diff toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/test_restaurants.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_restaurants.m	Tue Feb 10 15:05:51 2015 +0000
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+% 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
+% 
+%