PrologMethods/Transformation/nca.md

 # Neighborhood Components Analysis (NCA)
 
-An implementation of neighborhood components analysis, a distance learning technique that can be used for preprocessing. Given a labeled dataset, this uses NCA, which seeks to improve the k-nearest-neighbor classification, and returns the learned distance metric
+An implementation of neighborhood components analysis, a distance learning technique that can be used for preprocessing. Given a labeled dataset, this uses NCA, which seeks to improve the k-nearest-neighbor classification, and returns the learned distance metric.
+
+```prolog
+:- use_module('path/to/.../src/methods/nca/nca.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],
+nca(sgd, 0.01, 500000, 0.00001, 1, 5, 0.0001, 0.9, 50, 0.000000001, 100000000, 50, TrainData, 3, [0,1,0,1], DistancesList, _).
+```
 
 # Available Predicates
 
-* [nca/20](/PrologMethods/Transformation/nca#nca20)
+* [nca/17](/PrologMethods/Transformation/nca#nca17)
 
 ---
 
 [links/resources](/PrologMethods/Transformation/nca#connected-linksresources)
 
-## **_nca/20_**
+## **_nca/17_**
 
 Initialize the nca model and the selected optimizer.
 
 Then perform nca on the given data and return the learned distance.
 
 ```prolog
-%% part of the predicate definition
-nca(  +string,
+%% predicate definition 
+nca(OptimizerType, StepSize, MaxIterations, Tolerance, Shuffle, NumBasis, ArmijoConstant, Wolfe, MaxLine, MinStep, MaxStep, BatchSize, 
+        DataList, DataRows, PredictionList, DistanceList, ZCols) :-
+        StepSize > 0,
+        MaxIterations >= 0,
+        Tolerance >= 0,
+        NumBasis > 0,
+        ArmijoConstant > 0,
+        Wolfe > 0,
+        MaxLine > 0,
+        MinStep > 0,
+        MaxStep > 0,
+        MaxStep >= MinStep,
+        BatchSize > 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+        convert_list_to_float_array(PredictionList, array(Ysize, Y)),
+        ncaI(OptimizerType, StepSize, MaxIterations, Tolerance, Shuffle, NumBasis, ArmijoConstant, Wolfe, MaxLine, MinStep, MaxStep, BatchSize, 
+                X, Xsize, Xrows, Y, Ysize, Z, ZCols, ZRows),
+        convert_float_array_to_2d_list(Z, ZCols, ZRows, DistanceList).
+
+%% foreign c++ predicate definition 
+foreign(nca, c, ncaI( +string,
                       +float32, +integer, +float32,
                       +integer,
                       +integer, +float32, +float32, +integer, +float32, +float32, +integer,
                       +pointer(float_array), +integer, +integer,
                       +pointer(float_array), +integer,
-      -pointer(float_array), -integer, -integer)
+                      -pointer(float_array), -integer, -integer)).
 ```
 
 ### Parameters
@@ -52,8 +80,8 @@ nca(  +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::nca_C++\_documentation**](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1nca_1_1NCA.html)
-* [**MLpack::nca_Python_documentation**](https://www.mlpack.org/doc/stable/python_documentation.html#nca)
+* [**MLpack::nca_C++\_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/doxygen/classmlpack_1_1nca_1_1NCA.html)
+* [**MLpack::nca_Python_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/python_documentation.html#nca)
 
 added some of the links from the python documentation