diff toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/test_housing.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/test_housing.m	Tue Feb 10 15:05:51 2015 +0000
@@ -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
+% 
+
+