PrologMethods/Classification/decision_tree.md

@@ -3,11 +3,11 @@
 An implementation of an ID3-style decision tree for classification.
 
 ```prolog
-:- use_module('path/to/.../src/methods/adaboost/adaboost_test.pl').
+:- use_module('path/to/.../src/methods/decision_tree/decision_tree.pl').
 
 %% usage example
-adaboost_initModelWithTraining([5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5], 3, [0,0,1,0], 2, perceptron, 50, 0.0001),
-adaboost_classify([3,2,0, 5,1,4, 0,0,4, 3,3,5, 0,5,5, 2,5,5], 3, PredictionList, ProbabilitiesList, _).
+decision_tree_initModel([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 2, 10, 0.5, 0),
+decision_tree_classifyMatrix([3,2,0, 5,1,4, 0,0,4, 3,3,5, 0,5,5, 2,5,5], 3, PredictionsList, ProbabilitiesList, _).
 ```
 
 # Available Predicates
@@ -28,10 +28,21 @@ Construct the decision tree on the given data and labels, assuming that the data
 Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.
 
 ```prolog
-%% part of the predicate definition
-initModel(  +pointer(float_array), +integer, +integer,
+%% predicate definition 
+decision_tree_initModel(DataList, DataRows, LabelsList, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth) :-
+        NumClasses >= 0,
+        MinimumLeafSize > 0,
+        MinimumGainSplit > 0.0,
+        MinimumGainSplit < 1.0,
+        MaximumDepth >= 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+        convert_list_to_float_array(LabelsList, array(Ysize, Y)),
+        initModelI(X, Xsize, Xrownum, Y, Ysize, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth).
+
+%% foreign c++ predicate definition 
+foreign(initModel, c, initModelI(       +pointer(float_array), +integer, +integer,
                                         +pointer(float_array), +integer,
-            +integer, +integer, +float32, +integer).
+                                        +integer, +integer, +float32, +integer)).
 ```
 
 ### Parameters
@@ -52,10 +63,16 @@ initModel(  +pointer(float_array), +integer, +integer,
 Classify the given point and also return estimates of the probability for each class in the given vector.
 
 ```prolog
-%% part of the predicate definition
-classifyPoint(  +pointer(float_array), +integer,
+%% predicate definition 
+decision_tree_classifyPoint(DataList, Prediction, AssignList) :-
+        convert_list_to_float_array(DataList, array(Xsize, X)),
+        classifyPointI(X, Xsize, Prediction, Y, Ysize),
+        convert_float_array_to_list(Y, Ysize, AssignList).
+
+%% foreign c++ predicate definition 
+foreign(classifyPoint, c, classifyPointI(       +pointer(float_array), +integer,
                                                 -integer,
-                -pointer(float_array), -integer).
+                                                -pointer(float_array), -integer)).
 ```
 
 ### Parameters
@@ -73,10 +90,17 @@ classifyPoint(  +pointer(float_array), +integer,
 Classify the given points and also return estimates of the probabilities for each class in the given matrix.
 
 ```prolog
-%% part of the predicate definition
-classifyMatrix(  +pointer(float_array), +integer, +integer,
+%% predicate definition 
+decision_tree_classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols) :-
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+        classifyMatrixI(X, Xsize, Xrows, Y, Ysize, Z, ZCols, ZRows),
+        convert_float_array_to_list(Y, Ysize, PredictionList),
+        convert_float_array_to_2d_list(Z, ZCols, ZRows, ProbsList).
+
+%% foreign c++ predicate definition 
+foreign(classifyMatrix, c, classifyMatrixI(     +pointer(float_array), +integer, +integer,
                                                 -pointer(float_array), -integer,
-                 -pointer(float_array), -integer).
+                                                -pointer(float_array), -integer, -integer)).
 ```
 
 ### Parameters
@@ -96,11 +120,22 @@ Train the decision tree on the given data, assuming that all dimensions are nume
 This will overwrite the given model. Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.
 
 ```prolog
-%% part of the predicate definition
-train(  +pointer(float_array), +integer, +integer,
+%% predicate definition 
+decision_tree_train(DataList, DataRows, LabelsList, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy) :-
+        NumClasses >= 0,
+        MinimumLeafSize > 0,
+        MinimumGainSplit > 0.0,
+        MinimumGainSplit < 1.0,
+        MaximumDepth >= 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+        convert_list_to_float_array(LabelsList, array(Ysize, Y)),
+        trainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy).
+
+%% foreign c++ predicate definition 
+foreign(train, c, trainI(+pointer(float_array), +integer, +integer,
                          +pointer(float_array), +integer,
                          +integer, +integer, +float32, +integer,
-        [-float32]).
+                         [-float32])).
 ```
 
 ### Parameters