BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

8 files changed, 311 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/dtree/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/static/dtree/CVS/Entries
new file mode 100644
index 00000000..81dc9c31
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/dtree/CVS/Entries
@@ -0,0 +1,6 @@
+/test_housing.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/test_restaurants.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/test_zoo1.m/1.1.1.1/Wed May 29 15:59:54 2002//
+/tmp.dot/1.1.1.1/Wed May 29 15:59:54 2002//
+/transform_data_into_bnt_format.m/1.1.1.1/Wed May 29 15:59:54 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/examples/static/dtree/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/static/dtree/CVS/Repository
new file mode 100644
index 00000000..f45a3265
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/dtree/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/examples/static/dtree
diff --git a/sourcecodes/bnt-master/BNT/examples/static/dtree/CVS/Root b/sourcecodes/bnt-master/BNT/examples/static/dtree/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/dtree/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/examples/static/dtree/test_housing.m b/sourcecodes/bnt-master/BNT/examples/static/dtree/test_housing.m
new file mode 100644
index 00000000..40184b47
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/dtree/test_housing.m
@@ -0,0 +1,87 @@
+% Here the training data is adapted from UCI ML repository, 'housing' data
+% Input variables: 12 continous, one binary
+% Ouput variables: continous
+% The testing result trace is in the end of this script, it is same to the graph in page 219 of 
+% Leo Brieman etc. 1984 book titled "Classification and regression trees".
+
+dtreeCPD=tree_CPD;
+
+% load data
+fname = fullfile(BNT_HOME, 'examples', 'static', 'uci_data', 'housing', 'housing.data');
+data=load(fname);
+data=data';
+data=transform_data_into_bnt_format(data,[1:3,5:14]); 
+
+% learn decision tree from data 
+ns=1*ones(1,14);
+ns(4)=2;
+dtreeCPD1=learn_params(dtreeCPD,1:14,data,ns,[1:3,5:14],'stop_cases',5,'min_gain',0.006); 
+
+% evaluate on data
+[score,outputs]=evaluate_tree_performance(dtreeCPD1,1:14,data,ns,[1:3,5:14]);
+fprintf('Mean square deviation (using regression tree to predict) in old training data %6.3f\n',score);
+
+
+% show decision tree using graphpad
+% It should be easy, but still not implemented
+
+
+
+% >> test_housing
+% Create node 1 split at 6 gain  38.2205 Th 6.939000e+000. Mean  22.5328 Cases 506
+% Create node 2 split at 13 gain  14.4503 Th 1.437000e+001. Mean  19.9337 Cases 430
+% Create node 3 split at 8 gain   4.9809 Th 1.358000e+000. Mean  23.3498 Cases 255
+% Create node 4 split at 1 gain   0.7722 Th 1.023300e+001. Mean  45.5800 Cases 5
+% Create leaf node(samevalue) 5. Mean  50.0000 Std   0.0000 Cases 4 
+% Add subtree node 5 to 4. #nodes 5
+% Create leaf node(samevalue) 6. Mean  27.9000 Std   0.0000 Cases 1 
+% Add subtree node 6 to 4. #nodes 6
+% Add subtree node 4 to 3. #nodes 6
+% Create node 7 split at 6 gain   2.8497 Th 6.540000e+000. Mean  22.9052 Cases 250
+% Create node 8 split at 13 gain   0.5970 Th 7.560000e+000. Mean  21.6297 Cases 195
+% Create leaf node(nogain) 9. Mean  23.9698 Std   1.7568 Cases 43 
+% Add subtree node 9 to 8. #nodes 9
+% Create leaf node(nogain) 10. Mean  20.9678 Std   2.8242 Cases 152 
+% Add subtree node 10 to 8. #nodes 10
+% Add subtree node 8 to 7. #nodes 10
+% Create leaf node(nogain) 11. Mean  27.4273 Std   3.4512 Cases 55 
+% Add subtree node 11 to 7. #nodes 11
+% Add subtree node 7 to 3. #nodes 11
+% Add subtree node 3 to 2. #nodes 11
+% Create node 12 split at 1 gain   2.2467 Th 6.962150e+000. Mean  14.9560 Cases 175
+% Create node 13 split at 5 gain   0.5172 Th 5.240000e-001. Mean  17.1376 Cases 101
+% Create leaf node(nogain) 14. Mean  20.0208 Std   3.0672 Cases 24 
+% Add subtree node 14 to 13. #nodes 14
+% Create leaf node(nogain) 15. Mean  16.2390 Std   2.9746 Cases 77 
+% Add subtree node 15 to 13. #nodes 15
+% Add subtree node 13 to 12. #nodes 15
+% Create node 16 split at 5 gain   0.6133 Th 6.050000e-001. Mean  11.9784 Cases 74
+% Create leaf node(nogain) 17. Mean  16.6333 Std   4.5052 Cases 12 
+% Add subtree node 17 to 16. #nodes 17
+% Create leaf node(nogain) 18. Mean  11.0774 Std   3.0090 Cases 62 
+% Add subtree node 18 to 16. #nodes 18
+% Add subtree node 16 to 12. #nodes 18
+% Add subtree node 12 to 2. #nodes 18
+% Add subtree node 2 to 1. #nodes 18
+% Create node 19 split at 6 gain   6.0493 Th 7.420000e+000. Mean  37.2382 Cases 76
+% Create node 20 split at 1 gain   1.9900 Th 7.367110e+000. Mean  32.1130 Cases 46
+% Create node 21 split at 8 gain   0.6273 Th 1.877300e+000. Mean  33.3488 Cases 43
+% Create leaf node(samevalue) 22. Mean  45.6500 Std   6.1518 Cases 2 
+% Add subtree node 22 to 21. #nodes 22
+% Create leaf node(nogain) 23. Mean  32.7488 Std   3.5690 Cases 41 
+% Add subtree node 23 to 21. #nodes 23
+% Add subtree node 21 to 20. #nodes 23
+% Create leaf node(samevalue) 24. Mean  14.4000 Std   3.7363 Cases 3 
+% Add subtree node 24 to 20. #nodes 24
+% Add subtree node 20 to 19. #nodes 24
+% Create node 25 split at 1 gain   1.1001 Th 2.733970e+000. Mean  45.0967 Cases 30
+% Create leaf node(nogain) 26. Mean  45.8966 Std   4.4005 Cases 29 
+% Add subtree node 26 to 25. #nodes 26
+% Create leaf node(samevalue) 27. Mean  21.9000 Std   0.0000 Cases 1 
+% Add subtree node 27 to 25. #nodes 27
+% Add subtree node 25 to 19. #nodes 27
+% Add subtree node 19 to 1. #nodes 27
+% Mean square deviation (using regression tree to predict) in old training data  9.405
+% 
+
+
diff --git a/sourcecodes/bnt-master/BNT/examples/static/dtree/test_restaurants.m b/sourcecodes/bnt-master/BNT/examples/static/dtree/test_restaurants.m
new file mode 100644
index 00000000..9727847a
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/dtree/test_restaurants.m
@@ -0,0 +1,98 @@
+% Here the training data is adapted from Russell95 book. See restaurant.names for description.
+% (1) Use infomation-gain as the split testing score, we get the the same decision tree as the book Russell 95 (page 537),
+% and the Gain(Patrons) is 0.5409, equal to the result in Page 541 of Russell 95. (see below output trace)
+% (Note: the dtree in that book has small compilation error, the Type node is from YES of Hungry node, not NO.)
+% (2) Use gain-ratio (Quilan 93), the splitting defavorite attribute with more values. (e.g. the Type attribute here)
+
+dtreeCPD=tree_CPD;
+
+% load data
+fname = fullfile(BNT_HOME, 'examples', 'static', 'uci_data', 'restaurant', 'restaurant.data');
+data=load(fname);
+data=data';
+
+%make the data be BNT compliant (values for discrete nodes are from 1-n, here n is the node size)
+  % e.g. if the values are [0 1 6], they must be mapping to [1 2 3]
+%data=transform_data(data,'tmp.dat',[]); %here no cts nodes
+
+% learn decision tree from data 
+ns=2*ones(1,11);
+ns(5:6)=3;
+ns(9:10)=4;
+dtreeCPD1=learn_params(dtreeCPD,1:11,data,ns,[]);
+
+% evaluate on data
+[score,outputs]=evaluate_tree_performance(dtreeCPD1,1:11,data,ns,[]);
+fprintf('Accuracy in training data %6.3f\n',score);
+
+% show decision tree using graphpad
+
+
+
+% --------------------------Output trace: using Information-Gain------------------------------
+% The splits are Patron, Hungry, Type, Fri/Sat
+% *********************************
+% Create node 1 split at 5 gain 0.5409 Th 0. Class 1 Cases 12 Error 6 
+% Create leaf node(onecla) 2. Class 1 Cases 2 Error 0 
+% Add subtree node 2 to 1. #nodes 2
+% Create leaf node(onecla) 3. Class 2 Cases 4 Error 0 
+% Add subtree node 3 to 1. #nodes 3
+% Create node 4 split at 4 gain 0.2516 Th 0. Class 1 Cases 6 Error 2 
+% Create leaf node(onecla) 5. Class 1 Cases 2 Error 0 
+% Add subtree node 5 to 4. #nodes 5
+% Create node 6 split at 9 gain 0.5000 Th 0. Class 1 Cases 4 Error 2 
+% Create leaf node(nullset) 7. Father 6 Class 1
+% Create node 8 split at 3 gain 1.0000 Th 0. Class 1 Cases 2 Error 1 
+% Create leaf node(onecla) 9. Class 1 Cases 1 Error 0 
+% Add subtree node 9 to 8. #nodes 9
+% Create leaf node(onecla) 10. Class 2 Cases 1 Error 0 
+% Add subtree node 10 to 8. #nodes 10
+% Add subtree node 8 to 6. #nodes 10
+% Create leaf node(onecla) 11. Class 2 Cases 1 Error 0 
+% Add subtree node 11 to 6. #nodes 11
+% Create leaf node(onecla) 12. Class 1 Cases 1 Error 0 
+% Add subtree node 12 to 6. #nodes 12
+% Add subtree node 6 to 4. #nodes 12
+% Add subtree node 4 to 1. #nodes 12
+% ********************************
+% 
+% Note:
+% ***Create node 4 split at 4 gain 0.2516 Th 0. Class 1 Cases 6 Error 2 
+% This mean we create a new node number 4, it is splitting at the attribute 4, and info-gain is 0.2516, 
+% "Th 0" means threshhold for splitting continous attribute, "Class 1" means the majority class at node 4 is 1,
+% and "Cases 6" means it has 6 cases attached to it, "Error 2" means it has two errors if changing the class lable of 
+% all the cases in it to the majority class.
+% *** Add subtree node 12 to 6. #nodes 12
+% It means we add the child node 12 to node 6.
+% *** Create leaf node(onecla) 10. Class 2 Cases 1 Error 0 
+% here 'onecla' means all cases in this node belong to one class, so no need to split further. 
+%      'nullset' means no training cases belong to this node, we use its parent node majority class as its class
+% 
+% 
+% 
+% ---------------Output trace: using GainRatio-----------------------
+% The splits are Patron, Hungry, Fri/Sat, Price
+% 
+% 
+% Create node 1 split at 5 gain 0.3707 Th 0. Class 1 Cases 12 Error 6 
+% Create leaf node(onecla) 2. Class 1 Cases 2 Error 0 
+% Add subtree node 2 to 1. #nodes 2
+% Create leaf node(onecla) 3. Class 2 Cases 4 Error 0 
+% Add subtree node 3 to 1. #nodes 3
+% Create node 4 split at 4 gain 0.2740 Th 0. Class 1 Cases 6 Error 2 
+% Create leaf node(onecla) 5. Class 1 Cases 2 Error 0 
+% Add subtree node 5 to 4. #nodes 5
+% Create node 6 split at 3 gain 0.3837 Th 0. Class 1 Cases 4 Error 2 
+% Create leaf node(onecla) 7. Class 1 Cases 1 Error 0 
+% Add subtree node 7 to 6. #nodes 7
+% Create node 8 split at 6 gain 1.0000 Th 0. Class 2 Cases 3 Error 1 
+% Create leaf node(onecla) 9. Class 2 Cases 2 Error 0 
+% Add subtree node 9 to 8. #nodes 9
+% Create leaf node(nullset) 10. Father 8 Class 2
+% Create leaf node(onecla) 11. Class 1 Cases 1 Error 0 
+% Add subtree node 11 to 8. #nodes 11
+% Add subtree node 8 to 6. #nodes 11
+% Add subtree node 6 to 4. #nodes 11
+% Add subtree node 4 to 1. #nodes 11
+% 
+% 
diff --git a/sourcecodes/bnt-master/BNT/examples/static/dtree/test_zoo1.m b/sourcecodes/bnt-master/BNT/examples/static/dtree/test_zoo1.m
new file mode 100644
index 00000000..23c258b3
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/dtree/test_zoo1.m
@@ -0,0 +1,21 @@
+% Here the training data is adapted from UCI ML repository, 'zoo' data
+
+dtreeCPD=tree_CPD;
+
+% load data
+fname = fullfile(BNT_HOME, 'examples', 'static', 'uci_data', 'zoo', 'zoo1.data')
+data=load(fname);
+data=data';
+
+data=transform_data_into_bnt_format(data, []);
+
+% learn decision tree from data 
+ns=2*ones(1,17);
+ns(13)=6;
+ns(17)=7;
+dtreeCPD1=learn_params(dtreeCPD,1:17,data,ns,[],'stop_cases',5); % a node with less than 5 cases will not be splitted
+
+% evaluate on data
+[score,outputs]=evaluate_tree_performance(dtreeCPD1,1:17,data,ns,[]);
+fprintf('Accuracy in old training data %6.3f\n',score);
+
diff --git a/sourcecodes/bnt-master/BNT/examples/static/dtree/tmp.dot b/sourcecodes/bnt-master/BNT/examples/static/dtree/tmp.dot
new file mode 100644
index 00000000..de359ea7
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/dtree/tmp.dot
@@ -0,0 +1,31 @@
+digraph G {
+center = 1;
+size="4,4";
+n1 [ label = "1 :" ];
+n2 [ label = "2 :" ];
+n3 [ label = "3 :" ];
+n4 [ label = "4 :" ];
+n5 [ label = "5 :" ];
+n6 [ label = "6 :" ];
+n7 [ label = "7 :" ];
+n8 [ label = "8 :" ];
+n9 [ label = "9 :" ];
+n10 [ label = "10 :" ];
+n1 -> n5 [label="1.000"];
+n2 -> n7 [label="0.800"];
+n2 -> n10 [label="0.200"];
+n3 -> n2 [label="1.000"];
+n4 -> n8 [label="1.000"];
+n5 -> n3 [label="0.143"];
+n5 -> n5 [label="0.571"];
+n5 -> n8 [label="0.286"];
+n6 -> n4 [label="1.000"];
+n7 -> n6 [label="0.333"];
+n7 -> n9 [label="0.667"];
+n8 -> n1 [label="0.333"];
+n8 -> n5 [label="0.333"];
+n8 -> n10 [label="0.333"];
+n9 -> n2 [label="1.000"];
+n10 -> n9 [label="1.000"];
+
+}
\ No newline at end of file
diff --git a/sourcecodes/bnt-master/BNT/examples/static/dtree/transform_data_into_bnt_format.m b/sourcecodes/bnt-master/BNT/examples/static/dtree/transform_data_into_bnt_format.m
new file mode 100644
index 00000000..92739590
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/dtree/transform_data_into_bnt_format.m
@@ -0,0 +1,66 @@
+function [bnt_data, old_values] = transform_data_into_bnt_format(data,cnodes)
+% TRANSFORM_DATA_TO_BNT_FORMAT Ensures discrete variables have values 1,2,..,k
+% e.g., if the values of a discrete are [0 1 6], they must be mapped to [1 2 3]
+%
+% data(i,j) is the value for i-th node in j-th case.
+% bnt_data(i,j) is the new value.
+% old_values{i} are the original values for node i.
+% cnodes is the list of all continous nodes, e.g. [3 5] means the 3rd and 5th node is continuous
+%
+% Author: yimin.zhang@intel.com
+% Last updated: Jan. 22, 2002 by Kevin Murphy.
+
+num_nodes=size(data,1);
+num_cases=size(data,2);
+old_values=cell(1,num_nodes);
+
+for i=1:num_nodes
+  if (myismember(i,cnodes)==1)  %cts nodes no need to be transformed 
+    %just copy the data
+    bnt_data(i,:)=data(i,:);
+    continue;
+  end
+  values = data(i,:);
+  sort_v = sort(values); 
+  %remove the duplicate values in sort_v
+  v_set = unique(sort_v);  
+  
+  %transform the values
+  for j=1:size(values,2)
+    index = binary_search(v_set,values(j));
+    if (index==-1)
+      fprintf('value not found in tranforming data to bnt format.\n');   
+      return;
+    end
+    bnt_data(i,j)=index;
+  end
+  old_values{i}=v_set;
+end
+
+
+%%%%%%%%%%%%
+
+function index=binary_search(vector, value)
+% BI_SEARCH do binary search for value in the vector
+% Author: yimin.zhang@intel.com
+% Last updated: Jan. 19, 2002
+
+begin_index=1;
+end_index=size(vector,2); 
+index=-1;
+while (begin_index<=end_index)
+  mid=floor((begin_index+end_index)/2);
+  if (isstr(vector(mid)))
+    % need to write a strcmp to return three result (< = >)
+  else
+    if (value==vector(mid))
+      index=mid;
+      return;
+    elseif (value>vector(mid))
+      begin_index=mid+1;    
+    else
+      end_index=mid-1;
+    end
+  end
+end
+return;