diff --git a/src/methods/decision_tree/decision_tree.cpp b/src/methods/decision_tree/decision_tree.cpp
index e592413e030901118c192caab548ea66440095ab..8e6e7e794ce7e07ca7661c09fb35fb538df32084 100644
--- a/src/methods/decision_tree/decision_tree.cpp
+++ b/src/methods/decision_tree/decision_tree.cpp
@@ -37,7 +37,16 @@ void initModel( float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRow
// convert the Prolog array to arma::rowvec
Row<size_t> labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
-
+ try
+ {
+ decisionTreeObj = DecisionTree(data, labelsVector, numClasses, minimumLeafSize, minimumGainSplit, maximumDepth);
+ }
+ catch(const std::out_of_range& e)
+ {
+ raisePrologSystemExeption("Labels Vector is too short or its values are incorrect: should fit into [0,numClasses)!");
+ return;
+ }
+
decisionTreeObj = DecisionTree(data, labelsVector, numClasses, minimumLeafSize, minimumGainSplit, maximumDepth);
}
@@ -59,9 +68,17 @@ void classifyPoint( float *pointArr, SP_integer pointArrSize,
// create the ReturnVector
vec probsReturnVector;
+
-
- decisionTreeObj.Classify(pointVector, predictReturn, probsReturnVector);
+ try
+ {
+ decisionTreeObj.Classify(pointVector, predictReturn, probsReturnVector);
+ }
+ catch(const std::logic_error& e)
+ {
+ raisePrologSystemExeption("Given Point has the wrong Length!");
+ return;
+ }
// return prediction value
@@ -79,26 +96,33 @@ void classifyPoint( float *pointArr, SP_integer pointArrSize,
// description:
void classifyMatrix(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
float **predictArr, SP_integer *predictArrSize,
- float **probsArr, SP_integer *probsArrSize)
+ float **probsMatArr, SP_integer *probsMatColNum, SP_integer *probsMatRowNum)
{
// convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
-
+
// create the ReturnVector
Row< size_t > predictReturnVector;
+ // create the ReturnMat
+ mat probsReturnMat;
- // create the ReturnVector
- rowvec probsReturnVector;
-
- decisionTreeObj.Classify(data, predictReturnVector, probsReturnVector);
+ try
+ {
+ decisionTreeObj.Classify(data, predictReturnVector, probsReturnMat);
+ }
+ catch(const std::logic_error& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
// return the Vector
returnVectorInformation(predictReturnVector, predictArr, predictArrSize);
-
- // return the Vector
- returnVectorInformation(probsReturnVector, probsArr, probsArrSize);
+ /// return the Matrix
+ returnMatrixInformation(probsReturnMat, probsMatArr, probsMatColNum, probsMatRowNum);
+
}
// TODO:
@@ -122,5 +146,15 @@ double train( float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRow
// convert the Prolog array to arma::rowvec
Row<size_t> labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
- return decisionTreeObj.Train(data, labelsVector, numClasses, minimumLeafSize, minimumGainSplit, maximumDepth);
+ try
+ {
+ return decisionTreeObj.Train(data, labelsVector, numClasses, minimumLeafSize, minimumGainSplit, maximumDepth);
+ }
+ catch(const std::invalid_argument& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return 0.0;
+ }
+
+
}
diff --git a/src/methods/decision_tree/decision_tree.pl b/src/methods/decision_tree/decision_tree.pl
index c053b099e1869eb9a7f77ea9b0957bffb9d9ac4f..687ea26abd4555ae3b47f17c215387fe44af58c7 100644
--- a/src/methods/decision_tree/decision_tree.pl
+++ b/src/methods/decision_tree/decision_tree.pl
@@ -1,8 +1,8 @@
-:- module(decision_tree, [ initModel/9,
- classifyPoint/5,
- classifyMatrix/7,
- train/10]).
+:- module(decision_tree, [ initModel/7,
+ classifyPoint/3,
+ classifyMatrix/5,
+ train/8]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -20,6 +20,9 @@
%% definitions for the connected function
+foreign(initModel, c, initModelI( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +integer, +integer, +float32, +integer)).
%% --Input--
%% mat dataset,
%% vec labels,
@@ -34,11 +37,20 @@
%% Construct the decision tree on the given data and labels, assuming that the data is all of the numeric type.
%% Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.
%%
-foreign(initModel, c, initModel( +pointer(float_array), +integer, +integer,
- +pointer(float_array), +integer,
- +integer, +integer, +float32, +integer)).
-
-
+initModel(DataList, DataRows, LabelsList, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth) :-
+ NumClasses >= 0,
+ MinimumLeafSize > 0,
+ MinimumGainSplit > 0.0,
+ MinimumGainSplit < 1.0,
+ MaximumDepth >= 0,
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+ convert_list_to_float_array(LabelsList, array(Ysize, Y)),
+ initModelI(X, Xsize, Xrownum, Y, Ysize, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth).
+
+
+foreign(classifyPoint, c, classifyPointI(+pointer(float_array), +integer,
+ -integer,
+ -pointer(float_array), -integer)).
%% --Input--
%% vec point
%%
@@ -49,26 +61,36 @@ foreign(initModel, c, initModel( +pointer(float_array), +integer, +intege
%% --Description--
%% Classify the given point and also return estimates of the probability for each class in the given vector.
%%
-foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer,
- -integer,
- -pointer(float_array), -integer)).
+classifyPoint(DataList, Prediction, AssignList) :-
+ convert_list_to_float_array(DataList, array(Xsize, X)),
+ classifyPointI(X, Xsize, Prediction, Y, Ysize),
+ convert_float_array_to_list(Y, Ysize, AssignList).
+foreign(classifyMatrix, c, classifyMatrixI( +pointer(float_array), +integer, +integer,
+ -pointer(float_array), -integer,
+ -pointer(float_array), -integer, -integer)).
%% --Input--
%% mat data
%%
%% --Output--
%% vec predictions,
-%% vec probabilities
+%% mat probabilities
%%
%% --Description--
%% Classify the given points and also return estimates of the probabilities for each class in the given matrix.
%%
-foreign(classifyMatrix, c, classifyMatrix( +pointer(float_array), +integer, +integer,
- -pointer(float_array), -integer,
- -pointer(float_array), -integer)).
+classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ classifyMatrixI(X, Xsize, Xrows, Y, Ysize, Z, ZCols, ZRows),
+ convert_float_array_to_list(Y, Ysize, PredictionList),
+ convert_float_array_to_2d_list(Z, ZCols, ZRows, ProbsList).
+foreign(train, c, trainI(+pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +integer, +integer, +float32, +integer,
+ [-float32])).
%% --Input--
%% mat data,
%% vec labels,
@@ -84,10 +106,15 @@ foreign(classifyMatrix, c, classifyMatrix( +pointer(float_array), +integer,
%% Train the decision tree on the given data, assuming that all dimensions are numeric.
%% This will overwrite the given model. Setting minimumLeafSize and minimumGainSplit too small may cause the tree to overfit, but setting them too large may cause it to underfit.
%%
-foreign(train, c, train(+pointer(float_array), +integer, +integer,
- +pointer(float_array), +integer,
- +integer, +integer, +float32, +integer,
- [-float32])).
+train(DataList, DataRows, LabelsList, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy) :-
+ NumClasses >= 0,
+ MinimumLeafSize > 0,
+ MinimumGainSplit > 0.0,
+ MinimumGainSplit < 1.0,
+ MaximumDepth >= 0,
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+ convert_list_to_float_array(LabelsList, array(Ysize, Y)),
+ trainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy).
%% Defines the functions that get connected from main.cpp
diff --git a/src/methods/decision_tree/decision_tree_test.pl b/src/methods/decision_tree/decision_tree_test.pl
index 1bffbdaefd2d8ab44ac11041d764eeae9c0a0b17..209f0a7a3dc547d4b13fc23ae271997592e94592 100644
--- a/src/methods/decision_tree/decision_tree_test.pl
+++ b/src/methods/decision_tree/decision_tree_test.pl
@@ -1,56 +1,212 @@
+
+:- module(decision_tree_tests, [run_decision_tree_tests/0]).
+
:- use_module(library(plunit)).
:- use_module(decision_tree).
:- use_module('../../helper_files/helper.pl').
-reset_Model :-
- initModel(1,0,50,0.0001).
-
-:- begin_tests(lists).
-
-%% alpha tests
-test(alpha_std_init) :-
- reset_Model,
- alpha(0).
-test(alpha_wrong_input, fail) :-
- reset_Model,
- alpha(1).
-test(alpha_after_train, A =:= 9223372036854775808) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize),
- alpha(A).
-
-%% train tests
-test(correct_train) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train, fail) :-
- reset_Model,
- convert_list_to_float_array([],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train2, fail) :-
- reset_Model,
- convert_list_to_float_array([],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train4) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],2, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-:- end_tests(lists).
\ No newline at end of file
+reset_Model_With_Train :-
+ 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, [0,1,0,0], 2, 10, 0.5, 0).
+
+%%
+%% TESTING predicate initModel/7
+%%
+:- begin_tests(initModel).
+
+%% Failure Tests
+
+test(decision_tree_Negative_NumClass, 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, [0,0,0,0], -1, 1, 0.5, 0).
+
+test(decision_tree_Negative_LeafSize, 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, [0,0,0,0], 1, -1, 0.5, 0).
+
+test(decision_tree_Negative_GainSplit, 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, [0,0,0,0], 1, 1, -0.5, 0).
+
+test(decision_tree_Too_High_GainSplit, 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, [0,0,0,0], 1, 1, 1.5, 0).
+
+test(decision_tree_Negative_MaxDepth, 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, [0,0,0,0], 1, 1, 0.5, -1).
+
+test(decision_tree_Init_With_Wrong_Label_Dims1, [error(_,system_error('Labels Vector is too short or its values are incorrect: should fit into [0,numClasses)!'))]) :-
+ 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, [0,0], 1, 1, 0.5, 1).
+
+%% If the label vector is to long it seems to cause no problems
+test(decision_tree_Init_With_Wrong_Label_Dims2) :-
+ 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, [0,0,0,0,0,0], 1, 1, 0.5, 1).
+
+%% The same when the label values are out of range
+test(decision_tree_Init_With_Wrong_Label_Value) :-
+ 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, [0,5,0,-1], 1, 1, 0.5, 1).
+
+
+%% Successful Tests
+
+test(initModel_Direkt_Input_Use) :-
+ 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, [0,0,0,0], 2, 10, 0.5, 0).
+
+test(initModel_Direkt_CSV_Use) :-
+ 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, [0,1,0,1,1,0,1,1,1,0], 2, 2, 0.7, 3).
+
+:- end_tests(initModel).
+
+
+%%
+%% TESTING predicate classifyPoint/3
+%%
+:- begin_tests(classifyPoint).
+
+%% Failure Tests
+
+%% the point dimensionaly seems to not matter for mlpack
+%% so im not certain if this should be forced to fail
+test(classify_Point_With_Wrong_Dims) :-
+ reset_Model_With_Train,
+ classifyPoint([5.1,3.5,1.4,1.2,3.3], Prediction, AssignList),
+ print(Prediction),
+ print('\n'),
+ print(AssignList).
+
+
+%% Successful Tests
+
+test(classify_Point1) :-
+ reset_Model_With_Train,
+ classifyPoint([5.1,3.5,1.4], Prediction, AssignList),
+ print(Prediction),
+ print('\n'),
+ print(AssignList).
+
+test(classify_Point2) :-
+ reset_Model_With_Train,
+ classifyPoint([6.2,1.9,2.3], Prediction, AssignList),
+ print(Prediction),
+ print('\n'),
+ print(AssignList).
+
+:- end_tests(classifyPoint).
+
+
+%%
+%% TESTING predicate classifyMatrix/4
+%%
+:- begin_tests(classifyMatrix).
+
+%% Failure Tests
+
+%% the point dimensionaly seems to not matter for mlpack
+%% so im not certain if this should be forced to fail
+test(classify_Matrix_With_Wrong_Dims1) :-
+ reset_Model_With_Train,
+ classifyMatrix([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 5, PredictionList, ProbsList, _),
+ print(PredictionList),
+ print('\n'),
+ print(ProbsList).
+
+%% the point dimensionaly seems to not matter for mlpack
+%% so im not certain if this should be forced to fail
+test(classify_Matrix_With_Wrong_Dims2) :-
+ reset_Model_With_Train,
+ classifyMatrix([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 2, PredictionList, ProbsList, _),
+ print(PredictionList),
+ print('\n'),
+ print(ProbsList).
+
+
+%% Successful Tests
+
+test(classify_Matrix_Wierd_Trained_Labels) :-
+ 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, [0,5,0,-1], 1, 1, 0.5, 1),
+ classifyMatrix([5.1,3.5,1.4, 5.1,3.5,1.4, 5.1,3.5,1.4, 5.1,3.5,1.4, 5.1,3.5,1.4], 3, PredictionList, ProbsList, _),
+ print(PredictionList),
+ print('\n'),
+ print(ProbsList).
+
+test(classify_Matrix_Direkt_Input1) :-
+ reset_Model_With_Train,
+ classifyMatrix([5.1,3.5,1.4, 5.1,3.5,1.4, 5.1,3.5,1.4, 5.1,3.5,1.4, 5.1,3.5,1.4], 3, PredictionList, ProbsList, _),
+ print(PredictionList),
+ print('\n'),
+ print(ProbsList).
+
+test(classify_Matrix_Direkt_Input2) :-
+ reset_Model_With_Train,
+ classifyMatrix([2, 2, 3, 5, 1, 4, 1, 1, 4, 0, 3, 5, 0, 5, 5], 3, PredictionList, ProbsList, _),
+ print(PredictionList),
+ print('\n'),
+ print(ProbsList).
+
+test(classify_Matrix_CSV_Trained) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,30, Data),
+ train(Data, 4, [0,1,0,1,1,0,1,1,1,0, 0,1,0,1,1,0,1,1,1,0, 0,1,0,1,1,0,1,1,1,0], 2, 5, 0.0007, 0, _),
+ classifyMatrix([2, 2, 3, 5, 1, 4, 1, 1, 4, 0, 3, 5, 0, 5, 5, 2, 2, 6, 0, 1], 4, PredictionList, ProbsList, _),
+ print(PredictionList),
+ print('\n'),
+ print(ProbsList).
+
+:- end_tests(classifyMatrix).
+
+
+%%
+%% TESTING predicate train/8
+%%
+:- begin_tests(train).
+
+%% Failure Tests
+
+test(decision_tree_Train_Negative_NumClass, fail) :-
+ 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], -1, 1, 0.5, 0, _).
+
+test(decision_tree_Train_Negative_LeafSize, fail) :-
+ 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], 1, -1, 0.5, 0, _).
+
+test(decision_tree_Train_Negative_GainSplit, fail) :-
+ 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], 1, 1, -0.5, 0, _).
+
+test(decision_tree_Train_Too_High_GainSplit, fail) :-
+ 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], 1, 1, 1.5, 0, _).
+
+test(decision_tree_Train_Negative_MaxDepth, fail) :-
+ 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], 1, 1, 0.5, -1, _).
+
+test(decision_tree_Train_Wrong_Label_Dims1, [error(_,system_error('DecisionTree::Train(): number of points (4) does not match number of labels (2)!\n'))]) :-
+ 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], 1, 1, 0.5, 1, _).
+
+test(decision_tree_Train_Wrong_Label_Dims2, [error(_,system_error('DecisionTree::Train(): number of points (4) does not match number of labels (6)!\n'))]) :-
+ 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, [0,0,0,0,0,0], 1, 1, 0.5, 1, _).
+
+%% there seems to be no check for the label values
+test(decision_tree_Train_Wrong_Labels) :-
+ 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, [-1,0,0,5], 1, 1, 0.5, 1, _).
+
+
+%% Successful Tests
+
+test(initModel_Direkt_Input_Use, [true(Entropy =:= 0.0)]) :-
+ 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, 10, 0.5, 0, Entropy).
+
+test(initModel_Direkt_CSV_Use, [true(Entropy =:= 0.48)]) :-
+ 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, 2, 0.7, 3, Entropy).
+
+test(initModel_Direkt_Input_Use, [true(Entropy =:= 0.0)]) :-
+ reset_Model_With_Train,
+ 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], 3, 10, 0.7, 0, Entropy).
+
+test(initModel_Direkt_CSV_Use, [true(Entropy =:= 0.3767195767195767)]) :-
+ reset_Model_With_Train,
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,30, Data),
+ train(Data, 4, [0,1,0,1,1,0,1,1,1,0, 0,1,0,1,1,0,1,1,1,0, 0,1,0,1,1,0,1,1,1,0], 2, 5, 0.0005, 0, Entropy).
+
+:- end_tests(train).
+
+run_decision_tree_tests :-
+ run_tests.
diff --git a/test_all.pl b/test_all.pl
index 7b5dc51e503bdc3dbdd21445811efc902defc895..e4fe222b3433d0cb3513a86ce1a0826d30c888a7 100644
--- a/test_all.pl
+++ b/test_all.pl
@@ -4,6 +4,8 @@
:- use_module('src/methods/dbscan/dbscan_test.pl').
+:- use_module('src/methods/decision_tree/decision_tree_test.pl').
+
:- use_module('src/methods/sparse_coding/sparse_coding_test.pl').
:- use_module('src/helper_files/helper_tests.pl').
@@ -11,5 +13,6 @@
run :-
run_adaboost_tests,
run_dbscan_tests,
+ run_decision_tree_tests,
run_sparse_coding_tests,
run_helper_tests.
\ No newline at end of file