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Translated cliques_to_jtree()

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Waldir Leoncio 2022-12-22 14:05:13 +01:00
parent 76d31e593a
commit ac1e73c6fa
1 changed files with 42 additions and 51 deletions
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R/cliques_to_jtree.R
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@ -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))
}