diff --git a/src/methods/perceptron/perceptron.pl b/src/methods/perceptron/perceptron.pl
index 8b3c4a5f00049abd64c2dd68c1b25a2fcb254c10..b71547e9bad1b18e216e4404cdfd4ff0d2f1b9b3 100644
--- a/src/methods/perceptron/perceptron.pl
+++ b/src/methods/perceptron/perceptron.pl
@@ -32,6 +32,9 @@
%% Initilizes the perceptron model and its weight matrix but doesnt train it.
%%
initModelNoTrain(NumClasses, Dimensionality, MaxIterations) :-
+ NumClasses >= 0,
+ Dimensionality > 0,
+ MaxIterations >= 0,
initModelNoTrainI(NumClasses, Dimensionality, MaxIterations).
foreign(initModelNoTrain, c, initModelNoTrainI(+integer, +integer, +integer)).
@@ -48,6 +51,8 @@ foreign(initModelNoTrain, c, initModelNoTrainI(+integer, +integer, +integer)).
%% Initilizes the perceptron model and its weight matrix and trains it with the given data.
%%
initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, MaxIterations) :-
+ NumClasses >= 0,
+ MaxIterations >= 0,
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, MaxIterations).
@@ -100,6 +105,7 @@ foreign(classify, c, classifyI( +pointer(float_array), +integer, +integer,
%% 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) :-
+ NumClasses >= 0,
convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
convert_list_to_float_array(LabelsList, array(Ysize, Y)),
convert_list_to_float_array(WeightsList, array(Zsize, Z)),
diff --git a/src/methods/perceptron/perceptron_test.pl b/src/methods/perceptron/perceptron_test.pl
index e151fa1c2a221522beed62316e28beafb4aa6693..782c1ad1d9931d075ac5bc3a8131499a179ec224 100644
--- a/src/methods/perceptron/perceptron_test.pl
+++ b/src/methods/perceptron/perceptron_test.pl
@@ -6,38 +6,212 @@
:- use_module(perceptron).
:- use_module('../../helper_files/helper.pl').
-reset_Model :-
- initModel(1,0,50,0.0001).
+reset_Model_NoTrain :-
+ 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).
+
+
+
+%%
+%% TESTING predicate initModelNoTrain/3
+%%
+:- begin_tests(initModelNoTrain).
+
+%% Failure Tests
+
+test(perceptron_InitModelNoTrain_Negative_NumClasses, fail) :-
+ initModelNoTrain(-2, 3, 1000).
+
+test(perceptron_InitModelNoTrain_Negative_Dimensionality, fail) :-
+ initModelNoTrain(2, -3, 1000).
+
+test(perceptron_InitModelNoTrain_Negative_MaxIterations, fail) :-
+ initModelNoTrain(2, 3, -1000).
+
+
+%% Successful Tests
+
+test(perceptron_InitModelNoTrain_Normal_Use) :-
+ initModelNoTrain(2, 3, 1000).
+
+test(perceptron_InitModelNoTrain_Alternative_Input) :-
+ initModelNoTrain(0, 0, 1000).
+
+:- end_tests(initModelNoTrain).
+
+
%%
-%% TESTING predicate predicate/10
+%% TESTING predicate initModelWithTrain/5
%%
-:- begin_tests(predicate).
+:- begin_tests(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).
+
+
+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).
-test(testDescription, [error(domain_error('expectation' , culprit), _)]) :-
- reset_Model_No_Train(perceptron),
- train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,0,0,0], 2, culprit, 50, 0.0001, _).
-test(testDescription2, [error(_,system_error('The values of the Label have to start at 0 and be >= 0 and < the given numClass!'))]) :-
- reset_Model_No_Train(perceptron),
- train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,2], 2, perceptron, 50, 0.0001, _).
+test(random_forest_InitModelWithTrainNoWeights_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, 1000).
+
+test(random_forest_InitModelWithTrainNoWeights_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, 1000).
+
+test(random_forest_InitModelWithTrainNoWeights_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, 1000).
%% Successful Tests
-test(testDescription3, [true(Error =:= 1)]) :-
- reset_Model_No_Train(perceptron),
- train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,0,0,0], 2, perceptron, 50, 0.0001, Error).
+test(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).
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
- reset_Model_No_Train(perceptron),
+test(perceptron_InitModelWithTrain_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], 2, perceptron, 50, 0.0001, Error).
+ initModelWithTrain(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, 1000).
+
+:- end_tests(initModelWithTrain).
+
+
+
+%%
+%% TESTING predicate biases/1
+%%
+:- begin_tests(biases).
+
+%% Failure Tests
+
+test(perceptron_Biases_Before_Train, [error(_,system_error('Error'))]) :-
+ reset_Model_NoTrain,
+ biases(_).
+
+
+%% Successful Tests
+
+test(perceptron_Biases_AfterTrain) :-
+ reset_Model_WithTrain,
+ biases(Biases),
+ print('\nBiases: '),
+ print(Biases).
+
+:- end_tests(biases).
+
+
+
+%%
+%% TESTING predicate classify/3
+%%
+:- begin_tests(classify).
+
+%% Failure Tests
+
+test(perceptron_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, _).
+
+test(perceptron_Classify_Before_Train_Wrong_Dims, [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], 4, _).
+
+test(perceptron_Classify_Different_Dims_Than_Train) :-
+ 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, _).
+
+
+%% Successful Tests
+
+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),
+ print('\nPredicted Labels: '),
+ print(PredictList).
+
+:- end_tests(classify).
+
+
+
+%%
+%% TESTING predicate train/5
+%%
+:- begin_tests(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]).
+
+test(perceptron_Train_Too_Small_Data_Dims, [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], 4, [0,1,0], 2, [0.1,0.2,0.3]).
+
+
+test(perceptron_Train_Too_Short_Label, [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, [0.1,0.2,0.3,0.4]).
+
+test(perceptron_Train_Too_Long_Label, [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, [0.1,0.2,0.3,0.4]).
+
+test(perceptron_Train_Too_Many_Label_Classes, [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, [0.1,0.2,0.3,0.4]).
+
+
+test(perceptron_Train_Too_Short_Label, [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], 2, [0.1,0.2]).
+
+test(perceptron_Train_Too_Long_Label, [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], 2, [0.1,0.2,0.3,0.4,0.6,0.7]).
+
+test(perceptron_Train_Too_Many_Label_Classes, [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], 2, [-0.1,0.2,1.3,2.4]).
+
+
+%% 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]).
+
+
+:- end_tests(train).
+
+
+
+%%
+%% TESTING predicate weights/2
+%%
+:- begin_tests(weights).
+
+%% Failure Tests
+
+test(perceptron_Weights_Before_Train, [error(_,system_error('Error'))]) :-
+ reset_Model_NoTrain,
+ weights(_, _).
+
+
+%% Successful Tests
+
+test(perceptron_Weights_AfterTrain) :-
+ reset_Model_WithTrain,
+ weights(Weights, _),
+ print('\nWeights: '),
+ print(Weights).
-:- end_tests(predicate).
+:- end_tests(weights).
run_perceptron_tests :-
run_tests.