PrologMethods/Transformation/lmnn.md

@@ -2,31 +2,56 @@
 
 An implementation of Large Margin Nearest Neighbors (LMNN), a distance learning technique. Given a labeled dataset, this learns a transformation of the data that improves k-nearest-neighbor performance. This can be useful as a preprocessing step.
 
+```prolog
+:- use_module('path/to/.../src/methods/adaboost/adaboost.pl').
+
+%% usage example
+TrainData = [5.1,3.5,1.4, 4.9,3.0,1.4, 4.7,3.2,1.3, 4.6,3.1,1.5],
+lmnn(amsgrad, TrainData, 3, [0,1,0,1], 1, 0.5, 0.01, 50, 100000, 0.000001, 0, 0, 50, 1, 0, DistanceList, _).
+```
+
 # Available Predicates
 
-* [lmnn/20](/PrologMethods/Transformation/lmnn#lmnn20)
+* [lmnn/17](/PrologMethods/Transformation/lmnn#lmnn17)
 
 ---
 
 [links/resources](/PrologMethods/Transformation/lmnn#connected-linksresources)
 
-## **_lmnn/20_**
+## **_lmnn/17_**
 
 Is a single predicate that initiates the lmnn model with all the given params and then performs Large Margin Nearest Neighbors metric learning on the reference data.
 
 ```prolog
-%% part of the predicate definition
-lmnn(  +string,
+%% predicate definition 
+lmnn(Optimizer, DataList, DataRows, LabelsList, K, Regularization, StepSize, Passes, MaxIterations, Tolerance, Center, Shuffle, BatchSize, Range, Rank, DistanceList, ZCols) :-
+        K > 0,
+        Regularization >= 0.0,
+        StepSize >= 0.0,
+        Passes >= 0,
+        MaxIterations >= 0,
+        Tolerance >= 0.0,
+        BatchSize > 0,
+        Range > 0,
+        Rank >= 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+        convert_list_to_float_array(LabelsList, array(Ysize, Y)),
+        lmnnI(Optimizer, X, Xsize, Xrownum, Y, Ysize, K, Regularization, StepSize, Passes, MaxIterations, Tolerance, Center, Shuffle, BatchSize, Range, Rank, Z, ZCols, ZRows),
+        convert_float_array_to_2d_list(Z, ZCols, ZRows, DistanceList).
+        
+%% foreign c++ predicate definition 
+foreign(lmnn, c, lmnnI( +string,
                         +pointer(float_array), +integer, +integer,
                         +pointer(float_array), +integer,
                         +integer, 
                         +float32, +float32, +integer, +integer, +float32,
                         +integer, +integer,
                         +integer, +integer, +integer,
-       -pointer(float_array), -integer, -integer)
+                        -pointer(float_array), -integer, -integer)).
 ```
 
 ### Parameters
+
 | Name | Type | Description | Default |
 |------|------|-------------|---------|
 | optimizer | +string | Optimizer to use; "amsgrad", "bbsgd", "sgd", or "lbfgs". | amsgrad |
@@ -51,8 +76,8 @@ lmnn(  +string,
 
 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::lmnn_C++\_documentation**](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1lmnn_1_1LMNN.html)
-* [**MLpack::lmnn_Python_documentation**](https://www.mlpack.org/doc/stable/python_documentation.html#lmnn)
+* [**MLpack::lmnn_C++\_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/doxygen/classmlpack_1_1lmnn_1_1LMNN.html)
+* [**MLpack::lmnn_Python_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/python_documentation.html#lmnn)
 
 added some of the links from the python documentation