From aff5b3de15a094e988e92d5ffb6bcbcc8cb3681a Mon Sep 17 00:00:00 2001
From: Waldir Leoncio <w.l.netto@medisin.uio.no>
Date: Thu, 22 Dec 2022 14:05:23 +0100
Subject: [PATCH] Added more functions to translate

---
 R/minimum_spanning_tree.R            | 51 ++++++++++++++++++++++++++++
 tests/testthat/test-spatialMixture.R |  2 ++
 2 files changed, 53 insertions(+)
 create mode 100644 R/minimum_spanning_tree.R

diff --git a/R/minimum_spanning_tree.R b/R/minimum_spanning_tree.R
new file mode 100644
index 0000000..a9e7288
--- /dev/null
+++ b/R/minimum_spanning_tree.R
@@ -0,0 +1,51 @@
+minimum_spanning_tree <- function(C1, C2) stop("needs translation")
+# function A = minimum_spanning_tree(C1, C2)
+# %
+# % Find the minimum spanning tree using Prim's algorithm.
+# % C1(i,j) is the primary cost of connecting i to j.
+# % C2(i,j) is the (optional) secondary cost of connecting i to j, used to break ties.
+# % We assume that absent edges have 0 cost.
+# % To find the maximum spanning tree, used -1*C.
+# % See Aho, Hopcroft & Ullman 1983, "Data structures and algorithms", p 237.
+
+# % Prim's is O(V^2). Kruskal's algorithm is O(E log E) and hence is more efficient
+# % for sparse graphs, but is implemented in terms of a priority queue.
+
+# % We partition the nodes into those in U and those not in U.
+# % closest(i) is the vertex in U that is closest to i in V-U.
+# % lowcost(i) is the cost of the edge (i, closest(i)), or infinity is i has been used.
+# % In Aho, they say C(i,j) should be "some appropriate large value" if the edge is missing.
+# % We set it to infinity.
+# % However, since lowcost is initialized from C, we must distinguish absent edges from used nodes.
+
+# n = length(C1);
+# if nargin==1, C2 = zeros(n); end
+# A = zeros(n);
+
+# closest = ones(1,n);
+# used = zeros(1,n); % contains the members of U
+# used(1) = 1; % start with node 1
+# C1(find(C1==0))=inf;
+# C2(find(C2==0))=inf;
+# lowcost1 = C1(1,:);
+# lowcost2 = C2(1,:);
+
+# for i=2:n
+#   ks = find(lowcost1==min(lowcost1));
+#   k = ks(argmin(lowcost2(ks)));
+#   A(k, closest(k)) = 1;
+#   A(closest(k), k) = 1;
+#   lowcost1(k) = inf;
+#   lowcost2(k) = inf;
+#   used(k) = 1;
+#   NU = find(used==0);
+#   for ji=1:length(NU)
+#     for j=NU(ji)
+#       if C1(k,j) < lowcost1(j)
+# 	lowcost1(j) = C1(k,j);
+# 	lowcost2(j) = C2(k,j);
+# 	closest(j) = k;
+#       end
+#     end
+#   end
+# end
diff --git a/tests/testthat/test-spatialMixture.R b/tests/testthat/test-spatialMixture.R
index 4bf01e6..83b9a9f 100644
--- a/tests/testthat/test-spatialMixture.R
+++ b/tests/testthat/test-spatialMixture.R
@@ -42,5 +42,7 @@ test_that("lakseKlitik() and subfunctions produce expected output", {
   # TODO: test mysubset()
   # TODO: test findCliques()
   # TODO: test cliques_to_jtree()
+  # TODO: test minimum_spanning_tree()
+  # TODO: test myunion()
   # TODO: test lakseKlitik()
 })