Fixing some Errors in the tests.

src/helper_files/helper.hpp

@@ -7,6 +7,9 @@ using namespace arma;
 using namespace mlpack;
 using namespace std;
 
+
+// conversion from arma datatypes to arrays usable by prolog
+
 float *convertToArray(colvec vec);
 
 float *convertToArray(rowvec vec);
@@ -24,12 +27,17 @@ float *convertToArray(Row<size_t> vec);
 float *convertToArray(vector<vec> vec);
 
 
+// conversion from arrays to arma datatypes
+
 rowvec convertArrayToRowvec(float *arr, int vecSize);
 
 Row<size_t> convertArrayToVec(float *arr, int vecSize);
 
 mat convertArrayToMat(float *arr, int vecSize, int rowCount);
 
+
+// insert the Vector and Matrix informations into the prolog Variables
+
 void returnMatrixInformation(mat matrix, float **mat, SP_integer *matColNum, SP_integer *matRowNum);
 
 void returnMatrixInformation(Mat< size_t > matrix, float **mat, SP_integer *matColNum, SP_integer *matRowNum);

src/methods/adaboost/adaboost_test.pl

@@ -269,12 +269,12 @@ test(train_With_Bad_Labels_Too_Many_Classes, [error(_,system_error('The values o
         reset_Model_No_Train(perceptron),
         train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
 
-test(train_With_Bad_Labels_Negative, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
+test(train_With_Bad_Labels_Negative_Perceptron, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
         reset_Model_No_Train(perceptron),
         train([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, [0,-1,0,-1], 2, perceptron, 50, 0.0001, _).
 
 %% should cause an exeption but doesnt TODO:
-test(train_With_Bad_Labels_Negative) :-
+test(train_With_Bad_Labels_Negative_Decision_Stump) :-
         reset_Model_No_Train(decision_stump),
         train([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, [0,-1,0,-1], 2, decision_stump, 50, 0.0001, _).
 

src/methods/approx_kfn/approx_kfn.cpp

@@ -17,10 +17,10 @@ using namespace std;
 using namespace mlpack::neighbor;
 
 // Global Variable of the DrusillaSelect object so it can be accessed from all functions
-DrusillaSelect<mat> drusillaSelect = DrusillaSelect(0,0);
+DrusillaSelect<mat> drusillaSelect = DrusillaSelect(1,1);
 
 // Global Variable of the QDAFN object so it can be accessed from all functions
-QDAFN<mat> qdafn = QDAFN(0,0);
+QDAFN<mat> qdafn = QDAFN(1,1);
 
 // TODO: 
 // input:   const size_t 	l,

src/methods/approx_kfn/approx_kfn_test.pl

+
+:- module(approx_kfn_tests, [run_approx_kfn_tests/0]).
+
 :- use_module(library(plunit)).
 
 :- use_module(approx_kfn).
@@ -6,51 +9,36 @@
 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
+%%
+%% TESTING predicate predicate/10
+%%
+:- begin_tests(predicate).      
+
+%% Failure Tests
+                                            
+test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
+        reset_Model_No_Train(perceptron),
+        train([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, [0,0,0,0], 2, culprit, 50, 0.0001, _).
+
+test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
+        reset_Model_No_Train(perceptron),
+        train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
+        
+
+%% Successful Tests
+
+test(testDescription3, [true(Error =:= 1)]) :-
+        reset_Model_No_Train(perceptron),
+        train([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, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
+
+test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
+        reset_Model_No_Train(perceptron),
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+
+:- end_tests(predicate).
+
+run_approx_kfn_tests :-
+        run_tests.
+

src/methods/bayesian_linear_regression/bayesian_linear_regression.cpp

@@ -77,7 +77,8 @@ void omega(float **omegaArr, SP_integer *omegaArrSize)
 // input: 	const arma::mat &points, 
 //			arma::rowvec &predictions
 // output:
-void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum, float **predictionsArr, SP_integer *predictionsArrSize)
+void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum, 
+				float **predictionsArr, SP_integer *predictionsArrSize)
 {
 	// convert the Prolog array to arma::mat
 	mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum);
@@ -86,8 +87,16 @@ void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMat
 	rowvec predictionsReturnVector;
 	
 
+	try
+	{
 		// run the prediction and save the result in arma::rowvec
 		regressor.Predict(points, predictionsReturnVector);
+	}
+	catch(const std::exception& e)
+	{
+		raisePrologSystemExeption(e.what());
+		return;
+	}
 
 
 	// return the Vector
@@ -98,7 +107,9 @@ void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMat
 //			arma::rowvec &predictions, 
 //			arma::rowvec &std
 // output:
-void predictWithStd(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum, float **predictionsArr, SP_integer *predictionsArrSize, float **stdArr, SP_integer *stdArrSize)
+void predictWithStd(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum, 
+					float **predictionsArr, SP_integer *predictionsArrSize, 
+					float **stdArr, SP_integer *stdArrSize)
 {
 	// convert the Prolog array to arma::mat
 	mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum);
@@ -108,8 +119,16 @@ void predictWithStd(float *pointsMatArr, SP_integer pointsMatSize, SP_integer po
 	rowvec stdReturnVector;
 	
 
+	try
+	{
 		// run the prediction and save the result in arma::rowvec
 		regressor.Predict(points, predictionsReturnVector, stdReturnVector);
+	}
+	catch(const std::exception& e)
+	{
+		raisePrologSystemExeption(e.what());
+		return;
+	}
 
 
 	// return the Vectors
@@ -128,7 +147,8 @@ double responsesOffset()
 // input:	const arma::mat &		data, 
 //			const arma::rowvec &	responses
 // output: double
-double rmse(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *responsesArr, SP_integer responsesArrSize)
+double rmse(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, 
+			float *responsesArr, SP_integer responsesArrSize)
 {
 	// convert the Prolog array to arma::mat
 	mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
@@ -137,14 +157,23 @@ double rmse(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
 	rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
 	
 
+	try
+	{
 		// run the model function and return the error
 		return regressor.RMSE(data, responsesVector);
 	}
+	catch(const std::exception& e)
+	{
+		raisePrologSystemExeption(e.what());
+		return 0.0;
+	}
+}
 
 // input:	const arma::mat &		data, 
 //			const arma::rowvec &	responses
 // output:
-void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *responsesArr, SP_integer responsesArrSize)
+void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, 
+			float *responsesArr, SP_integer responsesArrSize)
 {
 	if(dataMatSize / dataMatRowNum != responsesArrSize)
 	{
@@ -158,9 +187,17 @@ void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
 	rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
 
 
+	try
+	{
 		// run the model function
 		regressor.Train(data, responsesVector);
 	}
+	catch(const std::exception& e)
+	{
+		raisePrologSystemExeption(e.what());
+		return;
+	}
+}
 
 // input:
 // output: double

src/methods/bayesian_linear_regression/bayesian_linear_regression_test.pl

+
+:- module(bayesian_linear_regression_tests, [run_bayesian_linear_regression_tests/0]).
+
 :- use_module(library(plunit)).
 
 %%:- compile(bayesian_linear_regression).
@@ -57,3 +60,37 @@ test(false_train4) :-
         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).
+
+%%
+%% TESTING predicate predicate/10
+%%
+:- begin_tests(predicate).      
+
+%% Failure Tests
+                                            
+test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
+        reset_Model_No_Train(perceptron),
+        train([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, [0,0,0,0], 2, culprit, 50, 0.0001, _).
+
+test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
+        reset_Model_No_Train(perceptron),
+        train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
+        
+
+%% Successful Tests
+
+test(testDescription3, [true(Error =:= 1)]) :-
+        reset_Model_No_Train(perceptron),
+        train([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, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
+
+test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
+        reset_Model_No_Train(perceptron),
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+
+:- end_tests(predicate).
+
+run_bayesian_linear_regression_tests :-
+        run_tests.
+

src/methods/hoeffding_tree/hoeffding_tree.cpp

@@ -17,6 +17,7 @@ using namespace mlpack::tree;
 
 // Global Variable of the HoeffdingTreeModel object so it can be accessed from all functions
 HoeffdingTreeModel hoeffdingTreeObj;
+bool isModelInitialized = false;
 
 
 // input:   const TreeType & 	            type = GINI_HOEFFDING,
@@ -48,19 +49,34 @@ void initAndBuildModel(char const *treeType,
     // convert the Prolog arrays to arma::rowvec
     Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
 
-    if (strcmp(treeType, "gini-hoeffding") == 0)
+    if (strcmp(treeType, "gini_hoeffding") == 0)
         hoeffdingTreeObj = HoeffdingTreeModel(HoeffdingTreeModel::GINI_HOEFFDING);
-    else if (strcmp(treeType, "gini-binary") == 0)
+    else if (strcmp(treeType, "gini_binary") == 0)
         hoeffdingTreeObj = HoeffdingTreeModel(HoeffdingTreeModel::GINI_BINARY);
-    else if (strcmp(treeType, "info-hoeffding") == 0)
+    else if (strcmp(treeType, "info_hoeffding") == 0)
         hoeffdingTreeObj = HoeffdingTreeModel(HoeffdingTreeModel::INFO_HOEFFDING);
-    else if (strcmp(treeType, "info-binary") == 0)
+    else if (strcmp(treeType, "info_binary") == 0)
         hoeffdingTreeObj = HoeffdingTreeModel(HoeffdingTreeModel::INFO_BINARY);
     else
-        cout << "wrong treeType input" << endl;
+    {
+        raisePrologDomainExeption(treeType, 1, "The given TreeType is unkown!", "initAndBuildModel");
+        return;
+    }
+    
 
+    try
+    {
         hoeffdingTreeObj.BuildModel(data, data::DatasetInfo(data.n_rows), labelsVector, numClasses, (batchTraining == 1), successProbability, maxSamples, checkInterval, minSamples, bins, observationsBeforeBinning);
     }
+    catch(const std::exception& e)
+    {
+        raisePrologSystemExeption(e.what());
+        isModelInitialized = false;
+        return;
+    }
+    
+    isModelInitialized = true;
+}
 
 
 // input:   const arma::mat & 	dataset,
@@ -74,6 +90,12 @@ void classify(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNu
                 float **predictArr, SP_integer *predictArrSize,
                 float **probsArr, SP_integer *probsArrSize)
 {
+    if (!isModelInitialized)
+    {
+        raisePrologSystemExeption("The model is not initialized!");
+        return;
+    }
+    
     // convert the Prolog arrays to arma::mat
     mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
 
@@ -84,7 +106,15 @@ void classify(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNu
     rowvec probsReturnVector;
 
 
+    try
+    {
         hoeffdingTreeObj.Classify(data, predictReturnVector, probsReturnVector);
+    }
+    catch(const std::exception& e)
+    {
+        raisePrologSystemExeption(e.what());
+        return;
+    }
 
     
     // return the Vector
@@ -106,11 +136,26 @@ void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
             float *labelsArr, SP_integer labelsArrSize,
             SP_integer batchTraining)
 {
+    if (!isModelInitialized)
+    {
+        raisePrologSystemExeption("The model is not initialized!");
+        return;
+    }
+
     // 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);
 
+
+    try
+    {
         hoeffdingTreeObj.Train(data, labelsVector, (batchTraining == 1));
     }
+    catch(const std::exception& e)
+    {
+        raisePrologSystemExeption("nice");
+        return;
+    }
+}

src/methods/hoeffding_tree/hoeffding_tree.pl

@@ -20,7 +20,7 @@
 
 
 %% --Input--
-%%              string  treeType                => "gini-hoeffding", "gini-binary", "info-hoeffding", "info-binary",
+%%              string  treeType                => "gini_hoeffding", "gini_binary", "info_hoeffding", "info_binary",
 %%              mat     dataset,
 %%              vec     labels,
 %%              int     numClasses,
@@ -43,6 +43,10 @@ initAndBuildModel(TreeType, DataList, DataRows, LabelsList, NumClasses, BatchTra
         SuccessProbability >= 0,
         SuccessProbability =< 1,
         MaxSamples >= 0,
+        CheckInterval > 0,
+        MinSamples >= 0,
+        Bins >= 0,
+        ObservationsBeforeBinning >= 0,
         convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
         convert_list_to_float_array(LabelsList, array(Ysize, Y)),
         initAndBuildModelI(TreeType, X, Xsize, Xrownum, Y, Ysize, NumClasses, BatchTraining, SuccessProbability, MaxSamples, CheckInterval, MinSamples, Bins, ObservationsBeforeBinning).

src/methods/hoeffding_tree/hoeffding_tree_test.pl

@@ -7,7 +7,9 @@
 :- use_module('../../helper_files/helper.pl').
 
 reset_Model :-
-        initAndBuildModel(1,0,50,0.0001).
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        initAndBuildModel(gini_binary, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100).
 
 %%
 %% TESTING predicate initAndBuildModel/12
@@ -16,26 +18,83 @@ reset_Model :-
 
 %% Failure Tests
 
-test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, culprit, 50, 0.0001, _).
+test(hoeffding_Init_Classify_Befor_Init, [error(_,system_error('The model is not initialized!'))]) :-
+        classify([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, _, _).
+                                            
+test(hoeffding_Init_Wrong_TreeType, [error(domain_error('The given TreeType is unkown!' , wrongType), _)]) :-
+        initAndBuildModel(wrongType, [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, [0,1,0,1], 2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_Negative_NumClass, fail) :-
+        initAndBuildModel(gini_binary, [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, [0,1,0,1], -2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_Bad_SuccessProbability, fail) :-
+        initAndBuildModel(gini_binary, [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, [0,1,0,1], 2, 0, -1.0, 5000, 100, 100, 10, 100),
+        initAndBuildModel(gini_binary, [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, [0,1,0,1], 2, 0, 2.0, 5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_Negative_MaxSamples, fail) :-
+        initAndBuildModel(gini_binary, [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, [0,1,0,1], 2, 0, 0.95, -5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_Negative_CheckInterval, fail) :-
+        initAndBuildModel(gini_binary, [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, [0,1,0,1], 2, 0, 0.95, 5000, -100, 100, 10, 100).
+
+test(hoeffding_Init_Negative_MinSamples, fail) :-
+        initAndBuildModel(gini_binary, [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, [0,1,0,1], 2, 0, 0.95, 5000, 100, -100, 10, 100).
+
+test(hoeffding_Init_Negative_Bins, fail) :-
+        initAndBuildModel(gini_binary, [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, [0,1,0,1], 2, 0, 0.95, 5000, 100, 100, -10, 100).
+
+test(hoeffding_Init_Negative_ObservationsBeforeBinning, fail) :-
+        initAndBuildModel(gini_binary, [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, [0,1,0,1], 2, 0, 0.95, 5000, 100, 100, 10, -100).
 
-test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
+
+test(hoeffding_Init_With_Wrong_Label_Dims1, [error(_,system_error('Labels Vector is too short or its values are incorrect: should fit into [0,numClasses)!'))]) :-
+        initAndBuildModel(gini_binary, [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, [0,1], 2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+%% If the label vector is to long it seems to cause no problems
+test(hoeffding_Init_With_Wrong_Label_Dims2) :-
+        initAndBuildModel(gini_binary, [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, [0,1,0,1,0,0,1], 2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+%% The same when the label values are out of range
+test(hoeffding_Init_With_Wrong_Label_Value) :-
+        initAndBuildModel(gini_binary, [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, [0,-1,0,-1], 2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_With_Too_Many_Label_Value, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
+        initAndBuildModel(gini_binary, [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, [1,1,0,2], 2, 0, 0.95, 5000, 100, 100, 10, 100).
         
 
 %% Successful Tests
 
-test(testDescription3, [true(Error =:= 1)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
+test(hoeffding_Init_GiniHoeffding_Direkt_Input) :-
+        initAndBuildModel(gini_hoeffding, [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, [0,1,0,1], 2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_GiniHoeffding_CSV_Input) :-
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        initAndBuildModel(gini_hoeffding, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_GiniBinary_Direkt_Input) :-
+        initAndBuildModel(gini_binary, [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, [0,1,0,1], 2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_GiniBinary_CSV_Input) :-
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        initAndBuildModel(gini_binary, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_InfoHoeffding_Direkt_Input) :-
+        initAndBuildModel(info_hoeffding, [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, [0,1,0,1], 2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_InfoHoeffding_CSV_Input) :-
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        initAndBuildModel(info_hoeffding, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100).
+
+test(hoeffding_Init_InfoBinary_Direkt_Input) :-
+        initAndBuildModel(info_binary, [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, [0,1,0,1], 2, 0, 0.95, 5000, 100, 100, 10, 100).
 
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
-        reset_Model_No_Train(perceptron),
+test(hoeffding_Init_InfoBinary_CSV_Input) :-
         open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
         take_csv_row(File, skipFirstRow,10, Data),
-        train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+        initAndBuildModel(info_binary, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100).
 
 :- end_tests(initAndBuildModel).
 
@@ -47,26 +106,56 @@ test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
 
 %% Failure Tests
                                             
-test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, culprit, 50, 0.0001, _).
-
-test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
+test(hoeffding_Classify_Different_Dims_To_Train, [error(_,system_error('Labels Vector is too short or its values are incorrect: should fit into [0,numClasses)!'))]) :-
+        reset_Model,
+        classify([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, PredicList, ProbsList),
+        print('\nPredictions: '),
+        print(PredicList),
+        print('\nProbabilities: '),
+        print(ProbsList).
         
 
 %% Successful Tests
 
-test(testDescription3, [true(Error =:= 1)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
-
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
-        reset_Model_No_Train(perceptron),
+test(hoeffding_Classify_GiniHoeffding) :-
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        initAndBuildModel(gini_hoeffding, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100),
+        classify([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, PredicList, ProbsList),
+        print('\nPredictions: '),
+        print(PredicList),
+        print('\nProbabilities: '),
+        print(ProbsList).
+
+test(hoeffding_Classify_GiniBinary) :-
         open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
         take_csv_row(File, skipFirstRow,10, Data),
-        train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+        initAndBuildModel(gini_binary, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100),
+        classify([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, PredicList, ProbsList),
+        print('\nPredictions: '),
+        print(PredicList),
+        print('\nProbabilities: '),
+        print(ProbsList).
+
+test(hoeffding_Classify_InfoHoeffding) :-
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        initAndBuildModel(info_hoeffding, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100),
+        classify([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, PredicList, ProbsList),
+        print('\nPredictions: '),
+        print(PredicList),
+        print('\nProbabilities: '),
+        print(ProbsList).
+
+test(hoeffding_Classify_InfoBinary) :-
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        initAndBuildModel(info_binary, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100),
+        classify([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, PredicList, ProbsList),
+        print('\nPredictions: '),
+        print(PredicList),
+        print('\nProbabilities: '),
+        print(ProbsList).
 
 :- end_tests(classify).
 
@@ -78,29 +167,38 @@ test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
 
 %% Failure Tests
                                             
-test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, culprit, 50, 0.0001, _).
+test(hoeffding_Train_With_Wrong_Label_Dims1, [error(_,system_error('Labels Vector is too short or its values are incorrect: should fit into [0,numClasses)!'))]) :-
+        reset_Model,
+        train([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, [0,1,0,0,0], 0).
 
-test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
+%% If the label vector is to long it seems to cause no problems
+test(hoeffding_Train_With_Wrong_Label_Dims2) :-
+        reset_Model,
+        train([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, [0,1,0,1,0,0,1], 0).
 
+%% The same when the label values are out of range
+test(hoeffding_Train_With_Wrong_Label_Value) :-
+        reset_Model,
+        train([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, [0,-1,0,-1], 0).
 
-%% Successful Tests
+test(hoeffding_Train_With_Too_Many_Label_Value, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
+        reset_Model,
+        train([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, [1,1,0,2], 0).
         
-test(testDescription3, [true(Error =:= 1)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
+test(hoeffding_Train_Bad_Data_Dims) :-
+        reset_Model,
+        train([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, [0,1,0,1], 0).
 
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
-        reset_Model_No_Train(perceptron),
-        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
-        take_csv_row(File, skipFirstRow,10, Data),
-        train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+
+%% Successful Tests
+
+test(testDescription3) :-
+        reset_Model,
+        train([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, [0,0,1,1,0], 1),
+        train([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, [0,0,1,1,0], 0).
 
 :- end_tests(train).
 
 run_hoeffding_tree_tests :-
-        run_tests.
+        run_tests(train).
 

src/methods/kde/kde.cpp

@@ -67,21 +67,22 @@ void initAndBuildModel(double bandwidth, double relError, double absError,
     }
     else
     {
-        raisePrologSystemExeption("The given KernelType is unknown!");
+        raisePrologDomainExeption(kernelType, 4, "The given KernelType is unknown!", "initAndBuildModel");
+        return;
     }
 
     // select the treeType
     mlpack::kde::KDEModel::TreeTypes treeT;
 
-    if (strcmp(treeType, "kd-tree") == 0)
+    if (strcmp(treeType, "kd_tree") == 0)
     {
         treeT = KDEModel::KD_TREE;
     }
-    else if (strcmp(treeType, "ball-tree") == 0)
+    else if (strcmp(treeType, "ball_tree") == 0)
     {
         treeT = KDEModel::BALL_TREE;
     }
-    else if (strcmp(treeType, "cover-tree") == 0)
+    else if (strcmp(treeType, "cover_tree") == 0)
     {
         treeT = KDEModel::COVER_TREE;
     }
@@ -89,42 +90,43 @@ void initAndBuildModel(double bandwidth, double relError, double absError,
     {
         treeT = KDEModel::OCTREE;
     }
-    else if (strcmp(treeType, "r-tree") == 0)
+    else if (strcmp(treeType, "r_tree") == 0)
     {
         treeT = KDEModel::R_TREE;
     }
     else
     {
-        raisePrologSystemExeption("The given TreeType is unknown!");
+        raisePrologDomainExeption(treeType, 5, "The given TreeType is unknown!", "initAndBuildModel");
+        return;
     }
     
     
+    try
+    {
         kdeModel = KDEModel(bandwidth, relError, absError, kernelT, treeT, (monteCarlo == 1), mcProb, initialSampleSize, mcEntryCoef, mcBreakCoef);
+        kdeModel.BuildModel(move(reference));
+    }
+    catch(const std::exception& e)
+    {
+        raisePrologSystemExeption(e.what());
+        return;
+    }
 
 
-    if (strcmp(algorithm, "dual-tree") == 0)
+    if (strcmp(algorithm, "dual_tree") == 0)
     {
         KDEMode& mode = kdeModel.Mode();
         mode = KDEMode::DUAL_TREE_MODE;
     }
-    else if (strcmp(algorithm, "single-tree") == 0)
+    else if (strcmp(algorithm, "single_tree") == 0)
     {
         KDEMode& mode = kdeModel.Mode();
         mode = KDEMode::SINGLE_TREE_MODE;
     }
     else
     {
-        raisePrologSystemExeption("The given Algorithm is unknown!");
-    }
-
-    
-    try
-    {
-        kdeModel.BuildModel(move(reference));
-    }
-    catch(const std::exception& e)
-    {
-        raisePrologSystemExeption(e.what());
+        raisePrologDomainExeption(algorithm, 6, "The given Algorithm is unknown!", "initAndBuildModel");
+        return;
     }
 }
 
@@ -151,6 +153,7 @@ void evaluateWithQuery(float *querySetMatArr, SP_integer querySetMatSize, SP_int
     catch(const std::exception& e)
     {
         raisePrologSystemExeption(e.what());
+        return;
     }
     
     
@@ -178,6 +181,7 @@ void evaluateNoQuery(float **estimationsArr, SP_integer *estimationsArrSize)
     catch(const std::exception& e)
     {
         raisePrologSystemExeption(e.what());
+        return;
     }
     
     

src/methods/kde/kde_test.pl

@@ -7,98 +7,295 @@
 :- use_module('../../helper_files/helper.pl').
 
 reset_Model :-
-        initModel(1,0,50,0.0001).
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
 
 
 %%
-%% TESTING predicate initAndBuildModel/10
+%% TESTING predicate initAndBuildModel/13
 %%
 :- begin_tests(initAndBuildModel).      
 
 %% Failure Tests
 
-test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, culprit, 50, 0.0001, _).
+%% only raises an exeption if the model hasnt been initilized once, so only after fully reseting the tests.
+test(kde_EvaluateWithQuery_Befor_Init, [error(_, system_error('no KDE model initialized'))]) :-
+        evaluateWithQuery([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, _).
 
-test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
+%% only raises an exeption if the model hasnt been initilized once, so only after fully reseting the tests.
+test(kde_EvaluateNoQuery_Befor_Init, [error(_, system_error('no KDE model initialized'))]) :-
+        evaluateNoQuery(_).
 
+test(kde_Init_Negative_Bandwidth, fail) :-
+        initAndBuildModel(-1.0, 0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
 
-%% Successful Tests
+test(kde_Init_Bad_RelError, fail) :-
+        initAndBuildModel(1.0, -0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 1.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Negative_AbsError, fail) :-
+        initAndBuildModel(1.0, 0.05, -1.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Wrong_KernelType, [error(domain_error('The given KernelType is unknown!' , wrongType), _)]) :-
+        initAndBuildModel(1.0, 0.05, 0.0, wrongType, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Wrong_TreeType, [error(domain_error('The given TreeType is unknown!' , wrongType), _)]) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, wrongType, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Wrong_Algorithm, [error(domain_error('The given Algorithm is unknown!' , wrongType), _)]) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, wrongType, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Bad_McProb, fail) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, dual_tree, 0, -0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 1.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Negative_InitialSampleSize, fail) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, -100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Negative_MCEntryCoef, fail) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 0.0, 0.4,
+                          [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).
+
+test(kde_Init_Bad_MCBreakCoef, fail) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, -0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 1.4,
+                          [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).
         
-test(testDescription3, [true(Error =:= 1)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
 
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
-        reset_Model_No_Train(perceptron),
+%% Successful Tests
+
+%% Gaussian Kernel
+test(kde_Init_Gaussian_KDTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Gaussian_BALLTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, ball_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, ball_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Gaussian_COVERTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, cover_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, cover_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Gaussian_OCTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, octree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, octree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Gaussian_RTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, r_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, gaussian, r_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+
+
+%% Epanechnikov Kernel
+test(kde_Init_Epanechnikov_KDTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, epanechnikov, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, epanechnikov, kd_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Epanechnikov_BALLTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, epanechnikov, ball_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, epanechnikov, ball_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Epanechnikov_COVERTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, epanechnikov, cover_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, epanechnikov, cover_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Epanechnikov_OCTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, epanechnikov, octree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, epanechnikov, octree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Epanechnikov_RTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, epanechnikov, r_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, epanechnikov, r_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+
+
+%% Laplacian Kernel
+test(kde_Init_Laplacian_KDTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, laplacian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, laplacian, kd_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Laplacian_BALLTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, laplacian, ball_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, laplacian, ball_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Laplacian_COVERTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, laplacian, cover_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, laplacian, cover_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Laplacian_OCTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, laplacian, octree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, laplacian, octree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Laplacian_RTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, laplacian, r_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, laplacian, r_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+
+
+%% Spherical Kernel
+test(kde_Init_Spherical_KDTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, spherical, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, spherical, kd_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Spherical_BALLTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, spherical, ball_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, spherical, ball_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Spherical_COVERTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, spherical, cover_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, spherical, cover_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Spherical_OCTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, spherical, octree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, spherical, octree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Spherical_RTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, spherical, r_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, spherical, r_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+
+%% Triangular Kernel
+test(kde_Init_Triangular_KDTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, triangular, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, triangular, kd_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Triangular_BALLTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, triangular, ball_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, triangular, ball_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Triangular_COVERTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, triangular, cover_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, triangular, cover_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Triangular_OCTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, triangular, octree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, triangular, octree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+test(kde_Init_Triangular_RTREE) :-
+        initAndBuildModel(1.0, 0.05, 0.0, triangular, r_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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),
+        initAndBuildModel(1.0, 0.05, 0.0, triangular, r_tree, single_tree, 0, 0.95, 100, 3.0, 0.4,
+                          [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).
+
+
+
+test(kde_Init_CSV_Input) :-
         open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
         take_csv_row(File, skipFirstRow,10, Data),
-        train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+        initAndBuildModel(2.2, 0.25, 0.5, gaussian, kd_tree, dual_tree, 1, 0.75, 50, 2.0, 0.2,
+                          Data, 4).
 
 :- end_tests(initAndBuildModel).
 
 
 %%
-%% TESTING predicate evaluateWithQuery/10
+%% TESTING predicate evaluateWithQuery/3
 %%
 :- begin_tests(evaluateWithQuery).      
 
 %% Failure Tests
-                                            
-test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, culprit, 50, 0.0001, _).
-
-test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
+test(kde_EvalWithQuery_Wrong_Query_Dims, [error(_, system_error('cannot evaluate KDE model: querySet and referenceSet dimensions don\'t match'))]) :-
+        reset_Model,
+        evaluateWithQuery([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, Estimation),
+        print('\nEstimation: '),
+        print(Estimation).
         
 
 %% Successful Tests
 
-test(testDescription3, [true(Error =:= 1)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
+test(kde_EvalWithQuery) :-
+        reset_Model,
+        evaluateWithQuery([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5], 3, Estimation),
+        print('\nEstimation: '),
+        print(Estimation).
 
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
-        reset_Model_No_Train(perceptron),
+test(kde_EvalWithQuery_CSV_Input) :-
         open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
         take_csv_row(File, skipFirstRow,10, Data),
-        train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+        initAndBuildModel(2.2, 0.25, 0.5, gaussian, kd_tree, dual_tree, 1, 0.75, 50, 2.0, 0.2,
+                          Data, 4),
+        evaluateWithQuery([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, Estimation),
+        print('\nEstimation: '),
+        print(Estimation).
 
 :- end_tests(evaluateWithQuery).
 
 
 %%
-%% TESTING predicate evaluateNoQuery/10
+%% TESTING predicate evaluateNoQuery/1
 %%
 :- begin_tests(evaluateNoQuery).      
 
 %% Failure Tests
         
-test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, culprit, 50, 0.0001, _).
-
-test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
-        
 
 %% Successful Tests
 
-test(testDescription3, [true(Error =:= 1)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
-
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
-        reset_Model_No_Train(perceptron),
-        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
-        take_csv_row(File, skipFirstRow,10, Data),
-        train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+test(kde_EvalNoQuery) :-
+        reset_Model,
+        evaluateNoQuery(Estimation),
+        print('\nEstimation: '),
+        print(Estimation).
 
 :- end_tests(evaluateNoQuery).
 

src/methods/kernel_pca/kernel_pca.cpp

@@ -23,7 +23,7 @@ using namespace mlpack::kernel;
 
 
 template<typename KernelType>
-void useNystroemKernelPCA(char const *nystroemMethod, KernelType kernel, bool centerTranformedData, mat data, mat transformedDataReturnMat, vec eigvalReturnVector, mat eigvecReturnMat, size_t returnDim)
+bool useNystroemKernelPCA(char const *nystroemMethod, KernelType kernel, bool centerTranformedData, mat data, mat transformedDataReturnMat, vec eigvalReturnVector, mat eigvecReturnMat, size_t returnDim)
 {
     if (strcmp(nystroemMethod, "kmeans") == 0)        
     {
@@ -42,8 +42,10 @@ void useNystroemKernelPCA(char const *nystroemMethod, KernelType kernel, bool ce
     }
     else
     {
-        raisePrologDomainExeption(nystroemMethod, 4, "The given NystoemMethod is unkown!", "kernel_pca");
+        raisePrologDomainExeption(nystroemMethod, 4, "The given NystroemMethod is unknown!", "kernel_pca");
+        return false;
     }
+    return true;
 }
 
 // TODO: 
@@ -62,7 +64,7 @@ void kernel_pca(char const *kernel, SP_integer centerTranformedData, SP_integer
                     float **transformedDataMatArr, SP_integer *transformedDataMatColNum, SP_integer *transformedDataMatRowNum,
                     float **eigvalArr, SP_integer *eigvalArrSize,
                     float **eigvecMatArr, SP_integer *eigvecMatColNum, SP_integer *eigvecMatRowNum, 
-                    SP_integer *newDimension)
+                    SP_integer newDimension)
 {
     // convert the Prolog array to arma::mat
     mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
@@ -75,20 +77,19 @@ void kernel_pca(char const *kernel, SP_integer centerTranformedData, SP_integer
     // create the ReturnMat
     mat eigvecReturnMat;
 
-    size_t returnDim = 0;
-    
     
     if (strcmp(kernel, "linear") == 0)        
     {
         LinearKernel lk;
         if(useNystroem == 1)
         {
-            useNystroemKernelPCA<LinearKernel>(nystroemMethod, lk, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            if(!useNystroemKernelPCA<LinearKernel>(nystroemMethod, lk, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension))
+                return;
         }
         else
         {
-            KernelPCA<LinearKernel, NaiveKernelRule<LinearKernel>>(lk, (centerTranformedData == 1))
-            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            KernelPCA<LinearKernel>(lk, (centerTranformedData == 1))
+            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension);
         }
     }
     else if (strcmp(kernel, "polynomial") == 0)
@@ -96,12 +97,13 @@ void kernel_pca(char const *kernel, SP_integer centerTranformedData, SP_integer
         PolynomialKernel pk(degree, offset);
         if(useNystroem == 1)
         {
-            useNystroemKernelPCA<PolynomialKernel>(nystroemMethod, pk, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            if(!useNystroemKernelPCA<PolynomialKernel>(nystroemMethod, pk, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension))
+                return;
         }
         else
         {
-            KernelPCA<PolynomialKernel, NaiveKernelRule<PolynomialKernel>>(pk, (centerTranformedData == 1))
-            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            KernelPCA<PolynomialKernel>(pk, (centerTranformedData == 1))
+            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension);
         }
     }
     else if (strcmp(kernel, "cosine") == 0)
@@ -109,12 +111,13 @@ void kernel_pca(char const *kernel, SP_integer centerTranformedData, SP_integer
         CosineDistance cd;
         if(useNystroem == 1)
         {
-            useNystroemKernelPCA<CosineDistance>(nystroemMethod, cd, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            if(!useNystroemKernelPCA<CosineDistance>(nystroemMethod, cd, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension))
+                return;
         }
         else
         {
-            KernelPCA<CosineDistance, NaiveKernelRule<CosineDistance>>(cd, (centerTranformedData == 1))
-            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            KernelPCA<CosineDistance>(cd, (centerTranformedData == 1))
+            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension);
         }
     }
     else if (strcmp(kernel, "gaussian") == 0)
@@ -122,12 +125,13 @@ void kernel_pca(char const *kernel, SP_integer centerTranformedData, SP_integer
         GaussianKernel gk(bandwidth);
         if(useNystroem == 1)
         {
-            useNystroemKernelPCA<GaussianKernel>(nystroemMethod, gk, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            if(!useNystroemKernelPCA<GaussianKernel>(nystroemMethod, gk, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension))
+                return;
         }
         else
         {
-            KernelPCA<GaussianKernel, NaiveKernelRule<GaussianKernel>>(gk, (centerTranformedData == 1))
-            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            KernelPCA<GaussianKernel>(gk, (centerTranformedData == 1))
+            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension);
         }
     }
     else if (strcmp(kernel, "epanechnikov") == 0)
@@ -135,12 +139,13 @@ void kernel_pca(char const *kernel, SP_integer centerTranformedData, SP_integer
         EpanechnikovKernel ek(bandwidth);
         if(useNystroem == 1)
         {
-            useNystroemKernelPCA<EpanechnikovKernel>(nystroemMethod, ek, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            if(!useNystroemKernelPCA<EpanechnikovKernel>(nystroemMethod, ek, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension))
+                return;
         }
         else
         {
-            KernelPCA<EpanechnikovKernel, NaiveKernelRule<EpanechnikovKernel>>(ek, (centerTranformedData == 1))
-            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            KernelPCA<EpanechnikovKernel>(ek, (centerTranformedData == 1))
+            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension);
         }
     }
     else if (strcmp(kernel, "laplacian") == 0)
@@ -148,12 +153,13 @@ void kernel_pca(char const *kernel, SP_integer centerTranformedData, SP_integer
         LaplacianKernel tk(bandwidth);
         if(useNystroem == 1)
         {
-            useNystroemKernelPCA<LaplacianKernel>(nystroemMethod, tk, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            if(!useNystroemKernelPCA<LaplacianKernel>(nystroemMethod, tk, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension))
+                return;
         }
         else
         {
-            KernelPCA<LaplacianKernel, NaiveKernelRule<LaplacianKernel>>(tk, (centerTranformedData == 1))
-            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            KernelPCA<LaplacianKernel>(tk, (centerTranformedData == 1))
+            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension);
         }
     }
     else if (strcmp(kernel, "hyptan") == 0)
@@ -161,19 +167,35 @@ void kernel_pca(char const *kernel, SP_integer centerTranformedData, SP_integer
         HyperbolicTangentKernel htk(scale, offset);
         if(useNystroem == 1)
         {
-            useNystroemKernelPCA<HyperbolicTangentKernel>(nystroemMethod, htk, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            if(!useNystroemKernelPCA<HyperbolicTangentKernel>(nystroemMethod, htk, (centerTranformedData == 1), data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension))
+                return;
         }
         else
         {
-            KernelPCA<HyperbolicTangentKernel, NaiveKernelRule<HyperbolicTangentKernel>>(htk, (centerTranformedData == 1))
-            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, returnDim);
+            KernelPCA<HyperbolicTangentKernel>(htk, (centerTranformedData == 1))
+            .Apply(data, transformedDataReturnMat, eigvalReturnVector, eigvecReturnMat, newDimension);
         }
     }
     else
     {
         raisePrologDomainExeption(kernel, 1, "The given Kernel is unkown!", "kernel_pca");
+        return;
     }
 
+    if (newDimension < eigvecReturnMat.n_rows && newDimension > 0 && !useNystroem)
+    transformedDataReturnMat.shed_rows(newDimension, transformedDataReturnMat.n_rows - 1);
+    else
+    {
+        transformedDataReturnMat.shed_rows(data.n_rows, transformedDataReturnMat.n_rows - 1);
+    }
+
+    if(isnan(transformedDataReturnMat[0]))
+    {
+        raisePrologSystemExeption("Matrix contains nan!");
+        return;
+    }
+
+
     // return the transformedData Matrix
     returnMatrixInformation(transformedDataReturnMat, transformedDataMatArr, transformedDataMatColNum, transformedDataMatRowNum);
     
@@ -182,6 +204,4 @@ void kernel_pca(char const *kernel, SP_integer centerTranformedData, SP_integer
 
     // return the eigvec Matrix
     returnMatrixInformation(eigvecReturnMat, eigvecMatArr, eigvecMatColNum, eigvecMatRowNum);
-
-    *newDimension = returnDim;
 }

src/methods/kernel_pca/kernel_pca.pl

@@ -43,6 +43,8 @@ kernel_pca(Kernel, CenterTransformedData, UseNystroem, NystroemMethod, Degree, O
                   EigVecList, EigVecCols,
                   NewDimension) :-
         Bandwidth > 0.0,
+        NewDimension >= 0,
+        Bandwidth =< DataRows,
         convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
         kernel_pcaI(Kernel, CenterTransformedData, UseNystroem, NystroemMethod, Degree, Offset, Bandwidth, Scale,
                     X, Xsize, Xrownum,
@@ -60,7 +62,7 @@ foreign(kernel_pca, c, kernel_pcaI(     +string, +integer, +integer, +string,
                                         -pointer(float_array), -integer, -integer,
                                         -pointer(float_array), -integer,
                                         -pointer(float_array), -integer, -integer,
-                                        -integer)).
+                                        +integer)).
 
 
 %% Defines the functions that get connected from main.cpp

src/methods/kernel_pca/kernel_pca_test.pl

@@ -6,8 +6,6 @@
 :- use_module(kernel_pca).
 :- use_module('../../helper_files/helper.pl').
 
-reset_Model :-
-        initModel(1,0,50,0.0001).
 
 %%
 %% TESTING predicate kernel_pca/16
@@ -16,26 +14,166 @@ reset_Model :-
 
 %% Failure Tests
               
-test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, culprit, 50, 0.0001, _).
 
-test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
+test(kernel_pca_Init_Wrong_Kernel, [error(domain_error('The given Kernel is unkown!' , wrongType), _)]) :-
+        kernel_pca(wrongType, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0, [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, _, _, _, _, _, 0).
+
+test(kernel_pca_Init_Wrong_NystroemMethod, [error(domain_error('The given NystroemMethod is unknown!' , wrongType), _)]) :-
+        kernel_pca(linear, 0, 1, wrongType, 1.0, 0.0, 1.0, 1.0, [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, _, _, _, _, _, 0).
+
+test(kernel_pca_Zero_Bandwidth, fail) :-
+        kernel_pca(linear, 0, 0, kmeans, 1.0, 0.0, 0.0, 1.0, [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, _, _, _, _, _, 0).
         
 
 %% Successful Tests
 
-test(testDescription3, [true(Error =:= 1)]) :-
-        reset_Model_No_Train(perceptron),
-        train([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, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
+test(kernel_pca_Linear_Random) :-
+        kernel_pca(linear, 0, 0, random, 1.0, 0.0, 1.0, 1.0,
+                   [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, TransformedList, _, EigValList, EigVecList, _, 0),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
+
+test(kernel_pca_Linear_Ordered) :-
+        kernel_pca(linear, 0, 0, ordered, 1.0, 0.0, 1.0, 1.0,
+                   [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, TransformedList, _, EigValList, EigVecList, _, 0),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
+
+test(kernel_pca_Linear_Kmeans) :-
+        kernel_pca(linear, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0,
+                   [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, TransformedList, _, EigValList, EigVecList, _, 0),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
+
+test(kernel_pca_Gaussian) :-
+        kernel_pca(gaussian, 0, 0, random, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(gaussian, 0, 0, ordered, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(gaussian, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0,
+                   [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, TransformedList, _, EigValList, EigVecList, _, 0),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
+
+test(kernel_pca_Polynomial) :-
+        kernel_pca(polynomial, 0, 0, random, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(polynomial, 0, 0, ordered, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(polynomial, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0,
+                   [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, TransformedList, _, EigValList, EigVecList, _, 0),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
+
+test(kernel_pca_Hyptan, [error(_, system_error('Matrix contains nan!'))]) :-
+        kernel_pca(hyptan, 0, 0, random, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(hyptan, 0, 0, ordered, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(hyptan, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0,
+                   [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, TransformedList, _, EigValList, EigVecList, _, 0),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
+
+test(kernel_pca_Laplacian) :-
+        kernel_pca(laplacian, 0, 0, random, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(laplacian, 0, 0, ordered, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(laplacian, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0,
+                   [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, TransformedList, _, EigValList, EigVecList, _, 0),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
+
+test(kernel_pca_Epanechnikov) :-
+        kernel_pca(epanechnikov, 0, 0, random, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(epanechnikov, 0, 0, ordered, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(epanechnikov, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0,
+                   [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, TransformedList, _, EigValList, EigVecList, _, 0),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
+
+test(kernel_pca_Cosine) :-
+        kernel_pca(cosine, 0, 0, random, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(cosine, 0, 0, ordered, 1.0, 0.0, 1.0, 1.0,
+                   [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, _, _, _, _, _, 0),
+        kernel_pca(cosine, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0,
+                   [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, TransformedList, _, EigValList, EigVecList, _, 0),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
+
+
+test(kernel_pca_CSV_Input) :-
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        kernel_pca(linear, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0, Data, 4, TransformedList, _, EigValList, EigVecList, _, 0),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
+
+test(kernel_pca_CSV_Input_Same_NewDims) :-
+        open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+        take_csv_row(File, skipFirstRow,10, Data),
+        kernel_pca(linear, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0, Data, 4, TransformedList, _, EigValList, EigVecList, _, 4),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
 
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
-        reset_Model_No_Train(perceptron),
+test(kernel_pca_CSV_Input_Smaller_NewDims) :-
         open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
         take_csv_row(File, skipFirstRow,10, Data),
-        train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+        kernel_pca(linear, 0, 0, kmeans, 1.0, 0.0, 1.0, 1.0, Data, 4, TransformedList, _, EigValList, EigVecList, _, 2),
+        print('\nTransformedData: '),
+        print(TransformedList),
+        print('\nEigenValues: '),
+        print(EigValList),
+        print('\nEigenVectors: '),
+        print(EigVecList).
 
 :- end_tests(kernel_pca).
 

src/methods/lars/lars.cpp

@@ -153,7 +153,7 @@ double computeError(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMa
 	if(dataMatSize / dataMatRowNum != responsesArrSize)
 	{
 		cout << "Target dim doesnt fit to the Data dim" << endl;
-		return;
+		return 0.0;
 	}
 	// convert the Prolog array to arma::mat
 	mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
@@ -221,7 +221,7 @@ double train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum
 	if(dataMatSize / dataMatRowNum != responsesArrSize)
 	{
 		cout << "Target dim doesnt fit to the Data dim" << endl;
-		return;
+		return 0.0;
 	}
 	// convert the Prolog array to arma::mat
 	mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);

test_all.pl

 
+:- use_module(library(plunit)).
+
+
+%% Just compile this file and call run_tests. to run every tests 
+
+
 :- use_module('src/methods/adaboost/adaboost_test.pl').
 
-:- use_module('src/methods/approx_kfn/approx_kfn_test.pl').
+%%:- use_module('src/methods/approx_kfn/approx_kfn_test.pl').
 
-:- use_module('src/methods/bayesian_linear_regression/bayesian_linear_regression_test.pl').
+%%:- use_module('src/methods/bayesian_linear_regression/bayesian_linear_regression_test.pl').
 
 :- use_module('src/methods/dbscan/dbscan_test.pl').
 
@@ -13,83 +19,53 @@
 
 :- use_module('src/methods/fastmks/fastmks_test.pl').
 
-:- use_module('src/methods/hoeffding_tree/hoeffding_tree_test.pl').
+%%:- use_module('src/methods/hoeffding_tree/hoeffding_tree_test.pl').
 
 :- use_module('src/methods/kde/kde_test.pl').
 
 :- use_module('src/methods/kernel_pca/kernel_pca_test.pl').
 
-:- use_module('src/methods/kfn/kfn_test.pl').
+%%:- use_module('src/methods/kfn/kfn_test.pl').
 
-:- use_module('src/methods/kmeans/kmeans_test.pl').
+%%:- use_module('src/methods/kmeans/kmeans_test.pl').
 
-:- use_module('src/methods/knn/knn_test.pl').
+%%:- use_module('src/methods/knn/knn_test.pl').
 
-:- use_module('src/methods/lars/lars_test.pl').
+%%:- use_module('src/methods/lars/lars_test.pl').
 
-:- use_module('src/methods/linear_regression/linear_regression_test.pl').
+%%:- use_module('src/methods/linear_regression/linear_regression_test.pl').
 
-:- use_module('src/methods/linear_SVM/linear_SVM_test.pl').
+%%:- use_module('src/methods/linear_SVM/linear_SVM_test.pl').
 
-:- use_module('src/methods/lmnn/lmnn_test.pl').
+%%:- use_module('src/methods/lmnn/lmnn_test.pl').
 
-:- use_module('src/methods/local_coordinate_coding/local_coordinate_coding_test.pl').
+%%:- use_module('src/methods/local_coordinate_coding/local_coordinate_coding_test.pl').
 
-:- use_module('src/methods/logistic_regression/logistic_regression_test.pl').
+%%:- use_module('src/methods/logistic_regression/logistic_regression_test.pl').
 
-:- use_module('src/methods/lsh/lsh_test.pl').
+%%:- use_module('src/methods/lsh/lsh_test.pl').
 
-:- use_module('src/methods/mean_shift/mean_shift_test.pl').
+%%:- use_module('src/methods/mean_shift/mean_shift_test.pl').
 
-:- use_module('src/methods/naive_bayes_classifier/naive_bayes_classifier_test.pl').
+%%:- use_module('src/methods/naive_bayes_classifier/naive_bayes_classifier_test.pl').
 
-:- use_module('src/methods/nca/nca_test.pl').
+%%:- use_module('src/methods/nca/nca_test.pl').
 
-:- use_module('src/methods/nmf/nmf_test.pl').
+%%:- use_module('src/methods/nmf/nmf_test.pl').
 
-:- use_module('src/methods/pca/pca_test.pl').
+%%:- use_module('src/methods/pca/pca_test.pl').
 
-:- use_module('src/methods/perceptron/perceptron_test.pl').
+%%:- use_module('src/methods/perceptron/perceptron_test.pl').
 
-:- use_module('src/methods/radical/radical_test.pl').
+%%:- use_module('src/methods/radical/radical_test.pl').
 
-:- use_module('src/methods/random_forest/random_forest_test.pl').
+%%:- use_module('src/methods/random_forest/random_forest_test.pl').
 
-:- use_module('src/methods/softmax_regression/softmax_regression_test.pl').
+%%:- use_module('src/methods/softmax_regression/softmax_regression_test.pl').
 
-:- use_module('src/methods/sparse_coding/sparse_coding_test.pl').
+%% better to run the parse_coding tests alone because the c++ Method writes out alot of Debug messages that make the tests hard to read.
+%%:- use_module('src/methods/sparse_coding/sparse_coding_test.pl').
 
 :- use_module('src/helper_files/helper_tests.pl').
 
-run :-
-        run_adaboost_tests,
-        run_approx_kfn_tests,
-        run_bayesian_linear_regression_tests,
-        run_dbscan_tests,
-        run_decision_tree_tests,
-        run_emst_tests,
-        run_fastmks_tests,
-        run_hoeffding_tree_tests,
-        run_kde_tests,
-        run_kernel_pca_tests,
-        run_kfn_tests,
-        run_kmeans_tests,
-        run_knn_tests,
-        run_lars_tests,
-        run_linear_regression_tests,
-        run_linear_SVM_tests,
-        run_lmnn_tests,
-        run_local_coordinate_coding_tests,
-        run_logistic_regression_tests,
-        run_lsh_tests,
-        run_mean_shift_tests,
-        run_naive_bayes_classifier_tests,
-        run_nca_tests,
-        run_nmf_tests,
-        run_pca_tests,
-        run_perceptron_tests,
-        run_radical_tests,
-        run_random_forest_tests,
-        run_softmax_regression_tests,
-        run_sparse_coding_tests,
-        run_helper_tests.
\ No newline at end of file
+