Skip to content
Snippets Groups Projects
Commit 2c6c97a5 authored by Jakhes's avatar Jakhes
Browse files

Finishing linear_regression tests

parent a42fdf0d
Branches
No related tags found
No related merge requests found
Show whitespace changes
Inline Side-by-side
src/methods/linear_regression/linear_regression.cpp
+ 112
− 34
View file @ 2c6c97a5
...@@ -16,6 +16,7 @@ using namespace mlpack::regression; ...@@ -16,6 +16,7 @@ using namespace mlpack::regression;
// Global Variable of the LinearRegression object so it can be accessed from all functions // Global Variable of the LinearRegression object so it can be accessed from all functions
LinearRegression regressor; LinearRegression regressor;
int trainingDimension = 0;
// input: const arma::mat &data, // input: const arma::mat &data,
// const arma::rowvec &responses, // const arma::rowvec &responses,
...@@ -27,20 +28,30 @@ void initModel(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowN ...@@ -27,20 +28,30 @@ void initModel(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowN
double lambda, double lambda,
SP_integer bool_intercept) SP_integer bool_intercept)
{ {
if(dataMatSize / dataMatRowNum != responsesArrSize) // 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)
{ {
cout << "Target dim doesnt fit to the Data dim" << endl; raisePrologSystemExeption("The number of data points does not match the number of labels!");
return; return;
} }
// 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::rowvec
rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize); rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
try
{
regressor = LinearRegression(data, responsesVector, lambda, bool_intercept); regressor = LinearRegression(data, responsesVector, lambda, bool_intercept);
} }
catch(const std::exception& e)
{
raisePrologSystemExeption(e.what());
return;
}
trainingDimension = data.n_rows;
}
// input: const arma::mat &data, // input: const arma::mat &data,
// const arma::rowvec &responses, // const arma::rowvec &responses,
...@@ -54,20 +65,36 @@ void initModelWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer ...@@ -54,20 +65,36 @@ void initModelWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer
double lambda, double lambda,
SP_integer bool_intercept) SP_integer bool_intercept)
{ {
if(dataMatSize / dataMatRowNum != responsesArrSize) // 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)
{ {
cout << "Target dim doesnt fit to the Data dim" << endl; raisePrologSystemExeption("The number of data points does not match the number of labels!");
return; return;
} }
// convert the Prolog array to arma::mat if (data.n_cols != weightsArrSize)
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum); {
raisePrologSystemExeption("The number of data points does not match the number of weights!");
return;
}
// convert the Prolog array to arma::rowvec // convert the Prolog array to arma::rowvec
rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize); rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
rowvec weightsVector = convertArrayToRowvec(weightsArr, weightsArrSize); rowvec weightsVector = convertArrayToRowvec(weightsArr, weightsArrSize);
try
{
regressor = LinearRegression(data, responsesVector, weightsVector, lambda, bool_intercept); regressor = LinearRegression(data, responsesVector, weightsVector, lambda, bool_intercept);
} }
catch(const std::exception& e)
{
raisePrologSystemExeption(e.what());
return;
}
trainingDimension = data.n_rows;
}
// input: const arma::mat &points, // input: const arma::mat &points,
// const arma::rowvec &responses // const arma::rowvec &responses
...@@ -75,27 +102,37 @@ void initModelWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer ...@@ -75,27 +102,37 @@ void initModelWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer
double computeError(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum, double computeError(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum,
float *responsesArr, SP_integer responsesArrSize) float *responsesArr, SP_integer responsesArrSize)
{ {
if(pointsMatSize / pointsMatRowNum != responsesArrSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
return 0;
}
// convert the Prolog array to arma::mat // convert the Prolog array to arma::mat
mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum); mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum);
// check if labels fit the data
if (points.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 // convert the Prolog array to arma::rowvec
rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize); rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
// run the model function // run the model function
try
{
return regressor.ComputeError(points, responsesVector); return regressor.ComputeError(points, responsesVector);
} }
catch(const std::exception& e)
{
raisePrologSystemExeption(e.what());
return 0.0;
}
}
// input: // input:
// output: const arma::vec & parameters b vector // output: const arma::vec & parameters b vector
void parameters(float **bArr, SP_integer *bArrSize) void parameters(float **bArr, SP_integer *bArrSize)
{ {
// create the ReturnVector // create the ReturnVector
rowvec bReturnVector = regressor.Parameters(); vec bReturnVector = regressor.Parameters();
// return the Vector // return the Vector
returnVectorInformation(bReturnVector, bArr, bArrSize); returnVectorInformation(bReturnVector, bArr, bArrSize);
...@@ -106,7 +143,7 @@ void parameters(float **bArr, SP_integer *bArrSize) ...@@ -106,7 +143,7 @@ void parameters(float **bArr, SP_integer *bArrSize)
void modifyParameters(float *newBArr, SP_integer newBArrSize) void modifyParameters(float *newBArr, SP_integer newBArrSize)
{ {
// convert the Prolog array to arma::rowvec // convert the Prolog array to arma::rowvec
vec newBVector = convertArrayToRowvec(newBArr, newBArrSize); vec newBVector = convertArrayToColvec(newBArr, newBArrSize);
// get parameter b vector ref // get parameter b vector ref
...@@ -124,13 +161,27 @@ void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMat ...@@ -124,13 +161,27 @@ void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMat
{ {
// convert the Prolog array to arma::mat // convert the Prolog array to arma::mat
mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum); mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum);
if (points.n_rows != trainingDimension)
{
raisePrologSystemExeption("The given Points have a diffrent Dimension than the trained Model!");
return;
}
// create the ReturnVector // create the ReturnVector
rowvec predicReturnVector; rowvec predicReturnVector;
// run the predict function on the model // run the predict function on the model
try
{
regressor.Predict(points, predicReturnVector); regressor.Predict(points, predicReturnVector);
}
catch(const std::exception& e)
{
raisePrologSystemExeption(e.what());
return;
}
// return the Vector // return the Vector
...@@ -145,20 +196,31 @@ double train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum ...@@ -145,20 +196,31 @@ double train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum
float *responsesArr, SP_integer responsesArrSize, float *responsesArr, SP_integer responsesArrSize,
SP_integer bool_intercept) SP_integer bool_intercept)
{ {
if(dataMatSize / dataMatRowNum != responsesArrSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
return 0;
}
// convert the Prolog array to arma::mat // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum); 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 0.0;
}
// convert the Prolog array to arma::rowvec // convert the Prolog array to arma::rowvec
rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize); rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
// run the model train function // run the model train function
try
{
return regressor.Train(data, responsesVector, (bool_intercept == 1)); return regressor.Train(data, responsesVector, (bool_intercept == 1));
} }
catch(const std::exception& e)
{
raisePrologSystemExeption(e.what());
return 0.0;
}
trainingDimension = data.n_rows;
}
// input: const arma::mat &data, // input: const arma::mat &data,
// const arma::rowvec &responses, // const arma::rowvec &responses,
...@@ -170,17 +232,33 @@ double trainWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer da ...@@ -170,17 +232,33 @@ double trainWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer da
float *weightsArr, SP_integer weightsArrSize, float *weightsArr, SP_integer weightsArrSize,
SP_integer bool_intercept) SP_integer bool_intercept)
{ {
if(dataMatSize / dataMatRowNum != responsesArrSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
return 0;
}
// convert the Prolog array to arma::mat // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum); 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;
}
if (data.n_cols != weightsArrSize)
{
raisePrologSystemExeption("The number of data points does not match the number of weights!");
return 0.0;
}
// convert the Prolog array to arma::rowvec // convert the Prolog array to arma::rowvec
rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize); rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
rowvec weightsVector = convertArrayToRowvec(weightsArr, weightsArrSize); rowvec weightsVector = convertArrayToRowvec(weightsArr, weightsArrSize);
// run the model train function // run the model train function
try
{
return regressor.Train(data, responsesVector, weightsVector, (bool_intercept == 1)); return regressor.Train(data, responsesVector, weightsVector, (bool_intercept == 1));
} }
catch(const std::exception& e)
{
raisePrologSystemExeption(e.what());
return 0.0;
}
trainingDimension = data.n_rows;
}
src/methods/linear_regression/linear_regression_test.pl
+ 28
− 23
View file @ 2c6c97a5
...@@ -21,13 +21,14 @@ reset_Model_WithWeights :- ...@@ -21,13 +21,14 @@ reset_Model_WithWeights :-
%% Failure Tests %% Failure Tests
test(linear_regression_InitModel_Too_Few_Labels, [error(_, system_error('Error'))]) :- test(linear_regression_InitModel_Too_Few_Labels, [error(_, system_error('The number of data points does not match the number of labels!'))]) :-
linear_regression_initModel([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0.0, 1). linear_regression_initModel([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 0.0, 1).
test(linear_regression_InitModel_Too_Many_Labels, [error(_, system_error('Error'))]) :- test(linear_regression_InitModel_Too_Many_Labels, [error(_, system_error('The number of data points does not match the number of labels!'))]) :-
linear_regression_initModel([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 0.0, 1). linear_regression_initModel([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 0.0, 1).
test(linear_regression_InitModel_Too_Many_Labelclasses, [error(_, system_error('Error'))]) :- %% doesnt cause error
test(linear_regression_InitModel_Too_Many_Labelclasses) :-
linear_regression_initModel([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 0.0, 1). linear_regression_initModel([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 0.0, 1).
...@@ -52,20 +53,21 @@ test(linear_regression_InitModel_CSV_Input) :- ...@@ -52,20 +53,21 @@ test(linear_regression_InitModel_CSV_Input) :-
%% Failure Tests %% Failure Tests
test(linear_regression_InitModelWithWeights_Too_Few_Labels, [error(_, system_error('Error'))]) :- test(linear_regression_InitModelWithWeights_Too_Few_Labels, [error(_, system_error('The number of data points does not match the number of labels!'))]) :-
linear_regression_initModelWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], [0.2,0.1,0.2,0.5], 0.0, 1). linear_regression_initModelWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], [0.2,0.1,0.2,0.5], 0.0, 1).
test(linear_regression_InitModelWithWeights_Too_Many_Labels, [error(_, system_error('Error'))]) :- test(linear_regression_InitModelWithWeights_Too_Many_Labels, [error(_, system_error('The number of data points does not match the number of labels!'))]) :-
linear_regression_initModelWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], [0.2,0.1,0.2,0.5], 0.0, 1). linear_regression_initModelWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], [0.2,0.1,0.2,0.5], 0.0, 1).
test(linear_regression_InitModelWithWeights_Too_Many_Labelclasses, [error(_, system_error('Error'))]) :- %% doesnt cause error
test(linear_regression_InitModelWithWeights_Too_Many_Labelclasses) :-
linear_regression_initModelWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], [0.2,0.1,0.2,0.5], 0.0, 1). linear_regression_initModelWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], [0.2,0.1,0.2,0.5], 0.0, 1).
test(linear_regression_InitModelWithWeights_Too_Few_Weights, [error(_, system_error('Error'))]) :- test(linear_regression_InitModelWithWeights_Too_Few_Weights, [error(_, system_error('The number of data points does not match the number of weights!'))]) :-
linear_regression_initModelWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], [0.2,0.5], 0.0, 1). linear_regression_initModelWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], [0.2,0.5], 0.0, 1).
test(linear_regression_InitModelWithWeights_Too_Many_Weights, [error(_, system_error('Error'))]) :- test(linear_regression_InitModelWithWeights_Too_Many_Weights, [error(_, system_error('The number of data points does not match the number of weights!'))]) :-
linear_regression_initModelWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], [0.2,0.1,0.2,0.1,0.2,0.5], 0.0, 1). linear_regression_initModelWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], [0.2,0.1,0.2,0.1,0.2,0.5], 0.0, 1).
...@@ -90,15 +92,16 @@ test(linear_regression_InitModelWithWeights_CSV_Input) :- ...@@ -90,15 +92,16 @@ test(linear_regression_InitModelWithWeights_CSV_Input) :-
%% Failure Tests %% Failure Tests
test(linear_regression_ComputeError_Too_Few_Labels, [error(_, system_error('Error'))]) :- test(linear_regression_ComputeError_Too_Few_Labels, [error(_, system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model, reset_Model,
linear_regression_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], _). linear_regression_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], _).
test(linear_regression_ComputeError_Too_Many_Labels, [error(_, system_error('Error'))]) :- test(linear_regression_ComputeError_Too_Many_Labels, [error(_, system_error('The number of data points does not match the number of labels!'))]) :-
reset_Model, reset_Model,
linear_regression_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], _). linear_regression_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], _).
test(linear_regression_ComputeError_Too_Many_Labelclasses, [error(_, system_error('Error'))]) :- %% doesnt cause error
test(linear_regression_ComputeError_Too_Many_Labelclasses) :-
reset_Model, reset_Model,
linear_regression_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], _). linear_regression_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], _).
...@@ -143,21 +146,21 @@ test(linear_regression_Parameters_Normal_Use) :- ...@@ -143,21 +146,21 @@ test(linear_regression_Parameters_Normal_Use) :-
%% Failure Tests %% Failure Tests
test(linear_regression_ModifyParameters_Too_Few_Parameters, [error(_, system_error('Error'))]) :- test(linear_regression_ModifyParameters_Too_Few_Parameters) :-
reset_Model, reset_Model,
linear_regression_modifyParameters([3,4]), linear_regression_modifyParameters([3,4]),
linear_regression_parameters(Parameters), linear_regression_parameters(Parameters),
print('\nParameters: '), print('\nParameters: '),
print(Parameters). print(Parameters).
test(linear_regression_ModifyParameters_Too_Many_Parameters, [error(_, system_error('Error'))]) :- test(linear_regression_ModifyParameters_Too_Many_Parameters) :-
reset_Model, reset_Model,
linear_regression_modifyParameters([1,2,1,2,3,4]), linear_regression_modifyParameters([1,2,1,2,3,4]),
linear_regression_parameters(Parameters), linear_regression_parameters(Parameters),
print('\nParameters: '), print('\nParameters: '),
print(Parameters). print(Parameters).
test(linear_regression_ModifyParameters_Bad_Parameter_Value, [error(_, system_error('Error'))]) :- test(linear_regression_ModifyParameters_Bad_Parameter_Value) :-
reset_Model, reset_Model,
linear_regression_modifyParameters([1,2,-3,4]), linear_regression_modifyParameters([1,2,-3,4]),
linear_regression_parameters(Parameters), linear_regression_parameters(Parameters),
...@@ -185,7 +188,7 @@ test(linear_regression_ModifyParameters_Normal_Use) :- ...@@ -185,7 +188,7 @@ test(linear_regression_ModifyParameters_Normal_Use) :-
%% Failure Tests %% Failure Tests
test(linear_regression_Predict_Diffrent_Dims_Than_Trained) :- test(linear_regression_Predict_Diffrent_Dims_Than_Trained, [error(_, system_error('The given Points have a diffrent Dimension than the trained Model!'))]) :-
reset_Model, reset_Model,
linear_regression_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, _). linear_regression_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, _).
...@@ -215,13 +218,14 @@ test(linear_regression_Predict_Normal_Use) :- ...@@ -215,13 +218,14 @@ test(linear_regression_Predict_Normal_Use) :-
%% Failure Tests %% Failure Tests
test(linear_regression_Train_Too_Few_Labels, [error(_, system_error('Error'))]) :- test(linear_regression_Train_Too_Few_Labels, [error(_, system_error('The number of data points does not match the number of labels!'))]) :-
linear_regression_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 1, _). linear_regression_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], 1, _).
test(linear_regression_Train_Too_Many_Labels, [error(_, system_error('Error'))]) :- test(linear_regression_Train_Too_Many_Labels, [error(_, system_error('The number of data points does not match the number of labels!'))]) :-
linear_regression_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 1, _). linear_regression_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], 1, _).
test(linear_regression_Train_Too_Many_Labelclasses, [error(_, system_error('Error'))]) :- %% doesnt cause error
test(linear_regression_Train_Too_Many_Labelclasses) :-
linear_regression_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 1, _). linear_regression_train([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], 1, _).
...@@ -250,20 +254,21 @@ test(linear_regression_Train_CSV_Input) :- ...@@ -250,20 +254,21 @@ test(linear_regression_Train_CSV_Input) :-
%% Failure Tests %% Failure Tests
test(linear_regression_TrainWithWeights_Too_Few_Labels, [error(_, system_error('Error'))]) :- test(linear_regression_TrainWithWeights_Too_Few_Labels, [error(_, system_error('The number of data points does not match the number of labels!'))]) :-
linear_regression_trainWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], [0.2,0.1,0.2,0.5], 1 ,_). linear_regression_trainWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1], [0.2,0.1,0.2,0.5], 1 ,_).
test(linear_regression_TrainWithWeights_Too_Many_Labels, [error(_, system_error('Error'))]) :- test(linear_regression_TrainWithWeights_Too_Many_Labels, [error(_, system_error('The number of data points does not match the number of labels!'))]) :-
linear_regression_trainWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], [0.2,0.1,0.2,0.5], 1 ,_). linear_regression_trainWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1,0,1], [0.2,0.1,0.2,0.5], 1 ,_).
test(linear_regression_TrainWithWeights_Too_Many_Labelclasses, [error(_, system_error('Error'))]) :- %% doesnt cause error
test(linear_regression_TrainWithWeights_Too_Many_Labelclasses) :-
linear_regression_trainWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], [0.2,0.1,0.2,0.5], 1 ,_). linear_regression_trainWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,2,3], [0.2,0.1,0.2,0.5], 1 ,_).
test(linear_regression_TrainWithWeights_Too_Few_Weights, [error(_, system_error('Error'))]) :- test(linear_regression_TrainWithWeights_Too_Few_Weights, [error(_, system_error('The number of data points does not match the number of weights!'))]) :-
linear_regression_trainWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], [0.2,0.5], 1 ,_). linear_regression_trainWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], [0.2,0.5], 1 ,_).
test(linear_regression_TrainWithWeights_Too_Many_Weights, [error(_, system_error('Error'))]) :- test(linear_regression_TrainWithWeights_Too_Many_Weights, [error(_, system_error('The number of data points does not match the number of weights!'))]) :-
linear_regression_trainWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], [0.2,0.1,0.2,0.1,0.2,0.5], 1 ,_). linear_regression_trainWithWeights([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5], 3, [0,1,0,1], [0.2,0.1,0.2,0.1,0.2,0.5], 1 ,_).
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment