Adding kde

src/methods/kde/Makefile

+splfr=/usr/local/sicstus4.7.1/bin/splfr
+
+METHOD_NAME=kde
+
+$(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/kde/kde.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 "kde_glue.h"
+#include <mlpack/methods/kde/kde_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::kde;
+
+// Global Variable of the KDEModel object so it can be accessed from all functions
+KDEModel kdeModel;
+
+// TODO: 
+// input:   const double 	    bandwidth = 1.0,
+//          const double 	    relError = KDEDefaultParams::relError,
+//          const double 	    absError = KDEDefaultParams::absError,
+//          const KernelTypes 	kernelType = KernelTypes::GAUSSIAN_KERNEL,
+//          const TreeTypes 	treeType = TreeTypes::KD_TREE,
+//          const bool 	        monteCarlo = KDEDefaultParams::mode,
+//          const double 	    mcProb = KDEDefaultParams::mcProb,
+//          const size_t 	    initialSampleSize = KDEDefaultParams::initialSampleSize,
+//          const double 	    mcEntryCoef = KDEDefaultParams::mcEntryCoef,
+//          const double 	    mcBreakCoef = KDEDefaultParams::mcBreakCoef,
+//          arma::mat && 	    referenceSet,
+// output: 
+// description: 
+void initAndBuildModel(double bandwidth, double relError, double absError, 
+                        char const *kernelType, char const *treeType, char const *algorithm,
+                        SP_integer monteCarlo, double mcProb, SP_integer initialSampleSize, double mcEntryCoef, double mcBreakCoef,
+                        float *referenceMatArr, SP_integer referenceMatSize, SP_integer referenceMatRowNum)
+{
+    // convert the Prolog arrays to arma::mat
+    mat reference = convertArrayToMat(referenceMatArr, referenceMatSize, referenceMatRowNum);
+
+    // select the kernelType
+    mlpack::kde::KDEModel::KernelTypes kernelT;
+
+    if (strcmp(kernelType, "gaussian") == 0)
+    {
+        kernelT = KDEModel::GAUSSIAN_KERNEL;
+    }
+    else if (strcmp(kernelType, "epanechnikov") == 0)
+    {
+        kernelT = KDEModel::EPANECHNIKOV_KERNEL;
+    }
+    else if (strcmp(kernelType, "laplacian") == 0)
+    {
+        kernelT = KDEModel::LAPLACIAN_KERNEL;
+    }
+    else if (strcmp(kernelType, "spherical") == 0)
+    {
+        kernelT = KDEModel::SPHERICAL_KERNEL;
+    }
+    else if (strcmp(kernelType, "triangular") == 0)
+    {
+        kernelT = KDEModel::TRIANGULAR_KERNEL;
+    }
+    else
+    {
+        cout << "wrong kernelType input" << endl;
+    }
+
+    // select the treeType
+    mlpack::kde::KDEModel::TreeTypes treeT;
+
+    if (strcmp(treeType, "kd-tree") == 0)
+    {
+        treeT = KDEModel::KD_TREE;
+    }
+    else if (strcmp(treeType, "ball-tree") == 0)
+    {
+        treeT = KDEModel::BALL_TREE;
+    }
+    else if (strcmp(treeType, "cover-tree") == 0)
+    {
+        treeT = KDEModel::COVER_TREE;
+    }
+    else if (strcmp(treeType, "octree") == 0)
+    {
+        treeT = KDEModel::OCTREE;
+    }
+    else if (strcmp(treeType, "r-tree") == 0)
+    {
+        treeT = KDEModel::R_TREE;
+    }
+    else
+    {
+        cout << "wrong treeType input" << endl;
+    }
+    
+    kdeModel = KDEModel(bandwidth, relError, absError, kernelT, treeT, (monteCarlo == 1), mcProb, initialSampleSize, mcEntryCoef, mcBreakCoef);
+
+    if (strcmp(algorithm, "dual-tree") == 0)
+    {
+        KDEMode& mode = kdeModel.Mode();
+        mode = KDEMode::DUAL_TREE_MODE;
+    }
+    else if (strcmp(algorithm, "single-tree") == 0)
+    {
+        KDEMode& mode = kdeModel.Mode();
+        mode = KDEMode::SINGLE_TREE_MODE;
+    }
+    else
+    {
+        cout << "wrong algorithm input" << endl;
+    }
+
+    kdeModel.BuildModel(move(reference));
+}
+
+// TODO: 
+// input:   arma::mat && 	    querySet,
+//          arma::vec & 	    estimations <-
+// output: 
+// description: 
+void evaluateWithQuery(float *querySetMatArr, SP_integer querySetMatSize, SP_integer querySetMatRowNum, float **estimationsArr, SP_integer *estimationsArrSize)
+{
+    // convert the Prolog arrays to arma::mat
+    mat querySet = convertArrayToMat(querySetMatArr, querySetMatSize, querySetMatRowNum);
+
+    // create the ReturnVector
+    vec estimationsReturnVector;
+
+    kdeModel.Evaluate(move(querySet), estimationsReturnVector);
+    
+    // return the Vector
+    returnVectorInformation(estimationsReturnVector, estimationsArr, estimationsArrSize);
+}
+
+// TODO: 
+// input:   arma::vec & 	    estimations <-
+// output: 
+// description: 
+void evaluateNoQuery(float **estimationsArr, SP_integer *estimationsArrSize)
+{
+    // create the ReturnVector
+    vec estimationsReturnVector;
+
+    kdeModel.Evaluate(estimationsReturnVector);
+    
+    // return the Vector
+    returnVectorInformation(estimationsReturnVector, estimationsArr, estimationsArrSize);
+}
\ No newline at end of file

src/methods/kde/kde.pl

+:- module(kde, [        initAndBuildModel/14,
+                        evaluateWithQuery/5,
+                        evaluateNoQuery/2]).
+
+%% 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--
+%%              float32 bandwidth               => 1.0,
+%%              float32 relError                => 0.05,
+%%              float32 absError                => 0.0,
+%%              string  kernelType              "gaussian", "epanechnikov", "laplacian", "spherical", "triangular",
+%%              string  treeType                "kd-tree", "ball-tree", "cover-tree", "octree", "r-tree",
+%%              string  algorithm               "dual-tree", "single-tree",
+%%              bool    monteCarlo              (1)true / (0)false => false,
+%%              float32 mcProb                  => 0.95,
+%%              int     initialSampleSize       => 100,
+%%              float32 mcEntryCoef             => 3.0,
+%%              float32 mcBreakCoef             => 0.4,
+%%              mat     referenceSet
+%%
+%% --Output--
+%%
+%% --Description--
+foreign(initAndBuildModel, c, initAndBuildModel(+float32, +float32, +float32, +string, +string, +string, +integer, +float32, +integer, +float32, +float32, +pointer(float_array), +integer, +integer)).
+
+%% TODO: 
+%% --Input--
+%%              mat     querySet
+%%
+%% --Output--
+%%              vec     estimations
+%%
+%% --Description--
+foreign(evaluateWithQuery, c, evaluateWithQuery(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer)).
+
+%% TODO: 
+%% --Input--
+%%
+%% --Output--
+%%              vec     estimations
+%%
+%% --Description--
+foreign(evaluateNoQuery, c, evaluateNoQuery(-pointer(float_array), -integer)).
+
+
+%% Defines the functions that get connected from main.cpp
+foreign_resource(kde, [ initAndBuildModel,
+                        evaluateWithQuery,
+                        evaluateNoQuery]).
+
+:- load_foreign_resource(kde).
\ No newline at end of file

src/methods/kde/kde_test.pl

+:- use_module(library(plunit)).
+
+:- use_module(kde).
+:- 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