diff --git a/src/methods/lmnn/lmnn.pl b/src/methods/lmnn/lmnn.pl
index 8bf6c13547854579821abf06a80e0dcaf5eb72a7..ed4db78c7465798f8d32bf27cd342461d54809e1 100644
--- a/src/methods/lmnn/lmnn.pl
+++ b/src/methods/lmnn/lmnn.pl
@@ -1,5 +1,5 @@
-:- module(lmnn, [lmnn/20]).
+:- module(lmnn, [lmnn/17]).
%% requirements of library(struct)
:- load_files(library(str_decl),
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 19da7aae4b94ae95c848f999e830bd00bb8ccb56..d62d2a66eac8db4f5114fbf4ede2df1897093f4c 100644
--- a/src/methods/local_coordinate_coding/local_coordinate_coding_test.pl
+++ b/src/methods/local_coordinate_coding/local_coordinate_coding_test.pl
@@ -1,5 +1,5 @@
-:- module(local_coordinate_tests, [run_local_coordinate_tests/0]).
+:- module(local_coordinate_coding_tests, [run_local_coordinate_coding_tests/0]).
:- use_module(library(plunit)).
@@ -39,6 +39,6 @@ test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
:- end_tests(predicate).
-run_local_coordinate_tests :-
+run_local_coordinate_coding_tests :-
run_tests.
diff --git a/src/methods/new_method/new_method_test.pl b/src/methods/new_method/new_method_test.pl
index 13e30b70ed3d396d0189ace2e8282ab6451338cf..78fdfd725191e5b9c2abe7b22269175add5456fc 100644
--- a/src/methods/new_method/new_method_test.pl
+++ b/src/methods/new_method/new_method_test.pl
@@ -41,3 +41,4 @@ test(testDescription4, [true(Error =:= 0.9797958971132711)]) :-
run_new_method_tests :-
run_tests.
+
diff --git a/src/methods/nmf/nmf.cpp b/src/methods/nmf/nmf.cpp
index 4ecb455736330589714961f4e5e9490e36560dbd..258bd7f3f072fae87c4fe31635e14096d7fced41 100644
--- a/src/methods/nmf/nmf.cpp
+++ b/src/methods/nmf/nmf.cpp
@@ -18,7 +18,6 @@ using namespace std;
using namespace mlpack::amf;
-// TODO:
// input: const TerminationPolicyType & terminationPolicy = TerminationPolicyType(),
// const InitializationRuleType & initializeRule = InitializationRuleType(),
// const UpdateRuleType & update = UpdateRuleType()
@@ -30,40 +29,54 @@ using namespace mlpack::amf;
// output: double
//
// description:
-void nmf(char const *updateRule, SP_integer maxIterations, double minResidue,
+// Initilizes the nmf model and applies it to the given data.
+//
+void nmf(char const *updateRule,
+ SP_integer maxIterations, double minResidue,
float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
SP_integer rank,
float **WMatArr, SP_integer *WMatColNum, SP_integer *WMatRowNum,
float **HMatArr, SP_integer *HMatColNum, SP_integer *HMatRowNum)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
// create the ReturnMats
mat WReturnMat;
mat HReturnMat;
- // decide which update rule to use
- if (strcmp(updateRule, "multdist") == 0)
- {
- AMF<SimpleResidueTermination, RandomAcolInitialization<>,NMFMultiplicativeDistanceUpdate>(SimpleResidueTermination(minResidue, maxIterations))
- .Apply(data, rank, WReturnMat, HReturnMat);
- }
- else if (strcmp(updateRule, "multdiv") == 0)
- {
- AMF<SimpleResidueTermination, RandomAcolInitialization<>,NMFMultiplicativeDivergenceUpdate>(SimpleResidueTermination(minResidue, maxIterations))
- .Apply(data, rank, WReturnMat, HReturnMat);
- }
- else if (strcmp(updateRule, "als") == 0)
+
+ try
{
- AMF<SimpleResidueTermination, RandomAcolInitialization<>,NMFALSUpdate>(SimpleResidueTermination(minResidue, maxIterations))
- .Apply(data, rank, WReturnMat, HReturnMat);
+ // decide which update rule to use
+ if (strcmp(updateRule, "multdist") == 0)
+ {
+ AMF<SimpleResidueTermination, RandomAcolInitialization<>,NMFMultiplicativeDistanceUpdate>(SimpleResidueTermination(minResidue, maxIterations))
+ .Apply(data, rank, WReturnMat, HReturnMat);
+ }
+ else if (strcmp(updateRule, "multdiv") == 0)
+ {
+ AMF<SimpleResidueTermination, RandomAcolInitialization<>,NMFMultiplicativeDivergenceUpdate>(SimpleResidueTermination(minResidue, maxIterations))
+ .Apply(data, rank, WReturnMat, HReturnMat);
+ }
+ else if (strcmp(updateRule, "als") == 0)
+ {
+ AMF<SimpleResidueTermination, RandomAcolInitialization<>,NMFALSUpdate>(SimpleResidueTermination(minResidue, maxIterations))
+ .Apply(data, rank, WReturnMat, HReturnMat);
+ }
+ else
+ {
+ raisePrologDomainExeption(updateRule, 1, "The given UpdateRule is unkown!", "nmf");
+ return;
+ }
}
- else
+ catch(const std::exception& e)
{
- cout << "wrong updateRule input" << endl;
+ raisePrologSystemExeption(e.what());
+ return;
}
+
// return the Matrices
returnMatrixInformation(WReturnMat, WMatArr, WMatColNum, WMatRowNum);
returnMatrixInformation(HReturnMat, HMatArr, HMatColNum, HMatRowNum);
diff --git a/src/methods/nmf/nmf.pl b/src/methods/nmf/nmf.pl
index 218c35e3b093a2c1921217242b19f456ec8f8dcf..eb25a4efb242148db2c661024c174b9540ce9f39 100644
--- a/src/methods/nmf/nmf.pl
+++ b/src/methods/nmf/nmf.pl
@@ -1,5 +1,5 @@
-:- module(nmf, [nmf/13]).
+:- module(nmf, [nmf/10]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -31,7 +31,13 @@
%% --Description--
%% Initilizes the nmf model and applies it to the given data.
%%
-foreign(nmf, c, nmf(+string, +integer, +float32,
+nmf(UpdateRule, MaxIterations, MinResidue, DataList, DataRows, Rank, WList, YCols, HList, ZCols) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ nmfI(UpdateRule, MaxIterations, MinResidue, X, Xsize, Xrows, Rank, Y, YCols, YRows, Z, ZCols, ZRows),
+ convert_float_array_to_2d_list(Y, YCols, YRows, WList),
+ convert_float_array_to_2d_list(Z, ZCols, ZRows, HList).
+
+foreign(nmf, c, nmfI( +string, +integer, +float32,
+pointer(float_array), +integer, +integer,
+integer,
-pointer(float_array), -integer, -integer,
diff --git a/src/methods/nmf/nmf_test.pl b/src/methods/nmf/nmf_test.pl
index 3132d0d859bd5228b4fb5f523f073d4b9dec55d0..04f2c7d6c1e367d4d80115d8bdf0b5481355d8e4 100644
--- a/src/methods/nmf/nmf_test.pl
+++ b/src/methods/nmf/nmf_test.pl
@@ -1,3 +1,6 @@
+
+:- module(nmf_tests, [run_nmf_tests/0]).
+
:- use_module(library(plunit)).
:- use_module(nmf).
@@ -6,51 +9,36 @@
reset_Model :-
initModel(1,0,50,0.0001).
-:- begin_tests(lists).
-
-%% alpha tests
-test(alpha_std_init) :-
- reset_Model,
- alpha(0).
-test(alpha_wrong_input, fail) :-
- reset_Model,
- alpha(1).
-test(alpha_after_train, A =:= 9223372036854775808) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize),
- alpha(A).
-
-%% train tests
-test(correct_train) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train, fail) :-
- reset_Model,
- convert_list_to_float_array([],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train2, fail) :-
- reset_Model,
- convert_list_to_float_array([],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train4) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],2, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-:- end_tests(lists).
\ No newline at end of file
+%%
+%% TESTING predicate predicate/10
+%%
+:- begin_tests(predicate).
+
+%% 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, _).
+
+
+%% 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),
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+
+:- end_tests(predicate).
+
+run_nmf_tests :-
+ run_tests.
+
diff --git a/src/methods/pca/pca.cpp b/src/methods/pca/pca.cpp
index f08c5a805526bf8ee2ffc9d6f7d845f54d06898a..cc7068d9785ba0003315e20d4e1d197c923e0780 100644
--- a/src/methods/pca/pca.cpp
+++ b/src/methods/pca/pca.cpp
@@ -20,7 +20,6 @@ using namespace std;
using namespace mlpack::pca;
-// TODO:
// input: const bool scaleData = false,
// const DecompositionPolicy & decomposition = DecompositionPolicy(),
// const arma::mat & data,
@@ -29,6 +28,8 @@ using namespace mlpack::pca;
// arma::mat & eigvec
// output:
// description:
+// Apply Principal Component Analysis to the provided data set.
+//
void pca(SP_integer scaleData, char const *decompositionPolicy,
float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
float **transformedMatArr, SP_integer *transformedMatColNum, SP_integer *transformedMatRowNum,
@@ -46,26 +47,35 @@ void pca(SP_integer scaleData, char const *decompositionPolicy,
mat eigVecReturnMat;
- // decide for the decomposition Policy
- if (strcmp(decompositionPolicy, "exact") == 0)
+ try
{
- PCA<ExactSVDPolicy>((scaleData == 1)).Apply(data, transformedReturnMat, eigValReturnVector, eigVecReturnMat);
+ // decide for the decomposition Policy
+ if (strcmp(decompositionPolicy, "exact") == 0)
+ {
+ PCA<ExactSVDPolicy>((scaleData == 1)).Apply(data, transformedReturnMat, eigValReturnVector, eigVecReturnMat);
+ }
+ else if (strcmp(decompositionPolicy, "randomized") == 0)
+ {
+ PCA<RandomizedSVDPolicy>((scaleData == 1)).Apply(data, transformedReturnMat, eigValReturnVector, eigVecReturnMat);
+ }
+ else if (strcmp(decompositionPolicy, "randomized-block-krylov") == 0)
+ {
+ PCA<RandomizedBlockKrylovSVDPolicy>((scaleData == 1)).Apply(data, transformedReturnMat, eigValReturnVector, eigVecReturnMat);
+ }
+ else if (strcmp(decompositionPolicy, "quic") == 0)
+ {
+ PCA<QUICSVDPolicy>((scaleData == 1)).Apply(data, transformedReturnMat, eigValReturnVector, eigVecReturnMat);
+ }
+ else
+ {
+ raisePrologDomainExeption(decompositionPolicy, 2, "The given DecompositionPolicy is unkown!", "pca");
+ return;
+ }
}
- else if (strcmp(decompositionPolicy, "randomized") == 0)
+ catch(const std::exception& e)
{
- PCA<RandomizedSVDPolicy>((scaleData == 1)).Apply(data, transformedReturnMat, eigValReturnVector, eigVecReturnMat);
- }
- else if (strcmp(decompositionPolicy, "randomized-block-krylov") == 0)
- {
- PCA<RandomizedBlockKrylovSVDPolicy>((scaleData == 1)).Apply(data, transformedReturnMat, eigValReturnVector, eigVecReturnMat);
- }
- else if (strcmp(decompositionPolicy, "quic") == 0)
- {
- PCA<QUICSVDPolicy>((scaleData == 1)).Apply(data, transformedReturnMat, eigValReturnVector, eigVecReturnMat);
- }
- else
- {
- cout << "wrong decompositionPolicy input" << endl;
+ raisePrologSystemExeption(e.what());
+ return;
}
@@ -77,13 +87,16 @@ void pca(SP_integer scaleData, char const *decompositionPolicy,
returnMatrixInformation(eigVecReturnMat, eigVecMatArr, eigVecMatColNum, eigVecMatRowNum);
}
-// TODO:
+
// input: const bool scaleData = false,
// const DecompositionPolicy & decomposition = DecompositionPolicy(),
// arma::mat & data,
// const size_t newDimension
// output:
// description:
+// Use PCA for dimensionality reduction on the given dataset.
+// Define the new dimensionality of the data with newDimension.
+//
double pcaDimReduction(SP_integer scaleData, char const *decompositionPolicy,
float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
SP_integer newDimension,
@@ -95,27 +108,35 @@ double pcaDimReduction(SP_integer scaleData, char const *decompositionPolicy,
double returnValue;
- // decide for the decomposition Policy
- if (strcmp(decompositionPolicy, "exact") == 0)
- {
- returnValue = PCA<ExactSVDPolicy>((scaleData == 1)).Apply(data, (size_t)newDimension);
- }
- else if (strcmp(decompositionPolicy, "randomized") == 0)
- {
- returnValue = PCA<RandomizedSVDPolicy>((scaleData == 1)).Apply(data, (size_t)newDimension);
- }
- else if (strcmp(decompositionPolicy, "randomized-block-krylov") == 0)
- {
- returnValue = PCA<RandomizedBlockKrylovSVDPolicy>((scaleData == 1)).Apply(data, (size_t)newDimension);
- }
- else if (strcmp(decompositionPolicy, "quic") == 0)
+ try
{
- returnValue = PCA<QUICSVDPolicy>((scaleData == 1)).Apply(data, (size_t)newDimension);
+ // decide for the decomposition Policy
+ if (strcmp(decompositionPolicy, "exact") == 0)
+ {
+ returnValue = PCA<ExactSVDPolicy>((scaleData == 1)).Apply(data, (size_t)newDimension);
+ }
+ else if (strcmp(decompositionPolicy, "randomized") == 0)
+ {
+ returnValue = PCA<RandomizedSVDPolicy>((scaleData == 1)).Apply(data, (size_t)newDimension);
+ }
+ else if (strcmp(decompositionPolicy, "randomized-block-krylov") == 0)
+ {
+ returnValue = PCA<RandomizedBlockKrylovSVDPolicy>((scaleData == 1)).Apply(data, (size_t)newDimension);
+ }
+ else if (strcmp(decompositionPolicy, "quic") == 0)
+ {
+ returnValue = PCA<QUICSVDPolicy>((scaleData == 1)).Apply(data, (size_t)newDimension);
+ }
+ else
+ {
+ raisePrologDomainExeption(decompositionPolicy, 2, "The given DecompositionPolicy is unkown!", "pca");
+ return 0.0;
+ }
}
- else
+ catch(const std::exception& e)
{
- cout << "wrong decompositionPolicy input" << endl;
- returnValue = -1.0;
+ raisePrologSystemExeption(e.what());
+ return 0.0;
}
@@ -125,13 +146,16 @@ double pcaDimReduction(SP_integer scaleData, char const *decompositionPolicy,
return returnValue;
}
-// TODO:
+
// input: const bool scaleData = false,
// const DecompositionPolicy & decomposition = DecompositionPolicy(),
// arma::mat & data,
// const double varRetained
// output:
// description:
+// Use PCA for dimensionality reduction on the given dataset.
+// Define to which variance the data should be reduced to.
+//
double pcaVarianceDimReduction(SP_integer scaleData, char const *decompositionPolicy,
float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
double varRetained,
@@ -143,27 +167,35 @@ double pcaVarianceDimReduction(SP_integer scaleData, char const *decompositionPo
double returnValue;
- // decide for the decomposition Policy
- if (strcmp(decompositionPolicy, "exact") == 0)
- {
- returnValue = PCA<ExactSVDPolicy>((scaleData == 1)).Apply(data, varRetained);
- }
- else if (strcmp(decompositionPolicy, "randomized") == 0)
- {
- returnValue = PCA<RandomizedSVDPolicy>((scaleData == 1)).Apply(data, varRetained);
- }
- else if (strcmp(decompositionPolicy, "randomized-block-krylov") == 0)
- {
- returnValue = PCA<RandomizedBlockKrylovSVDPolicy>((scaleData == 1)).Apply(data, varRetained);
- }
- else if (strcmp(decompositionPolicy, "quic") == 0)
+ try
{
- returnValue = PCA<QUICSVDPolicy>((scaleData == 1)).Apply(data, varRetained);
+ // decide for the decomposition Policy
+ if (strcmp(decompositionPolicy, "exact") == 0)
+ {
+ returnValue = PCA<ExactSVDPolicy>((scaleData == 1)).Apply(data, varRetained);
+ }
+ else if (strcmp(decompositionPolicy, "randomized") == 0)
+ {
+ returnValue = PCA<RandomizedSVDPolicy>((scaleData == 1)).Apply(data, varRetained);
+ }
+ else if (strcmp(decompositionPolicy, "randomized-block-krylov") == 0)
+ {
+ returnValue = PCA<RandomizedBlockKrylovSVDPolicy>((scaleData == 1)).Apply(data, varRetained);
+ }
+ else if (strcmp(decompositionPolicy, "quic") == 0)
+ {
+ returnValue = PCA<QUICSVDPolicy>((scaleData == 1)).Apply(data, varRetained);
+ }
+ else
+ {
+ raisePrologDomainExeption(decompositionPolicy, 2, "The given DecompositionPolicy is unkown!", "pca");
+ return 0.0;
+ }
}
- else
+ catch(const std::exception& e)
{
- cout << "wrong decompositionPolicy input" << endl;
- returnValue = -1.0;
+ raisePrologSystemExeption(e.what());
+ return 0.0;
}
diff --git a/src/methods/pca/pca.pl b/src/methods/pca/pca.pl
index 9b728665ffa8061f88b9d549458bd45c9924c461..05ec3dfdd1c8b1f235e4ea72dd2ea9e592d6cd07 100644
--- a/src/methods/pca/pca.pl
+++ b/src/methods/pca/pca.pl
@@ -1,7 +1,7 @@
-:- module(pca, [pca/13,
- pcaDimReduction/10,
- pcaVarianceDimReduction/10]).
+:- module(pca, [pca/9,
+ pcaDimReduction/8,
+ pcaVarianceDimReduction/8]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -32,7 +32,14 @@
%% --Description--
%% Apply Principal Component Analysis to the provided data set.
%%
-foreign(pca, c, pca( +integer, +string,
+pca(ScaleData, DecompositionPolicy, DataList, DataRows, TransformedList, TDataCols, EigValList, EigVecList, ZCols) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ pcaI(ScaleData, DecompositionPolicy, X, Xsize, Xrows, TData, TDataCols, TDataRows, Y, Ysize, Z, ZCols, ZRows),
+ convert_float_array_to_2d_list(TData, TDataCols, TDataRows, TransformedList),
+ convert_float_array_to_list(Y, Ysize, EigValList),
+ convert_float_array_to_2d_list(Z, ZCols, ZRows, EigVecList).
+
+foreign(pca, c, pcaI( +integer, +string,
+pointer(float_array), +integer, +integer,
-pointer(float_array), -integer, -integer,
-pointer(float_array), -integer,
@@ -53,7 +60,12 @@ foreign(pca, c, pca( +integer, +string,
%% Use PCA for dimensionality reduction on the given dataset.
%% Define the new dimensionality of the data with newDimension.
%%
-foreign(pcaDimReduction, c, pcaDimReduction( +integer, +string,
+pcaDimReduction(ScaleData, DecompositionPolicy, DataList, DataRows, NewDim, TransformedList, TDataCols, Variance) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ pcaDimReductionI(ScaleData, DecompositionPolicy, X, Xsize, Xrows, NewDim, TData, TDataCols, TDataRows, Variance),
+ convert_float_array_to_2d_list(TData, TDataCols, TDataRows, TransformedList).
+
+foreign(pcaDimReduction, c, pcaDimReductionI( +integer, +string,
+pointer(float_array), +integer, +integer,
+integer,
-pointer(float_array), -integer, -integer,
@@ -74,7 +86,12 @@ foreign(pcaDimReduction, c, pcaDimReduction( +integer, +string,
%% Use PCA for dimensionality reduction on the given dataset.
%% Define to which variance the data should be reduced to.
%%
-foreign(pcaVarianceDimReduction, c, pcaVarianceDimReduction( +integer, +string,
+pcaVarianceDimReduction(ScaleData, DecompositionPolicy, DataList, DataRows, VarRetained, TransformedList, TDataCols, Variance) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ pcaVarianceDimReductionI(ScaleData, DecompositionPolicy, X, Xsize, Xrows, VarRetained, TData, TDataCols, TDataRows, Variance),
+ convert_float_array_to_2d_list(TData, TDataCols, TDataRows, TransformedList).
+
+foreign(pcaVarianceDimReduction, c, pcaVarianceDimReductionI( +integer, +string,
+pointer(float_array), +integer, +integer,
+float32,
-pointer(float_array), -integer, -integer,
diff --git a/src/methods/pca/pca_test.pl b/src/methods/pca/pca_test.pl
index adc117cd53f19f59047e2667f78161565f7b15cb..98b031573734717ca4f4cbd3d537e3f61928d386 100644
--- a/src/methods/pca/pca_test.pl
+++ b/src/methods/pca/pca_test.pl
@@ -1,3 +1,6 @@
+
+:- module(pca_tests, [run_pca_tests/0]).
+
:- use_module(library(plunit)).
:- use_module(pca).
@@ -16,4 +19,37 @@ test(alpha_after_train) :-
convert_float_array_to_2d_list(EigVec, EigVecCols, EigVecRows, EigVecOut),
print(TOut).
-:- end_tests(lists).
\ No newline at end of file
+:- end_tests(lists).
+
+%%
+%% TESTING predicate predicate/10
+%%
+:- begin_tests(predicate).
+
+%% 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, _).
+
+
+%% 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),
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+
+:- end_tests(predicate).
+
+run_pca_tests :-
+ run_tests.
diff --git a/src/methods/perceptron/perceptron.cpp b/src/methods/perceptron/perceptron.cpp
index 2c8630c8e45bdfa7916aa1f5f6d8feee880568b9..e31d80bc8a9520634c46d94099ce2a0fc5a769b6 100644
--- a/src/methods/perceptron/perceptron.cpp
+++ b/src/methods/perceptron/perceptron.cpp
@@ -18,104 +18,151 @@ using namespace mlpack::perceptron;
// Global Variable of the GlobalMethodObject object so it can be accessed from all functions
Perceptron perceptronGlobal;
-// TODO:
+
// input: const size_t numClasses = 0,
// const size_t dimensionality = 0,
// const size_t maxIterations = 1000
// output:
// description:
+// Initilizes the perceptron model and its weight matrix but doesnt train it.
+//
void initModelNoTrain(SP_integer numClasses, SP_integer dimensionality, SP_integer maxIterations)
{
- perceptronGlobal = Perceptron(numClasses, dimensionality, maxIterations);
+ try
+ {
+ perceptronGlobal = Perceptron(numClasses, dimensionality, maxIterations);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
}
-// TODO:
+
// input: const MatType & data,
// const arma::Row< size_t > & labels,
// const size_t numClasses,
// const size_t maxIterations = 1000
// output:
// description:
-void initModelWithTrain(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize, SP_integer numClasses, SP_integer maxIterations)
+// Initilizes the perceptron model and its weight matrix and trains it with the given data.
+//
+void initModelWithTrain(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *labelsArr, SP_integer labelsArrSize,
+ SP_integer numClasses, SP_integer maxIterations)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
- // convert the Prolog arrays to arma::rowvec
+ // convert the Prolog array to arma::rowvec
Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
- perceptronGlobal = Perceptron(data, labelsVector, numClasses, maxIterations);
+
+ try
+ {
+ perceptronGlobal = Perceptron(data, labelsVector, numClasses, maxIterations);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
}
-// TODO:
+
// input:
// output: arma::vec& biases
// description:
+// Get the biases.
+//
void biases(float **biasesArr, SP_integer *biasesArrSize)
{
// create the ReturnVector
vec biasesReturnVector = perceptronGlobal.Biases();
- // return the Vector lenght
- *biasesArrSize = biasesReturnVector.n_elem;
-
- // return the Vector as Array
- *biasesArr = convertToArray(biasesReturnVector);
+ // return Vector
+ returnVectorInformation(biasesReturnVector, biasesArr, biasesArrSize);
}
-// TODO:
+
// input: const MatType & test,
// arma::Row< size_t > & predictedLabels <-
// output:
// description:
-void classify(float *testMatArr, SP_integer testMatSize, SP_integer testMatRowNum, float **predicLabeslsArr, SP_integer *predicLabeslsArrSize)
+// After training, use the weights matrix to classify test, and put the predicted classes in predictedLabels.
+//
+void classify(float *testMatArr, SP_integer testMatSize, SP_integer testMatRowNum,
+ float **predictLabelsArr, SP_integer *predictLabelsArrSize)
{
// convert the Prolog arrays to arma::mat
mat test = convertArrayToMat(testMatArr, testMatSize, testMatRowNum);
// create the ReturnVector
- Row< size_t > predicLabeslsReturnVector;
+ Row< size_t > predictLabelsReturnVector;
- perceptronGlobal.Classify(test, predicLabeslsReturnVector);
-
- // return the Vector lenght
- *predicLabeslsArrSize = predicLabeslsReturnVector.n_elem;
+
+ try
+ {
+ perceptronGlobal.Classify(test, predictLabelsReturnVector);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
- // return the Vector as Array
- *predicLabeslsArr = convertToArray(predicLabeslsReturnVector);
+
+ // return the Vector
+ returnVectorInformation(predictLabelsReturnVector, predictLabelsArr, predictLabelsArrSize);
}
-// TODO:
+
// input: const MatType & data,
// const arma::Row< size_t > & labels,
// const size_t numClasses,
// const arma::rowvec & instanceWeights = arma::rowvec()
// output:
// description:
-void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize, SP_integer numClasses, float *instanceWeightsArr, SP_integer instanceWeightsArrSize)
+// 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.
+//
+void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *labelsArr, SP_integer labelsArrSize,
+ SP_integer numClasses,
+ float *instanceWeightsArr, SP_integer instanceWeightsArrSize)
{
// convert the Prolog arrays to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
- // convert the Prolog arrays to arma::rowvec
+ // convert the Prolog array
Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
-
- // convert the Prolog arrays to arma::rowvec
rowvec instanceWeightsVector = convertArrayToRowvec(instanceWeightsArr, instanceWeightsArrSize);
+
// use the model function
- perceptronGlobal.Train(data, labelsVector, numClasses, instanceWeightsVector);
+ try
+ {
+ perceptronGlobal.Train(data, labelsVector, numClasses, instanceWeightsVector);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
}
-// TODO:
+
// input:
// output: const arma::mat& weights
// description:
+// Get the weight matrix.
+//
void weights(float **weightsMatArr, SP_integer *weightsMatColNum, SP_integer *weightsMatRowNum)
{
// create the ReturnMat
mat weightsReturnMat = perceptronGlobal.Weights();
+
// return the Mat
returnMatrixInformation(weightsReturnMat, weightsMatArr, weightsMatColNum, weightsMatRowNum);
}
diff --git a/src/methods/perceptron/perceptron.pl b/src/methods/perceptron/perceptron.pl
index de6493381d017f21f5bd90c23dc00cfe72f7a727..8b3c4a5f00049abd64c2dd68c1b25a2fcb254c10 100644
--- a/src/methods/perceptron/perceptron.pl
+++ b/src/methods/perceptron/perceptron.pl
@@ -1,10 +1,10 @@
:- module(perceptron, [ initModelNoTrain/3,
- initModelWithTrain/7,
- biases/2,
- classify/5,
- train/8,
- weights/3]).
+ initModelWithTrain/5,
+ biases/1,
+ classify/3,
+ train/5,
+ weights/2]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -31,12 +31,15 @@
%% --Description--
%% Initilizes the perceptron model and its weight matrix but doesnt train it.
%%
-foreign(initModelNoTrain, c, initModelNoTrain(+integer, +integer, +integer)).
+initModelNoTrain(NumClasses, Dimensionality, MaxIterations) :-
+ initModelNoTrainI(NumClasses, Dimensionality, MaxIterations).
+
+foreign(initModelNoTrain, c, initModelNoTrainI(+integer, +integer, +integer)).
%% --Input--
-%% mat data => data(float_array), dataSize, dataRowNum,
-%% vec labels => labels(float_array), labelsSize,
+%% mat data,
+%% vec labels,
%% int numClasses,
%% int maxIterations => 1000
%%
@@ -44,7 +47,12 @@ foreign(initModelNoTrain, c, initModelNoTrain(+integer, +integer, +integer)).
%% --Description--
%% Initilizes the perceptron model and its weight matrix and trains it with the given data.
%%
-foreign(initModelWithTrain, c, initModelWithTrain( +pointer(float_array), +integer, +integer,
+initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, MaxIterations) :-
+ 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).
+
+foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer, +integer)).
@@ -57,46 +65,65 @@ foreign(initModelWithTrain, c, initModelWithTrain( +pointer(float_array), +
%% --Description--
%% Get the biases.
%%
-foreign(biases, c, biases(-pointer(float_array), -integer)).
+biases(BiasesList) :-
+ biasesI(Y, Ysize),
+ convert_float_array_to_list(Y, Ysize, BiasesList).
+
+foreign(biases, c, biasesI(-pointer(float_array), -integer)).
%% --Input--
-%% mat test => test(float_array), testSize, testRowNum,
+%% mat test
%%
%% --Output--
-%% vec predicLabels => predicLabels(float_array), predicLabelsSize
+%% vec predicLabels
%%
%% --Description--
%% After training, use the weights matrix to classify test, and put the predicted classes in predictedLabels.
%%
-foreign(classify, c, classify( +pointer(float_array), +integer, +integer,
+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).
+
+foreign(classify, c, classifyI( +pointer(float_array), +integer, +integer,
-pointer(float_array), -integer)).
%% --Input--
-%% mat data => data(float_array), dataSize, dataRowNum,
-%% vec labels => labels(float_array), labelsSize,
+%% mat data,
+%% vec labels,
%% int numClasses,
-%% vec instWeights => instWeights(float_array), instWeightsSize = arma::rowvec()
+%% vec instWeights
%%
%% --Output--
%% --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.
%%
-foreign(train, c, train(+pointer(float_array), +integer, +integer,
- +pointer(float_array), +integer,
- +integer,
- +pointer(float_array), +integer)).
+train(DataList, DataRows, LabelsList, NumClasses, WeightsList) :-
+ 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)),
+ trainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, Z, Zsize).
+
+foreign(train, c, trainI( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +integer,
+ +pointer(float_array), +integer)).
%% --Input--
%%
%% --Output--
-%% mat weights => weights(float_array), weightsColNum, weightsRowNum,
+%% mat weights
%%
%% --Description--
%% Get the weight matrix.
%%
-foreign(weights, c, weights(-pointer(float_array), -integer, -integer)).
+weights(WeightsList, XCols) :-
+ weightsI(X, XCols, XRows),
+ convert_float_array_to_2d_list(X, XCols, XRows, WeightsList).
+
+foreign(weights, c, weightsI(-pointer(float_array), -integer, -integer)).
%% Defines the functions that get connected from main.cpp
diff --git a/src/methods/perceptron/perceptron_test.pl b/src/methods/perceptron/perceptron_test.pl
index 5562eaee7b879bb400701829fcf6a3ee9cea2339..e6fb253377fab011160714a9bd860c420ac03f78 100644
--- a/src/methods/perceptron/perceptron_test.pl
+++ b/src/methods/perceptron/perceptron_test.pl
@@ -1,3 +1,6 @@
+
+:- module(perceptron_tests, [run_perceptron_tests/0]).
+
:- use_module(library(plunit)).
:- use_module(perceptron).
@@ -6,51 +9,36 @@
reset_Model :-
initModel(1,0,50,0.0001).
-:- begin_tests(lists).
-
-%% alpha tests
-test(alpha_std_init) :-
- reset_Model,
- alpha(0).
-test(alpha_wrong_input, fail) :-
- reset_Model,
- alpha(1).
-test(alpha_after_train, A =:= 9223372036854775808) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize),
- alpha(A).
-
-%% train tests
-test(correct_train) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train, fail) :-
- reset_Model,
- convert_list_to_float_array([],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train2, fail) :-
- reset_Model,
- convert_list_to_float_array([],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train4) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],2, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-:- end_tests(lists).
\ No newline at end of file
+%%
+%% TESTING predicate predicate/10
+%%
+:- begin_tests(predicate).
+
+%% 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, _).
+
+
+%% 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),
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+
+:- end_tests(predicate).
+
+run_perceptron_tests :-
+ run_tests.
+
diff --git a/src/methods/radical/radical.cpp b/src/methods/radical/radical.cpp
index e2e9ce7912f4677915defc8ef8631ee374e59ac9..5aa2aa100189b3e55a7cc930c436921a60d92a34 100644
--- a/src/methods/radical/radical.cpp
+++ b/src/methods/radical/radical.cpp
@@ -18,7 +18,7 @@ using namespace mlpack::radical;
// Global Variable of the Radical object so it can be accessed from all functions
Radical radicalObj;
-// TODO:
+
// input: const double noiseStdDev = 0.175,
// const size_t replicates = 30,
// const size_t angles = 150,
@@ -26,18 +26,30 @@ Radical radicalObj;
// const size_t m = 0
// output:
// description:
+// Initilizes the radical model.
+//
void initModel(double noiseStdDev, SP_integer replicates, SP_integer angles, SP_integer sweeps, SP_integer m)
{
- radicalObj = Radical(noiseStdDev, replicates, angles, sweeps, m);
+ try
+ {
+ radicalObj = Radical(noiseStdDev, replicates, angles, sweeps, m);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ }
+
}
-// TODO:
+
// input: const arma::mat & matX,
// arma::mat & matY,
// arma::mat & matW,
// util::Timers & timers = IO::GetTimers()
// output:
// description:
+// Run RADICAL.
+//
void doRadical(float *xMatArr, SP_integer xMatSize, SP_integer xMatRowNum,
float **yMatArr, SP_integer *yMatColNum, SP_integer *yMatRowNum,
float **wMatArr, SP_integer *wMatColNum, SP_integer *wMatRowNum)
@@ -50,7 +62,14 @@ void doRadical(float *xMatArr, SP_integer xMatSize, SP_integer xMatRowNum,
mat wReturnMat;
- radicalObj.DoRadical(x, yReturnMat, wReturnMat);
+ try
+ {
+ radicalObj.DoRadical(x, yReturnMat, wReturnMat);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ }
// return the Matrices
@@ -58,16 +77,26 @@ void doRadical(float *xMatArr, SP_integer xMatSize, SP_integer xMatRowNum,
returnMatrixInformation(wReturnMat, wMatArr, wMatColNum, wMatRowNum);
}
-// TODO:
+
// input: const arma::mat & matX,
// util::Timers & timers = IO::GetTimers()
//
// output: double output
// description:
+// Two-dimensional version of RADICAL.
+//
double doRadical2D(float *xMatArr, SP_integer xMatSize, SP_integer xMatRowNum)
{
// convert the Prolog array to arma::mat
mat x = convertArrayToMat(xMatArr, xMatSize, xMatRowNum);
- return radicalObj.DoRadical2D(x);
+
+ try
+ {
+ return radicalObj.DoRadical2D(x);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ }
}
\ No newline at end of file
diff --git a/src/methods/radical/radical.pl b/src/methods/radical/radical.pl
index e123e847f1decdc923a9c4cc7c4504a9b80c5b3b..4663ceffc820f13db7822119713d90caee9614a1 100644
--- a/src/methods/radical/radical.pl
+++ b/src/methods/radical/radical.pl
@@ -1,7 +1,7 @@
:- module(radical, [ initModel/5,
- doRadical/9,
- doRadical2D/4]).
+ doRadical/6,
+ doRadical2D/3]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -31,7 +31,10 @@
%% --Description--
%% Initilizes the radical model.
%%
-foreign(initModel, c, initModel(+float32, +integer, +integer, +integer, +integer)).
+initModel(NoiseStdDev, Replicates, Angles, Sweeps, M) :-
+ initModelI(NoiseStdDev, Replicates, Angles, Sweeps, M).
+
+foreign(initModel, c, initModelI(+float32, +integer, +integer, +integer, +integer)).
%% --Input--
@@ -44,7 +47,13 @@ foreign(initModel, c, initModel(+float32, +integer, +integer, +integer, +integer
%% --Description--
%% Run RADICAL.
%%
-foreign(doRadical, c, doRadical(+pointer(float_array), +integer, +integer,
+doRadical(DataList, DataRows, YList, YCols, WList, ZCols) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ doRadicalI(X, Xsize, Xrows, Y, YCols, YRows, Z, ZCols, ZRows),
+ convert_float_array_to_2d_list(Y, YCols, YRows, YList),
+ convert_float_array_to_2d_list(Z, ZCols, ZRows, WList).
+
+foreign(doRadical, c, doRadicalI(+pointer(float_array), +integer, +integer,
-pointer(float_array), -integer, -integer,
-pointer(float_array), -integer, -integer)).
@@ -58,7 +67,11 @@ foreign(doRadical, c, doRadical(+pointer(float_array), +integer, +integer,
%% --Description--
%% Two-dimensional version of RADICAL.
%%
-foreign(doRadical2D, c, doRadical2D( +pointer(float_array), +integer, +integer,
+doRadical2D(DataList, DataRows, Result) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ doRadical2DI(X, Xsize, Xrows, Result).
+
+foreign(doRadical2D, c, doRadical2DI( +pointer(float_array), +integer, +integer,
[-float32])).
diff --git a/src/methods/radical/radical_test.pl b/src/methods/radical/radical_test.pl
index 8b05f87178e42f84cc4aa019a4db9fcb9c8194dd..54e9328b56c6f35aa411a0dec9d0dcd8e41a5a21 100644
--- a/src/methods/radical/radical_test.pl
+++ b/src/methods/radical/radical_test.pl
@@ -1,3 +1,6 @@
+
+:- module(radical_tests, [run_radical_tests/0]).
+
:- use_module(library(plunit)).
:- use_module(radical).
@@ -6,51 +9,36 @@
reset_Model :-
initModel(1,0,50,0.0001).
-:- begin_tests(lists).
-
-%% alpha tests
-test(alpha_std_init) :-
- reset_Model,
- alpha(0).
-test(alpha_wrong_input, fail) :-
- reset_Model,
- alpha(1).
-test(alpha_after_train, A =:= 9223372036854775808) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize),
- alpha(A).
-
-%% train tests
-test(correct_train) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train, fail) :-
- reset_Model,
- convert_list_to_float_array([],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train2, fail) :-
- reset_Model,
- convert_list_to_float_array([],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train4) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],2, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-:- end_tests(lists).
\ No newline at end of file
+%%
+%% TESTING predicate predicate/10
+%%
+:- begin_tests(predicate).
+
+%% 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, _).
+
+
+%% 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),
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+
+:- end_tests(predicate).
+
+run_radical_tests :-
+ run_tests.
+
diff --git a/src/methods/random_forest/random_forest.cpp b/src/methods/random_forest/random_forest.cpp
index cd23eacee8229a82e4983dd68a3009192e66f2c0..f831a2a03072551a059dee422473fc2338aab370 100644
--- a/src/methods/random_forest/random_forest.cpp
+++ b/src/methods/random_forest/random_forest.cpp
@@ -18,16 +18,18 @@ using namespace mlpack::tree;
// Global Variable of the RandomForest object so it can be accessed from all functions
RandomForest randomForest;
-// TODO:
+
// input:
// output:
// description:
+// Initilizes the model without training it.
+//
void initModelNoTrain(SP_integer)
{
randomForest = RandomForest();
}
-// TODO:
+
// input: const MatType & dataset,
// const arma::Row< size_t > & labels,
// const size_t numClasses,
@@ -38,19 +40,31 @@ void initModelNoTrain(SP_integer)
// DimensionSelectionType dimensionSelector = DimensionSelectionType()
// output:
// description:
-void initModelWithTrainNoWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize,
+// Initilizes the model and trains it but does not apply weights to it.
+//
+void initModelWithTrainNoWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *labelsArr, SP_integer labelsArrSize,
SP_integer numClasses, SP_integer numTrees, SP_integer minimumLeafSize, double minimumGainSplit, SP_integer maximumDepth)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
- // convert the Prolog arrays to arma::rowvec
+ // convert the Prolog array to arma::rowvec
Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
- randomForest = RandomForest(data, labelsVector, numClasses, numTrees, minimumLeafSize, minimumGainSplit, maximumDepth);
+
+ try
+ {
+ randomForest = RandomForest(data, labelsVector, numClasses, numTrees, minimumLeafSize, minimumGainSplit, maximumDepth);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
}
-// TODO:
+
// input: onst MatType & dataset,
// const arma::Row< size_t > & labels,
// const size_t numClasses,
@@ -62,29 +76,44 @@ void initModelWithTrainNoWeights(float *dataMatArr, SP_integer dataMatSize, SP_i
// DimensionSelectionType dimensionSelector = DimensionSelectionType()
// output:
// description:
-void initModelWithTrainWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize, SP_integer numClasses,
- float *weightsArr, SP_integer weightsArrSize, SP_integer numTrees, SP_integer minimumLeafSize, double minimumGainSplit, SP_integer maximumDepth)
+// Initilizes the model, trains it and applies weights to it.
+//
+void initModelWithTrainWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *labelsArr, SP_integer labelsArrSize,
+ SP_integer numClasses,
+ float *weightsArr, SP_integer weightsArrSize,
+ SP_integer numTrees, SP_integer minimumLeafSize, double minimumGainSplit, SP_integer maximumDepth)
{
// convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
-
- // convert the Prolog array to arma::rowvec
+ // convert the Prolog array to arma::Row< size_t >
Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
-
// convert the Prolog array to arma::rowvec
rowvec weightsVector = convertArrayToRowvec(weightsArr, weightsArrSize);
- randomForest = RandomForest(data, labelsVector, numClasses, weightsVector, numTrees, minimumLeafSize, minimumGainSplit, maximumDepth);
+ try
+ {
+ randomForest = RandomForest(data, labelsVector, numClasses, weightsVector, numTrees, minimumLeafSize, minimumGainSplit, maximumDepth);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
}
-// TODO:
+
// input: const VecType & point,
// size_t & prediction <-,
// arma::vec & probabilities <-
// output:
// description:
-void classifyPoint(float *pointArr, SP_integer pointArrSize, SP_integer *prediction, float **probsArr, SP_integer *probsArrSize)
+// Predict the class of the given point and return the predicted class probabilities for each class.
+//
+void classifyPoint(float *pointArr, SP_integer pointArrSize,
+ SP_integer *prediction,
+ float **probsArr, SP_integer *probsArrSize)
{
// convert the Prolog array to arma::rowvec
rowvec pointVector = convertArrayToRowvec(pointArr, pointArrSize);
@@ -94,61 +123,75 @@ void classifyPoint(float *pointArr, SP_integer pointArrSize, SP_integer *predict
size_t predicReturn;
- randomForest.Classify(pointVector, predicReturn, probsReturnVector);
+
+ try
+ {
+ randomForest.Classify(pointVector, predicReturn, probsReturnVector);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
+
// return the predic value
*prediction = predicReturn;
-
- // return the Vector lenght
- *probsArrSize = probsReturnVector.n_elem;
-
- // return the Vector as Array
- *probsArr = convertToArray(probsReturnVector);
-
+
+ // return the Vector
+ returnVectorInformation(probsReturnVector, probsArr, probsArrSize);
}
-// TODO:
+
// input: const MatType & data,
// arma::Row< size_t > & predictions <-,
// arma::mat & probabilities <-
// output:
// description:
-void classifyMatrix(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float **predicArr, SP_integer *predicArrSize, float **probsMatArr, SP_integer *probsMatColNum, SP_integer *probsMatRowNum)
+// Predict the classes of each point in the given dataset, also returning the predicted class probabilities for each point.
+//
+void classifyMatrix(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float **predicArr, SP_integer *predicArrSize,
+ float **probsMatArr, SP_integer *probsMatColNum, SP_integer *probsMatRowNum)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
// create the ReturnVector
Row< size_t > predicReturnVector;
-
// create the ReturnMat
mat probsReturnMat;
- randomForest.Classify(data, predicReturnVector, probsReturnMat);
-
- // return the Vector lenght
- *predicArrSize = predicReturnVector.n_elem;
-
- // return the Vector as Array
- *predicArr = convertToArray(predicReturnVector);
-
- // return the Matrix dimensions
- *probsMatColNum = probsReturnMat.n_cols;
- *probsMatRowNum = probsReturnMat.n_rows;
+
+ try
+ {
+ randomForest.Classify(data, predicReturnVector, probsReturnMat);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
- // return the Matrix as one long Array
- *probsMatArr = convertToArray(probsReturnMat);
+
+ // return the Vector
+ returnVectorInformation(predicReturnVector, predicArr, predicArrSize);
+ // return the Matrix
+ returnMatrixInformation(probsReturnMat, probsMatArr, probsMatColNum, probsMatRowNum);
}
-// TODO:
+
// input:
// output: const size_t number of trees
// description:
+// Get the number of trees in the forest.
+//
SP_integer numTrees()
{
return randomForest.NumTrees();
}
+
// TODO: doesnt accept warmStart parameter
//
// input: const MatType & data,
@@ -163,18 +206,30 @@ SP_integer numTrees()
//
// output: double average entropy of all trees trained
// description:
-double trainNoWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize,
+// Train the random forest on the given labeled training data with the given number of trees.
+//
+double trainNoWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *labelsArr, SP_integer labelsArrSize,
SP_integer numClasses, SP_integer numTrees, SP_integer minimumLeafSize, double minimumGainSplit, SP_integer maximumDepth)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
-
- // convert the Prolog arrays to arma::rowvec
+ // convert the Prolog array to arma::rowvec
Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
+
- return randomForest.Train(data, labelsVector, numClasses, numTrees, minimumLeafSize, minimumGainSplit, maximumDepth);
+ try
+ {
+ return randomForest.Train(data, labelsVector, numClasses, numTrees, minimumLeafSize, minimumGainSplit, maximumDepth);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return 0.0;
+ }
}
+
// TODO: doesnt accept warmStart parameter
//
// input: const MatType & data,
@@ -190,18 +245,26 @@ double trainNoWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer data
//
// output: double average entropy of all trees trained
// description:
+// Train the random forest on the given weighted labeled training data with the given number of trees.
+//
double trainWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize, SP_integer numClasses,
float *weightsArr, SP_integer weightsArrSize, SP_integer numTrees, SP_integer minimumLeafSize, double minimumGainSplit, SP_integer maximumDepth)
{
// convert the Prolog arrays to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
-
// convert the Prolog arrays to arma::rowvec
Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
-
// convert the Prolog arrays to arma::rowvec
rowvec weightsVector = convertArrayToRowvec(weightsArr, weightsArrSize);
- return randomForest.Train(data, labelsVector, numClasses, weightsVector, numTrees, minimumLeafSize, minimumGainSplit, maximumDepth);
+ try
+ {
+ return randomForest.Train(data, labelsVector, numClasses, weightsVector, numTrees, minimumLeafSize, minimumGainSplit, maximumDepth);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return 0.0;
+ }
}
diff --git a/src/methods/random_forest/random_forest.pl b/src/methods/random_forest/random_forest.pl
index 914d52d884ad0ffe31d7f15f580173a54504957f..a077c465b0e62e9e762453adb8e748524a561d2a 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/1,
- initModelWithTrainNoWeights/10,
- initModelWithTrainWithWeights/12,
- classifyPoint/5,
- classifyMatrix/8,
+:- module(random_forest, [ initModelNoTrain/0,
+ initModelWithTrainNoWeights/8,
+ initModelWithTrainWithWeights/9,
+ classifyPoint/3,
+ classifyMatrix/5,
numTrees/1,
- trainNoWeights/11,
- trainWithWeights/13]).
+ trainNoWeights/9,
+ trainWithWeights/10]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -32,7 +32,10 @@
%% --Description--
%% Initilizes the model without training it.
%%
-foreign(initModelNoTrain, c, initModelNoTrain(+integer)).
+initModelNoTrain :-
+ initModelNoTrainI(0).
+
+foreign(initModelNoTrain, c, initModelNoTrainI(+integer)).
%% --Input--
@@ -49,7 +52,12 @@ foreign(initModelNoTrain, c, initModelNoTrain(+integer)).
%% --Description--
%% Initilizes the model and trains it but does not apply weights to it.
%%
-foreign(initModelWithTrainNoWeights, c, initModelWithTrainNoWeights(
+initModelWithTrainNoWeights(DataList, DataRows, LabelsList, NumClasses, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+ convert_list_to_float_array(LabelsList, array(Ysize, Y)),
+ initModelWithTrainNoWeightsI(X, Xsize, Xrownum, Y, Ysize, NumClasses, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth).
+
+foreign(initModelWithTrainNoWeights, c, initModelWithTrainNoWeightsI(
+pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer, +integer, +integer, +float32, +integer)).
@@ -70,7 +78,13 @@ foreign(initModelWithTrainNoWeights, c, initModelWithTrainNoWeights(
%% --Description--
%% Initilizes the model, trains it and applies weights to it.
%%
-foreign(initModelWithTrainWithWeights, c, initModelWithTrainWithWeights(
+initModelWithTrainWithWeights(DataList, DataRows, LabelsList, NumClasses, WeightsList, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth) :-
+ 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)),
+ initModelWithTrainWithWeightsI(X, Xsize, Xrownum, Y, Ysize, NumClasses, Z, Zsize, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth).
+
+foreign(initModelWithTrainWithWeights, c, initModelWithTrainWithWeightsI(
+pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer,
@@ -89,9 +103,14 @@ foreign(initModelWithTrainWithWeights, c, initModelWithTrainWithWeights(
%% 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.
%%
-foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer,
- -integer,
- -pointer(float_array), -integer)).
+classifyPoint(DataList, Prediction, AssignList) :-
+ convert_list_to_float_array(DataList, array(Xsize, X)),
+ classifyPointI(X, Xsize, Prediction, Y, Ysize),
+ convert_float_array_to_list(Y, Ysize, AssignList).
+
+foreign(classifyPoint, c, classifyPointI( +pointer(float_array), +integer,
+ -integer,
+ -pointer(float_array), -integer)).
%% --Input--
@@ -105,7 +124,13 @@ foreign(classifyPoint, c, classifyPoint(+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.
%%
-foreign(classifyMatrix, c, classifyMatrix( +pointer(float_array), +integer, +integer,
+classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ classifyMatrixI(X, Xsize, Xrows, Y, Ysize, Z, ZCols, ZRows),
+ convert_float_array_to_list(Y, Ysize, PredictionList),
+ convert_float_array_to_2d_list(Z, ZCols, ZRows, ProbsList).
+
+foreign(classifyMatrix, c, classifyMatrixI( +pointer(float_array), +integer, +integer,
-pointer(float_array), -integer,
-pointer(float_array), -integer, -integer)).
@@ -118,7 +143,9 @@ foreign(classifyMatrix, c, classifyMatrix( +pointer(float_array), +integer,
%% --Description--
%% Get the number of trees in the forest.
%%
-foreign(numTrees, c, numTrees([-integer])).
+numTrees(NumTrees) :-
+ numTreesI(NumTrees).
+foreign(numTrees, c, numTreesI([-integer])).
%% --Input--
@@ -137,7 +164,12 @@ foreign(numTrees, c, numTrees([-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.
%%
-foreign(trainNoWeights, c, trainNoWeights( +pointer(float_array), +integer, +integer,
+trainNoWeights(DataList, DataRows, LabelsList, NumClasses, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+ convert_list_to_float_array(LabelsList, array(Ysize, Y)),
+ trainNoWeightsI(X, Xsize, Xrownum, Y, Ysize, NumClasses, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy).
+
+foreign(trainNoWeights, c, trainNoWeightsI( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer, +integer, +integer, +float32, +integer,
[-float32])).
@@ -160,7 +192,13 @@ foreign(trainNoWeights, c, trainNoWeights( +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.
%%
-foreign(trainWithWeights, c, trainWithWeights( +pointer(float_array), +integer, +integer,
+trainWithWeights(DataList, DataRows, LabelsList, NumClasses, WeightsList, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy) :-
+ 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)),
+ trainWithWeightsI(X, Xsize, Xrownum, Y, Ysize, NumClasses, Z, Zsize, NumTrees, MinimumLeafSize, MinimumGainSplit, MaximumDepth, Entropy).
+
+foreign(trainWithWeights, c, trainWithWeightsI( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer,
+pointer(float_array), +integer,
diff --git a/src/methods/random_forest/random_forest_test.pl b/src/methods/random_forest/random_forest_test.pl
index 82f0e14e20f00f4c173bfc55e83a2821eb320301..908724a554e9f7aa845d36bd13d7a995651b4adb 100644
--- a/src/methods/random_forest/random_forest_test.pl
+++ b/src/methods/random_forest/random_forest_test.pl
@@ -1,3 +1,6 @@
+
+:- module(random_forest_tests, [run_random_forest_tests/0]).
+
:- use_module(library(plunit)).
:- use_module(random_forest).
@@ -6,51 +9,36 @@
reset_Model :-
initModel(1,0,50,0.0001).
-:- begin_tests(lists).
-
-%% alpha tests
-test(alpha_std_init) :-
- reset_Model,
- alpha(0).
-test(alpha_wrong_input, fail) :-
- reset_Model,
- alpha(1).
-test(alpha_after_train, A =:= 9223372036854775808) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize),
- alpha(A).
-
-%% train tests
-test(correct_train) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train, fail) :-
- reset_Model,
- convert_list_to_float_array([],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train2, fail) :-
- reset_Model,
- convert_list_to_float_array([],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train4) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],2, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-:- end_tests(lists).
\ No newline at end of file
+%%
+%% TESTING predicate predicate/10
+%%
+:- begin_tests(predicate).
+
+%% 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, _).
+
+
+%% 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),
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+
+:- end_tests(predicate).
+
+run_random_forest_tests :-
+ run_tests.
+
diff --git a/src/methods/softmax_regression/softmax_regression.cpp b/src/methods/softmax_regression/softmax_regression.cpp
index f369c2c4413966226baee6ce119e03176f2c70e7..ddf2c2ce7134d7c30519d4031633188539336155 100644
--- a/src/methods/softmax_regression/softmax_regression.cpp
+++ b/src/methods/softmax_regression/softmax_regression.cpp
@@ -18,19 +18,21 @@ using namespace mlpack::regression;
// Global Variable of the SoftmaxRegression object so it can be accessed from all functions
SoftmaxRegression softmaxRegression;
-// TODO:
+
// input: const size_t inputSize = 0,
// const size_t numClasses = 0,
// const bool fitIntercept = false//
// output:
// description:
+// Initializes the softmax_regression model without training.
+//
void initModelNoTrain(SP_integer inputSize, SP_integer numClasses,
SP_integer fitIntercept)
{
softmaxRegression = SoftmaxRegression(inputSize, numClasses, (fitIntercept == 1));
}
-// TODO:
+
// input: const arma::mat & data,
// const arma::Row< size_t > & labels,
// const size_t numClasses,
@@ -39,36 +41,61 @@ void initModelNoTrain(SP_integer inputSize, SP_integer numClasses,
// OptimizerType optimizer = OptimizerType()
// output:
// description:
-void initModelWithTrain(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize, SP_integer numClasses, double lambda, SP_integer fitIntercept)
+// Initializes the softmax_regression model and trains it.
+//
+void initModelWithTrain(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *labelsArr, SP_integer labelsArrSize,
+ SP_integer numClasses, double lambda,
+ SP_integer fitIntercept)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
-
- // convert the Prolog arrays to arma::rowvec
+ // convert the Prolog array to arma::rowvec
Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
-
- softmaxRegression = SoftmaxRegression(data, labelsVector, numClasses, lambda, (fitIntercept == 1));
+
+
+ try
+ {
+ softmaxRegression = SoftmaxRegression(data, labelsVector, numClasses, lambda, (fitIntercept == 1));
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ }
}
-// TODO:
+
// input: const VecType & point
//
// output: size_t predicted label of point
// description:
+// Classify the given point.
+//
SP_integer classifyPoint(float *pointArr, SP_integer pointArrSize)
{
// convert the Prolog arrays to arma::rowvec
rowvec pointVector = convertArrayToRowvec(pointArr, pointArrSize);
- return softmaxRegression.Classify(pointVector);
+
+ try
+ {
+ return softmaxRegression.Classify(pointVector);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return 0;
+ }
}
-// TODO:
+
// input: const arma::mat & dataset,
// arma::Row< size_t > & labels <-,
// arma::mat & probabilities <-
// output:
// description:
+// Classify the given points, returning class probabilities and predicted class label for each point.
+//
void classifyMatrix(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float **labelsArr, SP_integer *labelsArrSize, float **probsMatArr, SP_integer *probsMatColNum, SP_integer *probsMatRowNum)
{
// convert the Prolog arrays to arma::mat
@@ -76,70 +103,82 @@ void classifyMatrix(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMa
// create the ReturnVector
Row< size_t > labelsReturnVector;
-
// create the ReturnMat
mat probsReturnMat;
- softmaxRegression.Classify(data, labelsReturnVector, probsReturnMat);
-
- // return the Vector lenght
- *labelsArrSize = labelsReturnVector.n_elem;
-
- // return the Vector as Array
- *labelsArr = convertToArray(labelsReturnVector);
+
+ try
+ {
+ softmaxRegression.Classify(data, labelsReturnVector, probsReturnMat);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return;
+ }
- // return the Matrix dimensions
- *probsMatColNum = probsReturnMat.n_cols;
- *probsMatRowNum = probsReturnMat.n_rows;
- // return the Matrix as one long Array
- *probsMatArr = convertToArray(probsReturnMat);
+ // return the Vector
+ returnVectorInformation(labelsReturnVector, labelsArr, labelsArrSize);
+ // return the Matrix
+ returnMatrixInformation(probsReturnMat, probsMatArr, probsMatColNum, probsMatRowNum);
}
-// TODO:
+
// input: const arma::mat & testData,
// const arma::Row< size_t > & labels
//
// output: double accuracy
// description:
+// Computes accuracy of the learned model given the feature data and the labels associated with each data point.
+//
double computeAccuracy(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
-
- // convert the Prolog arrays to arma::rowvec
+ // convert the Prolog array to arma::rowvec
Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
- return softmaxRegression.ComputeAccuracy(data, labelsVector);
+
+ try
+ {
+ return softmaxRegression.ComputeAccuracy(data, labelsVector);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return 0.0;
+ }
}
-// TODO:
+
// input:
// output: size_t
// description:
+// Gets the features size of the training data.
+//
SP_integer featureSize()
{
return softmaxRegression.FeatureSize();
}
-// TODO:
+
// input:
// output: arma::mat&
// description:
+// Get the model parameters.
+//
void parameters(float **parametersMatArr, SP_integer *parametersMatColNum, SP_integer *parametersMatRowNum)
{
// create the ReturnMat
mat parametersReturnMat = softmaxRegression.Parameters();
- // return the Matrix dimensions
- *parametersMatColNum = parametersReturnMat.n_cols;
- *parametersMatRowNum = parametersReturnMat.n_rows;
-
- // return the Matrix as one long Array
- *parametersMatArr = convertToArray(parametersReturnMat);
+
+ // return the Matrix
+ returnMatrixInformation(parametersReturnMat, parametersMatArr, parametersMatColNum, parametersMatRowNum);
}
-// TODO:
+
// input: const arma::mat & data,
// const arma::Row< size_t > & labels,
// const size_t numClasses,
@@ -147,13 +186,23 @@ void parameters(float **parametersMatArr, SP_integer *parametersMatColNum, SP_in
//
// output: double objective value of final point
// description:
+// Trains the softmax regression model with the given training data.
+//
double train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize, SP_integer numClasses)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
-
- // convert the Prolog arrays to arma::rowvec
+ // convert the Prolog array to arma::rowvec
Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
- return softmaxRegression.Train(data, labelsVector, numClasses);
+
+ try
+ {
+ return softmaxRegression.Train(data, labelsVector, numClasses);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
+ return 0.0;
+ }
}
diff --git a/src/methods/softmax_regression/softmax_regression.pl b/src/methods/softmax_regression/softmax_regression.pl
index 85443c33e539c0141064c62447c99ae8b29c1d24..4a34dd6b71d76ebf92836516bbb46655fca2c144 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/8,
- classifyPoint/3,
- classifyMatrix/8,
- computeAccuracy/6,
+ initModelWithTrain/6,
+ classifyPoint/2,
+ classifyMatrix/5,
+ computeAccuracy/4,
featureSize/1,
- parameters/3,
- train/7]).
+ parameters/2,
+ train/5]).
%% requirements of library(struct)
:- load_files(library(str_decl),
@@ -35,7 +35,10 @@
%% Initializes the softmax_regression model without training.
%% Be sure to use Train before calling Classif or ComputeAccuracy, otherwise the results may be meaningless.
%%
-foreign(initModelNoTrain, c, initModelNoTrain( +integer, +integer,
+initModelNoTrain(InputSize, NumClasses, FitIntercept) :-
+ initModelNoTrainI(InputSize, NumClasses, FitIntercept).
+
+foreign(initModelNoTrain, c, initModelNoTrainI( +integer, +integer,
+integer)).
@@ -51,7 +54,12 @@ foreign(initModelNoTrain, c, initModelNoTrain( +integer, +integer,
%% --Description--
%% Initializes the softmax_regression model and trains it.
%%
-foreign(initModelWithTrain, c, initModelWithTrain( +pointer(float_array), +integer, +integer,
+initModelWithTrain(DataList, DataRows, LabelsList, NumClasses, Lambda, FitIntercept) :-
+ 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, Lambda, FitIntercept).
+
+foreign(initModelWithTrain, c, initModelWithTrainI( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
+integer, +float32,
+integer)).
@@ -66,7 +74,11 @@ foreign(initModelWithTrain, c, initModelWithTrain( +pointer(float_array), +
%% --Description--
%% Classify the given point.
%%
-foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer,
+classifyPoint(DataList, Prediction) :-
+ convert_list_to_float_array(DataList, array(Xsize, X)),
+ classifyPointI(X, Xsize, Prediction).
+
+foreign(classifyPoint, c, classifyPointI(+pointer(float_array), +integer,
[-integer])).
@@ -80,7 +92,13 @@ foreign(classifyPoint, c, classifyPoint(+pointer(float_array), +integer,
%% --Description--
%% Classify the given points, returning class probabilities and predicted class label for each point.
%%
-foreign(classifyMatrix, c, classifyMatrix( +pointer(float_array), +integer, +integer,
+classifyMatrix(DataList, DataRows, PredictionList, ProbsList, ZCols) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrows, X)),
+ classifyMatrixI(X, Xsize, Xrows, Y, Ysize, Z, ZCols, ZRows),
+ convert_float_array_to_list(Y, Ysize, PredictionList),
+ convert_float_array_to_2d_list(Z, ZCols, ZRows, ProbsList).
+
+foreign(classifyMatrix, c, classifyMatrixI( +pointer(float_array), +integer, +integer,
-pointer(float_array), -integer,
-pointer(float_array), -integer, -integer)).
@@ -96,7 +114,11 @@ foreign(classifyMatrix, c, classifyMatrix( +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.
%%
-foreign(computeAccuracy, c, computeAccuracy( +pointer(float_array), +integer, +integer,
+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).
+foreign(computeAccuracy, c, computeAccuracyI( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
[-float32])).
@@ -109,7 +131,10 @@ foreign(computeAccuracy, c, computeAccuracy( +pointer(float_array), +integer,
%% --Description--
%% Gets the features size of the training data.
%%
-foreign(featureSize, c, featureSize([-integer])).
+featureSize(FeatureSize) :-
+ featureSizeI(FeatureSize).
+
+foreign(featureSize, c, featureSizeI([-integer])).
%% --Input--
%%
@@ -119,7 +144,11 @@ foreign(featureSize, c, featureSize([-integer])).
%% --Description--
%% Get the model parameters.
%%
-foreign(parameters, c, parameters(-pointer(float_array), -integer, -integer)).
+parameters(PraametersList, XCols) :-
+ parametersI(X, XCols, XRows),
+ convert_float_array_to_2d_list(X, XCols, XRows, PraametersList).
+
+foreign(parameters, c, parametersI(-pointer(float_array), -integer, -integer)).
%% --Input--
@@ -133,10 +162,15 @@ foreign(parameters, c, parameters(-pointer(float_array), -integer, -integer)).
%% --Description--
%% Trains the softmax regression model with the given training data.
%%
-foreign(train, c, train(+pointer(float_array), +integer, +integer,
- +pointer(float_array), +integer,
- +integer,
- [-float32])).
+train(DataList, DataRows, LabelsList, NumClasses, FinalValue) :-
+ convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+ convert_list_to_float_array(LabelsList, array(Ysize, Y)),
+ trainI(X, Xsize, Xrownum, Y, Ysize, NumClasses, FinalValue).
+
+foreign(train, c, trainI( +pointer(float_array), +integer, +integer,
+ +pointer(float_array), +integer,
+ +integer,
+ [-float32])).
%% Defines the functions that get connected from main.cpp
diff --git a/src/methods/softmax_regression/softmax_regression_test.pl b/src/methods/softmax_regression/softmax_regression_test.pl
index b09ee2acd83ea906f80effcd092b436e099b684d..2ed582c0e0e8d404f64e03c990cddb4598adc118 100644
--- a/src/methods/softmax_regression/softmax_regression_test.pl
+++ b/src/methods/softmax_regression/softmax_regression_test.pl
@@ -1,3 +1,6 @@
+
+:- module(softmax_regression_tests, [run_softmax_regression_tests/0]).
+
:- use_module(library(plunit)).
:- use_module(softmax_regression).
@@ -6,51 +9,36 @@
reset_Model :-
initModel(1,0,50,0.0001).
-:- begin_tests(lists).
-
-%% alpha tests
-test(alpha_std_init) :-
- reset_Model,
- alpha(0).
-test(alpha_wrong_input, fail) :-
- reset_Model,
- alpha(1).
-test(alpha_after_train, A =:= 9223372036854775808) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize),
- alpha(A).
-
-%% train tests
-test(correct_train) :-
- reset_Model,
- convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train, fail) :-
- reset_Model,
- convert_list_to_float_array([],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train2, fail) :-
- reset_Model,
- convert_list_to_float_array([],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2],0, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train3, fail) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],3, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-test(false_train4) :-
- reset_Model,
- convert_list_to_float_array([1,2,44,3],2, array(Xsize, Xrownum, X)),
- convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
- train(X,Xsize, Xrownum,Y, Ysize).
-:- end_tests(lists).
\ No newline at end of file
+%%
+%% TESTING predicate predicate/10
+%%
+:- begin_tests(predicate).
+
+%% 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, _).
+
+
+%% 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),
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris2.csv', read, File),
+ take_csv_row(File, skipFirstRow,10, Data),
+ train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 2, perceptron, 50, 0.0001, Error).
+
+:- end_tests(predicate).
+
+run_softmax_regression_tests :-
+ run_tests.
+
diff --git a/test_all.pl b/test_all.pl
index d9e4a0a4b87f355e0b2a4f41f0ac8a74ed0bf5fb..2b45b697f29b8b36e5a08d19df96a9c9e7595ecb 100644
--- a/test_all.pl
+++ b/test_all.pl
@@ -1,6 +1,4 @@
-:- use_module('src/methods/newMethod/newMethod_test.pl').
-
:- use_module('src/methods/adaboost/adaboost_test.pl').
:- use_module('src/methods/approx_kfn/approx_kfn_test.pl').
@@ -15,7 +13,7 @@
:- use_module('src/methods/fastmks/fastmks_test.pl').
-:- use_module('src/methods/hoefding_tree/hoefding_tree_test.pl').
+:- use_module('src/methods/hoeffding_tree/hoeffding_tree_test.pl').
:- use_module('src/methods/kde/kde_test.pl').
@@ -71,7 +69,7 @@ run :-
run_decision_tree_tests,
run_emst_tests,
run_fastmks_tests,
- run_hoefding_tree_tests,
+ run_hoeffding_tree_tests,
run_kde_tests,
run_kernel_pca_tests,
run_kfn_tests,