diff --git a/src/methods/adaboost/adaboost.pl b/src/methods/adaboost/adaboost.pl
index f7416a91884d424d9a0e0db539a3b16aa047c61f..a12de42f5ab70abe06ef61d4e85eb6246953a928 100644
--- a/src/methods/adaboost/adaboost.pl
+++ b/src/methods/adaboost/adaboost.pl
@@ -1,11 +1,11 @@
-:- module(adaboost, [ initModelWithTraining/7,
- initModelNoTraining/2,
- classify/5,
- numClasses/1,
- getTolerance/1,
- modifyTolerance/1,
- train/8]).
+:- module(adaboost, [ adaboost_initModelWithTraining/7,
+ adaboost_initModelNoTraining/2,
+ adaboost_classify/5,
+ adaboost_numClasses/1,
+ adaboost_getTolerance/1,
+ adaboost_modifyTolerance/1,
+ adaboost_train/8]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -38,7 +38,7 @@
%%
%% Initiates the Adaboostmodel and trains it, so classify can be used immediately.
%%
-initModelWithTraining(MatList, MatRows, VecList, NumClasses, Learner, Iterations, Tolerance) :-
+adaboost_initModelWithTraining(MatList, MatRows, VecList, NumClasses, Learner, Iterations, Tolerance) :-
NumClasses >= 0,
Iterations >= 0,
Tolerance > 0,
@@ -46,11 +46,11 @@ initModelWithTraining(MatList, MatRows, VecList, NumClasses, Learner, Iterations
convert_list_to_float_array(VecList, array(Ysize, Y)),
initModelWithTrainingI(X, Xsize, Xrownum, Y, Ysize, NumClasses, Learner, Iterations, Tolerance).
-foreign(initModelWithTraining, c, initModelWithTrainingI(+pointer(float_array), +integer, +integer,
- +pointer(float_array), +integer,
- +integer,
- +string,
- +integer , +float32)).
+foreign(initModelWithTraining, c, initModelWithTrainingI( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +integer,
+ +string,
+ +integer , +float32)).
@@ -64,7 +64,7 @@ foreign(initModelWithTraining, c, initModelWithTrainingI(+pointer(float_array),
%% Needs to be called first before all other predicates exept initModelWithTraining!
%% Initiates the Adaboostmodel but doesnt train it, so train has to be used first before classify can be used.
%%
-initModelNoTraining(Tolerance, Learner) :-
+adaboost_initModelNoTraining(Tolerance, Learner) :-
Tolerance > 0,
initModelNoTrainingI(Tolerance, Learner).
@@ -81,7 +81,7 @@ foreign(initModelNoTraining, c, initModelNoTrainingI(+float32, +string)).
%% --Description--
%% Classifies the given data into the number of classes the model was trained for.
%%
-classify(TestList, TestRows, PredicList, ProbsList, ZRows) :-
+adaboost_classify(TestList, TestRows, PredicList, ProbsList, ZRows) :-
convert_list_to_float_array(TestList, TestRows, array(Xsize, Xrownum, X)),
classifyI(X, Xsize, Xrownum, Y, Ysize, Z, ZCols, ZRows),
convert_float_array_to_list(Y, Ysize, PredicList),
@@ -102,7 +102,7 @@ foreign(classify, c, classifyI( +pointer(float_array), +integer, +integer,
%% --Description--
%% Returns the amount of classes defined in the model for classification.
%%
-numClasses(ClassesNum) :-
+adaboost_numClasses(ClassesNum) :-
numClassesI(ClassesNum).
foreign(numClasses, c, numClassesI([-integer])).
@@ -116,7 +116,7 @@ foreign(numClasses, c, numClassesI([-integer])).
%% --Description--
%% Returns the tolerance of the model.
%%
-getTolerance(Tolerance) :-
+adaboost_getTolerance(Tolerance) :-
getToleranceI(Tolerance).
foreign(getTolerance, c, getToleranceI([-float32])).
@@ -130,7 +130,7 @@ foreign(getTolerance, c, getToleranceI([-float32])).
%% --Description--
%% Modifies the tolerance of the model.
%%
-modifyTolerance(NewTolerance) :-
+adaboost_modifyTolerance(NewTolerance) :-
NewTolerance > 0,
modifyToleranceI(NewTolerance).
@@ -149,7 +149,7 @@ foreign(modifyTolerance, c, modifyToleranceI(+float32)).
%% float double upper bound training error
%%
%% --Description--
-train(MatList, MatRows, VecList, NumClasses, Learner, Iterations, Tolerance, Error) :-
+adaboost_train(MatList, MatRows, VecList, NumClasses, Learner, Iterations, Tolerance, Error) :-
NumClasses >= 0,
Iterations >= 0,
Tolerance >= 0,
@@ -157,7 +157,7 @@ train(MatList, MatRows, VecList, NumClasses, Learner, Iterations, Tolerance, Err
convert_list_to_float_array(VecList, array(Ysize, Y)),
trainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, Learner, Iterations, Tolerance, Error).
-foreign(train, c, trainI( +pointer(float_array), +integer, +integer,
+foreign(train, c, trainI( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer,
+string,
diff --git a/src/methods/adaboost/adaboost_test.pl b/src/methods/adaboost/adaboost_test.pl
index 1c1cae0c2c5105031f1d143a973ffa4be4a93bc0..fe9a54f870c2f58054d243e841179068756171e7 100644
--- a/src/methods/adaboost/adaboost_test.pl
+++ b/src/methods/adaboost/adaboost_test.pl
@@ -11,308 +11,309 @@
reset_Model_No_Train(Learner) :-
- initModelNoTraining(0.0001, Learner).
+ adaboost_initModelNoTraining(0.0001, Learner).
reset_Model_With_Train(Learner) :-
- initModelWithTraining([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, 0], 2, Learner, 50, 0.0001).
+ adaboost_initModelWithTraining([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, 0], 2, Learner, 50, 0.0001).
%%
-%% TESTING predicate initModelWithTraining/7
+%% TESTING predicate adaboost_initModelWithTraining/7
%%
-:- begin_tests(initModelWithTraining).
+:- begin_tests(adaboost_initModelWithTraining).
%% Failure Tests
test(initModelWithTraining_WrongInputTypes, fail) :-
- initModelWithTraining(wrong, 3, [0.2,0.2,0.2,0.2], 2, perceptron, 50, 0.0001).
+ adaboost_initModelWithTraining(wrong, 3, [0.2,0.2,0.2,0.2], 2, perceptron, 50, 0.0001).
test(initModelWithTraining_WrongTol, fail) :-
- initModelWithTraining([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, 0], -2, perceptron, 50, 0.0001).
+ adaboost_initModelWithTraining([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, 0], -2, perceptron, 50, 0.0001).
test(initModelWithTraining_WrongLearner, [error(domain_error('perceptron or decision_stump' ,wrongLearner), _)]) :-
- initModelWithTraining([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, 0], 2, wrongLearner, 50, 0.0001).
+ adaboost_initModelWithTraining([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, 0], 2, wrongLearner, 50, 0.0001).
test(initModelWithTraining_WrongIterations, fail) :-
- initModelWithTraining([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, 0], 2, perceptron, -50, 0.0001).
+ adaboost_initModelWithTraining([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, 0], 2, perceptron, -50, 0.0001).
test(initModelWithTraining_WrongTol, fail) :-
- initModelWithTraining([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, 0], 2, perceptron, 50, -10.0).
+ adaboost_initModelWithTraining([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, 0], 2, perceptron, 50, -10.0).
test(initModelWithTraining_MissmatchingLabels) :-
- initModelWithTraining([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, 0, 1 ,1], 2, perceptron, 50, 0.0001).
+ adaboost_initModelWithTraining([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, 0, 1 ,1], 2, perceptron, 50, 0.0001).
%% Successful Tests
test(initModelWithTraining_Perceptron) :-
- initModelWithTraining([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, 0], 2, perceptron, 50, 0.0001).
+ adaboost_initModelWithTraining([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, 0], 2, perceptron, 50, 0.0001).
test(initModelWithTraining_DecisionStump) :-
- initModelWithTraining([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3], 3, [0, 0, 1], 2, decision_stump, 50, 0.0001).
+ adaboost_initModelWithTraining([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3], 3, [0, 0, 1], 2, decision_stump, 50, 0.0001).
+
+:- end_tests(adaboost_initModelWithTraining).
-:- end_tests(initModelWithTraining).
%%
-%% TESTING predicate initModelNoTraining/2
+%% TESTING predicate adaboost_initModelNoTraining/2
%%
-:- begin_tests(initModelNoTrain).
+:- begin_tests(adaboost_initModelNoTraining).
%% Failure Tests
test(initModelNoTraining_WrongLearner, [error(domain_error('perceptron or decision_stump' ,wrongLearner), _)]) :-
- initModelNoTraining(0.0001, wrongLearner).
+ adaboost_initModelNoTraining(0.0001, wrongLearner).
test(initModelNoTraining_WrongTol, fail) :-
- initModelNoTraining(-1.0, perceptron).
+ adaboost_initModelNoTraining(-1.0, perceptron).
%% Successful Tests
test(initModelNoTraining_Perceptron) :-
- initModelNoTraining(0.001, perceptron).
+ adaboost_initModelNoTraining(0.001, perceptron).
test(initModelNoTraining_DecisionStump) :-
- initModelNoTraining(0.000014, decision_stump).
+ adaboost_initModelNoTraining(0.000014, decision_stump).
-:- end_tests(initModelNoTrain).
+:- end_tests(adaboost_initModelNoTraining).
%%
-%% TESTING predicate classify/8
+%% TESTING predicate adaboost_classify/8
%%
-:- begin_tests(classify).
+:- begin_tests(adaboost_classify).
%% Failure Tests
test(classify_on_untrained_model, [error(_,system_error('The model is not trained!'))]) :-
reset_Model_No_Train(perceptron),
- classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, _, _, _).
+ adaboost_classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, _, _, _).
test(classify_with_bad_data_input_perceptron, [error(_,system_error('The given data matrix has incorrect dimensions compared to the training data!'))]) :-
reset_Model_With_Train(perceptron),
- classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 2, _, _, _).
+ adaboost_classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 2, _, _, _).
%% should cause an exeption but doesnt TODO:
test(classify_with_bad_data_input_decision_stump) :-
reset_Model_With_Train(decision_stump),
- classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 5, _, _, _).
+ adaboost_classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 5, _, _, _).
%% Successful Tests
test(classify_perceptron) :-
reset_Model_No_Train(perceptron),
take_rows_from_iris_CSV(10, Records),
- train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, _),
- classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4,
+ adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, _),
+ adaboost_classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4,
[1.0,1.0,1.0,1.0,0.0],
[[0.0,1.0],[0.0,1.0],[0.0,1.0],[0.0,1.0],[1.0,0.0]], 2).
test(classify_decision_stump) :-
reset_Model_No_Train(decision_stump),
take_rows_from_iris_CSV(10, Records),
- train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, decision_stump, 50, 0.0001, _),
- classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4,
+ adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, decision_stump, 50, 0.0001, _),
+ adaboost_classify([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4,
[1.0,1.0,1.0,1.0,1.0],
[[0.0,1.0],[0.0,1.0],[0.0,1.0],[0.0,1.0],[0.0,1.0]], 2).
-:- end_tests(classify).
+:- end_tests(adaboost_classify).
%%
-%% TESTING predicate numClasses/1
+%% TESTING predicate adaboost_numClasses/1
%%
-:- begin_tests(numClasses).
+:- begin_tests(adaboost_numClasses).
test(numClasses_Perceptron_NoTrain, [true(Amount =:= 0)]) :-
reset_Model_No_Train(perceptron),
- numClasses(Amount).
+ adaboost_numClasses(Amount).
test(numClasses_Decision_Stump_NoTrain, [true(Amount =:= 0)]) :-
reset_Model_No_Train(decision_stump),
- numClasses(Amount).
+ adaboost_numClasses(Amount).
test(numClasses_Perceptron_WithTrain, [true(Amount =:= 2)]) :-
reset_Model_With_Train(perceptron),
- numClasses(Amount).
+ adaboost_numClasses(Amount).
test(numClasses_Decision_Stump_WithTrain, [true(Amount =:= 2)]) :-
reset_Model_With_Train(decision_stump),
- numClasses(Amount).
+ adaboost_numClasses(Amount).
test(numClasses_Custom_NumClasses, [true(Amount =:= 3)]) :-
- initModelWithTraining([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, 0], 3, perceptron, 50, 0.0001),
- numClasses(Amount).
+ adaboost_initModelWithTraining([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, 0], 3, perceptron, 50, 0.0001),
+ adaboost_numClasses(Amount).
test(numClasses_afterTrain_Perceptron, [true(Amount =:= 2)]) :-
reset_Model_No_Train(perceptron),
take_rows_from_iris_CSV(10, Records),
- train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, _),
- numClasses(Amount).
+ adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, _),
+ adaboost_numClasses(Amount).
-:- end_tests(numClasses).
+:- end_tests(adaboost_numClasses).
%%
-%% TESTING predicate getTolerance/1
+%% TESTING predicate adaboost_getTolerance/1
%%
-:- begin_tests(getTolerance).
+:- begin_tests(adaboost_getTolerance).
test(getTolerance_Perceptron_NoTrain, [true(Amount =:= 0.0001)]) :-
reset_Model_No_Train(perceptron),
- getTolerance(Amount).
+ adaboost_getTolerance(Amount).
test(getTolerance_Decision_Stump_NoTrain, [true(Amount =:= 0.0001)]) :-
reset_Model_No_Train(decision_stump),
- getTolerance(Amount).
+ adaboost_getTolerance(Amount).
test(getTolerance_Perceptron_WithTrain, [true(Amount =:= 0.0001)]) :-
reset_Model_With_Train(perceptron),
- getTolerance(Amount).
+ adaboost_getTolerance(Amount).
test(getTolerance_Decision_Stump_WithTrain, [true(Amount =:= 0.0001)]) :-
reset_Model_With_Train(decision_stump),
- getTolerance(Amount).
+ adaboost_getTolerance(Amount).
test(getTolerance_Custom_Tolerance, [true(Amount =:= 0.0009)]) :-
- initModelWithTraining([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, 0], 3, perceptron, 50, 0.0009),
- getTolerance(Amount).
+ adaboost_initModelWithTraining([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, 0], 3, perceptron, 50, 0.0009),
+ adaboost_getTolerance(Amount).
test(getTolerance_afterTrain, [true(Amount =:= 0.0005)]) :-
reset_Model_No_Train(perceptron),
take_rows_from_iris_CSV(10, Records),
- train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0005, _),
- getTolerance(Amount).
+ adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0005, _),
+ adaboost_getTolerance(Amount).
-:- end_tests(getTolerance).
+:- end_tests(adaboost_getTolerance).
%%
-%% TESTING predicate modifyTolerance/1
+%% TESTING predicate adaboost_modifyTolerance/1
%%
-:- begin_tests(modifyTolerance).
+:- begin_tests(adaboost_modifyTolerance).
%% Failure Tests
test(modifyTolerance_With_Negative_Input, fail) :-
reset_Model_No_Train(perceptron),
- modifyTolerance(-0.02).
+ adaboost_modifyTolerance(-0.02).
%% Successful Tests
test(modifyTolerance_Perceptron_NoTrain, [true(Amount =:= 0.02)]) :-
reset_Model_No_Train(perceptron),
- modifyTolerance(0.02),
- getTolerance(Amount).
+ adaboost_modifyTolerance(0.02),
+ adaboost_getTolerance(Amount).
test(modifyTolerance_Decision_Stump_NoTrain, [true(Amount =:= 0.02)]) :-
reset_Model_No_Train(decision_stump),
- modifyTolerance(0.02),
- getTolerance(Amount).
+ adaboost_modifyTolerance(0.02),
+ adaboost_getTolerance(Amount).
test(modifyTolerance_Perceptron_WithTrain, [true(Amount =:= 0.02)]) :-
reset_Model_With_Train(perceptron),
- modifyTolerance(0.02),
- getTolerance(Amount).
+ adaboost_modifyTolerance(0.02),
+ adaboost_getTolerance(Amount).
test(modifyTolerance_Decision_Stump_WithTrain, [true(Amount =:= 0.02)]) :-
reset_Model_With_Train(decision_stump),
- modifyTolerance(0.02),
- getTolerance(Amount).
+ adaboost_modifyTolerance(0.02),
+ adaboost_getTolerance(Amount).
test(modifyTolerance_Custom_Tolerance, [true(Amount =:= 0.02)]) :-
- initModelWithTraining([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, 0], 3, perceptron, 50, 0.0001),
- modifyTolerance(0.02),
- getTolerance(Amount).
+ adaboost_initModelWithTraining([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, 0], 3, perceptron, 50, 0.0001),
+ adaboost_modifyTolerance(0.02),
+ adaboost_getTolerance(Amount).
test(modifyTolerance_afterTrain, [true(Amount =:= 0.02)]) :-
reset_Model_No_Train(perceptron),
take_rows_from_iris_CSV(10, Records),
- train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, _),
- modifyTolerance(0.02),
- getTolerance(Amount).
+ adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, _),
+ adaboost_modifyTolerance(0.02),
+ adaboost_getTolerance(Amount).
-:- end_tests(modifyTolerance).
+:- end_tests(adaboost_modifyTolerance).
%%
-%% TESTING predicate train/8
+%% TESTING predicate adaboost_train/8
%%
-:- begin_tests(train).
+:- begin_tests(adaboost_train).
%% Failure Tests
test(train_With_Bad_NumClass_Input, fail) :-
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, _).
+ adaboost_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, _).
test(train_With_Bad_Learner_Input, [error(domain_error('perceptron or decision_stump' ,wrongLearner), _)]) :-
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, wrongLearner, 50, 0.0001, _).
+ adaboost_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, wrongLearner, 50, 0.0001, _).
test(train_With_Bad_Iterations_Input, fail) :-
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, _).
+ adaboost_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, _).
test(train_With_Bad_Tol_Input, fail) :-
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, _).
+ adaboost_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, _).
test(train_With_Bad_Labels_Too_Many_Classes, [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, _).
+ adaboost_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_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, _).
+ adaboost_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_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, _).
+ adaboost_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, _).
%% seems to be allowed
test(train_With_Too_Many_Labels) :-
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, [0,1,0,0,0,1], 2, perceptron, 50, 0.0001, _).
+ adaboost_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, [0,1,0,0,0,1], 2, perceptron, 50, 0.0001, _).
test(train_With_Too_Little_Labels, [error(_,system_error('The given Labels Vector is too short!'))]) :-
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], 2, decision_stump, 50, 0.0001, _).
+ adaboost_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], 2, decision_stump, 50, 0.0001, _).
test(train_With_Negative_RowNumber, fail) :-
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, [0,0,0,0], 2, decision_stump, 50, 0.0001, _).
+ adaboost_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, decision_stump, 50, 0.0001, _).
%% Successful Tests
test(train_With_Direct_Input_Perceptron, [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).
+ adaboost_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(train_With_Data_From_CSV_Perceptron, [true(Error =:= 0.9797958971132711)]) :-
reset_Model_No_Train(perceptron),
take_rows_from_iris_CSV(10, Records),
- train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+ adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
test(train_With_Direct_Input_Decision_Stump, [true(Error =:= 1)]) :-
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, [0,0,0,0], 2, decision_stump, 50, 0.0001, Error).
+ adaboost_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, decision_stump, 50, 0.0001, Error).
test(train_With_Data_From_CSV_Decision_Stump, [true(Error =:= 0.9797958971132711)]) :-
reset_Model_No_Train(decision_stump),
take_rows_from_iris_CSV(10, Records),
- train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, decision_stump, 50, 0.0001, Error).
+ adaboost_train(Records, 4, [0,1,0,1,1,0,1,1,1,0], 2, decision_stump, 50, 0.0001, Error).
test(train_After_InitTrain_Perceptron, [true(Error =:= 1)]) :-
reset_Model_With_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], 3, perceptron, 100, 0.01, Error).
+ adaboost_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, perceptron, 100, 0.01, Error).
test(train_After_InitTrain_Decision_Stump, [true(Error =:= 1)]) :-
reset_Model_With_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, [0,0,0,0], 3, decision_stump, 100, 0.01, Error).
+ adaboost_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, decision_stump, 100, 0.01, Error).
-:- end_tests(train).
+:- end_tests(adaboost_train).
run_adaboost_tests :-
run_tests.
diff --git a/src/methods/bayesian_linear_regression/bayesian_linear_regression.pl b/src/methods/bayesian_linear_regression/bayesian_linear_regression.pl
index 410a4ddad5616a6e02b66693aaf958516e389149..794e130245d70f69552fc7244ccf8d839b4672d2 100644
--- a/src/methods/bayesian_linear_regression/bayesian_linear_regression.pl
+++ b/src/methods/bayesian_linear_regression/bayesian_linear_regression.pl
@@ -1,15 +1,15 @@
-:- module(bayesian_linear_regression, [ initModel/4,
- alpha/1,
- beta/1,
- dataOffset/1,
- dataScale/1,
- omega/1,
- predict/3,
- predictWithStd/4,
- rmse/4,
- train/3,
- variance/1]).
+:- module(bayesian_linear_regression, [ blr_initModel/4,
+ blr_alpha/1,
+ blr_beta/1,
+ blr_dataOffset/1,
+ blr_dataScale/1,
+ blr_omega/1,
+ blr_predict/3,
+ blr_predictWithStd/4,
+ blr_rmse/4,
+ blr_train/3,
+ blr_variance/1]).
:- load_files(library(str_decl),
[when(compile_time), if(changed)]).
@@ -39,7 +39,7 @@
%% Initiates the Model so now train/5 can be called.
%% Before predict/5 or predictWitStd/7 can be used train/5 has to be called before
%%
-initModel(CenterData, ScaleData, NIterMax, Tol) :-
+blr_initModel(CenterData, ScaleData, NIterMax, Tol) :-
NIterMax >= 0,
Tol > 0,
initModelI(CenterData, ScaleData, NIterMax, Tol).
@@ -58,7 +58,7 @@ foreign(initModel, c, initModelI( +integer,
%% Get the precision (or inverse variance) of the gaussian prior.
%% train/5 should be called before.
%%
-alpha(Alpha) :-
+blr_alpha(Alpha) :-
alphaI(Alpha).
foreign(alpha, c, alphaI([-float32])).
@@ -73,7 +73,7 @@ foreign(alpha, c, alphaI([-float32])).
%% Get the precision (or inverse variance) beta of the model.
%% train/5 should be called before.
%%
-beta(Beta) :-
+blr_beta(Beta) :-
betaI(Beta).
foreign(beta, c, betaI([-float32])).
@@ -87,7 +87,7 @@ foreign(beta, c, betaI([-float32])).
%% --Description--
%% Get the mean vector computed on the features over the training points.
%%
-dataOffset(ResponsesList) :-
+blr_dataOffset(ResponsesList) :-
dataOffsetI(X, Xsize),
convert_float_array_to_list(X, Xsize, ResponsesList).
@@ -102,7 +102,7 @@ foreign(dataOffset, c, dataOffsetI(-pointer(float_array), -integer)).
%% --Description--
%% Get the vector of standard deviations computed on the features over the training points.
%%
-dataScale(DataOffsetList) :-
+blr_dataScale(DataOffsetList) :-
dataScaleI(X, Xsize),
convert_float_array_to_list(X, Xsize, DataOffsetList).
@@ -117,7 +117,7 @@ foreign(dataScale, c, dataScaleI(-pointer(float_array), -integer)).
%% --Description--
%% Get the solution vector.
%%
-omega(OmegaList) :-
+blr_omega(OmegaList) :-
omegaI(X, Xsize),
convert_float_array_to_list(X, Xsize, OmegaList).
@@ -133,7 +133,7 @@ foreign(omega, c, omegaI(-pointer(float_array), -integer)).
%% --Description--
%% Predict yi for each data point in the given data matrix using the currently-trained Bayesian Ridge model.
%%
-predict(PointsList, PointsRows, PredictionsList) :-
+blr_predict(PointsList, PointsRows, PredictionsList) :-
convert_list_to_float_array(PointsList, PointsRows, array(Xsize, Xrows, X)),
predictI(X, Xsize, Xrows, Y, Ysize),
convert_float_array_to_list(Y, Ysize, PredictionsList).
@@ -152,7 +152,7 @@ foreign(predict, c, predictI( +pointer(float_array), +integer, +integer,
%% --Description--
%% Predict yi and the standard deviation of the predictive posterior distribution for each data point in the given data matrix, using the currently-trained Bayesian Ridge estimator.
%%
-predictWithStd(PointsList, PointsRows, PredictionsList, STDList) :-
+blr_predictWithStd(PointsList, PointsRows, PredictionsList, STDList) :-
convert_list_to_float_array(PointsList, PointsRows, array(Xsize, Xrows, X)),
predictWithStdI(X, Xsize, Xrows, Y, Ysize, Z, Zsize),
convert_float_array_to_list(Y, Ysize, PredictionsList),
@@ -173,7 +173,7 @@ foreign(predictWithStd, c, predictWithStdI( +pointer(float_array), +integer,
%% --Description--
%% Compute the Root Mean Square Error between the predictions returned by the model and the true responses.
%%
-rmse(DataList, DataRows, ResponsesList, RMSE) :-
+blr_rmse(DataList, DataRows, ResponsesList, RMSE) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
rmseI(X, Xsize, Xrows, Y, Ysize, RMSE).
@@ -193,7 +193,7 @@ foreign(rmse, c, rmseI(+pointer(float_array), +integer, +integer,
%% Run BayesianLinearRegression.
%% The input matrix (like all mlpack matrices) should be column-major each column is an observation and each row is a dimension.
%%
-train(DataList, DataRows, ResponsesList) :-
+blr_train(DataList, DataRows, ResponsesList) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
trainI(X, Xsize, Xrows, Y, Ysize).
@@ -211,7 +211,7 @@ foreign(train, c, trainI( +pointer(float_array), +integer, +integer,
%% Get the estimate variance.
%% train/5 should be called before.
%%
-variance(Variance) :-
+blr_variance(Variance) :-
varianceI(Variance).
foreign(variance, c, varianceI([-float32])).
diff --git a/src/methods/bayesian_linear_regression/bayesian_linear_regression_test.pl b/src/methods/bayesian_linear_regression/bayesian_linear_regression_test.pl
index 62eb00c67c5a810335cbbb9190b738cf9dab3fa5..6f7fa10437a5e9e3c2306d979ddd563a712701df 100644
--- a/src/methods/bayesian_linear_regression/bayesian_linear_regression_test.pl
+++ b/src/methods/bayesian_linear_regression/bayesian_linear_regression_test.pl
@@ -9,211 +9,211 @@
reset_Model :-
- initModel(1,0,50,0.0001).
+ blr_initModel(1,0,50,0.0001).
%%
-%% TESTING predicate initModel/4
+%% TESTING predicate blr_initModel/4
%%
-:- begin_tests(initModel).
+:- begin_tests(blr_initModel).
%% Failure Tests
test(bay_lin_reg_InitModel_Negative_NIterMax, fail) :-
- initModel(0,0,-50,0.0001).
+ blr_initModel(0,0,-50,0.0001).
test(bay_lin_reg_InitModel_Negative_Tolerance, fail) :-
- initModel(0,0,50,-0.0001).
+ blr_initModel(0,0,50,-0.0001).
%% Successful Tests
test(bay_lin_reg_InitModel_Default_Inputs) :-
- initModel(1,0,50,0.0001).
+ blr_initModel(1,0,50,0.0001).
test(bay_lin_reg_InitModel_Alternative_Inputs) :-
- initModel(1,1,0,0.0071).
+ blr_initModel(1,1,0,0.0071).
-:- end_tests(initModel).
+:- end_tests(blr_initModel).
%%
-%% TESTING predicate alpha/1
+%% TESTING predicate blr_alpha/1
%%
-:- begin_tests(alpha).
+:- begin_tests(blr_alpha).
%% Failure Tests
test(bay_lin_reg_Alpha_Wrong_Input, fail) :-
reset_Model,
- alpha(1).
+ blr_alpha(1).
%% Successful Tests
test(bay_lin_reg_Alpha_Std_init, [true(Alpha =:= 0.0)]) :-
reset_Model,
- alpha(Alpha).
+ blr_alpha(Alpha).
test(bay_lin_reg_Alpha_After_Train, [true(Alpha =:= 0.12986500952614138)]) :-
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]),
- alpha(Alpha).
+ blr_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]),
+ blr_alpha(Alpha).
-:- end_tests(alpha).
+:- end_tests(blr_alpha).
%%
-%% TESTING predicate beta/1
+%% TESTING predicate blr_beta/1
%%
-:- begin_tests(beta).
+:- begin_tests(blr_beta).
%% Failure Tests
test(bay_lin_reg_Beta_Wrong_Input, fail) :-
reset_Model,
- beta(1).
+ blr_beta(1).
%% Successful Tests
test(bay_lin_reg_Beta_Std_init, [true(Beta =:= 0.0)]) :-
reset_Model,
- beta(Beta).
+ blr_beta(Beta).
test(bay_lin_reg_Beta_After_Train, [true(Beta =:= 2.317989668988762E+31)]) :-
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]),
- beta(Beta).
+ blr_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]),
+ blr_beta(Beta).
-:- end_tests(beta).
+:- end_tests(blr_beta).
%%
-%% TESTING predicate dataOffset/1
+%% TESTING predicate blr_dataOffset/1
%%
-:- begin_tests(dataOffset).
+:- begin_tests(blr_dataOffset).
%% Failure Tests
test(bay_lin_reg_DataOffset_Before_Train, [error(_,system_error('The Model is not Trained!'))]) :-
reset_Model,
- dataOffset(_).
+ blr_dataOffset(_).
%% Successful Tests
test(bay_lin_reg_DataOffset_DirektInput) :-
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]),
- dataOffset(ResponsesOffsetList),
+ blr_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]),
+ blr_dataOffset(ResponsesOffsetList),
print('\nResponsesOffset: '),
print(ResponsesOffsetList).
test(bay_lin_reg_DataOffset_CSV_Input) :-
open('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]),
- dataOffset(ResponsesOffsetList),
+ blr_train(Data, 4, [0,1,0,1,1,0,1,1,1,0]),
+ blr_dataOffset(ResponsesOffsetList),
print('\nResponsesOffset: '),
print(ResponsesOffsetList).
-:- end_tests(dataOffset).
+:- end_tests(blr_dataOffset).
%%
-%% TESTING predicate dataScale/1
+%% TESTING predicate blr_dataScale/1
%%
-:- begin_tests(dataScale).
+:- begin_tests(blr_dataScale).
%% Failure Tests
test(bay_lin_reg_DataScale_Before_Train, [error(_,system_error('The Model is not Trained!'))]) :-
reset_Model,
- dataScale(_).
+ blr_dataScale(_).
%% Successful Tests
test(bay_lin_reg_DataScale_DirektInput) :-
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]),
- dataScale(DataOffsetList),
+ blr_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]),
+ blr_dataScale(DataOffsetList),
print('\nDataOffset: '),
print(DataOffsetList).
test(bay_lin_reg_DataScale_CSV_Input) :-
open('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]),
- dataScale(DataOffsetList),
+ blr_train(Data, 4, [0,1,0,1,1,0,1,1,1,0]),
+ blr_dataScale(DataOffsetList),
print('\nDataOffset: '),
print(DataOffsetList).
-:- end_tests(dataScale).
+:- end_tests(blr_dataScale).
%%
-%% TESTING predicate omega/1
+%% TESTING predicate blr_omega/1
%%
-:- begin_tests(omega).
+:- begin_tests(blr_omega).
%% Failure Tests
test(bay_lin_reg_Omega_Before_Train, [error(_,system_error('The Model is not Trained!'))]) :-
reset_Model,
- omega(_).
+ blr_omega(_).
%% Successful Tests
test(bay_lin_reg_Omega_DirektInput) :-
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]),
- omega(OmegaList),
+ blr_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]),
+ blr_omega(OmegaList),
print('\nOmega: '),
print(OmegaList).
test(bay_lin_reg_Omega_CSV_Input) :-
open('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]),
- omega(OmegaList),
+ blr_train(Data, 4, [0,1,0,1,1,0,1,1,1,0]),
+ blr_omega(OmegaList),
print('\nOmega: '),
print(OmegaList).
-:- end_tests(omega).
+:- end_tests(blr_omega).
%%
-%% TESTING predicate predict/3
+%% TESTING predicate blr_predict/3
%%
-:- begin_tests(predict).
+:- begin_tests(blr_predict).
%% Failure Tests
test(bay_lin_reg_Predict_Before_Train, [error(_,system_error('The Model is not Trained!'))]) :-
reset_Model,
- predict([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, _).
+ blr_predict([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(bay_lin_reg_Predict_Different_Dims_Than_Trained, [error(_,system_error('each_col(): incompatible size; expected 4x1, got 3x1'))]) :-
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]),
- predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _).
+ blr_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]),
+ blr_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _).
%% Successful Tests
test(bay_lin_reg_Predict_Direct_Input) :-
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]),
- predict([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, PredictionsList),
+ blr_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]),
+ blr_predict([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, PredictionsList),
print('\nPredictions: '),
print(PredictionsList).
@@ -221,38 +221,38 @@ test(bay_lin_reg_Predict_CSV_Input) :-
reset_Model,
open('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]),
- predict([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, PredictionsList),
+ blr_train(Data, 4, [0,1,0,1,1,0,1,1,1,0]),
+ blr_predict([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, PredictionsList),
print('\nPredictions: '),
print(PredictionsList).
-:- end_tests(predict).
+:- end_tests(blr_predict).
%%
-%% TESTING predicate predictWithStd/3
+%% TESTING predicate blr_predictWithStd/3
%%
-:- begin_tests(predictWithStd).
+:- begin_tests(blr_predictWithStd).
%% Failure Tests
test(bay_lin_reg_PredictWithStd_Before_Train, [error(_,system_error('The Model is not Trained!'))]) :-
reset_Model,
- predictWithStd([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, _, _).
+ blr_predictWithStd([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(bay_lin_reg_PredictWithStd_Different_Dims_Than_Trained, [error(_,system_error('each_col(): incompatible size; expected 4x1, got 3x1'))]) :-
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]),
- predictWithStd([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _, _).
+ blr_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]),
+ blr_predictWithStd([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _, _).
%% Successful Tests
test(bay_lin_reg_PredictWithStd_Direct_Input) :-
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]),
- predictWithStd([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5], 3, PredictionsList, STDList),
+ blr_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]),
+ blr_predictWithStd([3, 2, 0, 5, 1, 4, 0, 0, 4, 3, 3, 5, 0, 5, 5], 3, PredictionsList, STDList),
print('\nPredictions: '),
print(PredictionsList),
print('\nSTD: '),
@@ -262,63 +262,63 @@ test(bay_lin_reg_PredictWithStd_CSV_Input) :-
reset_Model,
open('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]),
- predictWithStd([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, PredictionsList, STDList),
+ blr_train(Data, 4, [0,1,0,1,1,0,1,1,1,0]),
+ blr_predictWithStd([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, PredictionsList, STDList),
print('\nPredictions: '),
print(PredictionsList),
print('\nSTD: '),
print(STDList).
-:- end_tests(predictWithStd).
+:- end_tests(blr_predictWithStd).
%%
-%% TESTING predicate rmse/4
+%% TESTING predicate blr_rmse/4
%%
-:- begin_tests(rmse).
+:- begin_tests(blr_rmse).
%% Failure Tests
test(bay_lin_reg_RMSE_Before_Train, [error(_,system_error('The Model is not Trained!'))]) :-
reset_Model,
- rmse([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], _).
+ blr_rmse([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], _).
test(bay_lin_reg_RMSE_Too_Small_Label_Dims, [error(_,system_error('subtraction: incompatible matrix dimensions: 1x2 and 1x4'))]) :-
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]),
- rmse([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], _).
+ blr_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]),
+ blr_rmse([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], _).
test(bay_lin_reg_RMSE_Too_Large_Label_Dims, [error(_,system_error('subtraction: incompatible matrix dimensions: 1x6 and 1x4'))]) :-
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]),
- rmse([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, [0,0,1,0,0,0], _).
+ blr_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]),
+ blr_rmse([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, [0,0,1,0,0,0], _).
%% doesnt cause an exception
test(bay_lin_reg_RMSE_Wrong_Label_Value) :-
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]),
- rmse([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], _).
+ blr_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]),
+ blr_rmse([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], _).
test(bay_lin_reg_RMSE_Wrong_Data_Dims, [error(_,system_error('each_col(): incompatible size; expected 4x1, got 3x1'))]) :-
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]),
- rmse([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0,1], _).
+ blr_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]),
+ blr_rmse([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0,1], _).
test(bay_lin_reg_RMSE_Wrong_Amount_Off_DataPoints, [error(_,system_error('subtraction: incompatible matrix dimensions: 1x10 and 1x5'))]) :-
reset_Model,
open('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]),
- rmse([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, [0,1,0,1,1,0,1,1,1,0], _).
+ blr_train(Data, 4, [0,1,0,1,1,0,1,1,1,0]),
+ blr_rmse([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, [0,1,0,1,1,0,1,1,1,0], _).
%% Successful Tests
test(bay_lin_reg_RMSE_Direct_Input) :-
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]),
- rmse([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], RMSE),
+ blr_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]),
+ blr_rmse([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], RMSE),
print('\nRMSE: '),
print(RMSE).
@@ -326,71 +326,71 @@ test(bay_lin_reg_RMSE_CSV_Input) :-
reset_Model,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,5, Data),
- train(Data, 4, [0,1,0,1,1]),
- rmse([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, [0,1,0,1,1], RMSE),
+ blr_train(Data, 4, [0,1,0,1,1]),
+ blr_rmse([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, [0,1,0,1,1], RMSE),
print('\nRMSE: '),
print(RMSE).
-:- end_tests(rmse).
+:- end_tests(blr_rmse).
%%
-%% TESTING predicate train/3
+%% TESTING predicate blr_train/3
%%
-:- begin_tests(train).
+:- begin_tests(blr_train).
%% Failure Tests
test(bay_lin_reg_Train_Too_Small_Label_Dims, [error(_,system_error('Target dim doesnt fit to the Data dim'))]) :-
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]).
+ blr_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]).
test(bay_lin_reg_Train_Too_Large_Label_Dims, [error(_,system_error('Target dim doesnt fit to the Data dim'))]) :-
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, [0,0,1,0,0,0]).
+ blr_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, [0,0,1,0,0,0]).
%% doesnt cause an Exception
test(bay_lin_reg_Train_Wrong_Label_Value) :-
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, [0,5,0,-1]).
+ blr_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, [0,5,0,-1]).
%% Successful Tests
test(bay_lin_reg_Train_Direct_Input) :-
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]).
+ blr_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]).
test(bay_lin_reg_Train_CSV_Input) :-
reset_Model,
open('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]).
+ blr_train(Data, 4, [0,1,0,1,1,0,1,1,1,0]).
-:- end_tests(train).
+:- end_tests(blr_train).
%%
-%% TESTING predicate variance/1
+%% TESTING predicate blr_variance/1
%%
-:- begin_tests(variance).
+:- begin_tests(blr_variance).
%% Failure Tests
test(bay_lin_reg_Variance_Before_Train, [error(_,system_error('The Model is not Trained!'))]) :-
reset_Model,
- variance(_).
+ blr_variance(_).
%% Successful Tests
test(bay_lin_reg_Variance_After_Train, [true(Variance =:= 4.3140830754274083E-32)]) :-
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]),
- variance(Variance).
+ blr_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]),
+ blr_variance(Variance).
-:- end_tests(variance).
+:- end_tests(blr_variance).
run_bayesian_linear_regression_tests :-
diff --git a/src/methods/decision_tree/decision_tree.pl b/src/methods/decision_tree/decision_tree.pl
index 3bcb22aa147c1f522106d660632130b41a693dbe..16229538656dbab3222b90db952eb16deef6a8df 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/7,
- classifyPoint/3,
- classifyMatrix/5,
- train/8]).
+:- module(decision_tree, [ decision_tree_initModel/7,
+ decision_tree_classifyPoint/3,
+ decision_tree_classifyMatrix/5,
+ decision_tree_train/8]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -34,7 +34,7 @@
%% 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.
%%
-initModel(DataList, DataRows, LabelsList, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth) :-
+decision_tree_initModel(DataList, DataRows, LabelsList, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth) :-
NumClasses >= 0,
MinimumLeafSize > 0,
MinimumGainSplit > 0.0,
@@ -59,7 +59,7 @@ foreign(initModel, c, initModelI( +pointer(float_array), +integer, +intege
%% --Description--
%% Classify the given point and also return estimates of the probability for each class in the given vector.
%%
-classifyPoint(DataList, Prediction, AssignList) :-
+decision_tree_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).
@@ -79,7 +79,7 @@ foreign(classifyPoint, c, classifyPointI(+pointer(float_array), +integer,
%% --Description--
%% Classify the given points and also return estimates of the probabilities for each class in the given matrix.
%%
-classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols) :-
+decision_tree_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),
@@ -105,7 +105,7 @@ foreign(classifyMatrix, c, classifyMatrixI( +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.
%%
-train(DataList, DataRows, LabelsList, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy) :-
+decision_tree_train(DataList, DataRows, LabelsList, NumClasses, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy) :-
NumClasses >= 0,
MinimumLeafSize > 0,
MinimumGainSplit > 0.0,
diff --git a/src/methods/decision_tree/decision_tree_test.pl b/src/methods/decision_tree/decision_tree_test.pl
index 712d1c19d78d94df50850c1d890d9f477017fd69..c5ed2ac305808a67b434a58f156f234210933655 100644
--- a/src/methods/decision_tree/decision_tree_test.pl
+++ b/src/methods/decision_tree/decision_tree_test.pl
@@ -7,59 +7,59 @@
:- use_module('../../helper_files/helper.pl').
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).
+ decision_tree_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
+%% TESTING predicate decision_tree_initModel/7
%%
-:- begin_tests(initModel).
+:- begin_tests(decision_tree_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).
+ decision_tree_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).
+ decision_tree_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).
+ decision_tree_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).
+ decision_tree_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).
+ decision_tree_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).
+ decision_tree_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).
+ decision_tree_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).
+ decision_tree_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).
+ decision_tree_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('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).
+ decision_tree_initModel(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 2, 0.7, 3).
-:- end_tests(initModel).
+:- end_tests(decision_tree_initModel).
%%
-%% TESTING predicate classifyPoint/3
+%% TESTING predicate decision_tree_classifyPoint/3
%%
-:- begin_tests(classifyPoint).
+:- begin_tests(decision_tree_classifyPoint).
%% Failure Tests
@@ -67,7 +67,7 @@ test(initModel_Direkt_CSV_Use) :-
%% 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),
+ decision_tree_classifyPoint([5.1,3.5,1.4,1.2,3.3], Prediction, AssignList),
print(Prediction),
print('\n'),
print(AssignList).
@@ -77,25 +77,25 @@ test(classify_Point_With_Wrong_Dims) :-
test(classify_Point1) :-
reset_Model_With_Train,
- classifyPoint([5.1,3.5,1.4], Prediction, AssignList),
+ decision_tree_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),
+ decision_tree_classifyPoint([6.2,1.9,2.3], Prediction, AssignList),
print(Prediction),
print('\n'),
print(AssignList).
-:- end_tests(classifyPoint).
+:- end_tests(decision_tree_classifyPoint).
%%
-%% TESTING predicate classifyMatrix/4
+%% TESTING predicate decision_tree_classifyMatrix/4
%%
-:- begin_tests(classifyMatrix).
+:- begin_tests(decision_tree_classifyMatrix).
%% Failure Tests
@@ -103,7 +103,7 @@ test(classify_Point2) :-
%% 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, _),
+ decision_tree_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).
@@ -112,7 +112,7 @@ test(classify_Matrix_With_Wrong_Dims1) :-
%% 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, _),
+ decision_tree_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).
@@ -121,22 +121,22 @@ test(classify_Matrix_With_Wrong_Dims2) :-
%% 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, _),
+ decision_tree_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),
+ decision_tree_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, _),
+ decision_tree_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, _),
+ decision_tree_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).
@@ -144,69 +144,69 @@ test(classify_Matrix_Direkt_Input2) :-
test(classify_Matrix_CSV_Trained) :-
open('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, _),
+ decision_tree_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, _),
+ decision_tree_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).
+:- end_tests(decision_tree_classifyMatrix).
%%
-%% TESTING predicate train/8
+%% TESTING predicate decision_tree_train/8
%%
-:- begin_tests(train).
+:- begin_tests(decision_tree_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, _).
+ decision_tree_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, _).
+ decision_tree_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, _).
+ decision_tree_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, _).
+ decision_tree_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, _).
+ decision_tree_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, _).
+ decision_tree_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, _).
+ decision_tree_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, _).
+ decision_tree_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).
+ decision_tree_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('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).
+ decision_tree_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).
+ decision_tree_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('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).
+ decision_tree_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).
+:- end_tests(decision_tree_train).
run_decision_tree_tests :-
run_tests.
diff --git a/src/methods/fastmks/fastmks.pl b/src/methods/fastmks/fastmks.pl
index 09235703c243db64eb1d11dbfff8497d28fb43a5..65297349c09d3277fd4d0d64f2d2bccc9ac3d935 100644
--- a/src/methods/fastmks/fastmks.pl
+++ b/src/methods/fastmks/fastmks.pl
@@ -1,7 +1,7 @@
-:- module(fastmks, [ initModel/10,
- searchWithQuery/8,
- searchNoQuery/5]).
+:- module(fastmks, [ fastmks_initModel/10,
+ fastmks_searchWithQuery/8,
+ fastmks_searchNoQuery/5]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -35,7 +35,7 @@
%% --Description--
%% Initializes the model on the given reference set.
%%
-initModel(DataList, DataRows, Kernel, Degree, Offset, Bandwidth, Scale, SingleMode, Naive, Base) :-
+fastmks_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)),
@@ -61,7 +61,7 @@ foreign(initModel, c, initModelI( +pointer(float_array), +integer, +intege
%% --Description--
%% Search with a different query set.
%%
-searchWithQuery(DataList, DataRows, K, IndicesList, YCols, KernelsList, ZCols, Base) :-
+fastmks_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)),
@@ -87,7 +87,7 @@ foreign(searchWithQuery, c, searchWithQueryI( +pointer(float_array), +integer,
%% --Description--
%% Search with the reference set as the query set.
%%
-searchNoQuery(K, IndicesList, YCols, KernelsList, ZCols) :-
+fastmks_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),
diff --git a/src/methods/fastmks/fastmks_test.pl b/src/methods/fastmks/fastmks_test.pl
index 1d5aeeb3a429fbb3fc96a9d77b157034ac15a5c8..386121ee2e567526b287a92f1012b9f9186b2faf 100644
--- a/src/methods/fastmks/fastmks_test.pl
+++ b/src/methods/fastmks/fastmks_test.pl
@@ -9,29 +9,29 @@
reset_Model :-
open('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).
+ fastmks_initModel(Data, 4, linear, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.2).
%%
-%% TESTING predicate initModel/10
+%% TESTING predicate fastmks_initModel/10
%%
-:- begin_tests(initModel).
+:- begin_tests(fastmks_initModel).
%% Failure Tests
test(searchWithQuery_Fastmks_Search_Before_Init, [error(_,system_error('The Model hasnt been trained yet!'))]) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,5, Data),
- searchWithQuery(Data, 4, 2, _, _, _, _, 1.1).
+ fastmks_searchWithQuery(Data, 4, 2, _, _, _, _, 1.1).
test(searchNoQuery_Fastmks_Search_Before_Init, [error(_,system_error('The Model hasnt been trained yet!'))]) :-
- searchNoQuery(2, _, _, _, _).
+ fastmks_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).
+ fastmks_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).
+ fastmks_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
@@ -39,45 +39,45 @@ test(initModel_Fatsmks_Bad_Base_Input, fail) :-
test(iniModel_Fastmks_Linear) :-
open('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).
+ fastmks_initModel(Data, 4, linear, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.5).
test(iniModel_Fastmks_Polynomial) :-
open('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).
+ fastmks_initModel(Data, 4, polynomial, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.5).
test(iniModel_Fastmks_Cosine) :-
open('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).
+ fastmks_initModel(Data, 4, cosine, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.5).
test(iniModel_Fastmks_Gaussian) :-
open('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).
+ fastmks_initModel(Data, 4, gaussian, 0.5, 0.5, 0.5, 0.5, 0, 0, 1.5).
test(iniModel_Fastmks_Epanechnikov) :-
open('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).
+ fastmks_initModel(Data, 4, epanechnikov, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.5).
test(iniModel_Fastmks_Triangular) :-
open('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).
+ fastmks_initModel(Data, 4, triangular, 0.0, 0.0, 1.0, 0.0, 0, 0, 1.5).
test(iniModel_Fastmks_Hyptan) :-
open('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).
+ fastmks_initModel(Data, 4, hyptan, 0.0, 0.0, 1.0, 1.0, 0, 0, 1.5).
-:- end_tests(initModel).
+:- end_tests(fastmks_initModel).
%%
-%% TESTING predicate searchWithQuery/8
+%% TESTING predicate fastmks_searchWithQuery/8
%%
-:- begin_tests(searchWithQuery).
+:- begin_tests(fastmks_searchWithQuery).
%% Failure Tests
@@ -85,20 +85,20 @@ test(searchWithQuery_Fastmks_Negative_K, fail) :-
reset_Model,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,5, Data),
- searchWithQuery(Data, 4, -2, _, _, _, _, 1.1).
+ fastmks_searchWithQuery(Data, 4, -2, _, _, _, _, 1.1).
test(searchWithQuery_Fastmks_Negative_Base, fail) :-
reset_Model,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,5, Data),
- searchWithQuery(Data, 4, 2, _, _, _, _, -1.1).
+ fastmks_searchWithQuery(Data, 4, 2, _, _, _, _, -1.1).
%% Successful Tests
test(searchWithQuery_Fastmks_New_Query) :-
reset_Model,
- 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),
+ fastmks_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'),
@@ -108,32 +108,32 @@ test(searchWithQuery_Fastmks_Training_Data_Query) :-
reset_Model,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,5, Data),
- searchWithQuery(Data, 4, 2, IndicesList, _, KernelsList, _, 1.1),
+ fastmks_searchWithQuery(Data, 4, 2, IndicesList, _, KernelsList, _, 1.1),
print('Indices:\n'),
print(IndicesList),
print('Kernels:\n'),
print(KernelsList).
-:- end_tests(searchWithQuery).
+:- end_tests(fastmks_searchWithQuery).
%%
-%% TESTING predicate searchNoQuery/5
+%% TESTING predicate fastmks_searchNoQuery/5
%%
-:- begin_tests(searchNoQuery).
+:- begin_tests(fastmks_searchNoQuery).
%% Failure Tests
test(searchNoQuery_Fastmks_Negative_K, fail) :-
reset_Model,
- searchNoQuery(-2, _, _, _, _).
+ fastmks_searchNoQuery(-2, _, _, _, _).
%% Successful Tests
test(testDescription) :-
reset_Model,
- searchNoQuery(2, IndicesList, _, KernelsList, _),
+ fastmks_searchNoQuery(2, IndicesList, _, KernelsList, _),
print('Indices:\n'),
print(IndicesList),
print('Kernels:\n'),
@@ -141,13 +141,13 @@ test(testDescription) :-
test(testDescription) :-
reset_Model,
- searchNoQuery(5, IndicesList, _, KernelsList, _),
+ fastmks_searchNoQuery(5, IndicesList, _, KernelsList, _),
print('Indices:\n'),
print(IndicesList),
print('Kernels:\n'),
print(KernelsList).
-:- end_tests(searchNoQuery).
+:- end_tests(fastmks_searchNoQuery).
run_fastmks_tests :-
run_tests.
diff --git a/src/methods/hoeffding_tree/hoeffding_tree.pl b/src/methods/hoeffding_tree/hoeffding_tree.pl
index 9996abf949b859e17cd512c35815ad195b001dab..e656d2c491275050d60769820d4c857446e6fce3 100644
--- a/src/methods/hoeffding_tree/hoeffding_tree.pl
+++ b/src/methods/hoeffding_tree/hoeffding_tree.pl
@@ -1,7 +1,7 @@
-:- module(hoeffding_tree, [ initAndBuildModel/12,
- classify/4,
- train/4]).
+:- module(hoeffding_tree, [ hoeffding_tree_initAndBuildModel/12,
+ hoeffding_tree_classify/4,
+ hoeffding_tree_train/4]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -38,7 +38,7 @@
%% Construct the Hoeffding tree with the given parameters and given training data.
%% The tree may be trained either in batch mode (which looks at all points before splitting, and propagates these points to the created children for further training), or in streaming mode, where each point is only considered once. (In general, batch mode will give better-performing trees, but will have higher memory and runtime costs for the same dataset.)
%%
-initAndBuildModel(TreeType, DataList, DataRows, LabelsList, NumClasses, BatchTraining, SuccessProbability, MaxSamples, CheckInterval, MinSamples, Bins, ObservationsBeforeBinning) :-
+hoeffding_tree_initAndBuildModel(TreeType, DataList, DataRows, LabelsList, NumClasses, BatchTraining, SuccessProbability, MaxSamples, CheckInterval, MinSamples, Bins, ObservationsBeforeBinning) :-
NumClasses >= 0,
SuccessProbability >= 0,
SuccessProbability =< 1,
@@ -69,7 +69,7 @@ foreign(initAndBuildModel, c, initAndBuildModelI( +string,
%% Classify the given points, using this node and the entire (sub)tree beneath it.
%% The predicted labels for each point are returned, as well as an estimate of the probability that the prediction is correct for each point. This estimate is simply the MajorityProbability for the leaf that each point bins to.
%%
-classify(TestList, TestRows, PredicList, ProbsList) :-
+hoeffding_tree_classify(TestList, TestRows, PredicList, ProbsList) :-
convert_list_to_float_array(TestList, TestRows, array(Xsize, Xrownum, X)),
classifyI(X, Xsize, Xrownum, Y, Ysize, Z, Zsize),
convert_float_array_to_list(Y, Ysize, PredicList),
@@ -92,7 +92,7 @@ foreign(classify, c, classifyI( +pointer(float_array), +integer, +integer,
%% Train in streaming mode on the given dataset.
%% This takes one pass. Be sure that initAndBuildModel/14 has been called first!
%%
-train(DataList, DataRows, LabelsList, BatchTraining) :-
+hoeffding_tree_train(DataList, DataRows, LabelsList, BatchTraining) :-
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, BatchTraining).
diff --git a/src/methods/hoeffding_tree/hoeffding_tree_test.pl b/src/methods/hoeffding_tree/hoeffding_tree_test.pl
index 75280e5e39bb15cfc5d64639beb8d3abf022a998..e03e532ae8fd2dd27f028e92df8bbeedebf81cd2 100644
--- a/src/methods/hoeffding_tree/hoeffding_tree_test.pl
+++ b/src/methods/hoeffding_tree/hoeffding_tree_test.pl
@@ -9,106 +9,106 @@
reset_Model :-
open('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).
+ hoeffding_tree_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
+%% TESTING predicate hoeffding_tree_initAndBuildModel/12
%%
-:- begin_tests(initAndBuildModel).
+:- begin_tests(hoeffding_tree_initAndBuildModel).
%% Failure Tests
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, _, _).
+ hoeffding_tree_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).
+ hoeffding_tree_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).
+ hoeffding_tree_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).
+ hoeffding_tree_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),
+ hoeffding_tree_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).
+ hoeffding_tree_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).
+ hoeffding_tree_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).
+ hoeffding_tree_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).
+ hoeffding_tree_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).
+ hoeffding_tree_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_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).
+ hoeffding_tree_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).
+ hoeffding_tree_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).
+ hoeffding_tree_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).
+ hoeffding_tree_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(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).
+ hoeffding_tree_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('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).
+ hoeffding_tree_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).
+ hoeffding_tree_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('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).
+ hoeffding_tree_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).
+ hoeffding_tree_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('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).
+ hoeffding_tree_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).
+ hoeffding_tree_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(hoeffding_Init_InfoBinary_CSV_Input) :-
open('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).
+ hoeffding_tree_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).
+:- end_tests(hoeffding_tree_initAndBuildModel).
%%
-%% TESTING predicate classify/4
+%% TESTING predicate hoeffding_tree_classify/4
%%
-:- begin_tests(classify).
+:- begin_tests(hoeffding_tree_classify).
%% Failure Tests
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),
+ hoeffding_tree_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: '),
@@ -120,8 +120,8 @@ test(hoeffding_Classify_Different_Dims_To_Train, [error(_,system_error('Labels V
test(hoeffding_Classify_GiniHoeffding) :-
open('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),
+ hoeffding_tree_initAndBuildModel(gini_hoeffding, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100),
+ hoeffding_tree_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: '),
@@ -130,8 +130,8 @@ test(hoeffding_Classify_GiniHoeffding) :-
test(hoeffding_Classify_GiniBinary) :-
open('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),
- classify([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, PredicList, ProbsList),
+ hoeffding_tree_initAndBuildModel(gini_binary, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100),
+ hoeffding_tree_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: '),
@@ -140,8 +140,8 @@ test(hoeffding_Classify_GiniBinary) :-
test(hoeffding_Classify_InfoHoeffding) :-
open('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),
+ hoeffding_tree_initAndBuildModel(info_hoeffding, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100),
+ hoeffding_tree_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: '),
@@ -150,55 +150,55 @@ test(hoeffding_Classify_InfoHoeffding) :-
test(hoeffding_Classify_InfoBinary) :-
open('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),
+ hoeffding_tree_initAndBuildModel(info_binary, Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0, 0.95, 5000, 100, 100, 10, 100),
+ hoeffding_tree_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).
+:- end_tests(hoeffding_tree_classify).
%%
-%% TESTING predicate train/4
+%% TESTING predicate hoeffding_tree_train/4
%%
-:- begin_tests(train).
+:- begin_tests(hoeffding_tree_train).
%% Failure Tests
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).
+ hoeffding_tree_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).
%% 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).
+ hoeffding_tree_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).
+ hoeffding_tree_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).
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).
+ hoeffding_tree_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(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).
+ hoeffding_tree_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).
%% 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).
+ hoeffding_tree_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),
+ hoeffding_tree_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).
+:- end_tests(hoeffding_tree_train).
run_hoeffding_tree_tests :-
- run_tests(train).
+ run_tests(hoeffding_tree_train).
diff --git a/src/methods/kde/kde.pl b/src/methods/kde/kde.pl
index cec58620e6fdebe2d8c0a712452d297041f4a29e..1a2951a148518564271662363069ab7ed238f812 100644
--- a/src/methods/kde/kde.pl
+++ b/src/methods/kde/kde.pl
@@ -1,7 +1,7 @@
-:- module(kde, [ initAndBuildModel/13,
- evaluateWithQuery/3,
- evaluateNoQuery/1]).
+:- module(kde, [ kde_initAndBuildModel/13,
+ kde_evaluateWithQuery/3,
+ kde_evaluateNoQuery/1]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -39,7 +39,7 @@
%% --Description--
%% Build the KDE model with the given parameters and then trains it with the given reference data.
%%
-initAndBuildModel(Bandwidth, RelError, AbsError, KernelType, TreeType, Algorithm, MonteCarlo, McProb, InitialSampleSize, MCEntryCoef, MCBreakCoef, DataList, DataRows) :-
+kde_initAndBuildModel(Bandwidth, RelError, AbsError, KernelType, TreeType, Algorithm, MonteCarlo, McProb, InitialSampleSize, MCEntryCoef, MCBreakCoef, DataList, DataRows) :-
Bandwidth > 0.0,
RelError >= 0.0, RelError =< 1.0,
AbsError >= 0.0,
@@ -67,7 +67,7 @@ foreign(initAndBuildModel, c, initAndBuildModelI(+float32, +float32, +float32,
%% --Description--
%% initAndBuildModel/14 has to be called before.
%%
-evaluateWithQuery(QueryList, QueryRows, EstimationList) :-
+kde_evaluateWithQuery(QueryList, QueryRows, EstimationList) :-
convert_list_to_float_array(QueryList, QueryRows, array(Xsize, Xrownum, X)),
evaluateWithQueryI(X, Xsize, Xrownum, Y, Ysize),
convert_float_array_to_list(Y, Ysize, EstimationList).
@@ -87,7 +87,7 @@ foreign(evaluateWithQuery, c, evaluateWithQueryI(+pointer(float_array), +integer
%% If possible, it returns normalized estimations.
%% initAndBuildModel/14 has to be called before.
%%
-evaluateNoQuery(EstimationList) :-
+kde_evaluateNoQuery(EstimationList) :-
evaluateNoQueryI(Y, Ysize),
convert_float_array_to_list(Y, Ysize, EstimationList).
diff --git a/src/methods/kde/kde_test.pl b/src/methods/kde/kde_test.pl
index 6e8beba619b0e48e83bbc4663f07c48eba295e74..be92448dfdc98d9a4f8ed2b1ef277f7c8f485dbf 100644
--- a/src/methods/kde/kde_test.pl
+++ b/src/methods/kde/kde_test.pl
@@ -7,61 +7,61 @@
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initAndBuildModel(1.0, 0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+ kde_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/13
+%% TESTING predicate kde_initAndBuildModel/13
%%
-:- begin_tests(initAndBuildModel).
+:- begin_tests(kde_initAndBuildModel).
%% Failure Tests
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,
+ kde_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_Bad_RelError, fail) :-
- initAndBuildModel(1.0, -0.05, 0.0, gaussian, kd_tree, dual_tree, 0, 0.95, 100, 3.0, 0.4,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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).
@@ -69,164 +69,164 @@ test(kde_Init_Bad_MCBreakCoef, fail) :-
%% 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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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,
+ kde_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).
@@ -234,21 +234,21 @@ test(kde_Init_Triangular_RTREE) :-
test(kde_Init_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initAndBuildModel(2.2, 0.25, 0.5, gaussian, kd_tree, dual_tree, 1, 0.75, 50, 2.0, 0.2,
+ kde_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).
+:- end_tests(kde_initAndBuildModel).
%%
-%% TESTING predicate evaluateWithQuery/3
+%% TESTING predicate kde_evaluateWithQuery/3
%%
-:- begin_tests(evaluateWithQuery).
+:- begin_tests(kde_evaluateWithQuery).
%% Failure Tests
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),
+ kde_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).
@@ -257,26 +257,26 @@ test(kde_EvalWithQuery_Wrong_Query_Dims, [error(_, system_error('cannot evaluate
test(kde_EvalWithQuery) :-
reset_Model,
- evaluateWithQuery([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5], 3, Estimation),
+ kde_evaluateWithQuery([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5], 3, Estimation),
print('\nEstimation: '),
print(Estimation).
test(kde_EvalWithQuery_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initAndBuildModel(2.2, 0.25, 0.5, gaussian, kd_tree, dual_tree, 1, 0.75, 50, 2.0, 0.2,
+ kde_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),
+ kde_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).
+:- end_tests(kde_evaluateWithQuery).
%%
-%% TESTING predicate evaluateNoQuery/1
+%% TESTING predicate kde_evaluateNoQuery/1
%%
-:- begin_tests(evaluateNoQuery).
+:- begin_tests(kde_evaluateNoQuery).
%% Failure Tests
@@ -285,11 +285,11 @@ test(kde_EvalWithQuery_CSV_Input) :-
test(kde_EvalNoQuery) :-
reset_Model,
- evaluateNoQuery(Estimation),
+ kde_evaluateNoQuery(Estimation),
print('\nEstimation: '),
print(Estimation).
-:- end_tests(evaluateNoQuery).
+:- end_tests(kde_evaluateNoQuery).
run_kde_tests :-
run_tests.
diff --git a/src/methods/kernel_pca/kernel_pca.pl b/src/methods/kernel_pca/kernel_pca.pl
index 01a1c62f7af1e806edf43c94a85b11b25728bb32..687b496162379b4df8efe63289f6e05f36e04e63 100644
--- a/src/methods/kernel_pca/kernel_pca.pl
+++ b/src/methods/kernel_pca/kernel_pca.pl
@@ -27,12 +27,12 @@
%% float32 bandwidth needed by gaussian, epanechnikov, laplacian,
%% float32 scale needed by hyptan,
%% mat data
+%% int newDimension
%%
%% --Output--
%% mat transformedData,
%% vec eigenValues,
-%% mat eigenVectores,
-%% int newDimension
+%% mat eigenVectores
%%
%% --Description--
%% This program performs Kernel Principal Components Analysis (KPCA) on the specified dataset with the specified kernel. This will transform the data onto the kernel principal components, and optionally reduce the dimensionality by ignoring the kernel principal components with the smallest eigenvalues.
diff --git a/src/methods/kfn/kfn.pl b/src/methods/kfn/kfn.pl
index 21fc2b8fc0e25fe37af85755db7f8f6286d73b31..8a5f21c360cb75e37df77150514840aaacc77f8b 100644
--- a/src/methods/kfn/kfn.pl
+++ b/src/methods/kfn/kfn.pl
@@ -1,7 +1,7 @@
-:- module(kfn, [ initAndBuildModel/7,
- searchWithQuery/7,
- searchNoQuery/5]).
+:- module(kfn, [ kfn_initAndBuildModel/7,
+ kfn_searchWithQuery/7,
+ kfn_searchNoQuery/5]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -33,7 +33,7 @@
%% --Description--
%% Initialize the Model and build it.
%%
-initAndBuildModel(TreeType, SearchMode, RandomBasis, LeafSize, Epsilon, ReferenceList, ReferenceRows) :-
+kfn_initAndBuildModel(TreeType, SearchMode, RandomBasis, LeafSize, Epsilon, ReferenceList, ReferenceRows) :-
LeafSize >= 1,
Epsilon >= 0,
convert_list_to_float_array(ReferenceList, ReferenceRows, array(Xsize, Xrownum, X)),
@@ -56,7 +56,7 @@ foreign(initAndBuildModel, c, initAndBuildModelI(+string, +string,
%% --Description--
%% Perform neighbor search on the queryset.
%%
-searchWithQuery(QueryList, QueryRows, K, NeighborsList, YCols, DistancesList, ZCols) :-
+kfn_searchWithQuery(QueryList, QueryRows, K, NeighborsList, YCols, DistancesList, ZCols) :-
K > 0,
convert_list_to_float_array(QueryList, QueryRows, array(Xsize, Xrownum, X)),
searchWithQueryI(X, Xsize, Xrownum, K, Y, YCols, YRows, Z, ZCols, ZRows),
@@ -80,7 +80,7 @@ foreign(searchWithQuery, c, searchWithQueryI( +pointer(float_array), +integer,
%% --Description--
%% Perform monochromatic neighbor search.
%%
-searchNoQuery(K, NeighborsList, YCols, DistancesList, ZCols) :-
+kfn_searchNoQuery(K, NeighborsList, YCols, DistancesList, ZCols) :-
K > 0,
searchNoQueryI(K, Y, YCols, YRows, Z, ZCols, ZRows),
convert_float_array_to_2d_list(Y, YCols, YRows, NeighborsList),
diff --git a/src/methods/kfn/kfn_test.pl b/src/methods/kfn/kfn_test.pl
index 035a06a882681846a8cc8c030d8eb9f78ba9086f..d15258f57d481ccfae96b5bee7514243f5432843 100644
--- a/src/methods/kfn/kfn_test.pl
+++ b/src/methods/kfn/kfn_test.pl
@@ -7,149 +7,149 @@
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initAndBuildModel(kd, dual_tree, 0, 20, 0.0, [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).
+ kfn_initAndBuildModel(kd, dual_tree, 0, 20, 0.0, [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).
%%
-%% TESTING predicate initAndBuildModel/9
+%% TESTING predicate kfn_initAndBuildModel/9
%%
-:- begin_tests(initAndBuildModel).
+:- begin_tests(kfn_initAndBuildModel).
%% Failure Tests
test(kfn_InitAndBuildModel_Wrong_TreeType_Input, [error(domain_error('The given TreeType is unknown!' , wrongInput), _)]) :-
- initAndBuildModel(wrongInput, dual_tree, 0, 20, 0.0, [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).
+ kfn_initAndBuildModel(wrongInput, dual_tree, 0, 20, 0.0, [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).
test(kfn_InitAndBuildModel_Wrong_SearchMode_Input, [error(domain_error('The given SearchMode is unknown!' , wrongInput), _)]) :-
- initAndBuildModel(kd, wrongInput, 0, 20, 0.0, [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).
+ kfn_initAndBuildModel(kd, wrongInput, 0, 20, 0.0, [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).
test(kfn_InitAndBuildModel_Negative_LeafSize, fail) :-
- initAndBuildModel(kd, dual_tree, 0, 0, 0.0, [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).
+ kfn_initAndBuildModel(kd, dual_tree, 0, 0, 0.0, [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).
test(kfn_InitAndBuildModel_Negative_Epsilon, fail) :-
- initAndBuildModel(kd, dual_tree, 0, 20, -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).
+ kfn_initAndBuildModel(kd, dual_tree, 0, 20, -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).
%% Successful Tests
test(kfn_InitAndBuildModel_KD) :-
- initAndBuildModel(kd, naive, 0, 20, 0.005, [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(kd, single_tree, 0, 20, 0.005, [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(kd, dual_tree, 0, 20, 0.005, [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(kd, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(kd, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(kd, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(kd, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(kd, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_VP) :-
- initAndBuildModel(vp, naive, 0, 20, 0.005, [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(vp, single_tree, 0, 20, 0.005, [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(vp, dual_tree, 0, 20, 0.005, [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(vp, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(vp, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(vp, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(vp, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(vp, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_RP) :-
- initAndBuildModel(rp, naive, 0, 20, 0.005, [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(rp, single_tree, 0, 20, 0.005, [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(rp, dual_tree, 0, 20, 0.005, [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(rp, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(rp, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(rp, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(rp, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(rp, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_MAX_RP) :-
- initAndBuildModel(max_rp, naive, 0, 20, 0.005, [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(max_rp, single_tree, 0, 20, 0.005, [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(max_rp, dual_tree, 0, 20, 0.005, [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(max_rp, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(max_rp, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(max_rp, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(max_rp, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(max_rp, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_UB) :-
- initAndBuildModel(ub, naive, 0, 20, 0.005, [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(ub, single_tree, 0, 20, 0.005, [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(ub, dual_tree, 0, 20, 0.005, [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(ub, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(ub, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(ub, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(ub, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(ub, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_COVER) :-
- initAndBuildModel(cover, naive, 0, 20, 0.005, [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(cover, single_tree, 0, 20, 0.005, [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(cover, dual_tree, 0, 20, 0.005, [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(cover, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(cover, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(cover, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(cover, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(cover, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_R) :-
- initAndBuildModel(r, naive, 0, 20, 0.005, [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(r, single_tree, 0, 20, 0.005, [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(r, dual_tree, 0, 20, 0.005, [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(r, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(r, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_R_STAR) :-
- initAndBuildModel(r_star, naive, 0, 20, 0.005, [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(r_star, single_tree, 0, 20, 0.005, [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(r_star, dual_tree, 0, 20, 0.005, [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(r_star, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(r_star, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r_star, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r_star, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r_star, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_X) :-
- initAndBuildModel(x, naive, 0, 20, 0.005, [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(x, single_tree, 0, 20, 0.005, [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(x, dual_tree, 0, 20, 0.005, [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(x, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(x, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(x, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(x, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(x, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_BALL) :-
- initAndBuildModel(ball, naive, 0, 20, 0.005, [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(ball, single_tree, 0, 20, 0.005, [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(ball, dual_tree, 0, 20, 0.005, [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(ball, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(ball, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(ball, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(ball, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(ball, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_Hilbert_R) :-
- initAndBuildModel(hilbert_r, naive, 0, 20, 0.005, [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(hilbert_r, single_tree, 0, 20, 0.005, [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(hilbert_r, dual_tree, 0, 20, 0.005, [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(hilbert_r, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(hilbert_r, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(hilbert_r, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(hilbert_r, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(hilbert_r, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_R_Plus) :-
- initAndBuildModel(r_plus, naive, 0, 20, 0.005, [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(r_plus, single_tree, 0, 20, 0.005, [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(r_plus, dual_tree, 0, 20, 0.005, [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(r_plus, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(r_plus, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r_plus, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r_plus, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r_plus, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_R_Plus_Plus) :-
- initAndBuildModel(r_plus_plus, naive, 0, 20, 0.005, [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(r_plus_plus, single_tree, 0, 20, 0.005, [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(r_plus_plus, dual_tree, 0, 20, 0.005, [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(r_plus_plus, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(r_plus_plus, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r_plus_plus, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r_plus_plus, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(r_plus_plus, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_OCT) :-
- initAndBuildModel(oct, naive, 0, 20, 0.005, [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(oct, single_tree, 0, 20, 0.005, [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(oct, dual_tree, 0, 20, 0.005, [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(oct, greedy, 0, 20, 0.005, [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).
+ kfn_initAndBuildModel(oct, naive, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(oct, single_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(oct, dual_tree, 0, 20, 0.005, [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),
+ kfn_initAndBuildModel(oct, greedy, 0, 20, 0.005, [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(kfn_InitAndBuildModel_CSV_Input) :-
reset_Model,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initAndBuildModel(kd, dual_tree, 1, 20, 0.005, Data, 4).
+ kfn_initAndBuildModel(kd, dual_tree, 1, 20, 0.005, Data, 4).
-:- end_tests(initAndBuildModel).
+:- end_tests(kfn_initAndBuildModel).
%%
-%% TESTING predicate searchWithQuery/7
+%% TESTING predicate kfn_searchWithQuery/7
%%
-:- begin_tests(searchWithQuery).
+:- begin_tests(kfn_searchWithQuery).
%% Failure Tests
test(kfn_SearchWithQuery_Wrong_Query_Dims, [error(_,system_error('Queryset has Dim(4) but the Referenceset has Dim(3)'))]) :-
reset_Model,
- searchWithQuery([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 2, _, _, _, _).
+ kfn_searchWithQuery([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 2, _, _, _, _).
test(kfn_SearchWithQuery_Negative_K, fail) :-
reset_Model,
- searchWithQuery([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, _, _, _, _).
+ kfn_searchWithQuery([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, _, _, _, _).
test(kfn_SearchWithQuery_Too_Large_K, [error(_,system_error('Requested value of k (10) is greater than the number of points in the reference set (4)'))]) :-
reset_Model,
- searchWithQuery([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, 10, _, _, _, _).
+ kfn_searchWithQuery([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, 10, _, _, _, _).
%% Successful Tests
test(kfn_SearchWithQuery_Normal) :-
reset_Model,
- searchWithQuery([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, 2, NeighborsList, _, DistancesList, _),
+ kfn_searchWithQuery([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, 2, NeighborsList, _, DistancesList, _),
print('\nNeighbors: '),
print(NeighborsList),
print('\nDistances: '),
@@ -158,38 +158,38 @@ test(kfn_SearchWithQuery_Normal) :-
test(kfn_SearchWithQuery_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initAndBuildModel(kd, dual_tree, 0, 20, 0.0, Data, 4),
- searchWithQuery([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, 7, NeighborsList, _, DistancesList, _),
+ kfn_initAndBuildModel(kd, dual_tree, 0, 20, 0.0, Data, 4),
+ kfn_searchWithQuery([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, 7, NeighborsList, _, DistancesList, _),
print('\nNeighbors: '),
print(NeighborsList),
print('\nDistances: '),
print(DistancesList).
-:- end_tests(searchWithQuery).
+:- end_tests(kfn_searchWithQuery).
%%
-%% TESTING predicate searchNoQuery/10
+%% TESTING predicate kfn_searchNoQuery/10
%%
-:- begin_tests(searchNoQuery).
+:- begin_tests(kfn_searchNoQuery).
%% Failure Tests
test(kfn_SearchNoQuery_Negative_K, fail) :-
reset_Model,
- searchNoQuery(-1, _, _, _, _).
+ kfn_searchNoQuery(-1, _, _, _, _).
test(kfn_SearchNoQuery_Too_Large_K, [error(_,system_error('Requested value of k (15) is greater than the number of points in the reference set (4)'))]) :-
reset_Model,
- searchNoQuery(15, _, _, _, _).
+ kfn_searchNoQuery(15, _, _, _, _).
%% Successful Tests
test(kfn_SearchNoQuery_Normal) :-
reset_Model,
- searchNoQuery(2, NeighborsList, _, DistancesList, _),
+ kfn_searchNoQuery(2, NeighborsList, _, DistancesList, _),
print('\nNeighbors: '),
print(NeighborsList),
print('\nDistances: '),
@@ -198,14 +198,14 @@ test(kfn_SearchNoQuery_Normal) :-
test(kfn_SearchNoQuery_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initAndBuildModel(kd, dual_tree, 0, 20, 0.0, Data, 4),
- searchNoQuery(7, NeighborsList, _, DistancesList, _),
+ kfn_initAndBuildModel(kd, dual_tree, 0, 20, 0.0, Data, 4),
+ kfn_searchNoQuery(7, NeighborsList, _, DistancesList, _),
print('\nNeighbors: '),
print(NeighborsList),
print('\nDistances: '),
print(DistancesList).
-:- end_tests(searchNoQuery).
+:- end_tests(kfn_searchNoQuery).
run_kfn_tests :-
run_tests.
diff --git a/src/methods/knn/knn.pl b/src/methods/knn/knn.pl
index f4d0cac42953dfe2d9d4f1daa34c1c1037ca8ff7..c410a6c2dfe72ff869ce68790d4699d605351545 100644
--- a/src/methods/knn/knn.pl
+++ b/src/methods/knn/knn.pl
@@ -1,7 +1,7 @@
-:- module(knn, [ initAndBuildModel/9,
- searchWithQuery/7,
- searchNoQuery/5]).
+:- module(knn, [ knn_initAndBuildModel/9,
+ knn_searchWithQuery/7,
+ knn_searchNoQuery/5]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -34,7 +34,7 @@
%% --Description--
%% Initialize the Model and build it.
%%
-initAndBuildModel(TreeType, SearchMode, RandomBasis, LeafSize, Tau, Rho, Epsilon, ReferenceList, ReferenceRows) :-
+knn_initAndBuildModel(TreeType, SearchMode, RandomBasis, LeafSize, Tau, Rho, Epsilon, ReferenceList, ReferenceRows) :-
LeafSize >= 1,
Tau >= 0,
Rho >= 0,
@@ -60,7 +60,7 @@ foreign(initAndBuildModel, c, initAndBuildModelI( +string, +string,
%% --Description--
%% Perform neighbor search on the queryset.
%%
-searchWithQuery(QueryList, QueryRows, K, NeighborsList, YCols, DistancesList, ZCols) :-
+knn_searchWithQuery(QueryList, QueryRows, K, NeighborsList, YCols, DistancesList, ZCols) :-
K > 0,
convert_list_to_float_array(QueryList, QueryRows, array(Xsize, Xrownum, X)),
searchWithQueryI(X, Xsize, Xrownum, K, Y, YCols, YRows, Z, ZCols, ZRows),
@@ -83,7 +83,7 @@ foreign(searchWithQuery, c, searchWithQueryI( +pointer(float_array), +integer,
%% --Description--
%% Perform monochromatic neighbor search.
%%
-searchNoQuery(K, NeighborsList, YCols, DistancesList, ZCols) :-
+knn_searchNoQuery(K, NeighborsList, YCols, DistancesList, ZCols) :-
K > 0,
searchNoQueryI(K, Y, YCols, YRows, Z, ZCols, ZRows),
convert_float_array_to_2d_list(Y, YCols, YRows, NeighborsList),
diff --git a/src/methods/knn/knn_test.pl b/src/methods/knn/knn_test.pl
index 6b377527f8e6c9e6833c0981a538f37eb9c07b72..9372ef81ae2b2c9c371d1331bf869f733cfa752d 100644
--- a/src/methods/knn/knn_test.pl
+++ b/src/methods/knn/knn_test.pl
@@ -7,162 +7,162 @@
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 0.0, 0.0, [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).
+ knn_initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 0.0, 0.0, [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).
%%
-%% TESTING predicate initAndBuildModel/9
+%% TESTING predicate knn_initAndBuildModel/9
%%
-:- begin_tests(initAndBuildModel).
+:- begin_tests(knn_initAndBuildModel).
%% Failure Tests
test(knn_InitAndBuildModel_Wrong_TreeType_Input, [error(domain_error('The given TreeType is unknown!' , wrongInput), _)]) :-
- initAndBuildModel(wrongInput, dual_tree, 0, 20, 0.7, 0.0, 0.0, [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).
+ knn_initAndBuildModel(wrongInput, dual_tree, 0, 20, 0.7, 0.0, 0.0, [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).
test(knn_InitAndBuildModel_Wrong_SearchMode_Input, [error(domain_error('The given SearchMode is unknown!' , wrongInput), _)]) :-
- initAndBuildModel(kd, wrongInput, 0, 20, 0.7, 0.0, 0.0, [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).
+ knn_initAndBuildModel(kd, wrongInput, 0, 20, 0.7, 0.0, 0.0, [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).
test(knn_InitAndBuildModel_Negative_LeafSize, fail) :-
- initAndBuildModel(kd, dual_tree, 0, 0, 0.7, 0.0, 0.0, [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).
+ knn_initAndBuildModel(kd, dual_tree, 0, 0, 0.7, 0.0, 0.0, [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).
test(knn_InitAndBuildModel_Negative_Tau, fail) :-
- initAndBuildModel(kd, dual_tree, 0, 20, -0.7, 0.0, 0.0, [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).
+ knn_initAndBuildModel(kd, dual_tree, 0, 20, -0.7, 0.0, 0.0, [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).
test(knn_InitAndBuildModel_Bad_Rho_Input, fail) :-
- initAndBuildModel(kd, dual_tree, 0, 20, 0.7, -0.5, 0.0, [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),
- initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 1.5, 0.0, [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).
+ knn_initAndBuildModel(kd, dual_tree, 0, 20, 0.7, -0.5, 0.0, [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),
+ knn_initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 1.5, 0.0, [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).
test(knn_InitAndBuildModel_Negative_Epsilon, fail) :-
- initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 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).
+ knn_initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 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).
%% Successful Tests
test(knn_InitAndBuildModel_KD) :-
- initAndBuildModel(kd, naive, 0, 20, 0.7, 0.01, 0.005, [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(kd, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(kd, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(kd, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(kd, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(kd, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(kd, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_VP) :-
- initAndBuildModel(vp, naive, 0, 20, 0.7, 0.01, 0.005, [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(vp, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(vp, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(vp, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(vp, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(vp, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(vp, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(vp, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_RP) :-
- initAndBuildModel(rp, naive, 0, 20, 0.7, 0.01, 0.005, [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(rp, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(rp, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(rp, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(rp, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(rp, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(rp, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(rp, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_MAX_RP) :-
- initAndBuildModel(max_rp, naive, 0, 20, 0.7, 0.01, 0.005, [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(max_rp, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(max_rp, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(max_rp, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(max_rp, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(max_rp, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(max_rp, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(max_rp, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_UB) :-
- initAndBuildModel(ub, naive, 0, 20, 0.7, 0.01, 0.005, [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(ub, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(ub, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(ub, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(ub, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(ub, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(ub, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(ub, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_COVER) :-
- initAndBuildModel(cover, naive, 0, 20, 0.7, 0.01, 0.005, [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(cover, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(cover, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(cover, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(cover, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(cover, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(cover, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(cover, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_R) :-
- initAndBuildModel(r, naive, 0, 20, 0.7, 0.01, 0.005, [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(r, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(r, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(r, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(r, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_R_STAR) :-
- initAndBuildModel(r_star, naive, 0, 20, 0.7, 0.01, 0.005, [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(r_star, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(r_star, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(r_star, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(r_star, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r_star, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r_star, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r_star, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_X) :-
- initAndBuildModel(x, naive, 0, 20, 0.7, 0.01, 0.005, [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(x, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(x, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(x, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(x, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(x, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(x, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(x, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_BALL) :-
- initAndBuildModel(ball, naive, 0, 20, 0.7, 0.01, 0.005, [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(ball, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(ball, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(ball, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(ball, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(ball, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(ball, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(ball, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_Hilbert_R) :-
- initAndBuildModel(hilbert_r, naive, 0, 20, 0.7, 0.01, 0.005, [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(hilbert_r, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(hilbert_r, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(hilbert_r, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(hilbert_r, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(hilbert_r, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(hilbert_r, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(hilbert_r, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_R_Plus) :-
- initAndBuildModel(r_plus, naive, 0, 20, 0.7, 0.01, 0.005, [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(r_plus, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(r_plus, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(r_plus, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(r_plus, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r_plus, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r_plus, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r_plus, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_R_Plus_Plus) :-
- initAndBuildModel(r_plus_plus, naive, 0, 20, 0.7, 0.01, 0.005, [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(r_plus_plus, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(r_plus_plus, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(r_plus_plus, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(r_plus_plus, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r_plus_plus, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r_plus_plus, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(r_plus_plus, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_SPILL) :-
- initAndBuildModel(spill, naive, 0, 20, 0.7, 0.01, 0.005, [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(spill, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(spill, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(spill, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(spill, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(spill, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(spill, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(spill, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_OCT) :-
- initAndBuildModel(oct, naive, 0, 20, 0.7, 0.01, 0.005, [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(oct, single_tree, 0, 20, 0.7, 0.01, 0.005, [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(oct, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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(oct, greedy, 0, 20, 0.7, 0.01, 0.005, [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).
+ knn_initAndBuildModel(oct, naive, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(oct, single_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(oct, dual_tree, 0, 20, 0.7, 0.01, 0.005, [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),
+ knn_initAndBuildModel(oct, greedy, 0, 20, 0.7, 0.01, 0.005, [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(knn_InitAndBuildModel_CSV_Input) :-
reset_Model,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initAndBuildModel(kd, dual_tree, 1, 20, 0.7, 0.01, 0.005, Data, 4).
+ knn_initAndBuildModel(kd, dual_tree, 1, 20, 0.7, 0.01, 0.005, Data, 4).
-:- end_tests(initAndBuildModel).
+:- end_tests(knn_initAndBuildModel).
%%
-%% TESTING predicate searchWithQuery/7
+%% TESTING predicate knn_searchWithQuery/7
%%
-:- begin_tests(searchWithQuery).
+:- begin_tests(knn_searchWithQuery).
%% Failure Tests
test(knn_SearchWithQuery_Wrong_Query_Dims, [error(_,system_error('Queryset has Dim(4) but the Referenceset has Dim(3)'))]) :-
reset_Model,
- searchWithQuery([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 2, _, _, _, _).
+ knn_searchWithQuery([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 2, _, _, _, _).
test(knn_SearchWithQuery_Negative_K, fail) :-
reset_Model,
- searchWithQuery([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, _, _, _, _).
+ knn_searchWithQuery([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, _, _, _, _).
test(knn_SearchWithQuery_Too_Large_K, [error(_,system_error('Requested value of k (10) is greater than the number of points in the reference set (4)'))]) :-
reset_Model,
- searchWithQuery([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, 10, _, _, _, _).
+ knn_searchWithQuery([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, 10, _, _, _, _).
%% Successful Tests
test(knn_SearchWithQuery_Normal) :-
reset_Model,
- searchWithQuery([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, 2, NeighborsList, _, DistancesList, _),
+ knn_searchWithQuery([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, 2, NeighborsList, _, DistancesList, _),
print('\nNeighbors: '),
print(NeighborsList),
print('\nDistances: '),
@@ -171,38 +171,38 @@ test(knn_SearchWithQuery_Normal) :-
test(knn_SearchWithQuery_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 0.0, 0.0, Data, 4),
- searchWithQuery([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, 7, NeighborsList, _, DistancesList, _),
+ knn_initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 0.0, 0.0, Data, 4),
+ knn_searchWithQuery([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2, 5, 5, 0, 2], 4, 7, NeighborsList, _, DistancesList, _),
print('\nNeighbors: '),
print(NeighborsList),
print('\nDistances: '),
print(DistancesList).
-:- end_tests(searchWithQuery).
+:- end_tests(knn_searchWithQuery).
%%
-%% TESTING predicate searchNoQuery/10
+%% TESTING predicate knn_searchNoQuery/10
%%
-:- begin_tests(searchNoQuery).
+:- begin_tests(knn_searchNoQuery).
%% Failure Tests
test(knn_SearchNoQuery_Negative_K, fail) :-
reset_Model,
- searchNoQuery(-1, _, _, _, _).
+ knn_searchNoQuery(-1, _, _, _, _).
test(knn_SearchNoQuery_Too_Large_K, [error(_,system_error('Requested value of k (15) is greater than the number of points in the reference set (4)'))]) :-
reset_Model,
- searchNoQuery(15, _, _, _, _).
+ knn_searchNoQuery(15, _, _, _, _).
%% Successful Tests
test(knn_SearchNoQuery_Normal) :-
reset_Model,
- searchNoQuery(2, NeighborsList, _, DistancesList, _),
+ knn_searchNoQuery(2, NeighborsList, _, DistancesList, _),
print('\nNeighbors: '),
print(NeighborsList),
print('\nDistances: '),
@@ -211,14 +211,14 @@ test(knn_SearchNoQuery_Normal) :-
test(knn_SearchNoQuery_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 0.0, 0.0, Data, 4),
- searchNoQuery(7, NeighborsList, _, DistancesList, _),
+ knn_initAndBuildModel(kd, dual_tree, 0, 20, 0.7, 0.0, 0.0, Data, 4),
+ knn_searchNoQuery(7, NeighborsList, _, DistancesList, _),
print('\nNeighbors: '),
print(NeighborsList),
print('\nDistances: '),
print(DistancesList).
-:- end_tests(searchNoQuery).
+:- end_tests(knn_searchNoQuery).
run_knn_tests :-
run_tests.
diff --git a/src/methods/lars/lars.pl b/src/methods/lars/lars.pl
index a7449f565b9d1221410245c1f69749cc9e7ddd7f..d6ac26d39eae801a7cdd46b3fc6d29b3945117c0 100644
--- a/src/methods/lars/lars.pl
+++ b/src/methods/lars/lars.pl
@@ -1,16 +1,16 @@
-:- module(lars, [ initModelNoDataNoGram/4,
- initModelNoDataWithGram/6,
- initModelWithDataNoGram/8,
- initModelWithDataWithGram/10,
- activeSet/1,
- beta/1,
- betaPath/2,
- computeError/5,
- lambdaPath/1,
- matUtriCholFactor/2,
- predict/4,
- train/6]).
+:- module(lars, [ lars_initModelNoDataNoGram/4,
+ lars_initModelNoDataWithGram/6,
+ lars_initModelWithDataNoGram/8,
+ lars_initModelWithDataWithGram/10,
+ lars_activeSet/1,
+ lars_beta/1,
+ lars_betaPath/2,
+ lars_computeError/5,
+ lars_lambdaPath/1,
+ lars_matUtriCholFactor/2,
+ lars_predict/4,
+ lars_train/6]).
:- load_files(library(str_decl),
[when(compile_time), if(changed)]).
@@ -38,7 +38,7 @@
%% --Description--
%% Only initialize the LARS model.
%%
-initModelNoDataNoGram(UseCholesky, Lambda1, Lambda2, Tolerance) :-
+lars_initModelNoDataNoGram(UseCholesky, Lambda1, Lambda2, Tolerance) :-
initModelNoDataNoGramI(UseCholesky, Lambda1, Lambda2, Tolerance).
foreign(initModelNoDataNoGram, c, initModelNoDataNoGramI(+integer,
@@ -58,7 +58,7 @@ foreign(initModelNoDataNoGram, c, initModelNoDataNoGramI(+integer,
%% --Description--
%% Initialize LARS model, and pass in a precalculated Gram matrix but dont train the model.
%%
-initModelNoDataWithGram(UseCholesky, GramList, GramRows, Lambda1, Lambda2, Tolerance) :-
+lars_initModelNoDataWithGram(UseCholesky, GramList, GramRows, Lambda1, Lambda2, Tolerance) :-
convert_list_to_float_array(GramList, GramRows, array(Zsize, Zrownum, Z)),
initModelNoDataWithGramI(UseCholesky, Z, Zsize, Zrownum, Lambda1, Lambda2, Tolerance).
@@ -81,7 +81,7 @@ foreign(initModelNoDataWithGram, c, initModelNoDataWithGramI( +integer,
%% --Description--
%% Initialize LARS model, and train the model.
%%
-initModelWithDataNoGram(DataList, DataRows, ResponsesList, TransposeData, UseCholesky, Lambda1, Lambda2, Tolerance) :-
+lars_initModelWithDataNoGram(DataList, DataRows, ResponsesList, TransposeData, UseCholesky, Lambda1, Lambda2, Tolerance) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
initModelWithDataNoGramI(X, Xsize, Xrownum, Y, Ysize, TransposeData, UseCholesky, Lambda1, Lambda2, Tolerance).
@@ -109,7 +109,7 @@ foreign(initModelWithDataNoGram, c, initModelWithDataNoGramI( +pointer(float_a
%% --Description--
%% Initialize LARS model, pass in a precalculated Gram matrix and train the model.
%%
-initModelWithDataWithGram(DataList, DataRows, ResponsesList, TransposeData, UseCholesky, GramList, GramRows, Lambda1, Lambda2, Tolerance) :-
+lars_initModelWithDataWithGram(DataList, DataRows, ResponsesList, TransposeData, UseCholesky, GramList, GramRows, Lambda1, Lambda2, Tolerance) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
convert_list_to_float_array(GramList, GramRows, array(Zsize, Zrownum, Z)),
@@ -132,7 +132,7 @@ foreign(initModelWithDataWithGram, c, initModelWithDataWithGramI(+pointer(float_
%% --Description--
%% Get the set of active dimensions
%%
-activeSet(ActiveSetList) :-
+lars_activeSet(ActiveSetList) :-
activeSetI(Y, Ysize),
convert_float_array_to_list(Y, Ysize, ActiveSetList).
@@ -147,7 +147,7 @@ foreign(activeSet, c, activeSetI(-pointer(float_array), -integer)).
%% --Description--
%% Get the solution coefficients.
%%
-beta(BetaList) :-
+lars_beta(BetaList) :-
betaI(Y, Ysize),
convert_float_array_to_list(Y, Ysize, BetaList).
@@ -162,7 +162,7 @@ foreign(beta, c, betaI(-pointer(float_array), -integer)).
%% --Description--
%% Get the set of coefficients after each iteration. The solution is the last element.
%%
-betaPath(BetaList, XCols) :-
+lars_betaPath(BetaList, XCols) :-
betaPathI(X, XCols, XRows),
convert_float_array_to_2d_list(X, XCols, XRows, BetaList).
@@ -180,7 +180,7 @@ foreign(betaPath, c, betaPathI(-pointer(float_array), -integer, -integer)).
%% --Description--
%% Compute cost error of the given data matrix using the currently-trained LARS model.Only ||y-beta*X||2 is used to calculate cost error.
%%
-computeError(DataList, DataRows, ResponsesList, RowMajor, Error) :-
+lars_computeError(DataList, DataRows, ResponsesList, RowMajor, Error) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
computeErrorI(X, Xsize, Xrownum, Y, Ysize, RowMajor, Error).
@@ -199,7 +199,7 @@ foreign(computeError, c, computeErrorI( +pointer(float_array), +integer, +intege
%% --Description--
%% Get the set of values for lambda1 after each iteration; the solution is the last element.
%%
-lambdaPath(LambdaPathList) :-
+lars_lambdaPath(LambdaPathList) :-
lambdaPathI(Y, Ysize),
convert_float_array_to_list(Y, Ysize, LambdaPathList).
@@ -214,7 +214,7 @@ foreign(lambdaPath, c, lambdaPathI(-pointer(float_array), -integer)).
%% --Description--
%% Get the upper triangular cholesky factor.
%%
-matUtriCholFactor(FactorList, XCols) :-
+lars_matUtriCholFactor(FactorList, XCols) :-
matUtriCholFactorI(X, XCols, XRows),
convert_float_array_to_2d_list(X, XCols, XRows, FactorList).
@@ -231,7 +231,7 @@ foreign(matUtriCholFactor, c, matUtriCholFactorI(-pointer(float_array), -integer
%% --Description--
%% Predict y_i for each data point in the given data matrix using the currently-trained LARS model.
%%
-predict(PointsList, PointsRows, PredicList, RowMajor) :-
+lars_predict(PointsList, PointsRows, PredicList, RowMajor) :-
convert_list_to_float_array(PointsList, PointsRows, array(Xsize, Xrownum, X)),
predictI(X, Xsize, Xrownum, Y, Ysize, RowMajor),
convert_float_array_to_list(Y, Ysize, PredicList).
@@ -253,7 +253,7 @@ foreign(predict, c, predictI( +pointer(float_array), +integer, +integer,
%% --Description--
%% Train the LARS model with the given data.
%%
-train(DataList, DataRows, ResponsesList, BetaList, RowMajor, Error) :-
+lars_train(DataList, DataRows, ResponsesList, BetaList, RowMajor, Error) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
trainI(X, Xsize, Xrownum, Y, Ysize, Z, Zsize, RowMajor, Error),
diff --git a/src/methods/lars/lars_test.pl b/src/methods/lars/lars_test.pl
index 23e55df2df19392e7f53f3e6c678e8f805a06e0c..67e2cb15dc3c71fd90e1e22b6522d674e6b9c052 100644
--- a/src/methods/lars/lars_test.pl
+++ b/src/methods/lars/lars_test.pl
@@ -9,10 +9,10 @@
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initModelNoDataNoGram(1, 0.1, 0.3, 0.001),
+ lars_initModelNoDataNoGram(1, 0.1, 0.3, 0.001),
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)),
- trainNoBetaReturn(X,Xsize, Xrownum,Y, Ysize, 1, _).
+ lars_trainNoBetaReturn(X,Xsize, Xrownum,Y, Ysize, 1, _).
:- begin_tests(lists).
@@ -22,27 +22,27 @@ test(train, [true(A =:= 0)]) :-
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)),
- trainNoBetaReturn(X,Xsize, Xrownum,Y, Ysize, 1, A),
- predict(X, Xsize,Xrownum, Predic, PredicSize,0),
+ lars_trainNoBetaReturn(X,Xsize, Xrownum,Y, Ysize, 1, A),
+ lars_predict(X, Xsize,Xrownum, Predic, PredicSize,0),
convert_float_array_to_list(Predic, PredicSize, Result),
print(Result).
test(activeSet) :-
reset_Model,
- activeSet(ActSet, ActSetSize),
+ lars_activeSet(ActSet, ActSetSize),
convert_float_array_to_list(ActSet, ActSetSize, Result),
print(Result).
test(matUtriCholFactor) :-
reset_Model,
- matUtriCholFactor(Matrix, MatrixColNum, MatrixRowNum),
+ lars_matUtriCholFactor(Matrix, MatrixColNum, MatrixRowNum),
print(MatrixColNum),
convert_float_array_to_2d_list(Matrix, MatrixColNum, MatrixRowNum, Results),
print(Results).
test(betaPath) :-
reset_Model,
- betaPath(Matrix, MatrixColNum, MatrixRowNum),
+ lars_betaPath(Matrix, MatrixColNum, MatrixRowNum),
print(MatrixColNum),
convert_float_array_to_2d_list(Matrix, MatrixColNum, MatrixRowNum, Results),
print(Results).
diff --git a/src/methods/linear_SVM/linear_SVM.pl b/src/methods/linear_SVM/linear_SVM.pl
index 996531079d10358bd08c1623dd55d7c3d1d0b2cc..6959fa49beb4bda3e8c4af977e9c4fb49ff4c5ad 100644
--- a/src/methods/linear_SVM/linear_SVM.pl
+++ b/src/methods/linear_SVM/linear_SVM.pl
@@ -1,10 +1,10 @@
-:- module(linear_SVM, [ initModelWithTrain/8,
- initModelNoTrain/4,
- classify/5,
- classifyPoint/2,
- computeAccuracy/4,
- train/6]).
+:- module(linear_SVM, [ linear_SVM_initModelWithTrain/8,
+ linear_SVM_initModelNoTrain/4,
+ linear_SVM_classify/5,
+ linear_SVM_classifyPoint/2,
+ linear_SVM_computeAccuracy/4,
+ linear_SVM_train/6]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -36,7 +36,7 @@
%% --Description--
%% Initializes the linear_svm model with the given data and trains it.
%%
-initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, Lambda, Delta, FitIntercept, Optimizer) :-
+linear_SVM_initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, Lambda, Delta, FitIntercept, Optimizer) :-
NumClasses >= 0,
Lambda >= 0.0,
Delta >= 0.0,
@@ -62,7 +62,7 @@ foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +
%% --Description--
%% Initializes the linear_svm model with the given data but doesnt train it.
%%
-initModelNoTrain(NumClasses, Lambda, Delta, FitIntercept) :-
+linear_SVM_initModelNoTrain(NumClasses, Lambda, Delta, FitIntercept) :-
NumClasses >= 0,
Lambda >= 0.0,
Delta >= 0.0,
@@ -82,7 +82,7 @@ foreign(initModelNoTrain, c, initModelNoTrainI( +integer, +float32, +float32,
%% --Description--
%% Classify the given points, returning class scores and predicted class label for each point.
%%
-classify(DataList, DataRows, LabelsList, ScoresList, ZCols) :-
+linear_SVM_classify(DataList, DataRows, LabelsList, ScoresList, ZCols) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
classifyI(X, Xsize, Xrows, Y, Ysize, Z, ZCols, ZRows),
convert_float_array_to_list(Y, Ysize, LabelsList),
@@ -102,7 +102,7 @@ foreign(classify, c, classifyI( +pointer(float_array), +integer, +integer,
%% --Description--
%% Classify the given point.
%%
-classifyPoint(DataList, Prediction) :-
+linear_SVM_classifyPoint(DataList, Prediction) :-
convert_list_to_float_array(DataList, array(Xsize, X)),
classifyPointI(X, Xsize, Prediction).
@@ -121,7 +121,7 @@ foreign(classifyPoint, c, classifyPointI( +pointer(float_array), +integer,
%% Computes accuracy of the learned model given the feature data and the labels associated with each data point.
%% Predictions are made using the provided data and are compared with the actual labels.
%%
-computeAccuracy(DataList, DataRows, LabelsList, Accuracy) :-
+linear_SVM_computeAccuracy(DataList, DataRows, LabelsList, Accuracy) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(LabelsList, array(Ysize, Y)),
computeAccuracyI(X, Xsize, Xrownum, Y, Ysize, Accuracy).
@@ -143,7 +143,7 @@ foreign(computeAccuracy, c, computeAccuracyI( +pointer(float_array), +integer,
%% --Description--
%% Train the Linear_svm model with the given training data.
%%
-train(DataList, DataRows, LabelsList, NumClasses, Optimizer, ObjValue) :-
+linear_SVM_train(DataList, DataRows, LabelsList, NumClasses, Optimizer, ObjValue) :-
NumClasses >= 0,
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(LabelsList, array(Ysize, Y)),
diff --git a/src/methods/linear_SVM/linear_SVM_test.pl b/src/methods/linear_SVM/linear_SVM_test.pl
index 9ddaa7301faeb1f4e184db6c66aee5a434badb79..3b973e57ff6987196546f9ce6ae1196d21389ea6 100644
--- a/src/methods/linear_SVM/linear_SVM_test.pl
+++ b/src/methods/linear_SVM/linear_SVM_test.pl
@@ -7,272 +7,272 @@
:- use_module('../../helper_files/helper.pl').
reset_Model_NoTrain :-
- initModelNoTrain(2, 0.0001, 1.0, 0).
+ linear_SVM_initModelNoTrain(2, 0.0001, 1.0, 0).
reset_Model_WithTrain :-
- initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs).
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs).
:- begin_tests(lists).
%%
-%% TESTING predicate initModelWithTrain/8
+%% TESTING predicate linear_SVM_initModelWithTrain/8
%%
-:- begin_tests(initModelWithTrain).
+:- begin_tests(linear_SVM_initModelWithTrain).
%% Failure Tests
test(linear_SVM_InitModelWithTrain_Negative_NumClasses, fail) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs).
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Negative_Lambda, fail) :-
- initModelWithTrain([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, -0.0001, 1.0, 0, lbfgs).
+ linear_SVM_initModelWithTrain([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, -0.0001, 1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Negative_Delta, fail) :-
- initModelWithTrain([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, 0.0001, -1.0, 0, lbfgs).
+ linear_SVM_initModelWithTrain([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, 0.0001, -1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Wrong_Optimizer_Input, [error(domain_error('The given Optimizer is unkown!' , wrongInput), _)]) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, wrongInput).
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, wrongInput).
test(linear_SVM_InitModelWithTrain_Too_Few_Labels, [error(_,system_error('Error'))]) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs).
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Too_Many_Labels, [error(_,system_error('Error'))]) :-
- initModelWithTrain([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, [0,1,0,1,0,1], 2, 0.0001, 1.0, 0, lbfgs).
+ linear_SVM_initModelWithTrain([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, [0,1,0,1,0,1], 2, 0.0001, 1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Too_Many_LabelClasses, [error(_,system_error('Error'))]) :-
- initModelWithTrain([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, [0,1,2,3], 2, 0.0001, 1.0, 0, lbfgs).
+ linear_SVM_initModelWithTrain([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, [0,1,2,3], 2, 0.0001, 1.0, 0, lbfgs).
%% Successful Tests
test(linear_SVM_InitModelWithTrain_Normal_Use_LBFGS) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs).
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs).
test(linear_SVM_InitModelWithTrain_Normal_Use_PSGD) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, psgd).
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, psgd).
test(linear_SVM_InitModelWithTrain_CSV_Inpupt_LBFGS) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.000021, 2.0, 1, lbfgs).
+ linear_SVM_initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.000021, 2.0, 1, lbfgs).
-:- end_tests(initModelWithTrain).
+:- end_tests(linear_SVM_initModelWithTrain).
%%
-%% TESTING predicate initModelNoTrain/4
+%% TESTING predicate linear_SVM_initModelNoTrain/4
%%
-:- begin_tests(initModelNoTrain).
+:- begin_tests(linear_SVM_initModelNoTrain).
%% Failure Tests
test(linear_SVM_InitModelNoTrain_Negative_NumClasses, fail) :-
- initModelNoTrain(-2, 0.0001, 1.0, 0).
+ linear_SVM_initModelNoTrain(-2, 0.0001, 1.0, 0).
test(linear_SVM_InitModelNoTrain_Negative_Lambda, fail) :-
- initModelNoTrain(2, -0.0001, 1.0, 0).
+ linear_SVM_initModelNoTrain(2, -0.0001, 1.0, 0).
test(linear_SVM_InitModelNoTrain_Negative_Delta, fail) :-
- initModelNoTrain(2, 0.0001, -1.0, 0).
+ linear_SVM_initModelNoTrain(2, 0.0001, -1.0, 0).
%% Successful Tests
test(linear_SVM_InitModelNoTrain_Normal_Use) :-
- initModelNoTrain(2, 0.0001, 1.0, 0).
+ linear_SVM_initModelNoTrain(2, 0.0001, 1.0, 0).
test(linear_SVM_InitModelNoTrain_Alternative) :-
- initModelNoTrain(2, 0.042, 0.5, 1).
+ linear_SVM_initModelNoTrain(2, 0.042, 0.5, 1).
-:- end_tests(initModelNoTrain).
+:- end_tests(linear_SVM_initModelNoTrain).
%%
-%% TESTING predicate classify/5
+%% TESTING predicate linear_SVM_classify/5
%%
-:- begin_tests(classify).
+:- begin_tests(linear_SVM_classify).
%% Failure Tests
test(linear_SVM_Classify_Before_Train, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- 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, _, _, _).
+ linear_SVM_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(linear_SVM_Classify_Diffrent_Dims_Than_Train, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
- 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], 4, _, _, _).
+ linear_SVM_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], 4, _, _, _).
%% Successful Tests
test(linear_SVM_Classify_Normal_Use_LBFGS) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs),
- 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, LabelsList, ScoresList, _),
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs),
+ linear_SVM_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, LabelsList, ScoresList, _),
print('\nLabels: '),
print(LabelsList),
print('\nScores: '),
print(ScoresList).
test(linear_SVM_Classify_Normal_Use_PSGD) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, psgd),
- 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, LabelsList, ScoresList, _),
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, psgd),
+ linear_SVM_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, LabelsList, ScoresList, _),
print('\nLabels: '),
print(LabelsList),
print('\nScores: '),
print(ScoresList).
test(linear_SVM_Classify_CSV_Input) :-
- initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 2, 0.0001, 2.0, 1, lbfgs),
+ linear_SVM_initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 2, 0.0001, 2.0, 1, lbfgs),
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- classify(Data, 4, LabelsList, ScoresList, _),
+ linear_SVM_classify(Data, 4, LabelsList, ScoresList, _),
print('\nLabels: '),
print(LabelsList),
print('\nScores: '),
print(ScoresList).
-:- end_tests(classify).
+:- end_tests(linear_SVM_classify).
%%
-%% TESTING predicate classifyPoint/2
+%% TESTING predicate linear_SVM_classifyPoint/2
%%
-:- begin_tests(classifyPoint).
+:- begin_tests(linear_SVM_classifyPoint).
%% Failure Tests
test(linear_SVM_ClassifyPoint_Before_Train, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- classifyPoint([5.1,3.5,1.4], _).
+ linear_SVM_classifyPoint([5.1,3.5,1.4], _).
test(linear_SVM_ClassifyPoint_Diffrent_Dims_Than_Train, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
- classifyPoint([5.1,3.5,1.4,4.9], _).
+ linear_SVM_classifyPoint([5.1,3.5,1.4,4.9], _).
%% Successful Tests
test(linear_SVM_ClassifyPoint_Normal_Use_LBFGS) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs),
- classifyPoint([5.1,3.5,1.4], Label),
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs),
+ linear_SVM_classifyPoint([5.1,3.5,1.4], Label),
print('\nLabel: '),
print(Label).
test(linear_SVM_ClassifyPoint_Normal_Use_PSGD) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, psgd),
- classifyPoint([5.1,3.5,1.4], Label),
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, psgd),
+ linear_SVM_classifyPoint([5.1,3.5,1.4], Label),
print('\nLabel: '),
print(Label).
test(linear_SVM_ClassifyPoint_CSV_Input) :-
- initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 2, 0.0001, 2.0, 1, lbfgs),
+ linear_SVM_initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 2, 0.0001, 2.0, 1, lbfgs),
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,1, Data),
- classifyPoint(Data, Label),
+ linear_SVM_classifyPoint(Data, Label),
print('\nLabel: '),
print(Label).
-:- end_tests(classifyPoint).
+:- end_tests(linear_SVM_classifyPoint).
%%
-%% TESTING predicate computeAccuracy/4
+%% TESTING predicate linear_SVM_computeAccuracy/4
%%
-:- begin_tests(computeAccuracy).
+:- begin_tests(linear_SVM_computeAccuracy).
%% Failure Tests
test(linear_SVM_ComputeAccuracy_Before_Train, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- computeAccuracy([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], _).
+ linear_SVM_computeAccuracy([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], _).
test(linear_SVM_ComputeAccuracy_Too_Few_Labels, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
- computeAccuracy([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], _).
+ linear_SVM_computeAccuracy([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], _).
test(linear_SVM_ComputeAccuracy_Too_Many_Labels, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
- computeAccuracy([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, [0,1,0,1,0,1], _).
+ linear_SVM_computeAccuracy([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, [0,1,0,1,0,1], _).
test(linear_SVM_ComputeAccuracy_Too_Many_LabelClasses, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
- computeAccuracy([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, [0,1,2,3], _).
+ linear_SVM_computeAccuracy([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, [0,1,2,3], _).
test(linear_SVM_ComputeAccuracy_Wrong_Data_Dims, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
- computeAccuracy([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], _).
+ linear_SVM_computeAccuracy([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], _).
%% Successful Tests
test(linear_SVM_ComputeAccuracy_Normal_Use_LBFGS) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs),
- computeAccuracy([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], Accuracy),
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, lbfgs),
+ linear_SVM_computeAccuracy([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], Accuracy),
print('\nAccuracy: '),
print(Accuracy).
test(linear_SVM_ComputeAccuracy_Normal_Use_PSGD) :-
- initModelWithTrain([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, 0.0001, 1.0, 0, psgd),
- computeAccuracy([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], Accuracy),
+ linear_SVM_initModelWithTrain([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, 0.0001, 1.0, 0, psgd),
+ linear_SVM_computeAccuracy([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], Accuracy),
print('\nAccuracy: '),
print(Accuracy).
test(linear_SVM_ComputeAccuracy_CSV_Input) :-
- initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 2, 0.0001, 2.0, 1, lbfgs),
+ linear_SVM_initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 2, 0.0001, 2.0, 1, lbfgs),
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,1, Data),
- computeAccuracy(Data, 4, [0,1,0,1,1,0,1,1,1,0], Accuracy),
+ linear_SVM_computeAccuracy(Data, 4, [0,1,0,1,1,0,1,1,1,0], Accuracy),
print('\nAccuracy: '),
print(Accuracy).
-:- end_tests(computeAccuracy).
+:- end_tests(linear_SVM_computeAccuracy).
%%
-%% TESTING predicate train/6
+%% TESTING predicate linear_SVM_train/6
%%
-:- begin_tests(train).
+:- begin_tests(linear_SVM_train).
%% Failure Tests
test(linear_SVM_Train_Negative_NumClasses, fail) :-
reset_Model_NoTrain,
- 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, lbfgs, _).
+ linear_SVM_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, lbfgs, _).
test(linear_SVM_Train_Wrong_Optimizer_Input, [error(domain_error('The given Optimizer is unkown!' , wrongInput), _)]) :-
reset_Model_NoTrain,
- 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, wrongInput, _).
+ linear_SVM_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, wrongInput, _).
test(linear_SVM_Train_Too_Few_Labels, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- 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], 2, lbfgs, _).
+ linear_SVM_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], 2, lbfgs, _).
test(linear_SVM_Train_Too_Many_Labels, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- 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, [0,1,0,1,0,1], 2, lbfgs, _).
+ linear_SVM_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, [0,1,0,1,0,1], 2, lbfgs, _).
test(linear_SVM_Train_Too_Many_LabelClasses, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- 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, [0,1,2,3], 2, lbfgs, _).
+ linear_SVM_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, [0,1,2,3], 2, lbfgs, _).
%% Successful Tests
test(linear_SVM_Train_Normal_Use_LBFGS) :-
reset_Model_NoTrain,
- 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, lbfgs, ObjectiveValue),
+ linear_SVM_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, lbfgs, ObjectiveValue),
print('\nObjectiveValue: '),
print(ObjectiveValue).
test(linear_SVM_Train_Normal_Use_PSGD) :-
reset_Model_NoTrain,
- 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, psgd, ObjectiveValue),
+ linear_SVM_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, psgd, ObjectiveValue),
print('\nObjectiveValue: '),
print(ObjectiveValue).
@@ -280,11 +280,11 @@ test(linear_SVM_Train_CSV_Inpupt_LBFGS) :-
reset_Model_NoTrain,
open('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, lbfgs, ObjectiveValue),
+ linear_SVM_train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, lbfgs, ObjectiveValue),
print('\nObjectiveValue: '),
print(ObjectiveValue).
-:- end_tests(train).
+:- end_tests(linear_SVM_train).
run_linear_SVM_tests :-
run_tests.
diff --git a/src/methods/linear_regression/linear_regression.pl b/src/methods/linear_regression/linear_regression.pl
index cb85d07b3256b1e9e766e7391f749762f0c4dafb..dfc1ff849c7841cb6b5f945ceab7c2e8424cca9b 100644
--- a/src/methods/linear_regression/linear_regression.pl
+++ b/src/methods/linear_regression/linear_regression.pl
@@ -1,12 +1,12 @@
-:- module(linear_regression, [ initModel/5,
- initModelWithWeights/6,
- computeError/4,
- parameters/1,
- modifyParameters/1,
- predict/3,
- train/5,
- trainWithWeights/6]).
+:- module(linear_regression, [ linear_regression_initModel/5,
+ linear_regression_initModelWithWeights/6,
+ linear_regression_computeError/4,
+ linear_regression_parameters/1,
+ linear_regression_modifyParameters/1,
+ linear_regression_predict/3,
+ linear_regression_train/5,
+ linear_regression_trainWithWeights/6]).
:- load_files(library(str_decl),
[when(compile_time), if(changed)]).
@@ -34,7 +34,7 @@
%% --Description--
%% Initializes the linear_regression model and trains it but doesnt include weights.
%%
-initModel(DataList, DataRows, ResponsesList, Lambda, Intercept) :-
+linear_regression_initModel(DataList, DataRows, ResponsesList, Lambda, Intercept) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
initModelI(X, Xsize, Xrownum, Y, Ysize, Lambda, Intercept).
@@ -57,7 +57,7 @@ foreign(initModel, c, initModelI( +pointer(float_array), +integer, +intege
%% --Description--
%% Initializes the linear_regression model, trains it and adds weights to it.
%%
-initModelWithWeights(DataList, DataRows, ResponsesList, WeightsList, Lambda, Intercept) :-
+linear_regression_initModelWithWeights(DataList, DataRows, ResponsesList, WeightsList, Lambda, Intercept) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
convert_list_to_float_array(WeightsList, array(Zsize, Z)),
@@ -80,7 +80,7 @@ foreign(initModelWithWeights, c, initModelWithWeightsI( +pointer(float_a
%% --Description--
%% Calculate the L2 squared error on the given predictors and responses using this linear regression model.
%%
-computeError(DataList, DataRows, ResponsesList, Error) :-
+linear_regression_computeError(DataList, DataRows, ResponsesList, Error) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
computeErrorI(X, Xsize, Xrownum, Y, Ysize, Error).
@@ -98,7 +98,7 @@ foreign(computeError, c, computeErrorI(+pointer(float_array), +integer, +intege
%% --Description--
%% Get the parameters (the b vector).
%%
-parameters(ParametersList) :-
+linear_regression_parameters(ParametersList) :-
parametersI(Y, Ysize),
convert_float_array_to_list(Y, Ysize, ParametersList).
@@ -113,7 +113,7 @@ foreign(parameters, c, parametersI(-pointer(float_array), -integer)).
%% --Description--
%% Modify the parameters (the b vector).
%%
-modifyParameters(ParameterList) :-
+linear_regression_modifyParameters(ParameterList) :-
convert_list_to_float_array(ParameterList, array(Ysize, Y)),
modifyParametersI(Y, Ysize).
@@ -129,7 +129,7 @@ foreign(modifyParameters, c, modifyParametersI(+pointer(float_array), +integer)
%% --Description--
%% Calculate y_i for each data point in points.
%%
-predict(PointsList, PointsRows, PredicList) :-
+linear_regression_predict(PointsList, PointsRows, PredicList) :-
convert_list_to_float_array(PointsList, PointsRows, array(Xsize, Xrownum, X)),
predictI(X, Xsize, Xrownum, Y, Ysize),
convert_float_array_to_list(Y, Ysize, PredicList).
@@ -151,7 +151,7 @@ foreign(predict, c, predictI( +pointer(float_array), +integer, +integer,
%% Careful!
%% This will completely ignore and overwrite the existing model. This particular implementation does not have an incremental training algorithm.
%%
-train(DataList, DataRows, ResponsesList, Intercept, Error) :-
+linear_regression_train(DataList, DataRows, ResponsesList, Intercept, Error) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
trainI(X, Xsize, Xrownum, Y, Ysize, Intercept, Error).
@@ -176,7 +176,7 @@ foreign(train, c, trainI( +pointer(float_array), +integer, +integer,
%% Careful!
%% This will completely ignore and overwrite the existing model. This particular implementation does not have an incremental training algorithm.
%%
-trainWithWeights(DataList, DataRows, ResponsesList, WeightsList, Intercept, Error) :-
+linear_regression_trainWithWeights(DataList, DataRows, ResponsesList, WeightsList, Intercept, Error) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
convert_list_to_float_array(WeightsList, array(Zsize, Z)),
diff --git a/src/methods/local_coordinate_coding/local_coordinate_coding.pl b/src/methods/local_coordinate_coding/local_coordinate_coding.pl
index 9c7d2be226d054bb10bc91d2e901df04ba253f06..97c45803f284144917941ba1d9aae9def3171a05 100644
--- a/src/methods/local_coordinate_coding/local_coordinate_coding.pl
+++ b/src/methods/local_coordinate_coding/local_coordinate_coding.pl
@@ -1,10 +1,10 @@
-:- module(local_coordinate_coding, [ initModelWithTrain/7,
- initModelNoTrain/5,
- encode/4,
- objective/5,
- optimizeDictionary/5,
- train/3]).
+:- module(local_coordinate_coding, [ lcc_initModelWithTrain/7,
+ lcc_initModelNoTrain/5,
+ lcc_encode/4,
+ lcc_objective/5,
+ lcc_optimizeDictionary/5,
+ lcc_train/3]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -35,7 +35,7 @@
%% --Description--
%% Initializes the model and trains it so encode/6 can be called after
%%
-initModelWithTrain(DataList, DataRows, Normalize, Atoms, Lambda, MaxIterations, Tolerance) :-
+lcc_initModelWithTrain(DataList, DataRows, Normalize, Atoms, Lambda, MaxIterations, Tolerance) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
initModelWithTrainI(X, Xsize, Xrownum, Normalize, Atoms, Lambda, MaxIterations, Tolerance).
@@ -56,7 +56,7 @@ foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +
%% --Description--
%% Initializes the model but doesnt train it so train/4 has to be called befor encode/6 can be used.
%%
-initModelNoTrain(Normalize, Atoms, Lambda, MaxIterations, Tolerance) :-
+lcc_initModelNoTrain(Normalize, Atoms, Lambda, MaxIterations, Tolerance) :-
initModelNoTrainI(Normalize, Atoms, Lambda, MaxIterations, Tolerance).
foreign(initModelNoTrain, c, initModelNoTrainI( +integer,
@@ -72,7 +72,7 @@ foreign(initModelNoTrain, c, initModelNoTrainI( +integer,
%% --Description--
%% Code each point via distance-weighted LARS.
%%
-encode(DataList, DataRows, CodesList, YCols) :-
+lcc_encode(DataList, DataRows, CodesList, YCols) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
encodeI(X, Xsize, Xrows, Y, YCols, YRows),
convert_float_array_to_2d_list(Y, YCols, YRows, CodesList).
@@ -91,7 +91,7 @@ foreign(encode, c, encodeI( +pointer(float_array), +integer, +integer,
%% --Description--
%% Compute objective function given the list of adjacencies.
%%
-objective(DataList, DataRows, CodesList, ZCols, AdjacenciesList) :-
+lcc_objective(DataList, DataRows, CodesList, YCols, AdjacenciesList) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
objectiveI(X, Xsize, Xrows, Y, YCols, YRows, Z, Zsize),
convert_float_array_to_2d_list(Y, YCols, YRows, CodesList),
@@ -112,7 +112,7 @@ foreign(objective, c, objectiveI( +pointer(float_array), +integer, +intege
%% --Description--
%% Learn dictionary by solving linear system.
%%
-optimizeDictionary(DataList, DataRows, CodesList, ZCols, AdjacenciesList) :-
+lcc_optimizeDictionary(DataList, DataRows, CodesList, YCols, AdjacenciesList) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
optimizeDictionaryI(X, Xsize, Xrows, Y, YCols, YRows, Z, Zsize),
convert_float_array_to_2d_list(Y, YCols, YRows, CodesList),
@@ -132,7 +132,7 @@ foreign(optimizeDictionary, c, optimizeDictionaryI( +pointer(float_array), +
%% --Description--
%% Run local coordinate coding and train the model.
%%
-train(DataList, DataRows, ReturnValue) :-
+lcc_train(DataList, DataRows, ReturnValue) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
trainI(X, Xsize, Xrows, ReturnValue).
diff --git a/src/methods/local_coordinate_coding/local_coordinate_coding_test.pl b/src/methods/local_coordinate_coding/local_coordinate_coding_test.pl
index 5fe6b237aed5dd8d06ff3ad85db406abf8a99f84..0bdb5de3ca20b01aea1f96541b7685700f72fc57 100644
--- a/src/methods/local_coordinate_coding/local_coordinate_coding_test.pl
+++ b/src/methods/local_coordinate_coding/local_coordinate_coding_test.pl
@@ -7,7 +7,7 @@
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initModel(1,0,50,0.0001).
+ lcc_initModel(1,0,50,0.0001).
%%
%% TESTING predicate predicate/10
diff --git a/src/methods/logistic_regression/logistic_regression.pl b/src/methods/logistic_regression/logistic_regression.pl
index 86228c50007a5c6c0a67326dfcbf866c7fa30bfd..ecdc87b513dc2ae77c1fd008a21ab5993feb9c45 100644
--- a/src/methods/logistic_regression/logistic_regression.pl
+++ b/src/methods/logistic_regression/logistic_regression.pl
@@ -1,11 +1,11 @@
-:- module(logistic_regression, [ initModelNoOptimizer/4,
- initModelWithOptimizer/5,
- classifyPoint/3,
- classifyMatrix/6,
- computeAccuracy/5,
- computeError/4,
- train/4]).
+:- module(logistic_regression, [ logistic_regression_initModelNoOptimizer/4,
+ logistic_regression_initModelWithOptimizer/5,
+ logistic_regression_classifyPoint/3,
+ logistic_regression_classifyMatrix/6,
+ logistic_regression_computeAccuracy/5,
+ logistic_regression_computeError/4,
+ logistic_regression_train/4]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -33,7 +33,7 @@
%% --Description--
%% Initialize the logistic_regression model without specifing a optimizer.
%%
-initModelNoOptimizer(DataList, DataRows, ResponsesList, Lambda) :-
+logistic_regression_initModelNoOptimizer(DataList, DataRows, ResponsesList, Lambda) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
initModelNoOptimizerI(X, Xsize, Xrownum, Y, Ysize, Lambda).
@@ -54,7 +54,7 @@ foreign(initModelNoOptimizer, c, initModelNoOptimizerI( +pointer(float_array), +
%% --Description--
%% Initialize the logistic_regression model and specify the optimizer.
%%
-initModelWithOptimizer(DataList, DataRows, ResponsesList, Responses, Lambda) :-
+logistic_regression_initModelWithOptimizer(DataList, DataRows, ResponsesList, Responses, Lambda) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
initModelWithOptimizerI(X, Xsize, Xrownum, Y, Ysize, Responses, Lambda).
@@ -75,7 +75,7 @@ foreign(initModelWithOptimizer, c, initModelWithOptimizerI( +pointer(float_a
%% --Description--
%% Classify the given point.
%%
-classifyPoint(PointList, DecisionBoundary, PredicLabel) :-
+logistic_regression_classifyPoint(PointList, DecisionBoundary, PredicLabel) :-
convert_list_to_float_array(PointList, array(Xsize, X)),
classifyPointI(X, Xsize, DecisionBoundary, PredicLabel).
@@ -95,7 +95,7 @@ foreign(classifyPoint, c, classifyPointI( +pointer(float_array), +integer,
%% --Description--
%% Classify the given points, returning the predicted labels for each point.
%%
-classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols, DecisionBoundary) :-
+logistic_regression_classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols, DecisionBoundary) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
classifyMatrixI(X, Xsize, Xrows, Y, Ysize, Z, ZCols, ZRows, DecisionBoundary),
convert_float_array_to_list(Y, Ysize, PredictionList),
@@ -118,7 +118,7 @@ foreign(classifyMatrix, c, classifyMatrixI( +pointer(float_array), +integer,
%% --Description--
%% Compute the accuracy of the model on the given predictors and responses, using the given decision boundary.
%%
-computeAccuracy(DataList, DataRows, ResponsesList, DecisionBoundary, Accuracy) :-
+logistic_regression_computeAccuracy(DataList, DataRows, ResponsesList, DecisionBoundary, Accuracy) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
computeAccuracyI(X, Xsize, Xrownum, Y, Ysize, DecisionBoundary, Accuracy).
@@ -139,7 +139,7 @@ foreign(computeAccuracy, c, computeAccuracyI( +pointer(float_array), +integer,
%% --Description--
%% Compute the error of the model.
%%
-computeError(DataList, DataRows, ResponsesList, Error) :-
+logistic_regression_computeError(DataList, DataRows, ResponsesList, Error) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
computeErrorI(X, Xsize, Xrownum, Y, Ysize, Error).
@@ -159,7 +159,7 @@ foreign(computeError, c, computeErrorI( +pointer(float_array), +integer, +intege
%% --Description--
%% Train the logistic_regression model on the given input data.
%%
-train(DataList, DataRows, ResponsesList, Optimizer) :-
+logistic_regression_train(DataList, DataRows, ResponsesList, Optimizer) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
trainI(X, Xsize, Xrownum, Y, Ysize, Optimizer).
diff --git a/src/methods/logistic_regression/logistic_regression_test.pl b/src/methods/logistic_regression/logistic_regression_test.pl
index da8651e190a62ec57928d02c557e53dc7ad83518..f7d7fac2b850cab006a5a71b703ba2fb8ff46e7e 100644
--- a/src/methods/logistic_regression/logistic_regression_test.pl
+++ b/src/methods/logistic_regression/logistic_regression_test.pl
@@ -7,7 +7,7 @@
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initModel(1,0,50,0.0001).
+ logistic_regression_initModel(1,0,50,0.0001).
%%
%% TESTING predicate predicate/10
diff --git a/src/methods/lsh/lsh.pl b/src/methods/lsh/lsh.pl
index 0f6f40da06d02a73c6773efd8ef2a1f1b94a9954..d0df6d303a99f1f90cd505ecaeb72ca5001e523b 100644
--- a/src/methods/lsh/lsh.pl
+++ b/src/methods/lsh/lsh.pl
@@ -1,8 +1,8 @@
-:- module(lsh, [initAndTrainModel/7,
- computeRecall/5,
- searchWithQuery/9,
- searchNoQuery/7]).
+:- module(lsh, [lsh_initAndTrainModel/7,
+ lsh_computeRecall/5,
+ lsh_searchWithQuery/9,
+ lsh_searchNoQuery/7]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -33,7 +33,7 @@
%% --Description--
%% Initiatzes the model and trains it.
%%
-initAndTrainModel(ReferenceList, ReferenceRows, NumProj, NumTables, HashWidth, SecondHashSize, BucketSize) :-
+lsh_initAndTrainModel(ReferenceList, ReferenceRows, NumProj, NumTables, HashWidth, SecondHashSize, BucketSize) :-
NumProj >= 0,
NumTables >= 0,
HashWidth >= 0.0,
@@ -56,7 +56,7 @@ foreign(initAndTrainModel, c, initAndTrainModelI(+pointer(float_array), +integer
%% --Description--
%% Compute the recall (% of neighbors found) given the neighbors returned by searchWithQuery/12 or searchNoQuery/9 and a "ground truth" set of neighbors.
%%
-computeRecall(FoundNeighborsList, FoundNeighborsRows, RealNeighborsList, RealNeighborsRows, Percentage) :-
+lsh_computeRecall(FoundNeighborsList, FoundNeighborsRows, RealNeighborsList, RealNeighborsRows, Percentage) :-
convert_list_to_float_array(FoundNeighborsList, FoundNeighborsRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(RealNeighborsList, RealNeighborsRows, array(Ysize, Yrownum, Y)),
computeRecallI(X, Xsize, Xrownum, Y, Ysize, Yrownum, Percentage).
@@ -80,7 +80,7 @@ foreign(computeRecall, c, computeRecallI(+pointer(float_array), +integer, +integ
%% Compute the nearest neighbors of the points in the given query set and store the output in the given matrices.
%% The matrices will be set to the size of n columns by k rows, where n is the number of points in the query dataset and k is the number of neighbors being searched for.
%%
-searchWithQuery(QueryList, QueryRows, K, ResultingNeighborsList, YCols, DistancesList, ZCols, NumTablesToSearch, T) :-
+lsh_searchWithQuery(QueryList, QueryRows, K, ResultingNeighborsList, YCols, DistancesList, ZCols, NumTablesToSearch, T) :-
K > 0,
NumTablesToSearch >= 0,
T >= 0,
@@ -109,7 +109,7 @@ foreign(searchWithQuery, c, searchWithQueryI( +pointer(float_array), +integer,
%% Compute the nearest neighbors and store the output in the given matrices.
%% The matrices will be set to the size of n columns by k rows, where n is the number of points in the query dataset and k is the number of neighbors being searched for.
%%
-searchNoQuery(K, ResultingNeighborsList, YCols, DistancesList, ZCols, NumTablesToSearch, T) :-
+lsh_searchNoQuery(K, ResultingNeighborsList, YCols, DistancesList, ZCols, NumTablesToSearch, T) :-
K > 0,
NumTablesToSearch >= 0,
T >= 0,
diff --git a/src/methods/lsh/lsh_test.pl b/src/methods/lsh/lsh_test.pl
index d5088a5bea02a75fae36f73466a1e8a98d343d81..5d3a0cc4c08d6bcd64226d2b5ee0413c15e6401a 100644
--- a/src/methods/lsh/lsh_test.pl
+++ b/src/methods/lsh/lsh_test.pl
@@ -7,146 +7,146 @@
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initAndTrainModel([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, 20, 10, 0.0, 99901, 500).
+ lsh_initAndTrainModel([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, 20, 10, 0.0, 99901, 500).
%%
-%% TESTING predicate initAndTrainModel/7
+%% TESTING predicate lsh_initAndTrainModel/7
%%
-:- begin_tests(initAndTrainModel).
+:- begin_tests(lsh_initAndTrainModel).
%% Failure Tests
test(lsh_InitAndTrainModel_Negative_NumProj, fail) :-
- initAndTrainModel([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, -20, 10, 0.0, 99901, 500).
+ lsh_initAndTrainModel([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, -20, 10, 0.0, 99901, 500).
test(lsh_InitAndTrainModel_Negative_NumTables, fail) :-
- initAndTrainModel([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, 20, -10, 0.0, 99901, 500).
+ lsh_initAndTrainModel([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, 20, -10, 0.0, 99901, 500).
test(lsh_InitAndTrainModel_Negative_HashWidth, fail) :-
- initAndTrainModel([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, 20, 10, -1.0, 99901, 500).
+ lsh_initAndTrainModel([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, 20, 10, -1.0, 99901, 500).
test(lsh_InitAndTrainModel_Negative_SecondHashSize, fail) :-
- initAndTrainModel([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, 20, 10, 0.0, -99901, 500).
+ lsh_initAndTrainModel([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, 20, 10, 0.0, -99901, 500).
test(lsh_InitAndTrainModel_Negative_BucketSize, fail) :-
- initAndTrainModel([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, 20, 10, 0.0, 99901, -500).
+ lsh_initAndTrainModel([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, 20, 10, 0.0, 99901, -500).
%% Successful Tests
test(lsh_InitAndTrainModel_Normal_Use) :-
- initAndTrainModel([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, 20, 10, 0.0, 99901, 500).
+ lsh_initAndTrainModel([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, 20, 10, 0.0, 99901, 500).
test(lsh_InitAndTrainModel_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- train(Data, 4, 25, 15, 1.5, 99901, 200).
+ lsh_initAndTrainModel(Data, 4, 25, 15, 1.5, 99901, 200).
-:- end_tests(initAndTrainModel).
+:- end_tests(lsh_initAndTrainModel).
%%
-%% TESTING predicate computeRecall/5
+%% TESTING predicate lsh_computeRecall/5
%%
-:- begin_tests(computeRecall).
+:- begin_tests(lsh_computeRecall).
%% Failure Tests
test(lsh_ComputeRecall_Wrong_Dimensions, [error(_, system_error('Error'))]) :-
reset_Model,
- computeRecall([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, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _).
+ lsh_computeRecall([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, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _).
%% Successful Tests
test(lsh_ComputeRecall_Normal_Use) :-
reset_Model,
- computeRecall([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, [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, _).
+ lsh_computeRecall([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, [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, _).
-:- end_tests(computeRecall).
+:- end_tests(lsh_computeRecall).
%%
-%% TESTING predicate searchWithQuery/10
+%% TESTING predicate lsh_searchWithQuery/10
%%
-:- begin_tests(searchWithQuery).
+:- begin_tests(lsh_searchWithQuery).
%% Failure Tests
test(lsh_SearchWithQuery_Wrong_Dimensions, [error(_, system_error('Error'))]) :-
reset_Model,
- searchWithQuery([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 3, _, _, _, _, 0, 0).
+ lsh_searchWithQuery([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 3, _, _, _, _, 0, 0).
test(lsh_SearchWithQuery_Too_High_K, [error(_, system_error('Error'))]) :-
reset_Model,
- searchWithQuery([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, 10, _, _, _, _, 0, 0).
+ lsh_searchWithQuery([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, 10, _, _, _, _, 0, 0).
test(lsh_SearchWithQuery_Negative_K, fail) :-
reset_Model,
- searchWithQuery([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, -3, _, _, _, _, 0, 0).
+ lsh_searchWithQuery([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, -3, _, _, _, _, 0, 0).
test(lsh_SearchWithQuery_Negative_NumTablesToSearch, fail) :-
reset_Model,
- searchWithQuery([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, 3, _, _, _, _, -10, 0).
+ lsh_searchWithQuery([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, 3, _, _, _, _, -10, 0).
test(lsh_SearchWithQuery_Negative_T, fail) :-
reset_Model,
- searchWithQuery([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, 3, _, _, _, _, 0, -10).
+ lsh_searchWithQuery([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, 3, _, _, _, _, 0, -10).
%% Successful Tests
test(lsh_SearchWithQuery_Normal_Use) :-
reset_Model,
- searchWithQuery([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, 3, NeighborResultsList, _, DistancesList, _, 0, 0),
+ lsh_searchWithQuery([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, 3, NeighborResultsList, _, DistancesList, _, 0, 0),
print('\nNeighborResults: '),
print(NeighborResultsList),
print('\nDistances: '),
print(DistancesList).
-:- end_tests(searchWithQuery).
+:- end_tests(lsh_searchWithQuery).
%%
-%% TESTING predicate searchNoQuery/10
+%% TESTING predicate lsh_searchNoQuery/10
%%
-:- begin_tests(searchNoQuery).
+:- begin_tests(lsh_searchNoQuery).
%% Failure Tests
test(lsh_SearchNoQuery_Too_High_K, [error(_, system_error('Error'))]) :-
reset_Model,
- searchNoQuery(10, _, _, _, _, 0, 0).
+ lsh_searchNoQuery(10, _, _, _, _, 0, 0).
test(lsh_SearchNoQuery_Negative_K, fail) :-
reset_Model,
- searchNoQuery(-3, _, _, _, _, 0, 0).
+ lsh_searchNoQuery(-3, _, _, _, _, 0, 0).
test(lsh_SearchNoQuery_Negative_NumTablesToSearch, fail) :-
reset_Model,
- searchNoQuery(3, _, _, _, _, -10, 0).
+ lsh_searchNoQuery(3, _, _, _, _, -10, 0).
test(lsh_SearchNoQuery_Negative_T, fail) :-
reset_Model,
- searchNoQuery(3, _, _, _, _, 0, -10).
+ lsh_searchNoQuery(3, _, _, _, _, 0, -10).
%% Successful Tests
test(lsh_SearchNoQuery_Normal_Use) :-
reset_Model,
- searchNoQuery(3, NeighborResultsList, _, DistancesList, _, 0, 0),
+ lsh_searchNoQuery(3, NeighborResultsList, _, DistancesList, _, 0, 0),
print('\nNeighborResults: '),
print(NeighborResultsList),
print('\nDistances: '),
print(DistancesList).
-:- end_tests(searchNoQuery).
+:- end_tests(lsh_searchNoQuery).
run_lsh_tests :-
run_tests.
diff --git a/src/methods/naive_bayes_classifier/naive_bayes_classifier.pl b/src/methods/naive_bayes_classifier/naive_bayes_classifier.pl
index 898624de6e4b93cecc2587118f378d89772b58e3..ea840a47c57cbd13636622eae2544ea90b96b094 100644
--- a/src/methods/naive_bayes_classifier/naive_bayes_classifier.pl
+++ b/src/methods/naive_bayes_classifier/naive_bayes_classifier.pl
@@ -1,13 +1,13 @@
-:- module(naive_bayes_classifier, [ initModelWithTrain/6,
- initModelNoTrain/3,
+:- module(naive_bayes_classifier, [ nbc_initModelWithTrain/6,
+ nbc_initModelNoTrain/3,
- classifyMatrix/5,
- means/2,
- probabilities/2,
- trainMatrix/5,
- trainPoint/2,
- variances/2]).
+ nbc_classifyMatrix/5,
+ nbc_means/2,
+ nbc_probabilities/2,
+ nbc_trainMatrix/5,
+ nbc_trainPoint/2,
+ nbc_variances/2]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -37,7 +37,7 @@
%% --Description--
%% Initializes the classifier as per the input and then trains it by calculating the sample mean and variances.
%%
-initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, IncrementalVar, Epsilon) :-
+nbc_initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, IncrementalVar, Epsilon) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(LabelsList, array(Ysize, Y)),
initModelWithTrainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, IncrementalVar, Epsilon).
@@ -60,7 +60,7 @@ foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +
%% Initialize the Naive Bayes classifier without performing training.
%% All of the parameters of the model will be initialized to zero. Be sure to use train before calling classify, otherwise the results may be meaningless.
%%
-initModelNoTrain(NumClasses, IncrementalVar, Epsilon) :-
+nbc_initModelNoTrain(NumClasses, IncrementalVar, Epsilon) :-
NumClasses >= 0,
Epsilon >= 0,
initModelNoTrainI(NumClasses, IncrementalVar, Epsilon).
@@ -95,7 +95,7 @@ foreign(initModelNoTrain, c, initModelNoTrainI( +integer,
%% Classify the given points using the trained NaiveBayesClassifier model and also return estimates of the probabilities for each class in the given matrix.
%% The predicted labels for each point are stored in the given vector.
%%
-classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols) :-
+nbc_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),
@@ -114,7 +114,7 @@ foreign(classifyMatrix, c, classifyMatrixI( +pointer(float_array), +integer,
%% --Description--
%% Get the sample means for each class.
%%
-means(MeansList, XCols) :-
+nbc_means(MeansList, XCols) :-
meansI(X, XCols, XRows),
convert_float_array_to_2d_list(X, XCols, XRows, MeansList).
@@ -129,7 +129,7 @@ foreign(means, c, meansI(-pointer(float_array), -integer, -integer)).
%% --Description--
%% Get the sample probabilities for each class.
%%
-probabilities(ProbabilitiesList, XCols) :-
+nbc_probabilities(ProbabilitiesList, XCols) :-
probabilitiesI(X, XCols, XRows),
convert_float_array_to_2d_list(X, XCols, XRows, ProbabilitiesList).
@@ -147,7 +147,7 @@ foreign(probabilities, c, probabilitiesI(-pointer(float_array), -integer, -integ
%% --Description--
%% Train the Naive Bayes classifier on the given dataset. The data must be the same dimensionality as the existing model parameters.
%%
-trainMatrix(DataList, DataRows, LabelsList, NumClasses, IncrementalVar) :-
+nbc_trainMatrix(DataList, DataRows, LabelsList, NumClasses, IncrementalVar) :-
NumClasses >= 0,
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(LabelsList, array(Ysize, Y)),
@@ -169,7 +169,7 @@ foreign(trainMatrix, c, trainMatrixI( +pointer(float_array), +integer, +intege
%% Train the Naive Bayes classifier on the given point.
%% This will use the incremental algorithm for updating the model parameters. The data must be the same dimensionality as the existing model parameters.
%%
-trainPoint(DataList, Label) :-
+nbc_trainPoint(DataList, Label) :-
convert_list_to_float_array(DataList, array(Xsize, X)),
trainPointI(X, Xsize, Label).
@@ -185,7 +185,7 @@ foreign(trainPoint, c, trainPointI( +pointer(float_array), +integer,
%% --Description--
%% Get the sample variances for each class.
%%
-variances(VariancesList, XCols) :-
+nbc_variances(VariancesList, XCols) :-
variancesI(X, XCols, XRows),
convert_float_array_to_2d_list(X, XCols, XRows, VariancesList).
diff --git a/src/methods/naive_bayes_classifier/naive_bayes_classifier_test.pl b/src/methods/naive_bayes_classifier/naive_bayes_classifier_test.pl
index 8bc7b33a067a7386a8fef899400b3994fb7e9776..541088cb90c25ba3c1889fb35f48380ffb99ebbb 100644
--- a/src/methods/naive_bayes_classifier/naive_bayes_classifier_test.pl
+++ b/src/methods/naive_bayes_classifier/naive_bayes_classifier_test.pl
@@ -7,92 +7,92 @@
:- use_module('../../helper_files/helper.pl').
reset_Model_NoTrain :-
- initModelNoTrain(2, 0, 0.000001).
+ nbc_initModelNoTrain(2, 0, 0.000001).
reset_Model_WithTrain :-
- initModelWithTrain([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, 0.000001).
+ nbc_initModelWithTrain([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, 0.000001).
%%
-%% TESTING predicate initModelNoTrain/3
+%% TESTING predicate nbc_initModelNoTrain/3
%%
-:- begin_tests(initModelNoTrain).
+:- begin_tests(nbc_initModelNoTrain).
%% Failure Tests
test(nbc_InitModelNoTrain_Negative_NumClasses, fail) :-
- initModelNoTrain(-2, 0, 0.000001).
+ nbc_initModelNoTrain(-2, 0, 0.000001).
test(nbc_InitModelNoTrain_Negative_Epsilon, fail) :-
- initModelNoTrain(2, 0, -0.0001).
+ nbc_initModelNoTrain(2, 0, -0.0001).
%% Successful Tests
test(nbc_InitModelNoTrain_Normal_Use) :-
- initModelNoTrain(2, 0, 0.000001).
+ nbc_initModelNoTrain(2, 0, 0.000001).
-:- end_tests(initModelNoTrain).
+:- end_tests(nbc_initModelNoTrain).
%%
-%% TESTING predicate initModelWithTrain/6
+%% TESTING predicate nbc_initModelWithTrain/6
%%
-:- begin_tests(initModelWithTrain).
+:- begin_tests(nbc_initModelWithTrain).
%% Failure Tests
test(nbc_InitModelWithTrain_Negative_NumClasses, fail) :-
- initModelWithTrain([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, 0.000001).
+ nbc_initModelWithTrain([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, 0.000001).
test(nbc_InitModelWithTrain_Negative_Epsilon, fail) :-
- initModelWithTrain([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, -0.000001).
+ nbc_initModelWithTrain([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, -0.000001).
test(nbc_InitModelWithTrain_Too_Short_Label, [error(_,system_error('Error'))]) :-
- initModelWithTrain([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, 0.000001).
+ nbc_initModelWithTrain([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, 0.000001).
test(nbc_InitModelWithTrain_Too_Long_Label, [error(_,system_error('Error'))]) :-
- initModelWithTrain([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, [0,1,0,1,0,1], 2, 0, 0.000001).
+ nbc_initModelWithTrain([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, [0,1,0,1,0,1], 2, 0, 0.000001).
test(nbc_InitModelWithTrain_Too_Many_Label_Classes, [error(_,system_error('Error'))]) :-
- initModelWithTrain([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, [0,1,2,3], 2, 0, 0.000001).
+ nbc_initModelWithTrain([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, [0,1,2,3], 2, 0, 0.000001).
%% Successful Tests
test(nbc_InitModelWithTrain_Normal_Use) :-
- initModelWithTrain([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, 0.000001).
+ nbc_initModelWithTrain([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, 0.000001).
test(nbc_InitModelWithTrain_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 1, 0.0042).
+ nbc_initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 1, 0.0042).
-:- end_tests(initModelWithTrain).
+:- end_tests(nbc_initModelWithTrain).
%%
-%% TESTING predicate classifyMatrix/5
+%% TESTING predicate nbc_classifyMatrix/5
%%
-:- begin_tests(classifyMatrix).
+:- begin_tests(nbc_classifyMatrix).
%% Failure Tests
test(nbc_ClassifyMatrix_Before_Train, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- classifyMatrix([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, _, _, _).
+ nbc_classifyMatrix([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(nbc_ClassifyMatrix_Diffrent_Dims_To_Train, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
- classifyMatrix([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _, _, _).
+ nbc_classifyMatrix([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _, _, _).
%% Successful Tests
test(nbc_ClassifyMatrix_Normal_Use) :-
reset_Model_WithTrain,
- classifyMatrix([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, PredictionList, _, ProbabilitiesList),
+ nbc_classifyMatrix([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, PredictionList, _, ProbabilitiesList),
print('\nPredictions: '),
print(PredictionList),
print('\nProbabilities: '),
@@ -102,25 +102,25 @@ test(nbc_ClassifyMatrix_CSV_Input) :-
reset_Model_WithTrain,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- classifyMatrix(Data, 4, PredictionList, _, ProbabilitiesList),
+ nbc_classifyMatrix(Data, 4, PredictionList, _, ProbabilitiesList),
print('\nPredictions: '),
print(PredictionList),
print('\nProbabilities: '),
print(ProbabilitiesList).
-:- end_tests(classifyMatrix).
+:- end_tests(nbc_classifyMatrix).
%%
-%% TESTING predicate means/2
+%% TESTING predicate nbc_means/2
%%
-:- begin_tests(means).
+:- begin_tests(nbc_means).
%% Failure Tests
test(nbc_Means_Before_Train, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- means(MeansList, _),
+ nbc_means(MeansList, _),
print('\nMeans: '),
print(MeansList).
@@ -129,23 +129,23 @@ test(nbc_Means_Before_Train, [error(_,system_error('Error'))]) :-
test(nbc_Means_Normal_Use) :-
reset_Model_WithTrain,
- means(MeansList, _),
+ nbc_means(MeansList, _),
print('\nMeans: '),
print(MeansList).
-:- end_tests(means).
+:- end_tests(nbc_means).
%%
-%% TESTING predicate probabilities/2
+%% TESTING predicate nbc_probabilities/2
%%
-:- begin_tests(probabilities).
+:- begin_tests(nbc_probabilities).
%% Failure Tests
test(nbc_Probabilities_Before_Train, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- probabilities(ProbabilitiesList, _),
+ nbc_probabilities(ProbabilitiesList, _),
print('\nProbabilities: '),
print(ProbabilitiesList).
@@ -154,46 +154,46 @@ test(nbc_Probabilities_Before_Train, [error(_,system_error('Error'))]) :-
test(nbc_Probabilities_Normal_Use) :-
reset_Model_WithTrain,
- probabilities(ProbabilitiesList, _),
+ nbc_probabilities(ProbabilitiesList, _),
print('\nProbabilities: '),
print(ProbabilitiesList).
-:- end_tests(probabilities).
+:- end_tests(nbc_probabilities).
%%
-%% TESTING predicate trainMatrix/5
+%% TESTING predicate nbc_trainMatrix/5
%%
-:- begin_tests(trainMatrix).
+:- begin_tests(nbc_trainMatrix).
%% Failure Tests
test(nbc_TrainMatrix_Negative_NumClasses, fail) :-
reset_Model_NoTrain,
- trainMatrix([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).
+ nbc_trainMatrix([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).
test(nbc_TrainMatrix_Too_Short_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model_NoTrain,
- trainMatrix([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).
+ nbc_trainMatrix([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).
test(nbc_TrainMatrix_Too_Long_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model_NoTrain,
- trainMatrix([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, [0,1,0,1,0,1], 2, 0).
+ nbc_trainMatrix([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, [0,1,0,1,0,1], 2, 0).
test(nbc_TrainMatrix_Too_Many_Label_Classes, [error(_,system_error('The given Labels dont fit the format [0,Numclasses-1]!'))]) :-
reset_Model_NoTrain,
- trainMatrix([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, [0,1,2,3], 2, 0).
+ nbc_trainMatrix([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, [0,1,2,3], 2, 0).
test(nbc_TrainMatrix_After_InitTrain, [error(_,system_error('addition: incompatible matrix dimensions: 3x1 and 4x1'))]) :-
reset_Model_WithTrain,
- trainMatrix([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 2, 0).
+ nbc_trainMatrix([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 2, 0).
%% Successful Tests
test(nbc_TrainMatrix_Normal_Use) :-
reset_Model_NoTrain,
- trainMatrix([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).
+ nbc_trainMatrix([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).
@@ -201,51 +201,51 @@ test(nbc_TrainMatrix_CSV_Input) :-
reset_Model_NoTrain,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- trainMatrix(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0).
+ nbc_trainMatrix(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0).
-:- end_tests(trainMatrix).
+:- end_tests(nbc_trainMatrix).
%%
-%% TESTING predicate trainPoint/5
+%% TESTING predicate nbc_trainPoint/5
%%
-:- begin_tests(trainPoint).
+:- begin_tests(nbc_trainPoint).
%% Failure Tests
test(nbc_TrainPoint_Bad_Label, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- trainPoint([5.1,3.5,1.4], -5).
+ nbc_trainPoint([5.1,3.5,1.4], -5).
test(nbc_TrainPoint_Too_Long_Point, [error(_,system_error('Error'))]) :-
reset_Model_WithTrain,
- trainPoint([5.1,3.5,1.4,3.5], 0).
+ nbc_trainPoint([5.1,3.5,1.4,3.5], 0).
%% Successful Tests
%%test(nbc_TrainPoint_Normal_Use) :-
%% reset_Model_NoTrain,
-%% trainPoint([5.1,3.5,1.4], 1).
+%% nbc_trainPoint([5.1,3.5,1.4], 1).
test(nbc_TrainPoint_After_InitTrain) :-
reset_Model_WithTrain,
- trainPoint([5.1,3.5,1.4], 1).
+ nbc_trainPoint([5.1,3.5,1.4], 1).
-:- end_tests(trainPoint).
+:- end_tests(nbc_trainPoint).
%%
-%% TESTING predicate variances/2
+%% TESTING predicate nbc_variances/2
%%
-:- begin_tests(variances).
+:- begin_tests(nbc_variances).
%% Failure Tests
test(nbc_Variances_Before_Train, [error(_,system_error('Error'))]) :-
reset_Model_NoTrain,
- variances(VariancesList, _),
+ nbc_variances(VariancesList, _),
print('\nVariances: '),
print(VariancesList).
@@ -254,11 +254,11 @@ test(nbc_Variances_Before_Train, [error(_,system_error('Error'))]) :-
test(nbc_Variances_Normal_Use) :-
reset_Model_WithTrain,
- variances(VariancesList, _),
+ nbc_variances(VariancesList, _),
print('\nVariances: '),
print(VariancesList).
-:- end_tests(variances).
+:- end_tests(nbc_variances).
run_naive_bayes_classifier_tests :-
run_tests.
diff --git a/src/methods/perceptron/perceptron.pl b/src/methods/perceptron/perceptron.pl
index b71547e9bad1b18e216e4404cdfd4ff0d2f1b9b3..f829b50b4bb6912e84e44d16c0ca97b610930daa 100644
--- a/src/methods/perceptron/perceptron.pl
+++ b/src/methods/perceptron/perceptron.pl
@@ -1,10 +1,10 @@
-:- module(perceptron, [ initModelNoTrain/3,
- initModelWithTrain/5,
- biases/1,
- classify/3,
- train/5,
- weights/2]).
+:- module(perceptron, [ perceptron_initModelNoTrain/3,
+ perceptron_initModelWithTrain/5,
+ perceptron_biases/1,
+ perceptron_classify/3,
+ perceptron_train/5,
+ perceptron_weights/2]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -31,7 +31,7 @@
%% --Description--
%% Initilizes the perceptron model and its weight matrix but doesnt train it.
%%
-initModelNoTrain(NumClasses, Dimensionality, MaxIterations) :-
+perceptron_initModelNoTrain(NumClasses, Dimensionality, MaxIterations) :-
NumClasses >= 0,
Dimensionality > 0,
MaxIterations >= 0,
@@ -50,7 +50,7 @@ foreign(initModelNoTrain, c, initModelNoTrainI(+integer, +integer, +integer)).
%% --Description--
%% Initilizes the perceptron model and its weight matrix and trains it with the given data.
%%
-initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, MaxIterations) :-
+perceptron_initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, MaxIterations) :-
NumClasses >= 0,
MaxIterations >= 0,
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
@@ -70,7 +70,7 @@ foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +
%% --Description--
%% Get the biases.
%%
-biases(BiasesList) :-
+perceptron_biases(BiasesList) :-
biasesI(Y, Ysize),
convert_float_array_to_list(Y, Ysize, BiasesList).
@@ -85,7 +85,7 @@ foreign(biases, c, biasesI(-pointer(float_array), -integer)).
%% --Description--
%% After training, use the weights matrix to classify test, and put the predicted classes in predictedLabels.
%%
-classify(TestList, TestRows, PredictLabelList) :-
+perceptron_classify(TestList, TestRows, PredictLabelList) :-
convert_list_to_float_array(TestList, TestRows, array(Xsize, Xrows, X)),
classifyI(X, Xsize, Xrows, Y, Ysize),
convert_float_array_to_list(Y, Ysize, PredictLabelList).
@@ -104,7 +104,7 @@ foreign(classify, c, classifyI( +pointer(float_array), +integer, +integer,
%% --Description--
%% Train the perceptron on the given data for up to the maximum number of iterations. This training does not reset the model weights, so you can call train/8 on multiple datasets sequentially.
%%
-train(DataList, DataRows, LabelsList, NumClasses, WeightsList) :-
+perceptron_train(DataList, DataRows, LabelsList, NumClasses, WeightsList) :-
NumClasses >= 0,
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(LabelsList, array(Ysize, Y)),
@@ -125,7 +125,7 @@ foreign(train, c, trainI( +pointer(float_array), +integer, +integer,
%% --Description--
%% Get the weight matrix.
%%
-weights(WeightsList, XCols) :-
+perceptron_weights(WeightsList, XCols) :-
weightsI(X, XCols, XRows),
convert_float_array_to_2d_list(X, XCols, XRows, WeightsList).
diff --git a/src/methods/perceptron/perceptron_test.pl b/src/methods/perceptron/perceptron_test.pl
index bb8d3db9f922f434cde7c468fea7b9b5ca81b6d6..bd77ad9516396237224c309b45c16e70d18388a5 100644
--- a/src/methods/perceptron/perceptron_test.pl
+++ b/src/methods/perceptron/perceptron_test.pl
@@ -7,92 +7,92 @@
:- use_module('../../helper_files/helper.pl').
reset_Model_NoTrain :-
- initModelNoTrain(2, 3, 1000).
+ perceptron_initModelNoTrain(2, 3, 1000).
reset_Model_WithTrain :-
- initModelWithTrain([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, 1000).
+ perceptron_initModelWithTrain([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, 1000).
%%
-%% TESTING predicate initModelNoTrain/3
+%% TESTING predicate perceptron_initModelNoTrain/3
%%
-:- begin_tests(initModelNoTrain).
+:- begin_tests(perceptron_initModelNoTrain).
%% Failure Tests
test(perceptron_InitModelNoTrain_Negative_NumClasses, fail) :-
- initModelNoTrain(-2, 3, 1000).
+ perceptron_initModelNoTrain(-2, 3, 1000).
test(perceptron_InitModelNoTrain_Negative_Dimensionality, fail) :-
- initModelNoTrain(2, -3, 1000).
+ perceptron_initModelNoTrain(2, -3, 1000).
test(perceptron_InitModelNoTrain_Negative_MaxIterations, fail) :-
- initModelNoTrain(2, 3, -1000).
+ perceptron_initModelNoTrain(2, 3, -1000).
%% Successful Tests
test(perceptron_InitModelNoTrain_Normal_Use) :-
- initModelNoTrain(2, 3, 1000).
+ perceptron_initModelNoTrain(2, 3, 1000).
test(perceptron_InitModelNoTrain_Alternative_Input) :-
- initModelNoTrain(0, 1, 1000).
+ perceptron_initModelNoTrain(0, 1, 1000).
-:- end_tests(initModelNoTrain).
+:- end_tests(perceptron_initModelNoTrain).
%%
-%% TESTING predicate initModelWithTrain/5
+%% TESTING predicate perceptron_initModelWithTrain/5
%%
-:- begin_tests(initModelWithTrain).
+:- begin_tests(perceptron_initModelWithTrain).
%% Failure Tests
test(perceptron_InitModelWithTrain_Negative_NumClasses, fail) :-
- initModelWithTrain([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, 1000).
+ perceptron_initModelWithTrain([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, 1000).
test(perceptron_InitModelWithTrain_Negative_MaxIterations, fail) :-
- initModelWithTrain([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, -1000).
+ perceptron_initModelWithTrain([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, -1000).
test(random_forest_InitModelWithTrainNoWeights_Too_Short_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
- initModelWithTrain([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, 1000).
+ perceptron_initModelWithTrain([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, 1000).
test(random_forest_InitModelWithTrainNoWeights_Too_Long_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
- initModelWithTrain([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, [0,1,0,1,0,1], 2, 1000).
+ perceptron_initModelWithTrain([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, [0,1,0,1,0,1], 2, 1000).
test(random_forest_InitModelWithTrainNoWeights_Too_Many_Label_Classes, [error(_,system_error('The given Labels dont fit the format [0,Numclasses-1]!'))]) :-
- initModelWithTrain([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, [0,1,2,3], 2, 1000).
+ perceptron_initModelWithTrain([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, [0,1,2,3], 2, 1000).
%% Successful Tests
test(perceptron_InitModelWithTrain_Normal_Use) :-
- initModelWithTrain([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, 1000).
+ perceptron_initModelWithTrain([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, 1000).
test(perceptron_InitModelWithTrain_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 1000).
+ perceptron_initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 1000).
-:- end_tests(initModelWithTrain).
+:- end_tests(perceptron_initModelWithTrain).
%%
-%% TESTING predicate biases/1
+%% TESTING predicate perceptron_biases/1
%%
-:- begin_tests(biases).
+:- begin_tests(perceptron_biases).
%% Failure Tests
%% Doesnt cause an Error
test(perceptron_Biases_Before_Train) :-
reset_Model_NoTrain,
- biases(Biases),
+ perceptron_biases(Biases),
print('\nBiases: '),
print(Biases).
@@ -101,107 +101,107 @@ test(perceptron_Biases_Before_Train) :-
test(perceptron_Biases_AfterTrain) :-
reset_Model_WithTrain,
- biases(Biases),
+ perceptron_biases(Biases),
print('\nBiases: '),
print(Biases).
-:- end_tests(biases).
+:- end_tests(perceptron_biases).
%%
-%% TESTING predicate classify/3
+%% TESTING predicate perceptron_classify/3
%%
-:- begin_tests(classify).
+:- begin_tests(perceptron_classify).
%% Failure Tests
test(perceptron_Classify_Before_Train_Wrong_Dims, [error(_,system_error('matrix multiplication: incompatible matrix dimensions: 2x3 and 4x1'))]) :-
reset_Model_NoTrain,
- 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], 4, _).
+ perceptron_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], 4, _).
test(perceptron_Classify_Different_Dims_Than_Train, [error(_,system_error('matrix multiplication: incompatible matrix dimensions: 2x3 and 4x1'))]) :-
reset_Model_WithTrain,
- 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], 4, _).
+ perceptron_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], 4, _).
%% Successful Tests
test(perceptron_Classify_Before_Train) :-
reset_Model_NoTrain,
- classify([1.0,2.0,3.0], 3, _).
+ perceptron_classify([1.0,2.0,3.0], 3, _).
test(perceptron_Classify_Normal_Use) :-
reset_Model_WithTrain,
- 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, PredictList),
+ perceptron_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, PredictList),
print('\nPredicted Labels: '),
print(PredictList).
-:- end_tests(classify).
+:- end_tests(perceptron_classify).
%%
-%% TESTING predicate train/5
+%% TESTING predicate perceptron_train/5
%%
-:- begin_tests(train).
+:- begin_tests(perceptron_train).
%% Failure Tests
test(perceptron_Train_Negaitve_NumClasses, fail) :-
reset_Model_NoTrain,
- 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, [0.1,0.2,0.3,0.4]).
+ 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, [0.1,0.2,0.3,0.4]).
%% Seems to overide the dimensionality from reset_Model_NoTrain
test(perceptron_Train_Too_Small_Data_Dims) :-
reset_Model_NoTrain,
- 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], 4, [0,1,0], 2, [0.1,0.2,0.3]).
+ 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], 4, [0,1,0], 2, [0.1,0.2,0.3]).
test(perceptron_Train_Too_Short_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model_NoTrain,
- 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], 2, [0.1,0.2,0.3,0.4]).
+ 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], 2, [0.1,0.2,0.3,0.4]).
test(perceptron_Train_Too_Long_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model_NoTrain,
- 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, [0,1,0,1,0,1], 2, [0.1,0.2,0.3,0.4]).
+ 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, [0,1,0,1,0,1], 2, [0.1,0.2,0.3,0.4]).
test(perceptron_Train_Too_Many_Label_Classes, [error(_,system_error('The given Labels dont fit the format [0,Numclasses-1]!'))]) :-
reset_Model_NoTrain,
- 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, [0,1,2,3], 2, [0.1,0.2,0.3,0.4]).
+ 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, [0,1,2,3], 2, [0.1,0.2,0.3,0.4]).
test(perceptron_Train_Too_Short_Weights, [error(_,system_error('The number of data points does not match the number of weights!'))]) :-
reset_Model_NoTrain,
- 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, [0.1,0.2]).
+ 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, [0.1,0.2]).
test(perceptron_Train_Too_Long_Weights, [error(_,system_error('The number of data points does not match the number of weights!'))]) :-
reset_Model_NoTrain,
- 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, [0.1,0.2,0.3,0.4,0.6,0.7]).
+ 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, [0.1,0.2,0.3,0.4,0.6,0.7]).
%% Successful Tests
test(perceptron_Train_Normal_Use) :-
reset_Model_NoTrain,
- 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, [0.1,0.2,0.3,0.4]).
+ 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, [0.1,0.2,0.3,0.4]).
-:- end_tests(train).
+:- end_tests(perceptron_train).
%%
-%% TESTING predicate weights/2
+%% TESTING predicate perceptron_weights/2
%%
-:- begin_tests(weights).
+:- begin_tests(perceptron_weights).
%% Failure Tests
%% Doesnt cause an error
test(perceptron_Weights_Before_Train) :-
reset_Model_NoTrain,
- weights(Weights, _),
+ perceptron_weights(Weights, _),
print('\nWeights: '),
print(Weights).
@@ -210,11 +210,11 @@ test(perceptron_Weights_Before_Train) :-
test(perceptron_Weights_AfterTrain) :-
reset_Model_WithTrain,
- weights(Weights, _),
+ perceptron_weights(Weights, _),
print('\nWeights: '),
print(Weights).
-:- end_tests(weights).
+:- end_tests(perceptron_weights).
run_perceptron_tests :-
run_tests.
diff --git a/src/methods/radical/radical.pl b/src/methods/radical/radical.pl
index b51f77c7cb258ba1a4c0774e1d5e362ea178b161..902e2b7ea0ccad16dce5ba3d6c175e8571776494 100644
--- a/src/methods/radical/radical.pl
+++ b/src/methods/radical/radical.pl
@@ -1,5 +1,5 @@
-:- module(radical, [ initModel/5,
+:- module(radical, [ radical_initModel/5,
doRadical/6,
doRadical2D/3]).
@@ -31,7 +31,7 @@
%% --Description--
%% Initilizes the radical model.
%%
-initModel(NoiseStdDev, Replicates, Angles, Sweeps, M) :-
+radical_initModel(NoiseStdDev, Replicates, Angles, Sweeps, M) :-
NoiseStdDev >= 0,
Replicates > 0,
Angles > 0,
diff --git a/src/methods/radical/radical_test.pl b/src/methods/radical/radical_test.pl
index b915cbb79ef9988ebb7d21db1c590993d2baa72c..6fb60d37273d031bc8cd03601d777662620fd496 100644
--- a/src/methods/radical/radical_test.pl
+++ b/src/methods/radical/radical_test.pl
@@ -7,12 +7,12 @@
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initModel(0.175,30,150,0,0).
+ radical_initModel(0.175,30,150,0,0).
%%
-%% TESTING predicate initModel/5
+%% TESTING predicate radical_initModel/5
%%
-:- begin_tests(initModel).
+:- begin_tests(radical_initModel).
%% Failure Tests
@@ -29,24 +29,24 @@ test(radical_InitModel_DoRadical2D_No_Init) :-
print(Result).
test(radical_InitModel_Negative_NoiseStdDev, fail) :-
- initModel(-0.175,30,150,0,0).
+ radical_initModel(-0.175,30,150,0,0).
test(radical_InitModel_Negative_Replicates, fail) :-
- initModel(0.175,-30,150,0,0).
+ radical_initModel(0.175,-30,150,0,0).
test(radical_InitModel_Negative_Angles, fail) :-
- initModel(0.175,30,-150,0,0).
+ radical_initModel(0.175,30,-150,0,0).
test(radical_InitModel_Negative_Sweeps, fail) :-
- initModel(0.175,30,150,-1,0).
+ radical_initModel(0.175,30,150,-1,0).
%% Successful Tests
test(radical_InitModel_Normal_Use) :-
- initModel(0.175,30,150,0,0).
+ radical_initModel(0.175,30,150,0,0).
-:- end_tests(initModel).
+:- end_tests(radical_initModel).
diff --git a/src/methods/random_forest/random_forest.pl b/src/methods/random_forest/random_forest.pl
index b88d027883bfee2fd6ae87a3c4ac3a5436698cd3..c299e5096bad8dbe10fdb7bebba43a0b35f83b34 100644
--- a/src/methods/random_forest/random_forest.pl
+++ b/src/methods/random_forest/random_forest.pl
@@ -1,12 +1,12 @@
-:- module(random_forest, [ initModelNoTrain/0,
- initModelWithTrainNoWeights/8,
- initModelWithTrainWithWeights/9,
- classifyPoint/3,
- classifyMatrix/5,
- numTrees/1,
- trainNoWeights/9,
- trainWithWeights/10]).
+:- module(random_forest, [ random_forest_initModelNoTrain/0,
+ random_forest_initModelWithTrainNoWeights/8,
+ random_forest_initModelWithTrainWithWeights/9,
+ random_forest_classifyPoint/3,
+ random_forest_classifyMatrix/5,
+ random_forest_numTrees/1,
+ random_forest_trainNoWeights/9,
+ random_forest_trainWithWeights/10]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -32,7 +32,7 @@
%% --Description--
%% Initilizes the model without training it.
%%
-initModelNoTrain :-
+random_forest_initModelNoTrain :-
initModelNoTrainI(0).
foreign(initModelNoTrain, c, initModelNoTrainI(+integer)).
@@ -52,7 +52,7 @@ foreign(initModelNoTrain, c, initModelNoTrainI(+integer)).
%% --Description--
%% Initilizes the model and trains it but does not apply weights to it.
%%
-initModelWithTrainNoWeights(DataList, DataRows, LabelsList, NumClasses, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth) :-
+random_forest_initModelWithTrainNoWeights(DataList, DataRows, LabelsList, NumClasses, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth) :-
NumClasses >= 0,
NumTrees > 0,
MinimumLeafSize > 0,
@@ -83,7 +83,7 @@ foreign(initModelWithTrainNoWeights, c, initModelWithTrainNoWeightsI(
%% --Description--
%% Initilizes the model, trains it and applies weights to it.
%%
-initModelWithTrainWithWeights(DataList, DataRows, LabelsList, NumClasses, WeightsList, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth) :-
+random_forest_initModelWithTrainWithWeights(DataList, DataRows, LabelsList, NumClasses, WeightsList, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth) :-
NumClasses >= 0,
NumTrees > 0,
MinimumLeafSize > 0,
@@ -113,7 +113,7 @@ foreign(initModelWithTrainWithWeights, c, initModelWithTrainWithWeightsI(
%% Predict the class of the given point and return the predicted class probabilities for each class.
%% Random forest has to be train before using this.
%%
-classifyPoint(DataList, Prediction, AssignList) :-
+random_forest_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).
@@ -134,7 +134,7 @@ foreign(classifyPoint, c, classifyPointI( +pointer(float_array), +integer,
%% Predict the classes of each point in the given dataset, also returning the predicted class probabilities for each point.
%% Random forest has to be train before using this.
%%
-classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols) :-
+random_forest_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),
@@ -153,7 +153,7 @@ foreign(classifyMatrix, c, classifyMatrixI( +pointer(float_array), +integer,
%% --Description--
%% Get the number of trees in the forest.
%%
-numTrees(NumTrees) :-
+random_forest_numTrees(NumTrees) :-
numTreesI(NumTrees).
foreign(numTrees, c, numTreesI([-integer])).
@@ -174,7 +174,7 @@ foreign(numTrees, c, numTreesI([-integer])).
%% Train the random forest on the given labeled training data with the given number of trees.
%% The minimumLeafSize and minimumGainSplit parameters are given to each individual decision tree during tree building.
%%
-trainNoWeights(DataList, DataRows, LabelsList, NumClasses, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy) :-
+random_forest_trainNoWeights(DataList, DataRows, LabelsList, NumClasses, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy) :-
NumClasses >= 0,
NumTrees > 0,
MinimumLeafSize > 0,
@@ -207,7 +207,7 @@ foreign(trainNoWeights, c, trainNoWeightsI( +pointer(float_array), +integer,
%% Train the random forest on the given weighted labeled training data with the given number of trees.
%% The minimumLeafSize and minimumGainSplit parameters are given to each individual decision tree during tree building.
%%
-trainWithWeights(DataList, DataRows, LabelsList, NumClasses, WeightsList, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy) :-
+random_forest_trainWithWeights(DataList, DataRows, LabelsList, NumClasses, WeightsList, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy) :-
NumClasses >= 0,
NumTrees > 0,
MinimumLeafSize > 0,
diff --git a/src/methods/random_forest/random_forest_test.pl b/src/methods/random_forest/random_forest_test.pl
index ac6b4e1bddd63657154470110aabf8a079a580aa..249a498ba53a9b18a08eef3237c1f8792b4e8181 100644
--- a/src/methods/random_forest/random_forest_test.pl
+++ b/src/methods/random_forest/random_forest_test.pl
@@ -7,10 +7,10 @@
:- use_module('../../helper_files/helper.pl').
reset_Model_NoTrain :-
- initModelNoTrain.
+ random_forest_initModelNoTrain.
reset_Model_WithTrain :-
- initModelWithTrainNoWeights([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, 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainNoWeights([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, 20, 1, 0.0000001, 0).
%%
@@ -22,129 +22,129 @@ reset_Model_WithTrain :-
%% Successful Tests
test(random_forest_InitModelNoTrain) :-
- initModelNoTrain.
+ random_forest_initModelNoTrain.
:- end_tests(initModelNoTrainI).
%%
-%% TESTING predicate initModelWithTrainNoWeights/8
+%% TESTING predicate random_forest_initModelWithTrainNoWeights/8
%%
-:- begin_tests(initModelWithTrainNoWeights).
+:- begin_tests(random_forest_initModelWithTrainNoWeights).
%% Failure Tests
test(random_forest_InitModelWithTrainNoWeights_Negative_NumClasses, fail) :-
- initModelWithTrainNoWeights([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, 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainNoWeights([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, 20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainNoWeights_Negative_NumTrees, fail) :-
- initModelWithTrainNoWeights([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, -20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainNoWeights([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, -20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainNoWeights_Negative_MinLeafSize, fail) :-
- initModelWithTrainNoWeights([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, 20, -1, 0.0000001, 0).
+ random_forest_initModelWithTrainNoWeights([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, 20, -1, 0.0000001, 0).
test(random_forest_InitModelWithTrainNoWeights_Negative_MinGainSplit, fail) :-
- initModelWithTrainNoWeights([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, 20, 1, -0.0000001, 0).
+ random_forest_initModelWithTrainNoWeights([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, 20, 1, -0.0000001, 0).
test(random_forest_InitModelWithTrainNoWeights_Negative_MaxDepth, fail) :-
- initModelWithTrainNoWeights([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, 20, 1, 0.0000001, -1).
+ random_forest_initModelWithTrainNoWeights([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, 20, 1, 0.0000001, -1).
test(random_forest_InitModelWithTrainNoWeights_Too_Short_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
- initModelWithTrainNoWeights([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, 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainNoWeights([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, 20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainNoWeights_Too_Long_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
- initModelWithTrainNoWeights([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, [0,1,0,1,0,1], 2, 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainNoWeights([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, [0,1,0,1,0,1], 2, 20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainNoWeights_Too_Many_Label_Classes, [error(_,system_error('The given Labels dont fit the format [0,Numclasses-1]!'))]) :-
- initModelWithTrainNoWeights([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, [0,1,2,3], 2, 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainNoWeights([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, [0,1,2,3], 2, 20, 1, 0.0000001, 0).
%% Successful Tests
test(random_forest_InitModelWithTrainNoWeights_Normal_Use) :-
- initModelWithTrainNoWeights([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, 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainNoWeights([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, 20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainNoWeights_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initModelWithTrainNoWeights(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainNoWeights(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 20, 1, 0.0000001, 0).
-:- end_tests(initModelWithTrainNoWeights).
+:- end_tests(random_forest_initModelWithTrainNoWeights).
%%
-%% TESTING predicate initModelWithTrainWithWeights/9
+%% TESTING predicate random_forest_initModelWithTrainWithWeights/9
%%
-:- begin_tests(initModelWithTrainWithWeights).
+:- begin_tests(random_forest_initModelWithTrainWithWeights).
%% Failure Tests
test(random_forest_InitModelWithTrainWithWeights_Negative_NumClasses, fail) :-
- initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainWithWeights_Negative_NumTrees, fail) :-
- initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], -20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], -20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainWithWeights_Negative_MinLeafSize, fail) :-
- initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, -1, 0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, -1, 0.0000001, 0).
test(random_forest_initModelWithTrainWithWeights_Negative_MinGainSplit, fail) :-
- initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, -0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, -0.0000001, 0).
test(random_forest_InitModelWithTrainWithWeights_Negative_MaxDepth, fail) :-
- initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, -1).
+ random_forest_initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, -1).
test(random_forest_InitModelWithTrainWithWeights_Too_Short_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
- initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainWithWeights_Too_Long_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
- initModelWithTrainWithWeights([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, [0,1,0,1,0,1], 2, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights([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, [0,1,0,1,0,1], 2, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainWithWeights_Too_Many_Label_Classes, [error(_,system_error('The given Labels dont fit the format [0,Numclasses-1]!'))]) :-
- initModelWithTrainWithWeights([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, [0,1,2,3], 2, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights([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, [0,1,2,3], 2, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainWithWeights_Too_Short_Weights, [error(_,system_error('The number of data points does not match the number of weights!'))]) :-
- initModelWithTrainWithWeights([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, [0.5,0.1], 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights([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, [0.5,0.1], 20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainWithWeights_Too_Long_Weights, [error(_,system_error('The number of data points does not match the number of weights!'))]) :-
- initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2,0.43,2.0], 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2,0.43,2.0], 20, 1, 0.0000001, 0).
%% Successful Tests
test(random_forest_InitModelWithTrainWithWeights_Normal_Use) :-
- initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0).
test(random_forest_InitModelWithTrainWithWeights_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initModelWithTrainWithWeights(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, [0.5,0.1,0.7,1.2,0.5,3.4,0.9,0.5,0.5,1.2], 20, 1, 0.0000001, 0).
+ random_forest_initModelWithTrainWithWeights(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, [0.5,0.1,0.7,1.2,0.5,3.4,0.9,0.5,0.5,1.2], 20, 1, 0.0000001, 0).
-:- end_tests(initModelWithTrainWithWeights).
+:- end_tests(random_forest_initModelWithTrainWithWeights).
%%
-%% TESTING predicate classifyPoint/3
+%% TESTING predicate random_forest_classifyPoint/3
%%
-:- begin_tests(classifyPoint).
+:- begin_tests(random_forest_classifyPoint).
%% Failure Tests
test(random_forest_ClassifyPoint_Before_Train, [error(_,system_error('RandomForest::Classify(): no random forest trained!'))]) :-
reset_Model_NoTrain,
- classifyPoint([5.1,3.5,1.4], _, _).
+ random_forest_classifyPoint([5.1,3.5,1.4], _, _).
%% Point dim seems to not matter
test(random_forest_ClassifyPoint_Smaller_Dim_To_Train) :-
reset_Model_WithTrain,
- classifyPoint([5.1,3.5], Prediction, ProbabilitiesList),
+ random_forest_classifyPoint([5.1,3.5], Prediction, ProbabilitiesList),
print('\nPrediction: '),
print(Prediction),
print('\nProbabilities: '),
@@ -153,7 +153,7 @@ test(random_forest_ClassifyPoint_Smaller_Dim_To_Train) :-
%% Point dim seems to not matter
test(random_forest_ClassifyPoint_Larger_Dim_To_Train) :-
reset_Model_WithTrain,
- classifyPoint([5.1,3.5,1.4,4.3,0.4], Prediction, ProbabilitiesList),
+ random_forest_classifyPoint([5.1,3.5,1.4,4.3,0.4], Prediction, ProbabilitiesList),
print('\nPrediction: '),
print(Prediction),
print('\nProbabilities: '),
@@ -164,31 +164,31 @@ test(random_forest_ClassifyPoint_Larger_Dim_To_Train) :-
test(random_forest_ClassifyPoint_Normal_Use) :-
reset_Model_WithTrain,
- classifyPoint([5.1,3.5,1.4], Prediction, ProbabilitiesList),
+ random_forest_classifyPoint([5.1,3.5,1.4], Prediction, ProbabilitiesList),
print('\nPrediction: '),
print(Prediction),
print('\nProbabilities: '),
print(ProbabilitiesList).
-:- end_tests(classifyPoint).
+:- end_tests(random_forest_classifyPoint).
%%
-%% TESTING predicate classifyMatrix/5
+%% TESTING predicate random_forest_classifyMatrix/5
%%
-:- begin_tests(classifyMatrix).
+:- begin_tests(random_forest_classifyMatrix).
%% Failure Tests
test(random_forest_ClassifyMatrix_Before_Train, [error(_,system_error('RandomForest::Classify(): no random forest trained!'))]) :-
reset_Model_NoTrain,
- classifyMatrix([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, _, _, _).
+ random_forest_classifyMatrix([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, _, _, _).
%% Classify dim seems to not matter
test(random_forest_ClassifyMatrix_Smaller_Dim_To_Train) :-
reset_Model_WithTrain,
- classifyMatrix([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, Prediction, ProbabilitiesList, _),
+ random_forest_classifyMatrix([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, Prediction, ProbabilitiesList, _),
print('\nPrediction: '),
print(Prediction),
print('\nProbabilities: '),
@@ -197,7 +197,7 @@ test(random_forest_ClassifyMatrix_Smaller_Dim_To_Train) :-
%% Classify dim seems to not matter
test(random_forest_ClassifyMatrix_Larger_Dim_To_Train) :-
reset_Model_WithTrain,
- classifyMatrix([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 2, Prediction, ProbabilitiesList, _),
+ random_forest_classifyMatrix([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 2, Prediction, ProbabilitiesList, _),
print('\nPrediction: '),
print(Prediction),
print('\nProbabilities: '),
@@ -208,27 +208,27 @@ test(random_forest_ClassifyMatrix_Larger_Dim_To_Train) :-
test(random_forest_ClassifyMatrix_Normal_Use) :-
reset_Model_WithTrain,
- classifyMatrix([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, Prediction, ProbabilitiesList, _),
+ random_forest_classifyMatrix([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, Prediction, ProbabilitiesList, _),
print('\nPrediction: '),
print(Prediction),
print('\nProbabilities: '),
print(ProbabilitiesList).
-:- end_tests(classifyMatrix).
+:- end_tests(random_forest_classifyMatrix).
%%
-%% TESTING predicate numTrees/1
+%% TESTING predicate random_forest_numTrees/1
%%
-:- begin_tests(numTrees).
+:- begin_tests(random_forest_numTrees).
%% Failure Tests
%% doesnt cause an error
test(random_forest_NumTrees_Before_Train) :-
reset_Model_NoTrain,
- numTrees(NumTrees),
+ random_forest_numTrees(NumTrees),
print('\nNumber of Trees: '),
print(NumTrees).
@@ -237,60 +237,60 @@ test(random_forest_NumTrees_Before_Train) :-
test(random_forest_NumTrees_Normal_Use) :-
reset_Model_WithTrain,
- numTrees(NumTrees),
+ random_forest_numTrees(NumTrees),
print('\nNumber of Trees: '),
print(NumTrees).
-:- end_tests(numTrees).
+:- end_tests(random_forest_numTrees).
%%
-%% TESTING predicate trainNoWeights/9
+%% TESTING predicate random_forest_trainNoWeights/9
%%
-:- begin_tests(trainNoWeights).
+:- begin_tests(random_forest_trainNoWeights).
%% Failure Tests
test(random_forest_TrainNoWeights_Negative_NumClasses, fail) :-
reset_Model_NoTrain,
- trainNoWeights([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, 20, 1, 0.0000001, 0, _).
+ random_forest_trainNoWeights([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, 20, 1, 0.0000001, 0, _).
test(random_forest_TrainNoWeights_Negative_NumTrees, fail) :-
reset_Model_NoTrain,
- trainNoWeights([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, -20, 1, 0.0000001, 0, _).
+ random_forest_trainNoWeights([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, -20, 1, 0.0000001, 0, _).
test(random_forest_TrainNoWeights_Negative_MinLeafSize, fail) :-
reset_Model_NoTrain,
- trainNoWeights([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, 20, -1, 0.0000001, 0, _).
+ random_forest_trainNoWeights([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, 20, -1, 0.0000001, 0, _).
test(random_forest_TrainNoWeights_Negative_MinGainSplit, fail) :-
reset_Model_NoTrain,
- trainNoWeights([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, 20, 1, -0.0000001, 0, _).
+ random_forest_trainNoWeights([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, 20, 1, -0.0000001, 0, _).
test(random_forest_TrainNoWeights_Negative_MaxDepth, fail) :-
reset_Model_NoTrain,
- trainNoWeights([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, 20, 1, 0.0000001, -1, _).
+ random_forest_trainNoWeights([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, 20, 1, 0.0000001, -1, _).
test(random_forest_TrainNoWeights_Too_Short_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model_NoTrain,
- trainNoWeights([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, 20, 1, 0.0000001, 0, _).
+ random_forest_trainNoWeights([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, 20, 1, 0.0000001, 0, _).
test(random_forest_TrainNoWeights_Too_Long_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model_NoTrain,
- trainNoWeights([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, [0,1,0,1,0,1], 2, 20, 1, 0.0000001, 0, _).
+ random_forest_trainNoWeights([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, [0,1,0,1,0,1], 2, 20, 1, 0.0000001, 0, _).
test(random_forest_TrainNoWeights_Too_Many_Label_Classes, [error(_,system_error('The given Labels dont fit the format [0,Numclasses-1]!'))]) :-
reset_Model_NoTrain,
- trainNoWeights([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, [0,1,2,3], 2, 20, 1, 0.0000001, 0, _).
+ random_forest_trainNoWeights([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, [0,1,2,3], 2, 20, 1, 0.0000001, 0, _).
%% Successful Tests
test(random_forest_TrainNoWeights_Normal_Use) :-
reset_Model_NoTrain,
- trainNoWeights([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, 20, 1, 0.0000001, 0, Entropy),
+ random_forest_trainNoWeights([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, 20, 1, 0.0000001, 0, Entropy),
print('\nEntropy: '),
print(Entropy).
@@ -298,69 +298,69 @@ test(random_forest_TrainNoWeights_CSV_Input) :-
reset_Model_NoTrain,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- trainNoWeights(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 20, 1, 0.0000001, 0, Entropy),
+ random_forest_trainNoWeights(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 20, 1, 0.0000001, 0, Entropy),
print('\nEntropy: '),
print(Entropy).
-:- end_tests(trainNoWeights).
+:- end_tests(random_forest_trainNoWeights).
%%
-%% TESTING predicate trainWithWeights/10
+%% TESTING predicate random_forest_trainWithWeights/10
%%
-:- begin_tests(trainWithWeights).
+:- begin_tests(random_forest_trainWithWeights).
%% Failure Tests
test(random_forest_TrainWithWeights_Negative_NumClasses, fail) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0, _).
+ random_forest_trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0, _).
test(random_forest_TrainWithWeights_Negative_NumTrees, fail) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0.5,0.1,0.7,1.2], -20, 1, 0.0000001, 0, _).
+ random_forest_trainWithWeights([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, [0.5,0.1,0.7,1.2], -20, 1, 0.0000001, 0, _).
test(random_forest_TrainWithWeights_Negative_MinLeafSize, fail) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, -1, 0.0000001, 0, _).
+ random_forest_trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, -1, 0.0000001, 0, _).
test(random_forest_TrainWithWeights_Negative_MinGainSplit, fail) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, -0.0000001, 0, _).
+ random_forest_trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, -0.0000001, 0, _).
test(random_forest_TrainWithWeights_Negative_MaxDepth, fail) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, -1, _).
+ random_forest_trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, -1, _).
test(random_forest_TrainWithWeights_Too_Short_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0, _).
+ random_forest_trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0, _).
test(random_forest_TrainWithWeights_Too_Long_Label, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0,1,0,1,0,1], 2, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0, _).
+ random_forest_trainWithWeights([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, [0,1,0,1,0,1], 2, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0, _).
test(random_forest_TrainWithWeights_Too_Many_Label_Classes, [error(_,system_error('The given Labels dont fit the format [0,Numclasses-1]!'))]) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0,1,2,3], 2, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0, _).
+ random_forest_trainWithWeights([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, [0,1,2,3], 2, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0, _).
test(random_forest_TrainWithWeights_Too_Short_Weights, [error(_,system_error('The number of data points does not match the number of weights!'))]) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0.5,0.1], 20, 1, 0.0000001, 0, _).
+ random_forest_trainWithWeights([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, [0.5,0.1], 20, 1, 0.0000001, 0, _).
test(random_forest_TrainWithWeights_Too_Long_Weights, [error(_,system_error('The number of data points does not match the number of weights!'))]) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0.5,0.1,0.7,1.2,0.43,2.0], 20, 1, 0.0000001, 0, _).
+ random_forest_trainWithWeights([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, [0.5,0.1,0.7,1.2,0.43,2.0], 20, 1, 0.0000001, 0, _).
%% Successful Tests
test(random_forest_TrainWithWeights_Normal_Use) :-
reset_Model_NoTrain,
- trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0, Entropy),
+ random_forest_trainWithWeights([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, [0.5,0.1,0.7,1.2], 20, 1, 0.0000001, 0, Entropy),
print('\nEntropy: '),
print(Entropy).
@@ -368,11 +368,11 @@ test(random_forest_TrainWithWeights_CSV_Input) :-
reset_Model_NoTrain,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- trainWithWeights(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, [0.5,0.1,0.7,1.2,0.5,3.4,0.9,0.5,0.5,1.2], 20, 1, 0.0000001, 0, Entropy),
+ random_forest_trainWithWeights(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, [0.5,0.1,0.7,1.2,0.5,3.4,0.9,0.5,0.5,1.2], 20, 1, 0.0000001, 0, Entropy),
print('\nEntropy: '),
print(Entropy).
-:- end_tests(trainWithWeights).
+:- end_tests(random_forest_trainWithWeights).
run_random_forest_tests :-
run_tests.
diff --git a/src/methods/softmax_regression/softmax_regression.pl b/src/methods/softmax_regression/softmax_regression.pl
index 17e11624c49d642357995a3bf043b9c63483d944..2bfb6db902de0a43108a461a7bac9d043a66ad34 100644
--- a/src/methods/softmax_regression/softmax_regression.pl
+++ b/src/methods/softmax_regression/softmax_regression.pl
@@ -1,12 +1,12 @@
-:- module(softmax_regression, [ initModelNoTrain/3,
- initModelWithTrain/6,
- classifyPoint/2,
- classifyMatrix/5,
- computeAccuracy/4,
- featureSize/1,
- parameters/2,
- train/5]).
+:- module(softmax_regression, [ softmax_regression_initModelNoTrain/3,
+ softmax_regression_initModelWithTrain/6,
+ softmax_regression_classifyPoint/2,
+ softmax_regression_classifyMatrix/5,
+ softmax_regression_computeAccuracy/4,
+ softmax_regression_featureSize/1,
+ softmax_regression_parameters/2,
+ softmax_regression_train/5]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -35,7 +35,7 @@
%% Initializes the softmax_regression model without training.
%% Be sure to use Train before calling Classif or ComputeAccuracy, otherwise the results may be meaningless.
%%
-initModelNoTrain(InputSize, NumClasses, FitIntercept) :-
+softmax_regression_initModelNoTrain(InputSize, NumClasses, FitIntercept) :-
InputSize > 0,
NumClasses >= 0,
initModelNoTrainI(InputSize, NumClasses, FitIntercept).
@@ -56,7 +56,7 @@ foreign(initModelNoTrain, c, initModelNoTrainI( +integer, +integer,
%% --Description--
%% Initializes the softmax_regression model and trains it.
%%
-initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, Lambda, FitIntercept) :-
+softmax_regression_initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, Lambda, FitIntercept) :-
NumClasses >= 0,
Lambda >= 0,
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
@@ -78,7 +78,7 @@ foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +
%% --Description--
%% Classify the given point.
%%
-classifyPoint(DataList, Prediction) :-
+softmax_regression_classifyPoint(DataList, Prediction) :-
convert_list_to_float_array(DataList, array(Xsize, X)),
classifyPointI(X, Xsize, Prediction).
@@ -96,7 +96,7 @@ foreign(classifyPoint, c, classifyPointI(+pointer(float_array), +integer,
%% --Description--
%% Classify the given points, returning class probabilities and predicted class label for each point.
%%
-classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols) :-
+softmax_regression_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),
@@ -118,7 +118,7 @@ foreign(classifyMatrix, c, classifyMatrixI( +pointer(float_array), +integer,
%% Computes accuracy of the learned model given the feature data and the labels associated with each data point.
%% Predictions are made using the provided data and are compared with the actual labels.
%%
-computeAccuracy(DataList, DataRows, LabelsList, Accuracy) :-
+softmax_regression_computeAccuracy(DataList, DataRows, LabelsList, Accuracy) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(LabelsList, array(Ysize, Y)),
computeAccuracyI(X, Xsize, Xrownum, Y, Ysize, Accuracy).
@@ -135,7 +135,7 @@ foreign(computeAccuracy, c, computeAccuracyI( +pointer(float_array), +integer,
%% --Description--
%% Gets the features size of the training data.
%%
-featureSize(FeatureSize) :-
+softmax_regression_featureSize(FeatureSize) :-
featureSizeI(FeatureSize).
foreign(featureSize, c, featureSizeI([-integer])).
@@ -148,7 +148,7 @@ foreign(featureSize, c, featureSizeI([-integer])).
%% --Description--
%% Get the model parameters.
%%
-parameters(PrametersList, XCols) :-
+softmax_regression_parameters(PrametersList, XCols) :-
parametersI(X, XCols, XRows),
convert_float_array_to_2d_list(X, XCols, XRows, PrametersList).
@@ -166,7 +166,7 @@ foreign(parameters, c, parametersI(-pointer(float_array), -integer, -integer)).
%% --Description--
%% Trains the softmax regression model with the given training data.
%%
-train(DataList, DataRows, LabelsList, NumClasses, FinalValue) :-
+softmax_regression_train(DataList, DataRows, LabelsList, NumClasses, FinalValue) :-
NumClasses >= 0,
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(LabelsList, array(Ysize, Y)),
diff --git a/src/methods/softmax_regression/softmax_regression_test.pl b/src/methods/softmax_regression/softmax_regression_test.pl
index 6612ce1f49f7151a2bf2ba6af199bb31f27ee5e9..99dd60b36c20b2cf2a6bff6779112143fc378ce9 100644
--- a/src/methods/softmax_regression/softmax_regression_test.pl
+++ b/src/methods/softmax_regression/softmax_regression_test.pl
@@ -7,137 +7,137 @@
:- use_module('../../helper_files/helper.pl').
reset_Model_NoTrain :-
- initModelNoTrain(3, 2, 0).
+ softmax_regression_initModelNoTrain(3, 2, 0).
reset_Model_WithTrain :-
- initModelWithTrain([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, 0.0001, 0).
+ softmax_regression_initModelWithTrain([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, 0.0001, 0).
%%
-%% TESTING predicate initModelNoTrain/3
+%% TESTING predicate softmax_regression_initModelNoTrain/3
%%
-:- begin_tests(initModelNoTrain).
+:- begin_tests(softmax_regression_initModelNoTrain).
%% Failure Tests
test(softmax_InitNoTrain_Negative_InputSize, fail) :-
- initModelNoTrain(-1, 0, 0).
+ softmax_regression_initModelNoTrain(-1, 0, 0).
test(softmax_InitNoTrain_Negative_InputSize, fail) :-
- initModelNoTrain(3, -1, 0).
+ softmax_regression_initModelNoTrain(3, -1, 0).
%% Successful Tests
test(softmax_InitNoTrain_FitIntercept_False) :-
- initModelNoTrain(3, 2, 0).
+ softmax_regression_initModelNoTrain(3, 2, 0).
test(softmax_InitNoTrain_FitIntercept_True) :-
- initModelNoTrain(2, 3, 1).
+ softmax_regression_initModelNoTrain(2, 3, 1).
-:- end_tests(initModelNoTrain).
+:- end_tests(softmax_regression_initModelNoTrain).
%%
-%% TESTING predicate initModelWithTrain/6
+%% TESTING predicate softmax_regression_initModelWithTrain/6
%%
-:- begin_tests(initModelWithTrain).
+:- begin_tests(softmax_regression_initModelWithTrain).
%% Failure Tests
test(softmax_InitWithTrain_Negative_NumClass, fail) :-
- initModelWithTrain([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], -1, 0.0001, 0).
+ softmax_regression_initModelWithTrain([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], -1, 0.0001, 0).
test(softmax_InitWithTrain_Negative_Lambda, fail) :-
- initModelWithTrain([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, -0.0001, 0).
+ softmax_regression_initModelWithTrain([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, -0.0001, 0).
test(softmax_InitWithTrain_Wrong_Label_Dims1, [error(_,system_error('element-wise multiplication: incompatible matrix dimensions: 2x4 and 2x2'))]) :-
- initModelWithTrain([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, 0.0001, 0).
+ softmax_regression_initModelWithTrain([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, 0.0001, 0).
test(softmax_InitWithTrain_Wrong_Label_Dims2, [error(_,system_error('element-wise multiplication: incompatible matrix dimensions: 2x4 and 2x7'))]) :-
- initModelWithTrain([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, 0.0001, 0).
+ softmax_regression_initModelWithTrain([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, 0.0001, 0).
%% Doesnt cause exception
test(softmax_InitWithTrain_Wrong_Label_Value) :-
- initModelWithTrain([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, 0.0001, 0).
+ softmax_regression_initModelWithTrain([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, 0.0001, 0).
%% Doesnt cause exception
test(softmax_InitWithTrain_Too_Many_Label_Value) :-
- initModelWithTrain([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, 0.0001, 0).
+ softmax_regression_initModelWithTrain([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, 0.0001, 0).
%% Successful Tests
test(softmax_InitWithTrain_Direct_Input) :-
- initModelWithTrain([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, 0.0001, 0).
+ softmax_regression_initModelWithTrain([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, 0.0001, 0).
test(softmax_InitWithTrain_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.003, 1).
+ softmax_regression_initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.003, 1).
-:- end_tests(initModelWithTrain).
+:- end_tests(softmax_regression_initModelWithTrain).
%%
-%% TESTING predicate classifyPoint/2
+%% TESTING predicate softmax_regression_classifyPoint/2
%%
-:- begin_tests(classifyPoint).
+:- begin_tests(softmax_regression_classifyPoint).
%% Failure Tests
%% Doesnt cause an exception
test(softmax_ClassifyPoint_On_Untrained_Model) :-
reset_Model_NoTrain,
- classifyPoint([5.1,3.5,1.4], _).
+ softmax_regression_classifyPoint([5.1,3.5,1.4], _).
test(softmax_ClassifyPoint_With_Too_Big_Dims, [error(_,system_error('SoftmaxRegression::Classify(): dataset has 5 dimensions, but model has 3 dimensions!'))]) :-
reset_Model_WithTrain,
- classifyPoint([5.1,3.5,1.4,5.2,3.2], _).
+ softmax_regression_classifyPoint([5.1,3.5,1.4,5.2,3.2], _).
test(softmax_ClassifyPoint_With_Too_Small_Dims, [error(_,system_error('SoftmaxRegression::Classify(): dataset has 2 dimensions, but model has 3 dimensions!'))]) :-
reset_Model_WithTrain,
- classifyPoint([5.1,3.5], _).
+ softmax_regression_classifyPoint([5.1,3.5], _).
%% Successful Tests
test(softmax_ClassifyPoint) :-
reset_Model_WithTrain,
- classifyPoint([4.1,2.5,1.4], Prediction),
+ softmax_regression_classifyPoint([4.1,2.5,1.4], Prediction),
print('\nPrediction: '),
print(Prediction).
-:- end_tests(classifyPoint).
+:- end_tests(softmax_regression_classifyPoint).
%%
-%% TESTING predicate classifyMatrix/5
+%% TESTING predicate softmax_regression_classifyMatrix/5
%%
-:- begin_tests(classifyMatrix).
+:- begin_tests(softmax_regression_classifyMatrix).
%% Failure Tests
%% Doesnt cause an exception
test(softmax_ClassifyMatrix_On_Untrained_Model) :-
reset_Model_NoTrain,
- classifyMatrix([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5], 3, _, _, _).
+ softmax_regression_classifyMatrix([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5], 3, _, _, _).
test(softmax_ClassifyMatrix_With_Too_Big_Dims, [error(_,system_error('SoftmaxRegression::Classify(): dataset has 5 dimensions, but model has 3 dimensions!'))]) :-
reset_Model_WithTrain,
- classifyMatrix([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5], 5, _, _, _).
+ softmax_regression_classifyMatrix([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5], 5, _, _, _).
test(softmax_ClassifyMatrix_With_Too_Small_Dims, [error(_,system_error('SoftmaxRegression::Classify(): dataset has 2 dimensions, but model has 3 dimensions!'))]) :-
reset_Model_WithTrain,
- classifyMatrix([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5], 2, _, _, _).
+ softmax_regression_classifyMatrix([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5], 2, _, _, _).
%% Successful Tests
test(softmax_ClassifyMatrix_Direct_Input) :-
reset_Model_WithTrain,
- classifyMatrix([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5], 3, PredicList, ProbsList, _),
+ softmax_regression_classifyMatrix([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5], 3, PredicList, ProbsList, _),
print('\nPredicted Labels: '),
print(PredicList),
print('\nProbabilities: '),
@@ -146,50 +146,50 @@ test(softmax_ClassifyMatrix_Direct_Input) :-
test(softmax_ClassifyMatrix_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.003, 1),
- classifyMatrix(Data, 4, PredicList, ProbsList, _),
+ softmax_regression_initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.003, 1),
+ softmax_regression_classifyMatrix(Data, 4, PredicList, ProbsList, _),
print('\nPredicted Labels: '),
print(PredicList),
print('\nProbabilities: '),
print(ProbsList).
-:- end_tests(classifyMatrix).
+:- end_tests(softmax_regression_classifyMatrix).
%%
-%% TESTING predicate computeAccuracy/4
+%% TESTING predicate softmax_regression_computeAccuracy/4
%%
-:- begin_tests(computeAccuracy).
+:- begin_tests(softmax_regression_computeAccuracy).
%% Failure Tests
%% Doesnt cause an exception
test(softmax_ComputeAccuracy_On_Untrained_Model) :-
reset_Model_NoTrain,
- computeAccuracy([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], _).
+ softmax_regression_computeAccuracy([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], _).
test(softmax_ComputeAccuracy_Wrong_Label_Dims1, [error(_,system_error('The Labels Vector has the wrong Dimension!'))]) :-
reset_Model_WithTrain,
- computeAccuracy([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], _).
+ softmax_regression_computeAccuracy([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], _).
%% Doesnt cause exception
test(softmax_ComputeAccuracy_Wrong_Label_Dims2) :-
reset_Model_WithTrain,
- computeAccuracy([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], _).
+ softmax_regression_computeAccuracy([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], _).
%% The same when the label values are out of range
test(softmax_ComputeAccuracy_Wrong_Label_Value) :-
reset_Model_WithTrain,
- computeAccuracy([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], _).
+ softmax_regression_computeAccuracy([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], _).
%% Doesnt cause an exception
test(softmax_ComputeAccuracy_Too_Many_Label_Value) :-
reset_Model_WithTrain,
- computeAccuracy([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], _).
+ softmax_regression_computeAccuracy([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], _).
test(softmax_ComputeAccuracy_Wrong_Data_Dims, [error(_,system_error('SoftmaxRegression::Classify(): dataset has 4 dimensions, but model has 3 dimensions!'))]) :-
reset_Model_WithTrain,
- computeAccuracy([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], Accuracy),
+ softmax_regression_computeAccuracy([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], Accuracy),
print('\nAccuracy: '),
print(Accuracy).
@@ -198,25 +198,25 @@ test(softmax_ComputeAccuracy_Wrong_Data_Dims, [error(_,system_error('SoftmaxRegr
test(softmax_ComputeAccuracy_Direct_Input) :-
reset_Model_WithTrain,
- computeAccuracy([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], Accuracy),
+ softmax_regression_computeAccuracy([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], Accuracy),
print('\nAccuracy: '),
print(Accuracy).
test(softmax_ComputeAccuracy_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.003, 1),
- computeAccuracy([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2], 4, [0,1,0,1], Accuracy),
+ softmax_regression_initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.003, 1),
+ softmax_regression_computeAccuracy([3, 2, 0, 5, 1, 4, 1, 0, 4, 3, 3, 5, 0, 5, 5, 2], 4, [0,1,0,1], Accuracy),
print('\nAccuracy: '),
print(Accuracy).
-:- end_tests(computeAccuracy).
+:- end_tests(softmax_regression_computeAccuracy).
%%
-%% TESTING predicate featureSize/1
+%% TESTING predicate softmax_regression_featureSize/1
%%
-:- begin_tests(featureSize).
+:- begin_tests(softmax_regression_featureSize).
%% Failure Tests
@@ -227,31 +227,31 @@ test(softmax_ComputeAccuracy_CSV_Input) :-
test(softmax_FeatureSize_No_Train) :-
reset_Model_NoTrain,
- featureSize(FeatureSize),
+ softmax_regression_featureSize(FeatureSize),
print('\nFeatureSize: '),
print(FeatureSize).
test(softmax_FeatureSize_Direct_Input) :-
reset_Model_WithTrain,
- featureSize(FeatureSize),
+ softmax_regression_featureSize(FeatureSize),
print('\nFeatureSize: '),
print(FeatureSize).
test(softmax_FeatureSize_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.003, 1),
- featureSize(FeatureSize),
+ softmax_regression_initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.003, 1),
+ softmax_regression_featureSize(FeatureSize),
print('\nFeatureSize: '),
print(FeatureSize).
-:- end_tests(featureSize).
+:- end_tests(softmax_regression_featureSize).
%%
-%% TESTING predicate parameters/2
+%% TESTING predicate softmax_regression_parameters/2
%%
-:- begin_tests(parameters).
+:- begin_tests(softmax_regression_parameters).
%% Failure Tests
@@ -260,80 +260,80 @@ test(softmax_FeatureSize_CSV_Input) :-
test(softmax_Parameters_No_Train) :-
reset_Model_NoTrain,
- parameters(PrametersList, _),
+ softmax_regression_parameters(PrametersList, _),
print('\nParameters: '),
print(PrametersList).
test(softmax_Parameters_Direct_Input) :-
reset_Model_WithTrain,
- parameters(PrametersList, _),
+ softmax_regression_parameters(PrametersList, _),
print('\nParameters: '),
print(PrametersList).
test(softmax_Parameters_CSV_Input) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.003, 1),
- parameters(PrametersList, _),
+ softmax_regression_initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 0.003, 1),
+ softmax_regression_parameters(PrametersList, _),
print('\nParameters: '),
print(PrametersList).
-:- end_tests(parameters).
+:- end_tests(softmax_regression_parameters).
%%
-%% TESTING predicate train/5
+%% TESTING predicate softmax_regression_train/5
%%
-:- begin_tests(train).
+:- begin_tests(softmax_regression_train).
%% Failure Tests
test(softmax_Train_Negative_NumClass, fail) :-
reset_Model_NoTrain,
- 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], -1, _).
+ softmax_regression_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], -1, _).
test(softmax_Train_Wrong_Label_Dims1, [error(_,system_error('element-wise multiplication: incompatible matrix dimensions: 2x4 and 2x2'))]) :-
reset_Model_NoTrain,
- 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], 2, _).
+ softmax_regression_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], 2, _).
%% If the label vector is to long it seems to cause no problems
test(softmax_Train_Wrong_Label_Dims2, [error(_,system_error('element-wise multiplication: incompatible matrix dimensions: 2x4 and 2x7'))]) :-
reset_Model_NoTrain,
- 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, [0,1,0,1,0,0,1], 2, _).
+ softmax_regression_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, [0,1,0,1,0,0,1], 2, _).
%% The same when the label values are out of range
test(softmax_Train_Wrong_Label_Value) :-
reset_Model_NoTrain,
- 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, _).
+ softmax_regression_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, _).
%% doesnt cause a exeption
test(softmax_Train_Too_Many_Label_Value) :-
reset_Model_NoTrain,
- 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, [1,1,0,2], 2, _).
+ softmax_regression_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, [1,1,0,2], 2, _).
%% doesnt cause a exeption
test(softmax_Train_Wrong_Data_Dims) :-
reset_Model_NoTrain,
- 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], 4, [0,1,0], 2, _).
+ softmax_regression_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], 4, [0,1,0], 2, _).
%% Successful Tests
test(softmax_Train_Direct_Input) :-
reset_Model_NoTrain,
- 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, FinalValue),
+ softmax_regression_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, FinalValue),
print('\nFinalValue: '),
print(FinalValue).
test(softmax_Train_CSV_Input) :-
- initModelNoTrain(4, 2, 0),
+ softmax_regression_initModelNoTrain(4, 2, 0),
open('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, FinalValue),
+ softmax_regression_train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, FinalValue),
print('\nFinalValue: '),
print(FinalValue).
-:- end_tests(train).
+:- end_tests(softmax_regression_train).
run_softmax_regression_tests :-
diff --git a/src/methods/sparse_coding/sparse_coding.pl b/src/methods/sparse_coding/sparse_coding.pl
index 5b9c4f0fa99c136564190593325f008093c81aee..02aea9d87e69e54a78e2a7f01d7be3f5363d89b7 100644
--- a/src/methods/sparse_coding/sparse_coding.pl
+++ b/src/methods/sparse_coding/sparse_coding.pl
@@ -1,8 +1,8 @@
-:- module(sparse_coding, [ initModelWithTrain/8,
- initModelNoTrain/6,
- encode/4,
- train/3]).
+:- module(sparse_coding, [ sparse_coding_initModelWithTrain/8,
+ sparse_coding_initModelNoTrain/6,
+ sparse_coding_encode/4,
+ sparse_coding_train/3]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -17,7 +17,7 @@
float32 = float_32,
float_array = array(float32).
-%% definitions for the connected function
+%% definitions for the connected functionsparse_coding_
@@ -36,7 +36,7 @@
%% --Description--
%% Initializes sparse_coding model and trains it.
%%
-initModelWithTrain(MatList, MatRows, Atoms, Lambda1, Lambda2, MaxIterations, ObjTolerance, NewtonTolerance) :-
+sparse_coding_initModelWithTrain(MatList, MatRows, Atoms, Lambda1, Lambda2, MaxIterations, ObjTolerance, NewtonTolerance) :-
Atoms >= 0,
Lambda1 > 0,
Lambda2 >= 0,
@@ -64,7 +64,7 @@ foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array),
%% --Description--
%% Initializes sparse_coding model but will not train the model, and a subsequent call to Train will be required before the model can encode points with Encode.
%%
-initModelNoTrain(Atoms, Lambda1, Lambda2, MaxIterations, ObjTolerance, NewtonTolerance) :-
+sparse_coding_initModelNoTrain(Atoms, Lambda1, Lambda2, MaxIterations, ObjTolerance, NewtonTolerance) :-
Atoms >= 0,
Lambda1 > 0,
Lambda2 >= 0,
@@ -86,7 +86,7 @@ foreign(initModelNoTrain, c, initModelNoTrainI(+integer, +float32, +float32, +in
%% --Description--
%% Sparse code each point in the given dataset via LARS, using the current dictionary and store the encoded data in the codes matrix.
%%
-encode(DataList, DataRows, CodesList, YCols) :-
+sparse_coding_encode(DataList, DataRows, CodesList, YCols) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
encodeI(X, Xsize, Xrownum, Y, YCols, YRows),
convert_float_array_to_2d_list(Y, YCols, YRows, CodesList).
@@ -105,7 +105,7 @@ foreign(encode, c, encodeI( +pointer(float_array), +integer, +integer,
%% --Description--
%% Train the sparse coding model on the given dataset.
%%
-train(DataList, DataRows, ReturnValue) :-
+sparse_coding_train(DataList, DataRows, ReturnValue) :-
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
trainI(X, Xsize, Xrownum, ReturnValue).
diff --git a/src/methods/sparse_coding/sparse_coding_test.pl b/src/methods/sparse_coding/sparse_coding_test.pl
index 6290c0a4f210b89691b02b40c155875482b0e2d1..76b1149264671aa3ef7bc75710e6c94c32567f34 100644
--- a/src/methods/sparse_coding/sparse_coding_test.pl
+++ b/src/methods/sparse_coding/sparse_coding_test.pl
@@ -10,107 +10,107 @@
reset_Model_With_Train_A :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,20, Data),
- initModelWithTrain(Data,4,15,0.1,0.0,100,0.01,0.000001).
+ sparse_coding_initModelWithTrain(Data,4,15,0.1,0.0,100,0.01,0.000001).
reset_Model_No_Train_A :-
- initModelNoTrain(2,0.1,0.0,100,0.01,0.000001).
+ sparse_coding_initModelNoTrain(2,0.1,0.0,100,0.01,0.000001).
%%
-%% TESTING predicate initModelWithTrain/8
+%% TESTING predicate sparse_coding_initModelWithTrain/8
%%
-:- begin_tests(initModelWithTrain).
+:- begin_tests(sparse_coding_initModelWithTrain).
%% Failure Tests
test(initModelWithTrain_Negative_RowNum, fail) :-
- initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], -3,15,0.1,0.0,100,0.01,0.000001).
+ sparse_coding_initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], -3,15,0.1,0.0,100,0.01,0.000001).
test(initModelWithTrain_Negative_Atoms, fail) :-
- initModelWithTrain([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,-15,0.1,0.0,100,0.01,0.000001).
+ sparse_coding_initModelWithTrain([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,-15,0.1,0.0,100,0.01,0.000001).
test(initModelWithTrain_Negative_MaxIterations, fail) :-
- initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3,15,0.1,0.0,-100,0.01,0.000001).
+ sparse_coding_initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3,15,0.1,0.0,-100,0.01,0.000001).
test(initModelWithTrain_Negative_ObjTolerance, fail) :-
- initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3,15,0.1,0.0,100,-0.01,0.000001).
+ sparse_coding_initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3,15,0.1,0.0,100,-0.01,0.000001).
test(initModelWithTrain_Negative_NewtonTolerance, fail) :-
- initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3,15,0.1,0.0,100,0.01,-0.000001).
+ sparse_coding_initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3,15,0.1,0.0,100,0.01,-0.000001).
test(initModelWithTrain_Empty_List, fail) :-
- initModelWithTrain([], 3,15,0.1,0.0,100,0.01,-0.000001).
+ sparse_coding_initModelWithTrain([], 3,15,0.1,0.0,100,0.01,-0.000001).
test(initModelWithTrain_Data_Amount_Smaller_Than_Atoms, [error(_,system_error('There have to be more DataPoints than Atoms!'))]) :-
- initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4,10,0.1,0.0,2,0.01,0.000001).
+ sparse_coding_initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4,10,0.1,0.0,2,0.01,0.000001).
%% Successful Tests
test(initModelWithTrain_Direct_Input_Use) :-
- initModelWithTrain([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, 3,0.1,0.0,2,0.01,0.000001).
+ sparse_coding_initModelWithTrain([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, 3,0.1,0.0,2,0.01,0.000001).
test(initModelWithTrain_Changed_Input) :-
- initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 6,2,2.3,0.1,30,0.002,0.0001).
+ sparse_coding_initModelWithTrain([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 6,2,2.3,0.1,30,0.002,0.0001).
test(initModelWithTrain_CSV_Use) :-
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,20, Data),
- initModelWithTrain(Data,4,15,0.1,0.0,100,0.01,0.000001).
+ sparse_coding_initModelWithTrain(Data,4,15,0.1,0.0,100,0.01,0.000001).
-:- end_tests(initModelWithTrain).
+:- end_tests(sparse_coding_initModelWithTrain).
%%
-%% TESTING predicate initModelNoTrain/6
+%% TESTING predicate sparse_coding_initModelNoTrain/6
%%
-:- begin_tests(initModelNoTrain).
+:- begin_tests(sparse_coding_initModelNoTrain).
%% Failure Tests
test(initModelNoTrain_Negative_Atoms, fail) :-
- initModelNoTrain(-15,0.1,0.0,100,0.01,0.000001).
+ sparse_coding_initModelNoTrain(-15,0.1,0.0,100,0.01,0.000001).
test(initModelNoTrain_Negative_MaxIterations, fail) :-
- initModelNoTrain(15,0.1,0.0,-100,0.01,0.000001).
+ sparse_coding_initModelNoTrain(15,0.1,0.0,-100,0.01,0.000001).
test(initModelNoTrain_Negative_ObjTolerance, fail) :-
- initModelNoTrain(15,0.1,0.0,100,-0.01,0.000001).
+ sparse_coding_initModelNoTrain(15,0.1,0.0,100,-0.01,0.000001).
test(initModelNoTrain_Negative_NewtonTolerance, fail) :-
- initModelNoTrain(15,0.1,0.0,100,0.01,-0.000001).
+ sparse_coding_initModelNoTrain(15,0.1,0.0,100,0.01,-0.000001).
test(initModelNoTrain_Empty_List, fail) :-
- initModelNoTrain(15,0.1,0.0,100,0.01,-0.000001).
+ sparse_coding_initModelNoTrain(15,0.1,0.0,100,0.01,-0.000001).
%% Successful Tests
test(initModelNoTrain_Normal_Input_Use) :-
- initModelNoTrain(15,0.1,0.0,100,0.01,0.000001).
+ sparse_coding_initModelNoTrain(15,0.1,0.0,100,0.01,0.000001).
test(initModelNoTrain_Changed_Input) :-
- initModelNoTrain(5,2.3,0.2,200,0.002,0.0001).
+ sparse_coding_initModelNoTrain(5,2.3,0.2,200,0.002,0.0001).
-:- end_tests(initModelNoTrain).
+:- end_tests(sparse_coding_initModelNoTrain).
%%
-%% TESTING predicate encode/4
+%% TESTING predicate sparse_coding_encode/4
%%
-:- begin_tests(encode).
+:- begin_tests(sparse_coding_encode).
%% Failure Tests
test(encode_With_No_Trained_Model, [error(_,system_error('Matrix has not the correct dimensions!'))]) :-
reset_Model_No_Train_A,
- encode([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, CodesList, CodesRows),
+ sparse_coding_encode([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, CodesList, CodesRows),
print(CodesList),
print('\n'),
print(CodesRows).
test(encode_With_Not_Fitting_Data, [error(_,system_error('Matrix has not the correct dimensions!'))]) :-
reset_Model_No_Train_A,
- encode([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 2, CodesList, CodesRows),
+ sparse_coding_encode([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 2, CodesList, CodesRows),
print(CodesList),
print('\n'),
print(CodesRows).
@@ -120,7 +120,7 @@ test(encode_With_Not_Fitting_Data, [error(_,system_error('Matrix has not the cor
test(encode_Normal_Use1) :-
reset_Model_With_Train_A,
- encode([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, CodesList, CodesRows),
+ sparse_coding_encode([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, CodesList, CodesRows),
print(CodesList),
print('\n'),
print(CodesRows).
@@ -130,50 +130,50 @@ test(encode_Normal_Use2) :-
reset_Model_No_Train_A,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- train(Data, 4, _),
- encode([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, CodesList, CodesRows),
+ sparse_coding_train(Data, 4, _),
+ sparse_coding_encode([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, CodesList, CodesRows),
print(CodesList),
print('\n'),
print(CodesRows).
-:- end_tests(encode).
+:- end_tests(sparse_coding_encode).
%%
-%% TESTING predicate train/3
+%% TESTING predicate sparse_coding_train/3
%%
-:- begin_tests(train).
+:- begin_tests(sparse_coding_train).
%% Failure Tests
test(train_With_Negative_RowNum, fail) :-
reset_Model_No_Train_A,
- 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, _).
+ sparse_coding_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, _).
test(train_With_Empty_Data_List1, fail) :-
reset_Model_No_Train_A,
- train([], 1, _).
+ sparse_coding_train([], 1, _).
test(train_With_Empty_Data_List2, fail) :-
reset_Model_No_Train_A,
- train([], 0, _).
+ sparse_coding_train([], 0, _).
%% Successful Tests
test(train_With_Direkt_Input) :-
reset_Model_No_Train_A,
- 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, ObjectiveValue),
+ sparse_coding_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, ObjectiveValue),
print(ObjectiveValue).
test(train_With_CSV_Input) :-
reset_Model_No_Train_A,
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,20, Data),
- train(Data, 4, ObjectiveValue),
+ sparse_coding_train(Data, 4, ObjectiveValue),
print(ObjectiveValue).
-:- end_tests(train).
+:- end_tests(sparse_coding_train).
run_sparse_coding_tests :-