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Debugged the thinTree. Now works with tree indexing starting at 1 at the root. TODO : make it work with index 0 on a leaf.

release/4.3a0
jdurand7 2012-09-14 23:46:21 +00:00
parent 0357559827
commit 8d85d679cd
4 changed files with 36 additions and 16 deletions
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6
matlab/gtsam_tests/testThinTreeBayesNet.m
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@ -17,7 +17,11 @@
% * @author Jean-Guillaume Durand
% */
%% Clean the workspace
clc, clear all, close all;
%% Run the tests
import gtsam.*
bayesNet = thinTreeBayesNet(3,2);
[bayesNet tree] = thinTreeBayesNet(4,2);
EQUALITY('7 = bayesNet.size', 7, bayesNet.size);
bayesNet.saveGraph('thinTreeBayesNet.dot');

20
matlab/gtsam_tests/thinTree.m
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@ -49,12 +49,17 @@ classdef thinTree
% Function to return the ID's of a node's parents
function ids = getParents(obj, nodeID)
% Initialisation
ids = zeros(1,obj.w);
node = nodeID;
% Loop on w, the number of parents associated to one node
for i=1:obj.w
ids(i) = floor(node/2);
node = floor(node/2);
depthOfNode = obj.getNodeDepth(nodeID);
if depthOfNode == 1
ids = 1;
else
ids = zeros(1,min(obj.w, depthOfNode-1));
% Loop on w, the number of parents associated to one node
for i=1:min(obj.w, depthOfNode-1)
ids(i) = floor(node/2);
node = floor(node/2);
end
end
% Return
return
@ -74,5 +79,10 @@ classdef thinTree
function output = getNumberOfElements(obj)
output = 2^obj.depth - 1;
end
% Returns the depth of a node
function output = getNodeDepth(obj, nodeID)
output = ceil(log(nodeID+1)/log(2));
end
end % Methods
end % Class

24
matlab/gtsam_tests/thinTreeBayesNet.m
View File

@ -1,7 +1,7 @@
function [bayesNet, tree] = thinTreeBayesNet(d,w)
import gtsam.*
bayesNet = GaussianBayesNet;
tree = thinTree(3,2);
tree = thinTree(d,w);
% Filling the tree
@ -10,24 +10,30 @@ gc = gtsam.GaussianConditional(1, 5*rand(1), 5*rand(1), 3*rand(1));
% Getting it into the GaussianBayesNet
bayesNet.push_front(gc);
for i=1:2^tree.getNumberOfElements()
for i=1:tree.getNumberOfElements()
% Getting the parents of that node
parents = tree.getParents(i);
% Create and link the corresponding GaussianConditionals
if tree.getW == 1
if tree.getW == 1 || tree.getNodeDepth(i) <= 2
% Creation of the GaussianConditional
gc = gtsam.GaussianConditional(parents(1), 5*rand(1), 5*rand(1));
gc = gtsam.GaussianConditional(parents(1), 5*rand(1), 5*rand(1), i, 5*rand(1), 5*rand(1));
% Getting it into the GaussianBayesNet
bayesNet.push_front(gc);
% Getting it in the thinTree
t = tree.addContent({gc,parents}, i);
elseif tree.getW == 2
% Creation of the GaussianConditional
gc = gtsam.GaussianConditional(parents(2), 5*rand(1), 5*rand(1), parents(1), 5*rand(1), 5*rand(1));
tree = tree.addContent({gc,parents}, i);
elseif tree.getW == 2 && tree.getNodeDepth(i) > 2
% Creation of the GaussianConditional associated with the first
% parent
gc = gtsam.GaussianConditional(parents(1), 5*rand(1), 5*rand(1), i, 5*rand(1), 5*rand(1));
% Getting it into the GaussianBayesNet
bayesNet.push_front(gc);
% Creation of the GaussianConditionalj associated with the second
% parent
gc = gtsam.GaussianConditional(parents(2), 5*rand(1), 5*rand(1), i, 5*rand(1), 5*rand(1));
% Getting it into the GaussianBayesNet
bayesNet.push_front(gc);
% Getting it in the thinTree
t = tree.addContent({gc,parents}, i);
tree = tree.addContent({gc,parents}, i);
end
end
end