From 13671c964ca4c53c559575925be7c816254b0e4f Mon Sep 17 00:00:00 2001 From: Davis King Date: Sat, 2 Jan 2010 06:17:29 +0000 Subject: [PATCH] Fixed grammar. --HG-- extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403344 --- examples/bayes_net_ex.cpp | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/examples/bayes_net_ex.cpp b/examples/bayes_net_ex.cpp index b886ba557..7e2048663 100644 --- a/examples/bayes_net_ex.cpp +++ b/examples/bayes_net_ex.cpp @@ -166,17 +166,18 @@ int main() // First we need to create an undirected graph which contains set objects at each node and // edge. This long declaration does the trick. - typedef graph::compare_1b_c, set::compare_1b_c>::kernel_1a_c join_tree_type; + typedef set::compare_1b_c set_type; + typedef graph::kernel_1a_c join_tree_type; join_tree_type join_tree; - // Now we need populate the join_tree with data from our bayesian network. The next to + // Now we need to populate the join_tree with data from our bayesian network. The next // function calls do this. Explaining exactly what they do is outside the scope of this // example. Just think of them as filling join_tree with information that is useful // later on for dealing with our bayesian network. create_moral_graph(bn, join_tree); create_join_tree(join_tree, join_tree); - // Now we have a proper join_tree we can use it to obtain a solution to our + // Now that we have a proper join_tree we can use it to obtain a solution to our // bayesian network. Doing this is as simple as declaring an instance of // the bayesian_network_join_tree object as follows: bayesian_network_join_tree solution(bn, join_tree);