Adding naive_bayes_classifier

src/methods/naive_bayes_classifier/Makefile

+splfr=/usr/local/sicstus4.7.1/bin/splfr
+
+METHOD_NAME=naive_bayes_classifier
+
+$(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/naive_bayes_classifier/naive_bayes_classifier.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 "naive_bayes_classifier_glue.h"
+#include <mlpack/methods/naive_bayes/naive_bayes_classifier.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::naive_bayes;
+
+// Global Variable of the NaiveBayesClassifier object so it can be accessed from all functions
+NaiveBayesClassifier<mat> naiveBayesClassifier;
+
+// TODO: 
+// input: 	const MatType & 				data,
+//			const arma::Row< size_t > & 	labels,
+//			const size_t 					numClasses,
+//			const bool 						incrementalVariance = false,
+//			const double 					epsilon = 1e-10 
+// output: 
+// description: 
+void initModelWithTrain(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize,
+						 SP_integer numClasses, SP_integer incrementalVariance, double epsilon)
+{
+	// convert the Prolog arrays to arma::mat
+	mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+
+	// convert the Prolog arrays to arma::rowvec
+	Row<size_t> labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
+
+	naiveBayesClassifier = NaiveBayesClassifier(data, labelsVector, numClasses, (incrementalVariance == 1), epsilon);
+}
+
+// TODO: 
+// input: 	const size_t 	dimensionality = 0,
+//			const size_t 	numClasses = 0,
+//			const double 	epsilon = 1e-10 
+// output: 
+// description: 
+void initModelNoTrain(SP_integer dimensionality, SP_integer numClasses, double epsilon)
+{
+	naiveBayesClassifier = NaiveBayesClassifier(dimensionality, numClasses, epsilon);
+}
+
+/*
+// TODO: 	creates an error
+// input: 	const VecType & 		point,
+//			size_t & 				prediction <-,
+//			ProbabilitiesVecType & 	probabilities <-
+// output: 
+// description: 
+void classifyPoint(float *pointArr, SP_integer pointArrSize, SP_integer *prediction, float **probsArr, SP_integer *probsArrSize)
+{
+	// convert the Prolog arrays to arma::rowvec
+	vec pointVector = convertArrayToRowvec(pointArr, pointArrSize);
+
+	// get the ReturnVector
+	vec probsReturnVector;
+
+	size_t predictionReturn;
+
+	naiveBayesClassifier.Classify(pointVector, predictionReturn, probsReturnVector);
+
+	*prediction = predictionReturn;
+
+	// return the Vector lenght
+	*probsArrSize = probsReturnVector.n_elem;
+
+	// return the Vector as Array
+	*probsArr = convertToArray(probsReturnVector);
+}
+*/
+
+// TODO: 
+// input: 	const MatType & 		data,
+//			arma::Row< size_t > & 	predictions <-,
+//			ProbabilitiesMatType & 	probabilities <-
+// output: 
+// description: 
+void classifyMatrix(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float **predicArr, SP_integer *predicArrSize, float **probsMatArr, SP_integer *probsMatColNum, SP_integer *probsMatRowNum)
+{
+	// convert the Prolog arrays to arma::mat
+	mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+
+	// get the ReturnVector
+	Row<size_t> predicReturnVector;
+	// get the ReturnMat
+	mat probsReturnMat;
+
+	naiveBayesClassifier.Classify(data, predicReturnVector, probsReturnMat);
+
+	// return the Vector lenght
+	*predicArrSize = predicReturnVector.n_elem;
+
+	// return the Vector as Array
+	*predicArr = convertToArray(predicReturnVector);
+
+	// return the Matrix dimensions
+	*probsMatColNum = probsReturnMat.n_cols;
+	*probsMatRowNum = probsReturnMat.n_rows;
+
+	// return the Matrix as one long Array
+	*probsMatArr = convertToArray(probsReturnMat);
+}
+
+// TODO: 
+// input: 
+// output: 	mat	sample means
+// description: 
+void means(float **meansMatArr, SP_integer *meansMatColNum, SP_integer *meansMatRowNum)
+{
+	// get the ReturnMat
+	mat meansReturnMat = naiveBayesClassifier.Means();
+
+	// return the Matrix dimensions
+	*meansMatColNum = meansReturnMat.n_cols;
+	*meansMatRowNum = meansReturnMat.n_rows;
+
+	// return the Matrix as one long Array
+	*meansMatArr = convertToArray(meansReturnMat);
+}
+
+// TODO: 
+// input: 
+// output: 	mat	prior probabilities
+// description: 
+void probabilities(float **probsMatArr, SP_integer *probsMatColNum, SP_integer *probsMatRowNum)
+{
+	// get the ReturnMat
+	mat probsReturnMat = naiveBayesClassifier.Probabilities();
+
+	// return the Matrix dimensions
+	*probsMatColNum = probsReturnMat.n_cols;
+	*probsMatRowNum = probsReturnMat.n_rows;
+
+	// return the Matrix as one long Array
+	*probsMatArr = convertToArray(probsReturnMat);
+}
+
+// TODO: 
+// input: 	const MatType & 				data,
+//			const arma::Row< size_t > & 	labels,
+//			const size_t 					numClasses,
+//			const bool 						incremental = true
+// output: 
+// description: 
+void trainMatrix(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize,
+						 SP_integer numClasses, SP_integer incrementalVariance)
+{
+	// convert the Prolog arrays to arma::mat
+	mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+
+	// convert the Prolog arrays to arma::rowvec
+	Row<size_t> labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
+
+	naiveBayesClassifier.Train(data, labelsVector, numClasses, (incrementalVariance == 1));
+}
+
+// TODO: 
+// input: 	const VecType & 	point,
+//			const size_t 		label
+// output: 	
+// description: 
+void trainPoint(float *pointArr, SP_integer pointArrSize, SP_integer label)
+{
+	// convert the Prolog arrays to arma::rowvec
+	rowvec pointVector = convertArrayToRowvec(pointArr, pointArrSize);
+
+	naiveBayesClassifier.Train(pointVector, label);
+}
+
+// TODO: 
+// input: 
+// output: 	mat	sample variances
+// description: 
+void variances(float **variancesMatArr, SP_integer *variancesMatColNum, SP_integer *variancesMatRowNum)
+{
+	// get the ReturnMat
+	mat variancesReturnMat = naiveBayesClassifier.Variances();
+
+	// return the Matrix dimensions
+	*variancesMatColNum = variancesReturnMat.n_cols;
+	*variancesMatRowNum = variancesReturnMat.n_rows;
+
+	// return the Matrix as one long Array
+	*variancesMatArr = convertToArray(variancesReturnMat);
+}
+
+// in file 'Code/User/snippets/javascript.json'
+/*
+{
+  "For Loop": {
+    "prefix": ["for", "for-const"],
+    "body": ["for (const ${2:element} of ${1:array}) {", "\t$0", "}"],
+    "description": "A for loop."
+  }
+}
+*/
\ No newline at end of file

src/methods/naive_bayes_classifier/naive_bayes_classifier.pl

+:- module(naive_bayes_classifier, [       initModelWithTrain/8,
+                                          initModelNoTrain/3,
+                                          
+                                          classifyMatrix/8,
+                                          means/3,
+                                          probabilities/3,
+                                          trainMatrix/7,
+                                          trainPoint/3,
+                                          variances/3]).
+
+%% 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: 
+%% output: 
+%% description: 
+foreign(initModelWithTrain, c, initModelWithTrain(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer, +float32)).
+
+
+%% TODO: 
+%% input: 
+%% output: 
+%% description: 
+foreign(initModelNoTrain, c, initModelNoTrain(+integer, +integer, +float32)).
+
+%% TODO: 
+%% input: 
+%% output: 
+%% description: 
+%%foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer, -integer, -pointer(float_array), -integer)).
+
+%% TODO: 
+%% input: 
+%% output: 
+%% description: 
+foreign(classifyMatrix, c, classifyMatrix(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -pointer(float_array), -integer, -integer)).
+
+%% TODO: 
+%% input: 
+%% output: 
+%% description: 
+foreign(means, c, means(-pointer(float_array), -integer, -integer)).
+
+%% TODO: 
+%% input: 
+%% output: 
+%% description: 
+foreign(probabilities, c, probabilities(-pointer(float_array), -integer, -integer)).
+
+%% TODO: 
+%% input: 
+%% output: 
+%% description: 
+foreign(trainMatrix, c, trainMatrix(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer)).
+
+%% TODO: 
+%% input: 
+%% output: 
+%% description: 
+foreign(trainPoint, c, trainPoint(+pointer(float_array), +integer, +integer)).
+
+%% TODO: 
+%% input: 
+%% output: 
+%% description: 
+foreign(variances, c, variances(-pointer(float_array), -integer, -integer)).
+
+
+
+
+%% +integer , +float32, +string
+%% [-integer] , [-float32], [-string]
+
+%% matrix input
+%% +pointer(float_array), +integer, +integer
+
+%% array input
+%% +pointer(float_array), +integer
+
+%% matrix return
+%% -pointer(float_array), -integer, -integer
+
+%% array return
+%% -pointer(float_array), -integer
+
+%% Defines the functions that get connected from main.cpp
+foreign_resource(naive_bayes_classifier, [       initModelWithTrain,
+                                                 initModelNoTrain,
+                                                 
+                                                 classifyMatrix,
+                                                 means,
+                                                 probabilities,
+                                                 trainMatrix,
+                                                 trainPoint,
+                                                 variances]).
+
+:- load_foreign_resource(naive_bayes_classifier).
\ No newline at end of file

src/methods/naive_bayes_classifier/naive_bayes_classifier_test.pl

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