From 194e8e1c693e2a82497e54c250ebc446239a022f Mon Sep 17 00:00:00 2001
From: Jakhes <dean.schmitz@schmitzbauer.de>
Date: Wed, 9 Nov 2022 14:59:03 +0100
Subject: [PATCH] Changing Naming schema for the predicates
---
src/methods/adaboost/adaboost.pl | 40 ++--
src/methods/adaboost/adaboost_test.pl | 179 ++++++++--------
.../bayesian_linear_regression.pl | 44 ++--
.../bayesian_linear_regression_test.pl | 196 +++++++++---------
src/methods/decision_tree/decision_tree.pl | 16 +-
.../decision_tree/decision_tree_test.pl | 92 ++++----
src/methods/fastmks/fastmks.pl | 12 +-
src/methods/fastmks/fastmks_test.pl | 56 ++---
src/methods/hoeffding_tree/hoeffding_tree.pl | 12 +-
.../hoeffding_tree/hoeffding_tree_test.pl | 98 ++++-----
src/methods/kde/kde.pl | 12 +-
src/methods/kde/kde_test.pl | 158 +++++++-------
src/methods/kernel_pca/kernel_pca.pl | 4 +-
src/methods/kfn/kfn.pl | 12 +-
src/methods/kfn/kfn_test.pl | 164 +++++++--------
src/methods/knn/knn.pl | 12 +-
src/methods/knn/knn_test.pl | 178 ++++++++--------
src/methods/lars/lars.pl | 48 ++---
src/methods/lars/lars_test.pl | 14 +-
src/methods/linear_SVM/linear_SVM.pl | 24 +--
src/methods/linear_SVM/linear_SVM_test.pl | 140 ++++++-------
.../linear_regression/linear_regression.pl | 32 +--
.../local_coordinate_coding.pl | 24 +--
.../local_coordinate_coding_test.pl | 2 +-
.../logistic_regression.pl | 28 +--
.../logistic_regression_test.pl | 2 +-
src/methods/lsh/lsh.pl | 16 +-
src/methods/lsh/lsh_test.pl | 66 +++---
.../naive_bayes_classifier.pl | 32 +--
.../naive_bayes_classifier_test.pl | 114 +++++-----
src/methods/perceptron/perceptron.pl | 24 +--
src/methods/perceptron/perceptron_test.pl | 96 ++++-----
src/methods/radical/radical.pl | 4 +-
src/methods/radical/radical_test.pl | 18 +-
src/methods/random_forest/random_forest.pl | 32 +--
.../random_forest/random_forest_test.pl | 156 +++++++-------
.../softmax_regression/softmax_regression.pl | 32 +--
.../softmax_regression_test.pl | 148 ++++++-------
src/methods/sparse_coding/sparse_coding.pl | 18 +-
.../sparse_coding/sparse_coding_test.pl | 82 ++++----
40 files changed, 1219 insertions(+), 1218 deletions(-)
diff --git a/src/methods/adaboost/adaboost.pl b/src/methods/adaboost/adaboost.pl
index f7416a9..a12de42 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 1c1cae0..fe9a54f 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 410a4dd..794e130 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 62eb00c..6f7fa10 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 3bcb22a..1622953 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 712d1c1..c5ed2ac 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 0923570..6529734 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 1d5aeeb..386121e 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 9996abf..e656d2c 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 75280e5..e03e532 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 cec5862..1a2951a 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 6e8beba..be92448 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 01a1c62..687b496 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 21fc2b8..8a5f21c 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 035a06a..d15258f 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 f4d0cac..c410a6c 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 6b37752..9372ef8 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 a7449f5..d6ac26d 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 23e55df..67e2cb1 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 9965310..6959fa4 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 9ddaa73..3b973e5 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 cb85d07..dfc1ff8 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 9c7d2be..97c4580 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 5fe6b23..0bdb5de 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 86228c5..ecdc87b 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 da8651e..f7d7fac 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 0f6f40d..d0df6d3 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 d5088a5..5d3a0cc 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 898624d..ea840a4 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 8bc7b33..541088c 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 b71547e..f829b50 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 bb8d3db..bd77ad9 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 b51f77c..902e2b7 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 b915cbb..6fb60d3 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 b88d027..c299e50 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 ac6b4e1..249a498 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 17e1162..2bfb6db 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 6612ce1..99dd60b 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 5b9c4f0..02aea9d 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 6290c0a..76b1149 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 :-
--
GitLab