diff --git a/src/mlpack/methods/logistic_regression/logistic_regression_main.cpp b/src/mlpack/methods/logistic_regression/logistic_regression_main.cpp index 5981baf9b2..e087b20945 100644 --- a/src/mlpack/methods/logistic_regression/logistic_regression_main.cpp +++ b/src/mlpack/methods/logistic_regression/logistic_regression_main.cpp @@ -143,7 +143,7 @@ static void mlpackMain() const size_t batchSize = (size_t) CLI::GetParam("batch_size"); const size_t maxIterations = (size_t) CLI::GetParam("max_iterations"); const double decisionBoundary = CLI::GetParam("decision_boundary"); - + // One of training and input_model must be specified. RequireAtLeastOnePassed({ "training", "input_model" }, true); @@ -244,7 +244,7 @@ static void mlpackMain() Log::Fatal << "Can't get responses from training data " "since it has less than 2 rows." << endl; } - + // The initial predictors for y, Nx1. responses = arma::conv_to>::from( regressors.row(regressors.n_rows - 1)); @@ -293,7 +293,8 @@ static void mlpackMain() if (testSet.n_rows != model->Parameters().n_cols - 1) { Log::Fatal << "Test data dimensionality (" << testSet.n_rows << ") must " - << "be the same as the dimensionality of the Training Data (" << model->Parameters().n_cols-1 << ")!" << endl; + << "be the same as the dimensionality of the Training Data (" + << model->Parameters().n_cols-1 << ")!" << endl; } // We must perform predictions on the test set. Training (and the diff --git a/src/mlpack/methods/perceptron/perceptron_main.cpp b/src/mlpack/methods/perceptron/perceptron_main.cpp index e821a3d4ca..49a57518b2 100644 --- a/src/mlpack/methods/perceptron/perceptron_main.cpp +++ b/src/mlpack/methods/perceptron/perceptron_main.cpp @@ -179,7 +179,7 @@ static void mlpackMain() labelsIn = std::move(CLI::GetParam>("labels")); // Checking the size of the responses and training data - if (labelsIn.n_cols != trainingData.n_cols) + if (labelsIn.n_cols != trainingData.n_cols) { Log::Fatal << "The responses must have the same number of columns " "as the training set." << endl; @@ -187,7 +187,7 @@ static void mlpackMain() } else { - // Checking the size of training data if no labels are passed + // Checking the size of training data if no labels are passed if (trainingData.n_rows < 2) { Log::Fatal << "Can't get responses from training data " diff --git a/src/mlpack/tests/main_tests/logistic_regression_test.cpp b/src/mlpack/tests/main_tests/logistic_regression_test.cpp index 5f94debfba..34e090cb89 100644 --- a/src/mlpack/tests/main_tests/logistic_regression_test.cpp +++ b/src/mlpack/tests/main_tests/logistic_regression_test.cpp @@ -89,7 +89,7 @@ BOOST_AUTO_TEST_CASE(LRPridictionSizeCheck) arma::mat trainX = arma::randu(D, N); arma::Row trainY; // 10 responses - trainY << 0 << 1 << 0 << 1 << 1 << 1 << 0 << 1 << 0 << 0 << arma::endr; + trainY << 0 << 1 << 0 << 1 << 1 << 1 << 0 << 1 << 0 << 0 << arma::endr; arma::mat testX = arma::randu(D, M); SetInputParam("training", std::move(trainX)); @@ -119,7 +119,7 @@ BOOST_AUTO_TEST_CASE(LRWrongResponseSizeTest) arma::Row trainY; // response vector with wrong size // 8 responses - incorrect size - trainY << 0 << 0 << 1 << 0 << 1 << 1 << 1 << 0 << arma::endr; + trainY << 0 << 0 << 1 << 0 << 1 << 1 << 1 << 0 << arma::endr; SetInputParam("training", std::move(trainX)); SetInputParam("labels", std::move(trainY)); @@ -146,7 +146,7 @@ BOOST_AUTO_TEST_CASE(LRResponsesRepresentationTest) mlpackMain(); // get the output - const arma::Row testY1 = + const arma::Row testY1 = std::move(CLI::GetParam>("output")); // reset the settings @@ -319,7 +319,7 @@ BOOST_AUTO_TEST_CASE(LRTrainWithMoreThanTwoClasses) /** * Ensuring that max iteration for optimizers is non negative **/ -BOOST_AUTO_TEST_CASE(LRNonNegativeMaxIterationTest) +BOOST_AUTO_TEST_CASE(LRNonNegativeMaxIterationTest) { constexpr int N = 10; constexpr int D = 3; @@ -332,7 +332,7 @@ BOOST_AUTO_TEST_CASE(LRNonNegativeMaxIterationTest) SetInputParam("training", std::move(trainX)); SetInputParam("labels", std::move(trainY)); - SetInputParam("max_iterations", int (-1)); + SetInputParam("max_iterations", int(-1)); // Maximum iterations is negative. Should a runtime error Log::Fatal.ignoreInput = true; @@ -356,8 +356,8 @@ BOOST_AUTO_TEST_CASE(LRNonNegativeStepSizeTest) SetInputParam("training", std::move(trainX)); SetInputParam("labels", std::move(trainY)); - SetInputParam("optimizer", std::string ("sgd")); - SetInputParam("step_size", double (-0.01)); + SetInputParam("optimizer", std::string("sgd")); + SetInputParam("step_size", double(-0.01)); // step size for optimizer is negative. Should throw a runtime error Log::Fatal.ignoreInput = true; @@ -381,7 +381,7 @@ BOOST_AUTO_TEST_CASE(LRNonNegativeToleranceTest) SetInputParam("training", std::move(trainX)); SetInputParam("labels", std::move(trainY)); - SetInputParam("tolerance", double (-0.01)); + SetInputParam("tolerance", double(-0.01)); // tolerance is negative. Should throw a runtime error Log::Fatal.ignoreInput = true; @@ -405,7 +405,7 @@ BOOST_AUTO_TEST_CASE(LRMaxIterationsChangeTest) SetInputParam("training", trainX); SetInputParam("labels", trainY); - SetInputParam("max_iterations", int (1)); + SetInputParam("max_iterations", int(1)); // first solution mlpackMain(); @@ -422,7 +422,7 @@ BOOST_AUTO_TEST_CASE(LRMaxIterationsChangeTest) SetInputParam("training", std::move(trainX)); SetInputParam("labels", std::move(trainY)); - SetInputParam("max_iterations", int (100)); + SetInputParam("max_iterations", int(100)); // second solution mlpackMain(); @@ -431,7 +431,7 @@ BOOST_AUTO_TEST_CASE(LRMaxIterationsChangeTest) const arma::rowvec ¶meters2 = CLI::GetParam*>("output_model")->Parameters(); - // Check that the parameters (parameters1 and parameters2) are not equal + // Check that the parameters (parameters1 and parameters2) are not equal // which ensures Max Iteration changes the output model // arma::all function checks that each element of the vector is equal to zero BOOST_REQUIRE_MESSAGE(!arma::all((parameters1-parameters2) == 0), @@ -454,13 +454,13 @@ BOOST_AUTO_TEST_CASE(LRLambdaChangeTest) SetInputParam("training", trainX); SetInputParam("labels", trainY); - SetInputParam("lambda", double (0)); + SetInputParam("lambda", double(0)); // first solution mlpackMain(); // get the parameters of the output model obtained after first training - const arma::rowvec parameters1 = + const arma::rowvec parameters1 = std::move(CLI::GetParam*>("output_model") ->Parameters()); @@ -471,7 +471,7 @@ BOOST_AUTO_TEST_CASE(LRLambdaChangeTest) SetInputParam("training", std::move(trainX)); SetInputParam("labels", std::move(trainY)); - SetInputParam("lambda", double (1000)); + SetInputParam("lambda", double(1000)); // second solution mlpackMain(); @@ -504,7 +504,7 @@ BOOST_AUTO_TEST_CASE(LRStepSizeChangeTest) SetInputParam("training", trainX); SetInputParam("labels", trainY); SetInputParam("optimizer", std::string("sgd")); - SetInputParam("step_size", double (0.02)); + SetInputParam("step_size", double(0.02)); // first solution mlpackMain(); @@ -522,19 +522,19 @@ BOOST_AUTO_TEST_CASE(LRStepSizeChangeTest) SetInputParam("training", std::move(trainX)); SetInputParam("labels", std::move(trainY)); SetInputParam("optimizer", std::string("sgd")); - SetInputParam("step_size", double (1.02)); + SetInputParam("step_size", double(1.02)); // second solution mlpackMain(); // get the parameters of the output model obtained after second training const arma::rowvec ¶meters2 = - CLI::GetParam*>("output_model")->Parameters(); + CLI::GetParam*>("output_model")->Parameters(); // Check that the parameters (parameters1 and parameters2) are not equal // which ensures Step Size changes the output model // arma::all function checks that each element of the vector is equal to zero - BOOST_REQUIRE_MESSAGE(!arma::all((parameters1-parameters2) == 0), + BOOST_REQUIRE_MESSAGE(!arma::all((parameters1-parameters2) == 0), "Parameter(Step Size) has no effect on the output"); } @@ -555,9 +555,9 @@ BOOST_AUTO_TEST_CASE(LROptimizerChangeTest) SetInputParam("training", trainX); SetInputParam("labels", trainY); SetInputParam("optimizer", std::string("lbfgs")); - SetInputParam("max_iterations", int (1000)); + SetInputParam("max_iterations", int(1000)); - // first solution + // first solution mlpackMain(); // get the parameters of the output model obtained after first training @@ -569,24 +569,24 @@ BOOST_AUTO_TEST_CASE(LROptimizerChangeTest) bindings::tests::CleanMemory(); CLI::ClearSettings(); CLI::RestoreSettings(testName); - + SetInputParam("training", std::move(trainX)); SetInputParam("labels", std::move(trainY)); SetInputParam("optimizer", std::string("sgd")); - SetInputParam("max_iterations", int (1000)); + SetInputParam("max_iterations", int(1000)); // second solution mlpackMain(); // get the parameters of the output model obtained after second training - const arma::rowvec ¶meters2 = - CLI::GetParam*>("output_model")->Parameters(); + const arma::rowvec ¶meters2 = + CLI::GetParam*>("output_model")->Parameters(); // Check that the parameters (parameters1 and parameters2) are not equal which // ensures that different optimizer converge to different results // arma::all function checks that each element of the vector is equal to zero BOOST_REQUIRE_MESSAGE(!arma::all((parameters1-parameters2) == 0), - "Parameter(Step Size) has no effect on the output"); + "Parameter(Step Size) has no effect on the output"); } /** @@ -602,13 +602,13 @@ BOOST_AUTO_TEST_CASE(LRDecisionBoundaryTest) arma::Row trainY; // 10 responses - trainY << 1 << 0 << 0 << 1 << 0 << 1 << 0 << 1 << 0 << 1 << arma::endr; + trainY << 1 << 0 << 0 << 1 << 0 << 1 << 0 << 1 << 0 << 1 << arma::endr; arma::mat testX = arma::randu(D, M); SetInputParam("training", trainX); SetInputParam("labels", trainY); - SetInputParam("decision_boundary", double (1)); + SetInputParam("decision_boundary", double(1)); SetInputParam("test", testX); // first solution @@ -621,7 +621,8 @@ BOOST_AUTO_TEST_CASE(LRDecisionBoundaryTest) // ensures that decision boundary has some effect on the output // arma::all function checks that each element of the vector is equal to zero BOOST_REQUIRE_MESSAGE(arma::all(output1 == 0), - "Parameter(Decision Boudary) has no effect on the output"); + "Parameter(Decision Boudary) has + no effect on the output"); // reset the settings bindings::tests::CleanMemory(); @@ -630,7 +631,7 @@ BOOST_AUTO_TEST_CASE(LRDecisionBoundaryTest) SetInputParam("training", trainX); SetInputParam("labels", trainY); - SetInputParam("decision_boundary", double (0)); + SetInputParam("decision_boundary", double(0)); SetInputParam("test", testX); // second solution @@ -642,8 +643,8 @@ BOOST_AUTO_TEST_CASE(LRDecisionBoundaryTest) // Check that the parameters (parameters1 and parameters2) are not equal which // ensures that decision boundary has som effect on the output // arma::all function checks that each element of the vector is equal to one - BOOST_REQUIRE_MESSAGE(arma::all(output2 == 1), - "Parameter(Decision Boudary) has no effect on the output"); + BOOST_REQUIRE_MESSAGE(arma::all(output2 == 1), + "Parameter(Decision Boudary) has + no effect on the output"); } - BOOST_AUTO_TEST_SUITE_END(); \ No newline at end of file diff --git a/src/mlpack/tests/main_tests/perceptron_test.cpp b/src/mlpack/tests/main_tests/perceptron_test.cpp index aa2b9db067..56ed19d810 100644 --- a/src/mlpack/tests/main_tests/perceptron_test.cpp +++ b/src/mlpack/tests/main_tests/perceptron_test.cpp @@ -41,7 +41,7 @@ struct PerceptronTestFixture }; // reset the parameters -void resetSettings() +void resetSettings() { bindings::tests::CleanMemory(); CLI::ClearSettings(); @@ -263,7 +263,7 @@ BOOST_AUTO_TEST_CASE(PerceptronReTrainWithWrongClasses) // labels for the train data SetInputParam("labels", std::move(labelsX1)); - //training model using first training dataset + // training model using first training dataset mlpackMain(); // get the output model obtained after training @@ -283,15 +283,15 @@ BOOST_AUTO_TEST_CASE(PerceptronReTrainWithWrongClasses) // 10 responses labelsX2 << 0 << 1 << 4 << 1 << 2 << 1 << 0 << 3 << 3 << 0 << endr; - //last column of trainX2 contains the class labels + // last column of trainX2 contains the class labels SetInputParam("training", std::move(trainX2)); SetInputParam("input_model", model); - // re-training an existing model of 3 classes + // re-training an existing model of 3 classes // with training data of 5 classes. Should give runtime error - Log::Fatal.ignoreInput=true; + Log::Fatal.ignoreInput = true; BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error); - Log::Fatal.ignoreInput=false; + Log::Fatal.ignoreInput = false; } /** @@ -334,7 +334,7 @@ BOOST_AUTO_TEST_CASE(PerceptronWrongResponseSizeTest) arma::Row trainY; // response vector with wrong size // 8 responses - trainY << 0 << 0 << 1 << 0 << 1 << 1 << 1 << 0 << endr; + trainY << 0 << 0 << 1 << 0 << 1 << 1 << 1 << 0 << endr; SetInputParam("training", std::move(trainX)); SetInputParam("labels", std::move(trainY));