diff --git a/src/methods/kfn/kfn.cpp b/src/methods/kfn/kfn.cpp
index 3793829cf24ea0400a5da990192acce51a395d86..00f656cdb63cbaa7f2521e19c522e670e45e9afc 100644
--- a/src/methods/kfn/kfn.cpp
+++ b/src/methods/kfn/kfn.cpp
@@ -17,6 +17,7 @@ using namespace mlpack::neighbor;
// Global Variable of the NSModel object so it can be accessed from all functions
NSModel<FurthestNS> nsModelObj;
+int trainedDimension = 0;
// input: TreeTypes treeType = TreeTypes::KD_TREE,
@@ -47,23 +48,23 @@ void initAndBuildModel(char const *treeType, char const *algorithm,
tree = NSModel<FurthestNS>::COVER_TREE;
else if (strcmp(treeType, "r") == 0)
tree = NSModel<FurthestNS>::R_TREE;
- else if (strcmp(treeType, "r-star") == 0)
+ else if (strcmp(treeType, "r_star") == 0)
tree = NSModel<FurthestNS>::R_STAR_TREE;
else if (strcmp(treeType, "ball") == 0)
tree = NSModel<FurthestNS>::BALL_TREE;
else if (strcmp(treeType, "x") == 0)
tree = NSModel<FurthestNS>::X_TREE;
- else if (strcmp(treeType, "hilbert-r") == 0)
+ else if (strcmp(treeType, "hilbert_r") == 0)
tree = NSModel<FurthestNS>::HILBERT_R_TREE;
- else if (strcmp(treeType, "r-plus") == 0)
+ else if (strcmp(treeType, "r_plus") == 0)
tree = NSModel<FurthestNS>::R_PLUS_TREE;
- else if (strcmp(treeType, "r-plus-plus") == 0)
+ else if (strcmp(treeType, "r_plus_plus") == 0)
tree = NSModel<FurthestNS>::R_PLUS_PLUS_TREE;
else if (strcmp(treeType, "vp") == 0)
tree = NSModel<FurthestNS>::VP_TREE;
else if (strcmp(treeType, "rp") == 0)
tree = NSModel<FurthestNS>::RP_TREE;
- else if (strcmp(treeType, "max-rp") == 0)
+ else if (strcmp(treeType, "max_rp") == 0)
tree = NSModel<FurthestNS>::MAX_RP_TREE;
else if (strcmp(treeType, "ub") == 0)
tree = NSModel<FurthestNS>::UB_TREE;
@@ -92,6 +93,7 @@ void initAndBuildModel(char const *treeType, char const *algorithm,
nsModelObj.RandomBasis() = (randomBasis == 1);
+ trainedDimension = reference.n_rows;
nsModelObj.BuildModel(std::move(reference), size_t(leafSize), searchMode, epsilon);
}
@@ -110,6 +112,14 @@ void searchWithQuery( float *queryMatArr, SP_integer queryMatSize, SP_integer
float **neighborsMatArr, SP_integer *neighborsMatColNum, SP_integer *neighborsMatRowNum,
float **distancesMatArr, SP_integer *distancesMatColNum, SP_integer *distancesMatRowNum)
{
+ if (trainedDimension != queryMatRowNum)
+ {
+ std::ostringstream stream;
+ stream << "Queryset has Dim(" << queryMatRowNum << ") but the Referenceset has Dim(" << trainedDimension << ")";
+ raisePrologSystemExeption(stream.str().c_str());
+ return;
+ }
+
// convert the Prolog array to arma::mat
mat query = convertArrayToMat(queryMatArr, queryMatSize, queryMatRowNum);
@@ -162,4 +172,4 @@ void searchNoQuery( SP_integer k,
// return the Matrices
returnMatrixInformation(neighborsReturnMat, neighborsMatArr, neighborsMatColNum, neighborsMatRowNum);
returnMatrixInformation(distancesReturnMat, distancesMatArr, distancesMatColNum, distancesMatRowNum);
-}
\ No newline at end of file
+}
diff --git a/src/methods/kfn/kfn.pl b/src/methods/kfn/kfn.pl
index 838df2d407e7579a0bd61c55121abbb8d58064df..21fc2b8fc0e25fe37af85755db7f8f6286d73b31 100644
--- a/src/methods/kfn/kfn.pl
+++ b/src/methods/kfn/kfn.pl
@@ -21,7 +21,7 @@
%% --Input--
-%% string treeType => "kd", "vp", "rp", "max-rp", "ub", "cover", "r", "r-star", "x", "ball", "hilbert-r", "r-plus", "r-plus-plus", "spill", "oct",
+%% string treeType => "kd", "vp", "rp", "max_rp", "ub", "cover", "r", "r_star", "x", "ball", "hilbert_r", "r_plus", "r_plus_plus", "oct",
%% string searchMode => "naive", "single_tree", "dual_tree", "greedy",
%% bool randomBasis => (1)true / (0)false,
%% int leafSize => 20,
@@ -34,6 +34,8 @@
%% Initialize the Model and build it.
%%
initAndBuildModel(TreeType, SearchMode, RandomBasis, LeafSize, Epsilon, ReferenceList, ReferenceRows) :-
+ LeafSize >= 1,
+ Epsilon >= 0,
convert_list_to_float_array(ReferenceList, ReferenceRows, array(Xsize, Xrownum, X)),
initAndBuildModelI(TreeType, SearchMode, RandomBasis, LeafSize, Epsilon, X, Xsize, Xrownum).
@@ -55,6 +57,7 @@ foreign(initAndBuildModel, c, initAndBuildModelI(+string, +string,
%% Perform neighbor search on the queryset.
%%
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),
convert_float_array_to_2d_list(Y, YCols, YRows, NeighborsList),
@@ -78,6 +81,7 @@ foreign(searchWithQuery, c, searchWithQueryI( +pointer(float_array), +integer,
%% Perform monochromatic neighbor search.
%%
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),
convert_float_array_to_2d_list(Z, ZCols, ZRows, DistancesList).
diff --git a/src/methods/kfn/kfn_test.pl b/src/methods/kfn/kfn_test.pl
index 3e74176831b05961006cbc22c88d4793f6206df5..035a06a882681846a8cc8c030d8eb9f78ba9086f 100644
--- a/src/methods/kfn/kfn_test.pl
+++ b/src/methods/kfn/kfn_test.pl
@@ -7,97 +7,203 @@
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initModel(1,0,50,0.0001).
+ 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/7
+%% TESTING predicate initAndBuildModel/9
%%
:- begin_tests(initAndBuildModel).
%% Failure Tests
-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(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).
-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(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).
+
+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).
+
+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).
%% 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(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
- reset_Model_No_Train(perceptron),
+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).
+
+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).
+
+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).
+
+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).
+
+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).
+
+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).
+
+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).
+
+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).
+
+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).
+
+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).
+
+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).
+
+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).
+
+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).
+
+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).
+
+test(kfn_InitAndBuildModel_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], 2, perceptron, 50, 0.0001, Error).
+ initAndBuildModel(kd, dual_tree, 1, 20, 0.005, Data, 4).
:- end_tests(initAndBuildModel).
+
%%
%% TESTING predicate searchWithQuery/7
%%
:- begin_tests(searchWithQuery).
%% Failure Tests
-
-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(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, _, _, _, _).
+
+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, _, _, _, _).
+
+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, _, _, _, _).
%% 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(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, _),
+ print('\nNeighbors: '),
+ print(NeighborsList),
+ print('\nDistances: '),
+ print(DistancesList).
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
- reset_Model_No_Train(perceptron),
+test(kfn_SearchWithQuery_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).
+ 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, _),
+ print('\nNeighbors: '),
+ print(NeighborsList),
+ print('\nDistances: '),
+ print(DistancesList).
:- end_tests(searchWithQuery).
+
%%
-%% TESTING predicate searchNoQuery/5
+%% TESTING predicate searchNoQuery/10
%%
:- begin_tests(searchNoQuery).
%% Failure Tests
-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(kfn_SearchNoQuery_Negative_K, fail) :-
+ reset_Model,
+ searchNoQuery(-1, _, _, _, _).
-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(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, _, _, _, _).
%% 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(kfn_SearchNoQuery_Normal) :-
+ reset_Model,
+ searchNoQuery(2, NeighborsList, _, DistancesList, _),
+ print('\nNeighbors: '),
+ print(NeighborsList),
+ print('\nDistances: '),
+ print(DistancesList).
-test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
- reset_Model_No_Train(perceptron),
+test(kfn_SearchNoQuery_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).
+ initAndBuildModel(kd, dual_tree, 0, 20, 0.0, Data, 4),
+ searchNoQuery(7, NeighborsList, _, DistancesList, _),
+ print('\nNeighbors: '),
+ print(NeighborsList),
+ print('\nDistances: '),
+ print(DistancesList).
:- end_tests(searchNoQuery).