PrologMethods/Clustering/mean_shift.md

@@ -2,25 +2,41 @@
 
 A fast implementation of mean-shift clustering using dual-tree range search. Given a dataset, this uses the mean shift algorithm to produce and return a clustering of the data.
 
+```prolog
+:- use_module('path/to/.../src/methods/mean_shift/mean_shift.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],
+meanShift(1, 1000, TrainData, 3, AssignList, CentroidsList, _, 0, 0).
+```
+
 # Available Predicates
 
-* [meanShift/12](/PrologMethods/Clustering/mean_shift#meanshift12)
+* [meanShift/9](/PrologMethods/Clustering/mean_shift#meanshift9)
 
 ---
 
 [links/resources](/PrologMethods/Clustering/mean_shift#connected-linksresources)
 
-## **_meanShift/12_**
+## **_meanShift/9_**
 
 Perform mean shift clustering on the data, returning a list of cluster assignments and centroids.
 
 ```prolog
-%% part of the predicate definition
-meanShift(  +float32, +integer, 
+%% predicate definition 
+meanShift(Radius, MaxIterations, DataList, DataRows, AssignmentsList, CentroidsList, ZCols, ForceConvergence, UseSeeds) :-
+        MaxIterations >= 0,
+        convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+        meanShiftI(Radius, MaxIterations, X, Xsize, Xrownum, Y, Ysize, Z, ZCols, ZRows, ForceConvergence, UseSeeds),
+        convert_float_array_to_list(Y, Ysize, AssignmentsList),
+        convert_float_array_to_2d_list(Z, ZCols, ZRows, CentroidsList).
+
+%% foreign c++ predicate definition 
+foreign(meanShift, c, meanShiftI(+float32, +integer, 
                                 +pointer(float_array), +integer, +integer, 
                                 -pointer(float_array), -integer,
                                 -pointer(float_array), -integer, -integer, 
-            +integer, +integer).
+                                +integer, +integer)).
 ```
 
 ### Parameters
@@ -40,8 +56,8 @@ meanShift(  +float32, +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::mean_shift_C++\_documentation**](https://www.mlpack.org/doc/stable/doxygen/classmlpack_1_1meanshift_1_1MeanShift.html)
-* [**MLpack::mean_shift_Python_documentation**](https://www.mlpack.org/doc/stable/python_documentation.html#mean_shift)
+* [**MLpack::mean_shift_C++\_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/doxygen/classmlpack_1_1meanshift_1_1MeanShift.html)
+* [**MLpack::mean_shift_Python_documentation**](https://www.mlpack.org/doc/mlpack-3.4.2/python_documentation.html#mean_shift)
 
 added some of the links from the python documentation