Fix minor spelling and style issues.

src/mlpack/tests/ann_layer_test.cpp

@@ -355,7 +355,7 @@ BOOST_AUTO_TEST_CASE(SimpleDropoutLayerTest)
 
 /**
  * Perform dropout x times using ones as input, sum the number of ones and
- * validate that the layer is is producing approximately the right number of
+ * validate that the layer is producing approximately the correct number of
  * ones.
  */
 BOOST_AUTO_TEST_CASE(DropoutProbabilityTest)

src/mlpack/tests/main_tests/decision_tree_test.cpp

@@ -77,14 +77,14 @@ BOOST_AUTO_TEST_CASE(DecisionTreeOutputDimensionTest)
 
   // Check that number of output points are equal to number of input points.
   BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::Row<size_t>>("predictions").n_cols,
-                      testSize);
+      testSize);
   BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::mat>("probabilities").n_cols,
-                      testSize);
+      testSize);
 
   // Check number of output rows equals number of classes in case of
   // probabilities and 1 for predictions.
-  BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::Row<size_t>>("predictions").n_rows,
-                      1);
+  BOOST_REQUIRE_EQUAL(
+      CLI::GetParam<arma::Row<size_t>>("predictions").n_rows, 1);
   BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::mat>("probabilities").n_rows, 3);
 }
 
@@ -160,20 +160,20 @@ BOOST_AUTO_TEST_CASE(DecisionModelReuseTest)
   // Input trained model.
   SetInputParam("test", std::move(testData));
   SetInputParam("input_model",
-                std::move(CLI::GetParam<DecisionTreeModel>("output_model")));
+      std::move(CLI::GetParam<DecisionTreeModel>("output_model")));
 
   mlpackMain();
 
   // Check that number of output points are equal to number of input points.
   BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::Row<size_t>>("predictions").n_cols,
-                      testSize);
+      testSize);
   BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::mat>("probabilities").n_cols,
-                      testSize);
+      testSize);
 
   // Check number of output rows equals number of classes in case of
   // probabilities and 1 for predicitions.
-  BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::Row<size_t>>("predictions").n_rows,
-                      1);
+  BOOST_REQUIRE_EQUAL(
+      CLI::GetParam<arma::Row<size_t>>("predictions").n_rows, 1);
   BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::mat>("probabilities").n_rows, 3);
 
   // Check that initial predictions and predictions using saved model are same.

src/mlpack/tests/main_tests/emst_test.cpp

@@ -122,11 +122,9 @@ BOOST_AUTO_TEST_CASE(EMSTFirstTwoOutputRowsIntegerTest)
   for (size_t i = 0; i < CLI::GetParam<arma::mat>("output").n_cols; i++)
   {
     BOOST_REQUIRE_CLOSE(CLI::GetParam<arma::mat>("output")(0, i),
-                        boost::math::iround(
-                        CLI::GetParam<arma::mat>("output")(0, i)), 1e-5);
+        boost::math::iround(CLI::GetParam<arma::mat>("output")(0, i)), 1e-5);
     BOOST_REQUIRE_CLOSE(CLI::GetParam<arma::mat>("output")(1, i),
-                        boost::math::iround(
-                        CLI::GetParam<arma::mat>("output")(1, i)), 1e-5);
+        boost::math::iround(CLI::GetParam<arma::mat>("output")(1, i)), 1e-5);
   }
 }
 

src/mlpack/tests/main_tests/linear_regression_test.cpp

@@ -171,7 +171,7 @@ BOOST_AUTO_TEST_CASE(LRWrongResponseSizeTest)
 }
 
 /**
- * Ensuring that test data dimensionality is is checked.
+ * Ensuring that test data dimensionality is checked.
  */
 BOOST_AUTO_TEST_CASE(LRWrongDimOfDataTest1)
 {
@@ -193,7 +193,7 @@ BOOST_AUTO_TEST_CASE(LRWrongDimOfDataTest1)
 }
 
 /**
- * Ensuring that test data dimensionality is is checked when model is loaded.
+ * Ensuring that test data dimensionality is checked when model is loaded.
  */
 BOOST_AUTO_TEST_CASE(LRWrongDimOfDataTest2)
 {

src/mlpack/tests/main_tests/preprocess_imputer_test.cpp

@@ -122,9 +122,9 @@ BOOST_AUTO_TEST_CASE(PreprocessImputerListwiseDimensionTest)
     if (std::to_string(inputData(0, i)) == "nan" ||
         std::to_string(inputData(1, i)) == "nan" ||
         std::to_string(inputData(2, i)) == "nan")
-      {
-        countNaN++;
-      }
+    {
+      countNaN++;
+    }
   }
 
   // Input custom data points and labels.

src/mlpack/tests/main_tests/preprocess_split_test.cpp

@@ -105,9 +105,9 @@ BOOST_AUTO_TEST_CASE(PreprocessSplitLabelLessDimensionTest)
 
   // Now check that the output has desired dimensions.
   BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::mat>("training").n_cols,
-                      std::ceil(0.9 * inputSize));
+      std::ceil(0.9 * inputSize));
   BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::mat>("test").n_cols,
-                      std::floor(0.1 * inputSize));
+      std::floor(0.1 * inputSize));
 }
 
 /**
@@ -161,8 +161,8 @@ BOOST_AUTO_TEST_CASE(PreprocessSplitZeroTestRatioTest)
 
   BOOST_REQUIRE_EQUAL(
       CLI::GetParam<arma::Mat<size_t>>("training_labels").n_cols, labelSize);
-  BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::Mat<size_t>>("test_labels").n_cols,
-      0);
+  BOOST_REQUIRE_EQUAL(
+      CLI::GetParam<arma::Mat<size_t>>("test_labels").n_cols, 0);
 }
 
 /**

src/mlpack/tests/nystroem_method_test.cpp

@@ -157,7 +157,7 @@ BOOST_AUTO_TEST_CASE(GermanTest)
   // The bandwidth of the kernel is selected to be the half the average
   // distance between each point and the mean of the dataset.  This isn't
   // _exactly_ what the paper says, but I've modified what it said because our
-  // formulation of what the Gaussian kernel is is different.
+  // formulation of what the Gaussian kernel is different.
   GaussianKernel gk(16.461);
 
   // Calculate the true kernel matrix.