PrologMethods/Classification/decision_tree.md

@@ -2,18 +2,26 @@
 
 An implementation of an ID3-style decision tree for classification.
 
+```prolog
+:- use_module('path/to/.../src/methods/adaboost/adaboost_test.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, _).
+```
+
 # Available Predicates
 
-* [initModel/9](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#initmodel9)
-* [classifyPoint/5](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#classifypoint5)
-* [classifyMatrix/7](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#classifymatrix7)
-* [train/10](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#train10)
+* [decision_tree_initModel/7](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#decision_tree_initmodel7)
+* [decision_tree_classifyPoint/3](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#decision_tree_classifypoint3)
+* [decision_tree_classifyMatrix/5](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#decision_tree_classifymatrix5)
+* [decision_tree_train/8](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#decision_tree_train8)
 
 ---
 
 [links/resources](https://gitlab.cs.uni-duesseldorf.de/stups/abschlussarbeiten/prolog-mlpack-libary/-/wikis/PrologMethods/Classification/decision_tree#connected-linksresources)
 
-## **_initModel/9_**
+## **_decision_tree_initModel/7_**
 
 Construct the decision tree on the given data and labels, assuming that the data is all of the numeric type.
 
@@ -27,6 +35,7 @@ initModel(  +pointer(float_array), +integer, +integer,
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | dataset | +matrix | Training dataset | - |
@@ -38,7 +47,7 @@ initModel(  +pointer(float_array), +integer, +integer,
 
 ---
 
-## **_classifyPoint/5_**
+## **_decision_tree_classifyPoint/3_**
 
 Classify the given point and also return estimates of the probability for each class in the given vector.
 
@@ -50,6 +59,7 @@ classifyPoint(  +pointer(float_array), +integer,
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | point | +vector | Point to classify. | - |
@@ -58,7 +68,7 @@ classifyPoint(  +pointer(float_array), +integer,
 
 ---
 
-## **_classifyMatrix/7_**
+## **_decision_tree_classifyMatrix/5_**
 
 Classify the given points and also return estimates of the probabilities for each class in the given matrix.
 
@@ -70,6 +80,7 @@ classifyMatrix(  +pointer(float_array), +integer, +integer,
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | data | +matrix | Set of points to classify. | - |
@@ -78,7 +89,7 @@ classifyMatrix(  +pointer(float_array), +integer, +integer,
 
 ---
 
-## **_train/10_**
+## **_decision_tree_train/8_**
 
 Train the decision tree on the given data, assuming that all dimensions are numeric.
 
@@ -93,6 +104,7 @@ train(  +pointer(float_array), +integer, +integer,
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | dataset | +matrix | Training dataset | - |
@@ -109,8 +121,8 @@ train(  +pointer(float_array), +integer, +integer,
 
 If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.
 
-* [**MLpack::decision_tree_C++\_documentation**](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1tree_1_1DecisionTree.html)
-* [**MLpack::decision_tree_Python_documentation**](https://www.mlpack.org/doc/stable/python_documentation.html#decision_tree)
+* [**MLpack::decision_tree_C++\_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/doxygen/classmlpack_1_1tree_1_1DecisionTree.html)
+* [**MLpack::decision_tree_Python_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/python_documentation.html#decision_tree)
 
 added some of the links from the python documentation