diff --git a/src/methods/lars/lars.cpp b/src/methods/lars/lars.cpp
index b16291ec2fe743685a128e5181a57ce2c46e4555..4c93aa3fe5f7a2aeee4c38b00c4602f1d9b46d87 100644
--- a/src/methods/lars/lars.cpp
+++ b/src/methods/lars/lars.cpp
@@ -24,108 +24,47 @@ bool isModelTrained = false;
// const double lambda2,
// const double tolerance
// output:
-void initModelNoDataNoGram(SP_integer useCholesky,
- double lambda1, double lambda2, double tol)
+void initAndTrainModel(SP_integer useCholesky,
+ double lambda1, double lambda2, double tol,
+ float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *responsesArr, SP_integer responsesArrSize,
+ SP_integer transposeData, double *error)
{
+ bool transpose = (transposeData == 1);
regressor = new LARS((useCholesky == 1), lambda1, lambda2, tol);
- isModelTrained = false;
-}
-
-// input: const bool useCholesky,
-// const arma::mat & gramMatrix,
-// const double lambda1,
-// const double lambda2,
-// const double tolerance
-// output:
-void initModelNoDataWithGram(SP_integer useCholesky,
- float *gramArr, SP_integer gramSize, SP_integer gramRowNum,
- double lambda1, double lambda2, double tol)
-{
- mat gramMatrix = convertArrayToMat(gramArr, gramSize, gramRowNum);
-
- regressor = new LARS((useCholesky == 1), gramMatrix, lambda1, lambda2, tol);
- isModelTrained = false;
-}
-// input: const arma::mat & dataMatrix,
-// const arma::rowvec & responses,
-// const bool transposeData,
-// const bool useCholesky,
-// const double lambda1,
-// const double lambda2,
-// const double tolerance
-// output:
-void initModelWithDataNoGram(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
- float *responsesArr, SP_integer responsesArrSize,
- SP_integer transposeData,
- SP_integer useCholesky,
- double lambda1, double lambda2, double tol)
-{
// convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
// check if labels fit the data
- if (data.n_cols != responsesArrSize)
- {
- raisePrologSystemExeption("The number of data points does not match the number of labels!");
- return;
- }
- // convert the Prolog array to arma::rowvec
- rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
-
-
-
- try
+ if (!transpose && data.n_cols != responsesArrSize)
{
- regressor = new LARS(data, responsesVector, (transposeData == 1), (useCholesky == 1), lambda1, lambda2, tol);
+ raisePrologSystemExeption("The number of data points does not match the number of labels!");
+ return;
}
- catch(const std::exception& e)
+ if (transpose && data.n_rows != responsesArrSize)
{
- raisePrologSystemExeption(e.what());
+ raisePrologSystemExeption("The number of data points does not match the number of labels!");
return;
}
- isModelTrained = true;
-}
-
-// input: const arma::mat & dataMatrix,
-// const arma::rowvec & responses,
-// const bool transposeData,
-// const bool useCholesky,
-// const arma::mat & gramMatrix,
-// const double lambda1,
-// const double lambda2,
-// const double tolerance
-// output:
-void initModelWithDataWithGram(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
- float *responsesArr, SP_integer responsesArrSize,
- SP_integer transposeData,
- SP_integer useCholesky,
- float *gramMatArr, SP_integer gramMatSize, SP_integer gramMatRowNum,
- double lambda1, double lambda2, double tol)
-{
- // convert the Prolog array to arma::mat
- mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
- // check if labels fit the data
- if (data.n_cols != responsesArrSize)
- {
- raisePrologSystemExeption("The number of data points does not match the number of labels!");
- return;
- }
// convert the Prolog array to arma::rowvec
rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
- // convert the Prolog array to arma::mat
- mat gram = convertArrayToMat(gramMatArr, gramMatSize, gramMatRowNum);
+ // create the ReturnVector
+ vec beta;
+
+ // run the model function
try
- {
- regressor = new LARS(data, responsesVector, (transposeData == 1), (useCholesky == 1), gram, lambda1, lambda2, tol);
- }
- catch(const std::exception& e)
- {
- raisePrologSystemExeption(e.what());
+ {
+ *error = regressor.Train(data.t(), responsesVector, beta, transpose);
+ }
+ catch(const std::exception& e)
+ {
+ raisePrologSystemExeption(e.what());
return;
- }
+ }
+
isModelTrained = true;
}
@@ -168,7 +107,7 @@ void betaPath(float **betaPathArr, SP_integer *betaPathColNum, SP_integer *betaP
return;
}
// get the betaPath matrix
- vector<vec> matrix = regressor.BetaPath();
+ std::vector<vec> matrix = regressor.BetaPath();
// return the matrix dimensions
*betaPathColNum = matrix[0].size();
@@ -191,13 +130,15 @@ double computeError(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMa
raisePrologSystemExeption("The Model is not trained!");
return 0.0;
}
- if(dataMatSize / dataMatRowNum != responsesArrSize)
- {
- cout << "Target dim doesnt fit to the Data dim" << endl;
- return 0.0;
- }
// convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+ // check if labels and weights fit the data
+ if (data.n_cols != responsesArrSize)
+ {
+ raisePrologSystemExeption("The number of data points does not match the number of labels!");
+ return 0.0;
+ }
+
// convert the Prolog array to arma::rowvec
rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
@@ -205,7 +146,7 @@ double computeError(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMa
double result;
try
{
- result = regressor.ComputeError(data, responsesVector, (rowMajor == 1));
+ result = regressor.ComputeError(data.t(), responsesVector, (rowMajor == 1));
}
catch(const std::exception& e)
{
@@ -262,13 +203,18 @@ void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMat
}
// convert the Prolog array to arma::mat
mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum);
+ if(points.n_rows != regressor.Beta().n_elem)
+ {
+ raisePrologSystemExeption("The given Datapoints have a diffrent Dimension than trained!");
+ return;
+ }
// create the ReturnVector
rowvec predicReturnVector;
try
{
- regressor.Predict(points, predicReturnVector, (rowMajor == 1));
+ regressor.Predict(points.t(), predicReturnVector, (rowMajor == 1));
}
catch(const std::exception& e)
{
@@ -279,48 +225,4 @@ void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMat
// return the Vector
returnVectorInformation(predicReturnVector, predicArr, predicArrSize);
-}
-
-// input: const arma::mat &data,
-// const arma::rowvec &responses,
-// arma::vec& beta,
-// const bool transposeData
-//
-// output: double minimum cost error
-double train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
- float *responsesArr, SP_integer responsesArrSize,
- float **betaArr, SP_integer *betaArrSize,
- SP_integer transposeData)
-{
- if(dataMatSize / dataMatRowNum != responsesArrSize)
- {
- cout << "Target dim doesnt fit to the Data dim" << endl;
- return 0.0;
- }
- // convert the Prolog array to arma::mat
- mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
- // convert the Prolog array to arma::rowvec
- rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
-
- // create the ReturnVector
- vec betaReturnVector;
-
-
- // run the model function
- double error;
- try
- {
- error = regressor.Train(data, responsesVector, betaReturnVector, (transposeData == 1));
- }
- catch(const std::exception& e)
- {
- raisePrologSystemExeption(e.what());
- return 0.0;
- }
-
-
- // return the Vector
- returnVectorInformation(betaReturnVector, betaArr, betaArrSize);
- isModelTrained = true;
- return error;
-}
+}
\ No newline at end of file
diff --git a/src/methods/lars/lars.pl b/src/methods/lars/lars.pl
index 6f647dddc495013019980aba35421c4258788c3f..eb7c4d0a064ac6d859a1fd949ead6e72b775b2ce 100644
--- a/src/methods/lars/lars.pl
+++ b/src/methods/lars/lars.pl
@@ -1,16 +1,12 @@
-:- module(lars, [ lars_initModelNoDataNoGram/4,
- lars_initModelNoDataWithGram/6,
- lars_initModelWithDataNoGram/8,
- lars_initModelWithDataWithGram/10,
+:- module(lars, [ lars_initAndTrainModel/9,
lars_activeSet/1,
lars_beta/1,
lars_betaPath/2,
lars_computeError/5,
lars_lambdaPath/1,
lars_matUtriCholFactor/2,
- lars_predict/4,
- lars_train/6]).
+ lars_predict/4]).
:- load_files(library(str_decl),
[when(compile_time), if(changed)]).
@@ -32,99 +28,27 @@
%% float32 lambda1 => 0.0,
%% float32 lambda2 => 0.0,
%% float32 tolerance => 1e-16
-%%
-%% --Output--
-%%
-%% --Description--
-%% Only initialize the LARS model.
-%%
-lars_initModelNoDataNoGram(UseCholesky, Lambda1, Lambda2, Tolerance) :-
- Tolerance > 0,
- initModelNoDataNoGramI(UseCholesky, Lambda1, Lambda2, Tolerance).
-
-foreign(initModelNoDataNoGram, c, initModelNoDataNoGramI(+integer,
- +float32, +float32, +float32)).
-
-
-
-%% --Input--
-%% bool useCholesky => (1)true / (0)false =>false,
-%% mat gramMatrix,
-%% float32 lambda1 => 0.0,
-%% float32 lambda2 => 0.0,
-%% float32 tolerance => 1e-16
-%%
-%% --Output--
-%%
-%% --Description--
-%% Initialize LARS model, and pass in a precalculated Gram matrix but dont train the model.
-%%
-lars_initModelNoDataWithGram(UseCholesky, GramList, GramRows, Lambda1, Lambda2, Tolerance) :-
- Tolerance > 0,
- convert_list_to_float_array(GramList, GramRows, array(Zsize, Zrownum, Z)),
- initModelNoDataWithGramI(UseCholesky, Z, Zsize, Zrownum, Lambda1, Lambda2, Tolerance).
-
-foreign(initModelNoDataWithGram, c, initModelNoDataWithGramI( +integer,
- +pointer(float_array), +integer, +integer,
- +float32, +float32, +float32)).
-
-
-%% --Input--
-%% mat data,
-%% vec responses,
-%% bool transposeData => (1)true / (0)false =>true,
-%% bool useCholesky => (1)true / (0)false =>false,
-%% float32 lambda1 => 0.0,
-%% float32 lambda2 => 0.0,
-%% float32 tolerance => 1e-16
-%%
-%% --Output--
-%%
-%% --Description--
-%% Initialize LARS model, and train the model.
-%%
-lars_initModelWithDataNoGram(DataList, DataRows, ResponsesList, TransposeData, UseCholesky, Lambda1, Lambda2, Tolerance) :-
- Tolerance > 0,
- convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
- convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
- initModelWithDataNoGramI(X, Xsize, Xrownum, Y, Ysize, TransposeData, UseCholesky, Lambda1, Lambda2, Tolerance).
-
-foreign(initModelWithDataNoGram, c, initModelWithDataNoGramI( +pointer(float_array), +integer, +integer,
- +pointer(float_array), +integer,
- +integer,
- +integer,
- +float32, +float32, +float32)).
-
-
-
-%% --Input--
%% mat data,
%% vec responses,
-%% bool transposeData => (1)true / (0)false =>true,
-%% bool useCholesky => (1)true / (0)false =>false,
-%% mat gramMatrix,
-%% float32 lambda1 => 0.0,
-%% float32 lambda2 => 0.0,
-%% float32 tolerance => 1e-16
+%% bool rowMajor => (1)true / (0)false => true
%%
%% --Output--
+%% float32 error
%%
%% --Description--
-%% Initialize LARS model, pass in a precalculated Gram matrix and train the model.
+%% Only initialize the LARS model.
%%
-lars_initModelWithDataWithGram(DataList, DataRows, ResponsesList, TransposeData, UseCholesky, GramList, GramRows, Lambda1, Lambda2, Tolerance) :-
+lars_initAndTrainModel(UseCholesky, Lambda1, Lambda2, Tolerance, DataList, DataPointsDim, ResponsesList, RowMajor, Error) :-
Tolerance > 0,
- convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
+ convert_list_to_float_array(DataList, DataPointsDim, array(Xsize, Xrownum, X)),
convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
- convert_list_to_float_array(GramList, GramRows, array(Zsize, Zrownum, Z)),
- initModelWithDataWithGramI(X, Xsize, Xrownum, Y, Ysize, TransposeData, UseCholesky, Z, Zsize, Zrownum, Lambda1, Lambda2, Tolerance).
+ initAndTrainModelI(UseCholesky, Lambda1, Lambda2, Tolerance, X, Xsize, Xrownum, Y, Ysize, RowMajor, Error).
-foreign(initModelWithDataWithGram, c, initModelWithDataWithGramI(+pointer(float_array), +integer, +integer,
+foreign(initAndTrainModel, c, initAndTrainModelI( +integer,
+ +float32, +float32, +float32,
+ +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
- +integer,
- +integer,
- +pointer(float_array), +integer, +integer,
- +float32, +float32, +float32)).
+ +integer, -float32)).
@@ -245,43 +169,14 @@ foreign(predict, c, predictI( +pointer(float_array), +integer, +integer,
+integer)).
-%% --Input--
-%% mat data,
-%% vec responses,
-%% bool rowMajor => (1)true / (0)false => true
-%%
-%% --Output--
-%% vec beta,
-%% float32 error
-%%
-%% --Description--
-%% Train the LARS model with the given data.
-%%
-lars_train(DataList, DataRows, ResponsesList, BetaList, RowMajor, Error) :-
- convert_list_to_float_array(DataList, DataRows, array(Xsize, Xrownum, X)),
- convert_list_to_float_array(ResponsesList, array(Ysize, Y)),
- trainI(X, Xsize, Xrownum, Y, Ysize, Z, Zsize, RowMajor, Error),
- convert_float_array_to_list(Z, Zsize, BetaList).
-
-foreign(train, c, trainI( +pointer(float_array), +integer, +integer,
- +pointer(float_array), +integer,
- -pointer(float_array), -integer,
- +integer,
- [-float32])).
-
-
%% Defines what functions should be connected from main.cpp
-foreign_resource(lars, [initModelNoDataNoGram,
- initModelNoDataWithGram,
- initModelWithDataNoGram,
- initModelWithDataWithGram,
+foreign_resource(lars, [initAndTrainModel,
activeSet,
beta,
betaPath,
computeError,
lambdaPath,
matUtriCholFactor,
- predict,
- train]).
+ predict]).
:- load_foreign_resource(lars).
diff --git a/src/methods/lars/lars_test.pl b/src/methods/lars/lars_test.pl
index f3bb0e48a56e9a7a17cba3dd787b3257ca3e8e94..7332dbd3a788c8a98025522daca365dcea7ca8eb 100644
--- a/src/methods/lars/lars_test.pl
+++ b/src/methods/lars/lars_test.pl
@@ -8,128 +8,46 @@
:- use_module(lars).
:- use_module('../../helper_files/helper.pl').
-reset_Model_NoTrain :-
- lars_initModelNoDataNoGram(0, 0.0, 0.0, 1.0e-16).
reset_Model_WithTrain :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, 0, 0.0, 0.0, 1.0e-16).
+ lars_initAndTrainModel(0, 0.0, 0.0, 1.0e-16, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, _).
%%
-%% TESTING predicate lars_initModelNoDataNoGram/4
+%% TESTING predicate lars_initAndTrainModel/9
%%
-:- begin_tests(lars_initModelNoDataNoGram).
+:- begin_tests(lars_initAndTrainModel).
%% Failure Tests
-test(lars_InitModelNoDataNoGram_Negative_Tolerance, fail) :-
- lars_initModelNoDataNoGram(0, 0.0, 0.0, -1.0e-16).
-
-
-%% Successful Tests
+test(lars_initAndTrainModel_Negative_Tolerance, fail) :-
+ lars_initAndTrainModel(0, 0.0, 0.0, -1.0e-16, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, _).
-test(lars_InitModelNoDataNoGram_Normal_Use) :-
- lars_initModelNoDataNoGram(0, 0.0, 0.0, 1.0e-16).
+test(lars_initAndTrainModel_Too_Few_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
+ lars_initAndTrainModel(0, 0.0, 0.0, 1.0e-16, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0, _).
-test(lars_InitModelNoDataNoGram_Alternative_Use) :-
- lars_initModelNoDataNoGram(1, -1.0, 1.0, 1.0e-16).
+test(lars_initAndTrainModel_Too_Many_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
+ lars_initAndTrainModel(0, 0.0, 0.0, 1.0e-16, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 0, _).
-:- end_tests(lars_initModelNoDataNoGram).
+%% doesnt cause an error
+test(lars_initAndTrainModel_Too_Many_Labelclasses) :-
+ lars_initAndTrainModel(0, 0.0, 0.0, 1.0e-16, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 0, _).
-
-%%
-%% TESTING predicate lars_initModelNoDataWithGram/6
-%%
-:- begin_tests(lars_initModelNoDataWithGram).
-
-%% Failure Tests
-
-test(lars_InitModelNoDataWithGram_Negative_Tolerance, fail) :-
- lars_initModelNoDataWithGram(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, 0.0, 0.0, -1.0e-16).
+test(lars_initAndTrainModel_Wrongly_Transposed, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
+ lars_initAndTrainModel(0, 0.0, 0.0, 1.0e-16, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, _).
%% Successful Tests
-test(lars_InitModelNoDataWithGram_Normal_Use) :-
- lars_initModelNoDataWithGram(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, 0.0, 0.0, 1.0e-16).
-
-test(lars_InitModelNoDataWithGram_Alternative_Use) :-
- lars_initModelNoDataWithGram(1, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, -1.0, 1.0, 1.0e-16).
-
-:- end_tests(lars_initModelNoDataWithGram).
-
-
-
-%%
-%% TESTING predicate lars_initModelWithDataNoGram/8
-%%
-:- begin_tests(lars_initModelWithDataNoGram).
-
-%% Failure Tests
-
-test(lars_InitModelWithDataNoGram_Negative_Tolerance, fail) :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, 0, 0.0, 0.0, -1.0e-16).
-
-
-test(lars_InitModelWithDataNoGram_Too_Few_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0, 0, 0.0, 0.0, 1.0e-16).
-
-test(lars_InitModelWithDataNoGram_Too_Many_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 0, 0, 0.0, 0.0, 1.0e-16).
-
-%% doesnt cause error
-test(lars_InitModelWithDataNoGram_Too_Many_Labelclasses) :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 1, 0, 0.0, 0.0, 1.0e-16).
-
-
-%% Successful Tests
-
-test(lars_InitModelWithDataNoGram_Normal_Use) :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, 0, 0.0, 0.0, 1.0e-16).
-
-test(lars_InitModelWithDataNoGram_Alternative_Use) :-
- lars_initModelWithDataNoGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, 1, 1.0, -1.0, 1.0e-16).
-
-:- end_tests(lars_initModelWithDataNoGram).
-
-
-
-%%
-%% TESTING predicate lars_initModelWithDataWithGram/10
-%%
-:- begin_tests(lars_initModelWithDataWithGram).
-
-%% Failure Tests
-
-test(lars_InitModelWithDataWithGram_Negative_Tolerance, fail) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, -1.0e-16).
-
-
-test(lars_InitModelWithDataWithGram_Too_Few_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, 1.0e-16).
-
-test(lars_InitModelWithDataWithGram_Too_Many_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 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, 0.0, 0.0, 1.0e-16).
-
-test(lars_InitModelWithDataWithGram_Too_Many_Labelclasses, [error(_,system_error('Error'))]) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 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, 0.0, 0.0, 1.0e-16).
-
-
-test(lars_InitModelWithDataWithGram_Diffrent_Dimensions, [error(_,system_error('Error'))]) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 0.0, 0.0, 1.0e-16).
-
-
-%% Successful Tests
-
-test(lars_InitModelWithDataWithGram_Normal_Use) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 0.0, 0.0, 1.0e-16).
+test(lars_initAndTrainModel_Normal_Use) :-
+ lars_initAndTrainModel(0, 0.0, 0.0, 1.0e-16, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, _).
-test(lars_InitModelWithDataWithGram_Alternative_Use) :-
- lars_initModelWithDataWithGram([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, 0, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 1.0, -1.0, 1.0e-16).
+test(lars_initAndTrainModel_Alternative_Use) :-
+ lars_initAndTrainModel(1, -1.0, 1.0, 1.0e-16, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 0, _).
-:- end_tests(lars_initModelWithDataWithGram).
+:- end_tests(lars_initAndTrainModel).
@@ -139,10 +57,6 @@ test(lars_InitModelWithDataWithGram_Alternative_Use) :-
:- begin_tests(lars_activeSet).
%% Failure Tests
-
-test(lars_ActiveSet_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
- reset_Model_NoTrain,
- lars_activeSet(_).
%% Successful Tests
@@ -163,19 +77,15 @@ test(lars_ActiveSet_Normal_Use) :-
:- begin_tests(lars_beta).
%% Failure Tests
-
-test(lars_Beta_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
- reset_Model_NoTrain,
- lars_beta(_).
%% Successful Tests
-%%test(lars_Beta_Normal_Use) :-
-%% reset_Model_WithTrain,
-%% lars_beta(BetaList),
-%% print('\nBeta : '),
-%% print(BetaList).
+test(lars_Beta_Normal_Use) :-
+ reset_Model_WithTrain,
+ lars_beta(BetaList),
+ print('\nBeta : '),
+ print(BetaList).
:- end_tests(lars_beta).
@@ -187,10 +97,6 @@ test(lars_Beta_Before_Train, [error(_,system_error('The Model is not trained!'))
:- begin_tests(lars_betaPath).
%% Failure Tests
-
-test(lars_BetaPath_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
- reset_Model_NoTrain,
- lars_betaPath(_, _).
%% Successful Tests
@@ -212,29 +118,25 @@ test(lars_BetaPath_Normal_Use) :-
%% Failure Tests
-test(lars_ComputeError_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
- reset_Model_NoTrain,
- lars_computeError([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, _).
-
-
-test(lars_ComputeError_Too_Few_Labels, [error(_,system_error('Error'))]) :-
+test(lars_ComputeError_Too_Few_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model_WithTrain,
- lars_computeError([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0, _).
+ lars_computeError([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 1, _).
-test(lars_ComputeError_Too_Many_Labels, [error(_,system_error('Error'))]) :-
+test(lars_ComputeError_Too_Many_Labels, [error(_,system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model_WithTrain,
- lars_computeError([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 0, _).
+ lars_computeError([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 1, _).
-test(lars_ComputeError_Too_Many_Labelclasses, [error(_,system_error('Error'))]) :-
+%% doenst cause an error
+test(lars_ComputeError_Too_Many_Labelclasses) :-
reset_Model_WithTrain,
- lars_computeError([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 0, _).
+ lars_computeError([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 1, _).
%% Successful Tests
test(lars_ComputeError_Normal_Use) :-
reset_Model_WithTrain,
- lars_computeError([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, Error),
+ lars_computeError([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, Error),
print('\nError : '),
print(Error).
@@ -245,10 +147,10 @@ test(lars_ComputeError_Alternative_Use) :-
print(Error).
test(lars_ComputeError_CSV_Input) :-
- reset_Model_WithTrain,
+ lars_initAndTrainModel(0, 0.0, 0.0, 1.0e-16, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 0, _),
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- lars_computeError(Data, 4, [0,1,0,1,1,0,1,1,1,0], 0, Error),
+ lars_computeError(Data, 4, [0,1,0,1,1,0,1,1,1,0], 1, Error),
print('\nError : '),
print(Error).
@@ -262,11 +164,6 @@ test(lars_ComputeError_CSV_Input) :-
:- begin_tests(lars_lambdaPath).
%% Failure Tests
-
-test(lars_LambdaPath_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
- reset_Model_NoTrain,
- lars_lambdaPath(_).
-
%% Successful Tests
@@ -286,11 +183,6 @@ test(lars_LambdaPath_Normal_Use) :-
:- begin_tests(lars_matUtriCholFactor).
%% Failure Tests
-
-test(lars_MatUtriCholFactor_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
- reset_Model_NoTrain,
- lars_matUtriCholFactor(_, _).
-
%% Successful Tests
@@ -311,104 +203,34 @@ test(lars_MatUtriCholFactor_Normal_Use) :-
%% Failure Tests
-test(lars_Predict_Before_Train, [error(_,system_error('The Model is not trained!'))]) :-
- reset_Model_NoTrain,
- lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 1, _).
-
-
-test(lars_Predict_Diffrent_Dims, [error(_,system_error('Error'))]) :-
+test(lars_Predict_Diffrent_Dims, [error(_,system_error('The given Datapoints have a diffrent Dimension than trained!'))]) :-
reset_Model_WithTrain,
- lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, 1, _).
+ lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, _, 1).
%% Successful Tests
test(lars_Predict_Normal_Use) :-
reset_Model_WithTrain,
- lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 1, PredictList),
+ lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, PredictList, 1),
print('\nPrediction: '),
print(PredictList).
test(lars_Predict_Alternative_Use) :-
reset_Model_WithTrain,
- lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, 1, PredictList),
+ lars_predict([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, PredictList, 1),
print('\nPrediction: '),
print(PredictList).
test(lars_Predict_CSV_Input) :-
- reset_Model_WithTrain,
+ lars_initAndTrainModel(0, 0.0, 0.0, 1.0e-16, [5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 0, _),
open('src/data_csv/iris2.csv', read, File),
take_csv_row(File, skipFirstRow,10, Data),
- lars_predict(Data, 4, 1, PredictList),
+ lars_predict(Data, 4, PredictList, 1),
print('\nPrediction: '),
print(PredictList).
:- end_tests(lars_predict).
-
-
-%%
-%% TESTING predicate lars_train/10
-%%
-:- begin_tests(lars_train).
-
-%% Failure Tests\
-
-test(lars_Train_Diffrent_Dims, [error(_,system_error('Error'))]) :-
- reset_Model_WithTrain,
- lars_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 4, [0,1,0], 0, _, _).
-
-
-test(lars_Train_Too_Few_Labels, [error(_,system_error('Error'))]) :-
- reset_Model_NoTrain,
- lars_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0, _, _).
-
-test(lars_Train_Too_Many_Labels, [error(_,system_error('Error'))]) :-
- reset_Model_NoTrain,
- lars_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 0, _, _).
-
-test(lars_Train_Too_Many_Labelclasses, [error(_,system_error('Error'))]) :-
- reset_Model_NoTrain,
- lars_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 0, _, _).
-
-
-%% Successful Tests
-
-test(lars_Train_Normal_Use) :-
- reset_Model_NoTrain,
- lars_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, BetaList, Error),
- print('\nBeta: '),
- print(BetaList),
- print('\nError: '),
- print(Error).
-
-test(lars_Train_Normal_Use) :-
- reset_Model_WithTrain,
- lars_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 0, BetaList, Error),
- print('\nBeta: '),
- print(BetaList),
- print('\nError: '),
- print(Error).
-
-test(lars_Train_Alternative_Use) :-
- reset_Model_NoTrain,
- lars_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], 1, BetaList, Error),
- print('\nBeta: '),
- print(BetaList),
- print('\nError: '),
- print(Error).
-
-test(lars_Train_CSV_Input) :-
- reset_Model_NoTrain,
- open('src/data_csv/iris2.csv', read, File),
- take_csv_row(File, skipFirstRow,10, Data),
- lars_train(Data, 4, [0,1,0,1,1,0,1,1,1,0], 0, BetaList, Error),
- print('\nBeta: '),
- print(BetaList),
- print('\nError: '),
- print(Error).
-
-:- end_tests(lars_train).
-
run_lars_tests :-
- run_tests(lars_predict).
\ No newline at end of file
+ run_tests.