) - # % - # % The best jtree is the maximal spanning tree which minimizes the sum of the costs on each edge, - # % where cost(i,j) = w(C(i)) + w(C(j)), and w(C) is the weight of clique C, - # % which is the total number of values C can take on. - # % - # % For details, see - # % - Jensen and Jensen, "Optimal Junction Trees", UAI 94. - # % - # % Input: - # % cliques{i} = nodes in clique i - # % ns(i) = number of values node i can take on - # % Output: - # % jtree(i,j) = 1 iff cliques i and j aer connected - # % root = the clique that should be used as root - # % B(i,j) = 1 iff node j occurs in clique i - # % w(i) = weight of clique i + # MK_JTREE Make an optimal junction tree. + # [jtree, root, B, w] = mk_jtree(cliques, ns) + # A junction tree is a tree that satisfies the jtree property, which says: + # for each pair of cliques U, V with intersection S, all cliques on the path between U and V + # contain S. (This ensures that local propagation leads to # global consistency.) + # We can create a junction tree by computing the maximal spanning tree of the junction graph. + # (The junction graph connects all cliques, and the weight of an edge (i, j) is + # |C(i) intersect C(j)|, where C(i) is the i'th clique.) + # The best jtree is the maximal spanning tree which minimizes the sum of the costs on each edge, + # where cost[i, j] <- w(C(i)) + w(C(j)), and w(C) is the weight of clique C, + # which is the total number of values C can take on. + # For details, see + # - Jensen and Jensen, "Optimal Junction Trees", UAI 94. + # Input: + # cliques{i} = nodes in clique i + # ns[i] <- number of values node i can take on + # Output: + # jtree[i, j] <- 1 iff cliques i and j aer connected + # root <- the clique that should be used as root + # B[i, j] <- 1 iff node j occurs in clique i + # w[i] <- weight of clique i + num_cliques <- length(cliques) + w <- zeros(num_cliques, 1) + B <- zeros(num_cliques, 1) + for (i in 1:num_cliques) { + B[i, cliques[[i]]] <- 1 + w[i] <- prod(ns(cliques[[i]])) + } + # C1[i, j] <- length(intersect(cliques{i}, cliques{j})) + # The length of the intersection of two sets is the dot product of their bit vector representation. + C1 <- B %*% t(B) + C1 <- setdiag(C1, 0) - # num_cliques = length(cliques); - # w = zeros(num_cliques, 1); - # B = sparse(num_cliques, 1); - # for i=1:num_cliques - # B(i, cliques{i}) = 1; - # w(i) = prod(ns(cliques{i})); - # end + # C2[i, j] <- w(i) + w(j) + num_cliques <- length(w) + W <- repmat(w, c(1, num_cliques)) + C2 <- W + t(W) + C2 <- setdiag(C2, 0) + jtree <- zeros(minimum_spanning_tree(-C1, C2))# Using - C1 gives * maximum * spanning tree - # % C1(i,j) = length(intersect(cliques{i}, cliques{j})); - # % The length of the intersection of two sets is the dot product of their bit vector representation. - # C1 = B*B'; - # C1 = setdiag(C1, 0); - - # % C2(i,j) = w(i) + w(j) - # num_cliques = length(w); - # W = repmat(w, 1, num_cliques); - # C2 = W + W'; - # C2 = setdiag(C2, 0); - - # jtree = sparse(minimum_spanning_tree(-C1, C2)); % Using -C1 gives *maximum* spanning tree - - # % The root is arbitrary, but since the first pass is towards the root, - # % we would like this to correspond to going forward in time in a DBN. - # root = num_cliques; + # The root is arbitrary, but since the first pass is towards the root, + # we would like this to correspond to going forward in time in a DBN. + root <- num_cliques + return(list("jtree" = jtree, "root" = root, "B" = B, "w" = w)) }