diff --git a/src/helper_files/helper.cpp b/src/helper_files/helper.cpp
index 2321d2e4fe610eac4e3e1f8fdb5db6cc6919a91a..05b47f5c46d2a981a617b0bf739bdd89d9efa8e3 100644
--- a/src/helper_files/helper.cpp
+++ b/src/helper_files/helper.cpp
@@ -33,6 +33,21 @@ float *convertToArray(mat matrix)
return convertToArray(vector);
}
+float *convertToArray(arma::Mat< unsigned long > matrix)
+{
+ int vectorSize = matrix.size();
+
+ // using malloc so the memory is still valid outside of the function
+ float *arr = (float *)malloc (sizeof(float) * vectorSize);
+
+ // save the data in a normal array so it can be send to prolog
+ for(int i = 0; i < vectorSize; i++)
+ {
+ arr[i] = (double)matrix[i];
+ }
+ return arr;
+}
+
float *convertToArray(vector<size_t> vec)
{
colvec newVec = conv_to<colvec>::from(vec);
@@ -62,7 +77,6 @@ float *convertToArray(vector<vec> matrix)
vec c = arma::join_cols(a, b);
newVec = c;
}
- cout << newVec << endl;
return convertToArray(newVec);
}
diff --git a/src/helper_files/helper.hpp b/src/helper_files/helper.hpp
index 2900ca5d2618a4e24b44195b7ae0c0d6bfbe99f1..15dee9381356ed95961cd303c656f2e7db6c061e 100644
--- a/src/helper_files/helper.hpp
+++ b/src/helper_files/helper.hpp
@@ -13,6 +13,8 @@ float *convertToArray(rowvec vec);
float *convertToArray(mat matrix);
+float *convertToArray(arma::Mat< size_t > matrix);
+
float *convertToArray(vector<size_t> vec);
float *convertToArray(vector<double> vec);
diff --git a/src/methods/fastmks/fastmks.cpp b/src/methods/fastmks/fastmks.cpp
index 7240f26019c06c61cbdf650420b6ab09a8c1d24a..2137686dd17a9dc2e4407bc453e1e26bfe1d15ea 100644
--- a/src/methods/fastmks/fastmks.cpp
+++ b/src/methods/fastmks/fastmks.cpp
@@ -18,8 +18,9 @@ using namespace mlpack::kernel;
// Global Variable of the FastMKSModel object so it can be accessed from all functions
FastMKSModel fastMKSModel;
+bool isModelTrained = false;
+
-// TODO:
// input: util::Timers & timers,
// arma::mat && referenceData,
// TKernelType & kernel,
@@ -32,9 +33,16 @@ FastMKSModel fastMKSModel;
// const double base
// output:
// description:
-void initModel(float *referenceMatArr, SP_integer referenceMatSize, SP_integer referenceMatRowNum, char const *kernel, double degree, double offset, double bandwidth, double scale, SP_integer singleMode, SP_integer naive, double base)
+// Initializes the model on the given reference set.
+//
+void initModel(float *referenceMatArr, SP_integer referenceMatSize, SP_integer referenceMatRowNum,
+ char const *kernel,
+ double degree, double offset, double bandwidth, double scale,
+ SP_integer singleMode,
+ SP_integer naive,
+ double base)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat reference = convertArrayToMat(referenceMatArr, referenceMatSize, referenceMatRowNum);
if (strcmp(kernel, "linear") == 0)
@@ -42,46 +50,59 @@ void initModel(float *referenceMatArr, SP_integer referenceMatSize, SP_integer r
fastMKSModel = FastMKSModel(0);
LinearKernel lk;
fastMKSModel.BuildModel(move(reference), lk, (singleMode == 1), (naive == 1), base);
+ isModelTrained = true;
}
else if (strcmp(kernel, "polynomial") == 0)
{
PolynomialKernel pk(degree, offset);
fastMKSModel.KernelType() = FastMKSModel::POLYNOMIAL_KERNEL;
fastMKSModel.BuildModel(move(reference), pk, (singleMode == 1), (naive == 1), base);
+ isModelTrained = true;
}
else if (strcmp(kernel, "cosine") == 0)
{
CosineDistance cd;
fastMKSModel.KernelType() = FastMKSModel::COSINE_DISTANCE;
fastMKSModel.BuildModel(move(reference), cd, (singleMode == 1), (naive == 1), base);
+ isModelTrained = true;
}
else if (strcmp(kernel, "gaussian") == 0)
{
GaussianKernel gk(bandwidth);
fastMKSModel.KernelType() = FastMKSModel::GAUSSIAN_KERNEL;
fastMKSModel.BuildModel(move(reference), gk, (singleMode == 1), (naive == 1), base);
+ isModelTrained = true;
}
else if (strcmp(kernel, "epanechnikov") == 0)
{
EpanechnikovKernel ek(bandwidth);
fastMKSModel.KernelType() = FastMKSModel::EPANECHNIKOV_KERNEL;
fastMKSModel.BuildModel(move(reference), ek, (singleMode == 1), (naive == 1), base);
+ isModelTrained = true;
}
else if (strcmp(kernel, "triangular") == 0)
{
TriangularKernel tk(bandwidth);
fastMKSModel.KernelType() = FastMKSModel::TRIANGULAR_KERNEL;
fastMKSModel.BuildModel(move(reference), tk, (singleMode == 1), (naive == 1), base);
+ isModelTrained = true;
}
else if (strcmp(kernel, "hyptan") == 0)
{
+
HyperbolicTangentKernel htk(scale, offset);
fastMKSModel.KernelType() = FastMKSModel::HYPTAN_KERNEL;
fastMKSModel.BuildModel(move(reference), htk, (singleMode == 1), (naive == 1), base);
+ isModelTrained = true;
+ }
+ else
+ {
+ raisePrologDomainExeption(kernel, 2, "The given kernel is unkown!", "initModel");
}
+
}
-// TODO:
+
// input: util::Timers & timers,
// const arma::mat & querySet,
// const size_t k,
@@ -90,62 +111,77 @@ void initModel(float *referenceMatArr, SP_integer referenceMatSize, SP_integer r
// const double base
// output:
// description:
-void searchWithQuery(float *querySetMatArr, SP_integer querySetMatSize, SP_integer querySetMatRowNum, SP_integer k, float **indicesMatArr, SP_integer *indicesMatColNum, SP_integer *indicesMatRowNum, float **kernelsMatArr, SP_integer *kernelsMatColNum, SP_integer *kernelsMatRowNum, double base)
+// Search with a different query set.
+//
+void searchWithQuery(float *querySetMatArr, SP_integer querySetMatSize, SP_integer querySetMatRowNum,
+ SP_integer k,
+ float **indicesMatArr, SP_integer *indicesMatColNum, SP_integer *indicesMatRowNum,
+ float **kernelsMatArr, SP_integer *kernelsMatColNum, SP_integer *kernelsMatRowNum,
+ double base)
{
- // convert the Prolog arrays to arma::mat
+ if (!isModelTrained)
+ {
+ raisePrologSystemExeption("The Model hasnt been trained yet!");
+ return;
+ }
+
+ // convert the Prolog array to arma::mat
mat querySet = convertArrayToMat(querySetMatArr, querySetMatSize, querySetMatRowNum);
- // create the ReturnMat
+ // create the ReturnMats
arma::Mat< size_t > indicesReturnMat;
-
- // create the ReturnMat
mat kernelsReturnMat;
- fastMKSModel.Search(querySet, k, indicesReturnMat, kernelsReturnMat, base);
-
- // return the Matrix dimensions
- *indicesMatColNum = indicesReturnMat.n_cols;
- *indicesMatRowNum = indicesReturnMat.n_rows;
- // return the Matrix as one long Array
- *indicesMatArr = convertToArray(indicesReturnMat);
-
- // return the Matrix dimensions
- *kernelsMatColNum = kernelsReturnMat.n_cols;
- *kernelsMatRowNum = kernelsReturnMat.n_rows;
+ try
+ {
+ fastMKSModel.Search(querySet, k, indicesReturnMat, kernelsReturnMat, base);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ }
+
- // return the Matrix as one long Array
- *kernelsMatArr = convertToArray(kernelsReturnMat);
+ // return the Matrices
+ returnMatrixInformation(indicesReturnMat, indicesMatArr, indicesMatColNum, indicesMatRowNum);
+ returnMatrixInformation(kernelsReturnMat, kernelsMatArr, kernelsMatColNum, kernelsMatRowNum);
}
-// TODO:
+
// input: util::Timers & timers,
// const size_t k,
// arma::Mat< size_t > & indices <-,
// arma::mat & kernels <-
// output:
// description:
-void searchNoQuery(SP_integer k, float **indicesMatArr, SP_integer *indicesMatColNum, SP_integer *indicesMatRowNum, float **kernelsMatArr, SP_integer *kernelsMatColNum, SP_integer *kernelsMatRowNum)
+// Search with the reference set as the query set.
+//
+void searchNoQuery(SP_integer k,
+ float **indicesMatArr, SP_integer *indicesMatColNum, SP_integer *indicesMatRowNum,
+ float **kernelsMatArr, SP_integer *kernelsMatColNum, SP_integer *kernelsMatRowNum)
{
- // create the ReturnMat
+ if (!isModelTrained)
+ {
+ raisePrologSystemExeption("The Model hasnt been trained yet!");
+ return;
+ }
+ // create the ReturnMats
arma::Mat< size_t > indicesReturnMat;
-
- // create the ReturnMat
- mat kernelsReturnMat;
-
- fastMKSModel.Search(k, indicesReturnMat, kernelsReturnMat);
-
- // return the Matrix dimensions
- *indicesMatColNum = indicesReturnMat.n_cols;
- *indicesMatRowNum = indicesReturnMat.n_rows;
+ arma::mat kernelsReturnMat;
- // return the Matrix as one long Array
- *indicesMatArr = convertToArray(indicesReturnMat);
- // return the Matrix dimensions
- *kernelsMatColNum = kernelsReturnMat.n_cols;
- *kernelsMatRowNum = kernelsReturnMat.n_rows;
-
- // return the Matrix as one long Array
- *kernelsMatArr = convertToArray(kernelsReturnMat);
+ try
+ {
+ fastMKSModel.Search((size_t)k, indicesReturnMat, kernelsReturnMat);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
+
+
+ returnMatrixInformation(indicesReturnMat, indicesMatArr, indicesMatColNum, indicesMatRowNum);
+ returnMatrixInformation(kernelsReturnMat, kernelsMatArr, kernelsMatColNum, kernelsMatRowNum);
}
diff --git a/src/methods/fastmks/fastmks.pl b/src/methods/fastmks/fastmks.pl
index 134fcaaddfe6fbe7bacb74f0134c387bb7569896..70cde77fa4bedf41fc59edb1cb6eaf6e7be9f8d8 100644
--- a/src/methods/fastmks/fastmks.pl
+++ b/src/methods/fastmks/fastmks.pl
@@ -1,7 +1,7 @@
-:- module(fastmks, [ initModel/11,
- searchWithQuery/11,
- searchNoQuery/7]).
+:- module(fastmks, [ initModel/10,
+ searchWithQuery/8,
+ searchNoQuery/5]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -19,6 +19,11 @@
%% definitions for the connected function
+foreign(initModel, c, initModelI( +pointer(float_array), +integer, +integer,
+ +string,
+ +float32, +float32, +float32, +float32,
+ +integer, +integer,
+ +float32)).
%% --Input--
%% mat referenceData,
%% string kernel "linear", "polynomial", "cosine", "gaussian", "epanechnikov", "triangular", "hyptan"
@@ -35,13 +40,19 @@
%% --Description--
%% Initializes the model on the given reference set.
%%
-foreign(initModel, c, initModel(+pointer(float_array), +integer, +integer,
- +string,
- +float32, +float32, +float32, +float32,
- +integer, +integer,
- +float32)).
+initModel(DataList, DataRows, Kernel, Degree, Offset, Bandwidth, Scale, SingleMode, Naive, Base) :-
+ Base > 1.0,
+ Bandwidth > 0.0,
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ initModelI(X, Xsize, Xrows, Kernel, Degree, Offset, Bandwidth, Scale, SingleMode, Naive, Base).
+
+foreign(searchWithQuery, c, searchWithQueryI( +pointer(float_array), +integer, +integer,
+ +integer,
+ -pointer(float_array), -integer, -integer,
+ -pointer(float_array), -integer, -integer,
+ +float32)).
%% --Input--
%% mat querySet,
%% int k,
@@ -54,13 +65,19 @@ foreign(initModel, c, initModel(+pointer(float_array), +integer, +integer,
%% --Description--
%% Search with a different query set.
%%
-foreign(searchWithQuery, c, searchWithQuery( +pointer(float_array), +integer, +integer,
- +integer,
- -pointer(float_array), -integer, -integer,
- -pointer(float_array), -integer, -integer,
- +float32)).
+searchWithQuery(DataList, DataRows, K, IndicesList, YCols, KernelsList, ZCols, Base) :-
+ K > 0,
+ Base > 1.0,
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ searchWithQueryI(X, Xsize, Xrows, K, Y, YCols, YRows, Z, ZCols, ZRows, Base),
+ convert_float_array_to_2d_list(Y, YCols, YRows, IndicesList),
+ convert_float_array_to_2d_list(Z, ZCols, ZRows, KernelsList).
+
+foreign(searchNoQuery, c, searchNoQueryI( +integer,
+ -pointer(float_array), -integer, -integer,
+ -pointer(float_array), -integer, -integer)).
%% --Input--
%% int k
%%
@@ -71,9 +88,11 @@ foreign(searchWithQuery, c, searchWithQuery( +pointer(float_array), +integer,
%% --Description--
%% Search with the reference set as the query set.
%%
-foreign(searchNoQuery, c, searchNoQuery(+integer,
- -pointer(float_array), -integer, -integer,
- -pointer(float_array), -integer, -integer)).
+searchNoQuery(K, IndicesList, YCols, KernelsList, ZCols) :-
+ K > 0,
+ searchNoQueryI(K, Y, YCols, YRows, Z, ZCols, ZRows),
+ convert_float_array_to_2d_list(Y, YCols, YRows, IndicesList),
+ convert_float_array_to_2d_list(Z, ZCols, ZRows, KernelsList).
%% Defines the functions that get connected from main.cpp
@@ -81,4 +100,5 @@ foreign_resource(fastmks, [ initModel,
searchWithQuery,
searchNoQuery]).
+
:- load_foreign_resource(fastmks).
diff --git a/src/methods/fastmks/fastmks_test.pl b/src/methods/fastmks/fastmks_test.pl
index b33843d3fc0f4ac3c1171762a9ce01567ec375db..1ba17d322316918212569df26e4022412fbd7596 100644
--- a/src/methods/fastmks/fastmks_test.pl
+++ b/src/methods/fastmks/fastmks_test.pl
@@ -1,56 +1,154 @@
+
+:- module(fastmks_tests, [run_fastmks_tests/0]).
+
:- use_module(library(plunit)).
:- use_module(fastmks).
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initModel(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),
+ initModel(Data, 4, linear, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.2).
-:- 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) :-
+%%
+%% TESTING predicate initModel/10
+%%
+:- begin_tests(initModel).
+
+%% Failure Tests
+
+test(searchWithQuery_Fastmks_Search_Before_Init, [error(_,system_error('The Model hasnt been trained yet!'))]) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,5, Data),
+ searchWithQuery(Data, 4, 2, _, _, _, _, 1.1).
+
+test(searchNoQuery_Fastmks_Search_Before_Init, [error(_,system_error('The Model hasnt been trained yet!'))]) :-
+ searchNoQuery(2, _, _, _, _).
+
+test(initModel_Fatsmks_WrongKernel_Input, [error(domain_error('The given kernel is unkown!' , wrongKernel), _)]) :-
+ initModel([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, wrongKernel, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.2).
+
+test(initModel_Fatsmks_Bad_Base_Input, fail) :-
+ initModel([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, linear, 0.0, 0.0, 1.0, 0.0, 0, 0, -0.1).
+
+
+%% Successful Tests
+
+test(iniModel_Fastmks_Linear) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ initModel(Data, 4, linear, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.5).
+
+test(iniModel_Fastmks_Polynomial) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ initModel(Data, 4, polynomial, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.5).
+
+test(iniModel_Fastmks_Cosine) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ initModel(Data, 4, cosine, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.5).
+
+test(iniModel_Fastmks_Gaussian) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ initModel(Data, 4, gaussian, 0.5, 0.5, 0.5, 0.5, 0, 0, 1.5).
+
+test(iniModel_Fastmks_Epanechnikov) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ initModel(Data, 4, epanechnikov, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.5).
+
+test(iniModel_Fastmks_Triangular) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ initModel(Data, 4, triangular, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.5).
+
+test(iniModel_Fastmks_Hyptan) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ initModel(Data, 4, hyptan, 0.0, 0.0, 1.0, 1.0, 0, 0, 1.5).
+
+:- end_tests(initModel).
+
+
+%%
+%% TESTING predicate searchWithQuery/8
+%%
+:- begin_tests(searchWithQuery).
+
+%% Failure Tests
+
+test(searchWithQuery_Fastmks_Negative_K, fail) :-
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).
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,5, Data),
+ searchWithQuery(Data, 4, -2, _, _, _, _, 1.1).
-%% train tests
-test(correct_train) :-
+test(searchWithQuery_Fastmks_Negative_Base, fail) :-
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) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,5, Data),
+ searchWithQuery(Data, 4, 2, _, _, _, _, -1.1).
+
+
+%% Successful Tests
+
+test(searchWithQuery_Fastmks_New_Query) :-
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) :-
+ searchWithQuery([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, 2, IndicesList, _, KernelsList, _, 1.1),
+ print('Indices:\n'),
+ print(IndicesList),
+ print('Kernels:\n'),
+ print(KernelsList).
+
+test(searchWithQuery_Fastmks_Training_Data_Query) :-
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) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,5, Data),
+ searchWithQuery(Data, 4, 2, IndicesList, _, KernelsList, _, 1.1),
+ print('Indices:\n'),
+ print(IndicesList),
+ print('Kernels:\n'),
+ print(KernelsList).
+
+:- end_tests(searchWithQuery).
+
+
+%%
+%% TESTING predicate searchNoQuery/5
+%%
+:- begin_tests(searchNoQuery).
+
+%% Failure Tests
+
+test(searchNoQuery_Fastmks_Negative_K, 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) :-
+ searchNoQuery(-2, _, _, _, _).
+
+
+%% Successful Tests
+
+test(testDescription) :-
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) :-
+ searchNoQuery(2, IndicesList, _, KernelsList, _),
+ print('Indices:\n'),
+ print(IndicesList),
+ print('Kernels:\n'),
+ print(KernelsList).
+
+test(testDescription) :-
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
+ searchNoQuery(5, IndicesList, _, KernelsList, _),
+ print('Indices:\n'),
+ print(IndicesList),
+ print('Kernels:\n'),
+ print(KernelsList).
+
+:- end_tests(searchNoQuery).
+
+run_fastmks_tests :-
+ run_tests.
+
diff --git a/test_all.pl b/test_all.pl
index e4fe222b3433d0cb3513a86ce1a0826d30c888a7..749d0c92d1249e5a03f5388ece6608b30108fefd 100644
--- a/test_all.pl
+++ b/test_all.pl
@@ -6,6 +6,10 @@
:- use_module('src/methods/decision_tree/decision_tree_test.pl').
+:- use_module('src/methods/emst/emst_test.pl').
+
+:- use_module('src/methods/fastmks/fastmks_test.pl').
+
:- use_module('src/methods/sparse_coding/sparse_coding_test.pl').
:- use_module('src/helper_files/helper_tests.pl').
@@ -14,5 +18,7 @@ run :-
run_adaboost_tests,
run_dbscan_tests,
run_decision_tree_tests,
+ run_emst_tests,
+ run_fastmks_tests,
run_sparse_coding_tests,
run_helper_tests.
\ No newline at end of file