Adding kfn

src/methods/kfn/Makefile

+splfr=/usr/local/sicstus4.7.1/bin/splfr
+
+METHOD_NAME=kfn
+
+$(METHOD_NAME).so: $(METHOD_NAME).pl $(METHOD_NAME).cpp
+	$(splfr) -larmadillo -fopenmp -lmlpack -lstdc++ -cxx --struct $(METHOD_NAME).pl $(METHOD_NAME).cpp ../../helper_files/helper.cpp
+clean:
+	rm $(METHOD_NAME).so

src/methods/kfn/kfn.cpp

+#include <sicstus/sicstus.h>
+/* ex_glue.h is generated by splfr from the foreign/[2,3] facts.
+   Always include the glue header in your foreign resource code.
+*/
+#include "kfn_glue.h"
+#include <mlpack/methods/neighbor_search/ns_model.hpp>
+#include <mlpack/core.hpp>
+
+// including helper functions for converting between arma structures and arrays
+#include "../../helper_files/helper.hpp"
+
+// some of the most used namespaces
+using namespace arma;
+using namespace mlpack;
+using namespace std;
+using namespace mlpack::neighbor;
+
+// Global Variable of the NSModel object so it can be accessed from all functions
+NSModel<FurthestNS> nsModelObj;
+
+// TODO: 
+// input:   TreeTypes 	                treeType = TreeTypes::KD_TREE,
+//          bool 	                    randomBasis = false,
+//          util::Timers & 	            timers,
+//          arma::mat && 	            referenceSet,
+//          const NeighborSearchMode 	searchMode,
+//          const double 	            epsilon = 0
+// output: 
+// description: 
+void initAndBuildModel(char const *treeType, char const *algorithm,
+                        SP_integer randomBasis, SP_integer leafSize, double epsilon,
+                        float *referenceMatArr, SP_integer referenceMatSize, SP_integer referenceMatRowNum)
+{
+    // convert the Prolog array to arma::mat
+    mat reference = convertArrayToMat(referenceMatArr, referenceMatSize, referenceMatRowNum);
+
+
+    //  select the treeType
+    NSModel<FurthestNS>::TreeTypes tree = NSModel<FurthestNS>::KD_TREE;
+
+    if (strcmp(treeType, "kd") == 0)
+        tree = NSModel<FurthestNS>::KD_TREE;
+    else if (strcmp(treeType, "cover") == 0)
+        tree = NSModel<FurthestNS>::COVER_TREE;
+    else if (strcmp(treeType, "r") == 0)
+        tree = NSModel<FurthestNS>::R_TREE;
+    else if (strcmp(treeType, "r-star") == 0)
+        tree = NSModel<FurthestNS>::R_STAR_TREE;
+    else if (strcmp(treeType, "ball") == 0)
+        tree = NSModel<FurthestNS>::BALL_TREE;
+    else if (strcmp(treeType, "x") == 0)
+        tree = NSModel<FurthestNS>::X_TREE;
+    else if (strcmp(treeType, "hilbert-r") == 0)
+        tree = NSModel<FurthestNS>::HILBERT_R_TREE;
+    else if (strcmp(treeType, "r-plus") == 0)
+        tree = NSModel<FurthestNS>::R_PLUS_TREE;
+    else if (strcmp(treeType, "r-plus-plus") == 0)
+        tree = NSModel<FurthestNS>::R_PLUS_PLUS_TREE;
+    else if (strcmp(treeType, "vp") == 0)
+        tree = NSModel<FurthestNS>::VP_TREE;
+    else if (strcmp(treeType, "rp") == 0)
+        tree = NSModel<FurthestNS>::RP_TREE;
+    else if (strcmp(treeType, "max-rp") == 0)
+        tree = NSModel<FurthestNS>::MAX_RP_TREE;
+    else if (strcmp(treeType, "ub") == 0)
+        tree = NSModel<FurthestNS>::UB_TREE;
+    else if (strcmp(treeType, "oct") == 0)
+        tree = NSModel<FurthestNS>::OCTREE;
+    else
+        cout << "wrong treeType input" << endl;
+
+
+    //  select the searchMode
+    NeighborSearchMode searchMode = DUAL_TREE_MODE;
+
+    if (strcmp(algorithm, "naive") == 0)
+        searchMode = NAIVE_MODE;
+    else if (strcmp(algorithm, "single_tree") == 0)
+        searchMode = SINGLE_TREE_MODE;
+    else if (strcmp(algorithm, "dual_tree") == 0)
+        searchMode = DUAL_TREE_MODE;
+    else if (strcmp(algorithm, "greedy") == 0)
+        searchMode = GREEDY_SINGLE_TREE_MODE;
+    else
+        cout << "wrong algorithm input" << endl;
+
+    
+    nsModelObj.TreeType() = tree;
+    nsModelObj.RandomBasis() = (randomBasis == 1);
+
+    
+    nsModelObj.BuildModel(std::move(reference), size_t(leafSize), searchMode, epsilon);
+}
+
+// TODO: 
+// input:   	util::Timers & 	        timers,
+//              arma::mat && 	        querySet,
+//              const size_t 	        k,
+//              arma::Mat< size_t > & 	neighbors <-,
+//              arma::mat & 	        distances <-
+// output: 
+// description: 
+void searchWithQuery(   float *queryMatArr, SP_integer queryMatSize, SP_integer queryMatRowNum,
+                        SP_integer k,
+                        float **neighborsMatArr, SP_integer *neighborsMatColNum, SP_integer *neighborsMatRowNum,
+                        float **distancesMatArr, SP_integer *distancesMatColNum, SP_integer *distancesMatRowNum)
+{
+    // convert the Prolog array to arma::mat
+    mat query = convertArrayToMat(queryMatArr, queryMatSize, queryMatRowNum);
+
+    // create the ReturnMat
+    Mat< size_t > neighborsReturnMat;
+
+    // create the ReturnMat
+    mat distancesReturnMat;
+    
+
+    nsModelObj.Search(move(query), (size_t)k, neighborsReturnMat, distancesReturnMat);
+
+
+    // return the Matrix
+    returnMatrixInformation(neighborsReturnMat, neighborsMatArr, neighborsMatColNum, neighborsMatRowNum);
+    
+    // return the Matrix
+    returnMatrixInformation(distancesReturnMat, distancesMatArr, distancesMatColNum, distancesMatRowNum);
+}
+
+// TODO: 
+// input:   	util::Timers & 	        timers,
+//              const size_t 	        k,
+//              arma::Mat< size_t > & 	neighbors <-,
+//              arma::mat & 	        distances <-
+// output: 
+// description: 
+void searchNoQuery(     SP_integer k,
+                        float **neighborsMatArr, SP_integer *neighborsMatColNum, SP_integer *neighborsMatRowNum,
+                        float **distancesMatArr, SP_integer *distancesMatColNum, SP_integer *distancesMatRowNum)
+{
+    // create the ReturnMat
+    Mat< size_t > neighborsReturnMat;
+
+    // create the ReturnMat
+    mat distancesReturnMat;
+    
+
+    nsModelObj.Search((size_t)k, neighborsReturnMat, distancesReturnMat);
+
+
+    // return the Matrix
+    returnMatrixInformation(neighborsReturnMat, neighborsMatArr, neighborsMatColNum, neighborsMatRowNum);
+    
+    // return the Matrix
+    returnMatrixInformation(distancesReturnMat, distancesMatArr, distancesMatColNum, distancesMatRowNum);
+}
\ No newline at end of file

src/methods/kfn/kfn.pl

+:- module(kfn, [        initAndBuildModel/8,
+                        searchWithQuery/10,
+                        searchNoQuery/7]).
+
+%% requirements of library(struct)
+:- load_files(library(str_decl),
+        [when(compile_time), if(changed)]).
+
+%% needed for using the array type
+:- use_module(library(structs)).
+:- use_module('../../helper_files/helper.pl').
+
+%% type definitions for the float array
+:- foreign_type
+        float32          = float_32,
+        float_array      = array(float32).
+
+%% definitions for the connected function
+
+%% TODO: 
+%% --Input--
+%%              string  treeType        => "kd", "vp", "rp", "max-rp", "ub", "cover", "r", "r-star", "x", "ball", "hilbert-r", "r-plus", "r-plus-plus", "spill", "oct",
+%%              string  searchMode      => "naive", "single_tree", "dual_tree", "greedy",
+%%              bool    randomBasis     => (1)true / (0)false,
+%%              int     leafSize        => 20,
+%%              float32 epsilon         => 0.0,
+%%              mat     referenceSet
+%%
+%% --Output--
+%%
+%% --Description--
+foreign(initAndBuildModel, c, initAndBuildModel(+string, +string,
+                                                +integer, +integer, +float32,
+                                                +pointer(float_array), +integer, +integer)).
+
+%% TODO: 
+%% --Input--
+%%              mat     querySet,
+%%              int     k
+%%
+%% --Output--
+%%              mat     neighbors,
+%%              mat     distances
+%%
+%% --Description--
+foreign(searchWithQuery, c, searchWithQuery(    +pointer(float_array), +integer, +integer,
+                                                +integer,
+                                                -pointer(float_array), -integer, -integer,
+                                                -pointer(float_array), -integer, -integer)).
+
+%% TODO: 
+%% --Input--
+%%              int     k
+%%
+%% --Output--
+%%              mat     neighbors,
+%%              mat     distances
+%%
+%% --Description--
+foreign(searchNoQuery, c, searchNoQuery(        +integer,
+                                                -pointer(float_array), -integer, -integer,
+                                                -pointer(float_array), -integer, -integer)).
+
+
+%% Defines the functions that get connected from kfn.cpp
+foreign_resource(kfn, [ initAndBuildModel,
+                        searchWithQuery,
+                        searchNoQuery]).
+
+:- load_foreign_resource(kfn).
\ No newline at end of file

src/methods/kfn/kfn_test.pl

+:- use_module(library(plunit)).
+
+:- use_module(kfn).
+:- use_module('../../helper_files/helper.pl').
+
+reset_Model :-
+        initModel(1,0,50,0.0001).
+
+:- begin_tests(lists).
+
+%% alpha tests
+test(alpha_std_init) :-
+        reset_Model,
+        alpha(0).
+test(alpha_wrong_input, fail) :-
+        reset_Model,
+        alpha(1).
+test(alpha_after_train, A =:= 9223372036854775808) :-
+        reset_Model,
+        convert_list_to_float_array([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, array(Xsize, Xrownum, X)),
+        convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
+        train(X,Xsize, Xrownum,Y, Ysize),
+        alpha(A).
+
+%% train tests
+test(correct_train) :-
+        reset_Model,
+        convert_list_to_float_array([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, array(Xsize, Xrownum, X)),
+        convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
+        train(X,Xsize, Xrownum,Y, Ysize).
+test(false_train, fail) :-
+        reset_Model,
+        convert_list_to_float_array([],3, array(Xsize, Xrownum, X)),
+        convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
+        train(X,Xsize, Xrownum,Y, Ysize).
+test(false_train2, fail) :-
+        reset_Model,
+        convert_list_to_float_array([],0, array(Xsize, Xrownum, X)),
+        convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
+        train(X,Xsize, Xrownum,Y, Ysize).
+test(false_train3, fail) :-
+        reset_Model,
+        convert_list_to_float_array([1,2],0, array(Xsize, Xrownum, X)),
+        convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
+        train(X,Xsize, Xrownum,Y, Ysize).
+test(false_train3, fail) :-
+        reset_Model,
+        convert_list_to_float_array([1,2,44,3],3, array(Xsize, Xrownum, X)),
+        convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
+        train(X,Xsize, Xrownum,Y, Ysize).
+test(false_train4) :-
+        reset_Model,
+        convert_list_to_float_array([1,2,44,3],2, array(Xsize, Xrownum, X)),
+        convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
+        train(X,Xsize, Xrownum,Y, Ysize).
+:- end_tests(lists).
\ No newline at end of file