PrologMethods/Geometry/approx_kfn.md

@@ -2,6 +2,16 @@
 
 An implementation of two strategies for furthest neighbor search. This can be used to compute the furthest neighbor of query point(s) from a set of points.
 
+```prolog
+:- use_module('path/to/.../src/methods/approx_kfn/approx_kfn.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],
+TestData = [3,2,0, 5,1,4, 0,0,4, 3,3,5, 0,5,5, 2,5,5],
+initDrusillaModelWithTrain(TrainData, 3, 2, 2),
+searchDrusilla(TestData, 3, 2, NeighborsList, _, DistancesList, _).
+```
+
 # Available Predicates
 
 * [initDrusillaModelNoTrain/2](/PrologMethods/Geometry/approx_kfn#initdrusillamodelnotrain2)
@@ -25,7 +35,12 @@ Initiates the DrusillaSearch Model but doesn’t train it.
 
 ```prolog
 %% part of the definition
-initDrusillaModelNoTrain(  +integer, +integer).
+initDrusillaModelNoTrain(L, M) :-
+        L > 0,
+        M > 0,
+        initDrusillaModelNoTrainI(L, M).
+
+foreign(initDrusillaModelNoTrain, c, initDrusillaModelNoTrainI(+integer, +integer)).
 ```
 
 ### Parameters
@@ -45,8 +60,14 @@ Afterwards **searchDrusilla** can be used.
 
 ```prolog
 %% part of the definition
-initDrusillaModelWithTrain( +pointer(float_array), +integer, +integer, 
-                            +integer, +integer).
+initDrusillaModelWithTrain(DataList, DataRows, L, M) :-
+        L > 0,
+        M > 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+        initDrusillaModelWithTrainI(X, Xsize, Xrownum, L, M).
+        
+foreign(initDrusillaModelWithTrain, c, initDrusillaModelWithTrainI(     +pointer(float_array), +integer, +integer, 
+                                                                        +integer, +integer)).
 ```
 
 ### Parameters
@@ -65,10 +86,17 @@ Run Search on the given Queryset with the Drusilla Search Policy.
 
 ```prolog
 %% part of the predicate definition
-searchDrusilla( +pointer(float_array), +integer, +integer, 
+searchDrusilla(DataList, DataRows, K, NeighborsList, YCols, DistancesList, ZCols) :-
+        K > 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+        searchDrusillaI(X, Xsize, Xrownum, K, Y, YCols, YRows, Z, ZCols, ZRows),
+        convert_float_array_to_2d_list(Y, YCols, YRows, NeighborsList),
+        convert_float_array_to_2d_list(Z, ZCols, ZRows, DistancesList).
+
+foreign(searchDrusilla, c, searchDrusillaI( +pointer(float_array), +integer, +integer, 
                                             +integer, 
                                             -pointer(float_array), -integer, -integer, 
-                -pointer(float_array), -integer, -integer).
+                                            -pointer(float_array), -integer, -integer)).
 ```
 
 ### Parameters
@@ -88,8 +116,14 @@ Trains the DrusillaSearch Model with the given reference Set.
 
 ```prolog
 %% part of the predicate definition
-trainDrusilla(  +pointer(float_array), +integer, +integer, 
-                +integer, +integer).
+trainDrusilla(DataList, DataRows, L, M) :-
+        L > 0,
+        M > 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+        trainDrusillaI(X, Xsize, Xrownum, L, M).
+
+foreign(trainDrusilla, c, trainDrusillaI( +pointer(float_array), +integer, +integer, 
+                                          +integer, +integer)).
 ```
 
 ### Parameters
@@ -110,7 +144,12 @@ Initiates the QDAFNSearch Model but doesn’t train it.
 
 ```prolog
 %% part of the definition
-initQDAFNModelNoTrain(  +integer, +integer).
+initQDAFNModelNoTrain(L, M) :-
+        L > 0,
+        M > 0,
+        initQDAFNModelNoTrainI(L, M).
+
+foreign(initQDAFNModelNoTrain, c, initQDAFNModelNoTrainI(+integer, +integer)).
 ```
 
 ### Parameters
@@ -130,8 +169,14 @@ Afterwards **searchQDAFN** can be used.
 
 ```prolog
 %% part of the definition
-initQDAFNModelWithTrain( +pointer(float_array), +integer, +integer, 
-                         +integer, +integer).
+initQDAFNModelWithTrain(DataList, DataRows, L, M) :-
+        L > 0,
+        M > 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+        initQDAFNModelWithTrainI(X, Xsize, Xrownum, L, M).
+
+foreign(initQDAFNModelWithTrain, c, initQDAFNModelWithTrainI( +pointer(float_array), +integer, +integer, 
+                                                              +integer, +integer)).
 ```
 
 ### Parameters
@@ -150,10 +195,17 @@ Run Search on the given Queryset with the QDAFN Search Policy.
 
 ```prolog
 %% part of the predicate definition
-searchQDAFN( +pointer(float_array), +integer, +integer, 
+searchQDAFN(DataList, DataRows, K, NeighborsList, YCols, DistancesList, ZCols) :-
+        K > 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+        searchQDAFNI(X, Xsize, Xrownum, K, Y, YCols, YRows, Z, ZCols, ZRows),
+        convert_float_array_to_2d_list(Y, YCols, YRows, NeighborsList),
+        convert_float_array_to_2d_list(Z, ZCols, ZRows, DistancesList).
+
+foreign(searchQDAFN, c, searchQDAFNI( +pointer(float_array), +integer, +integer, 
                                       +integer, 
                                       -pointer(float_array), -integer, -integer, 
-             -pointer(float_array), -integer, -integer).
+                                      -pointer(float_array), -integer, -integer)).
 ```
 
 ### Parameters
@@ -173,8 +225,14 @@ Trains the QDAFNSearch Model with the given reference Set.
 
 ```prolog
 %% part of the predicate definition
-trainQDAFN(  +pointer(float_array), +integer, +integer, 
-             +integer, +integer).
+trainQDAFN(DataList, DataRows, L, M) :-
+        L > 0,
+        M > 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+        trainQDAFNI(X, Xsize, Xrownum, L, M).
+
+foreign(trainQDAFN, c, trainQDAFNI( +pointer(float_array), +integer, +integer, 
+                                    +integer, +integer)).
 ```
 
 ### Parameters
@@ -191,9 +249,9 @@ trainQDAFN(  +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::DrusillaSearch_C++\_documentation**](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1neighbor_1_1DrusillaSelect.html)
-* [**MLpack::QDAFN_C++\_documentation**](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1neighbor_1_1QDAFN.html)
-* [**MLpack::Approx_KFN_Python_documentation**](https://www.mlpack.org/doc/stable/python_documentation.html#approx_kfn)
+* [**MLpack::DrusillaSearch_C++\_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/doxygen/classmlpack_1_1neighbor_1_1DrusillaSelect.html)
+* [**MLpack::QDAFN_C++\_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/doxygen/classmlpack_1_1neighbor_1_1QDAFN.html)
+* [**MLpack::Approx_KFN_Python_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/python_documentation.html#approx_kfn)
 
 added some of the links from the python documentation