diff --git a/src/mlpack/core/data/scaler_methods/max_abs_scaler.hpp b/src/mlpack/core/data/scaler_methods/max_abs_scaler.hpp
index f7c0abca2e..6a03883347 100644
--- a/src/mlpack/core/data/scaler_methods/max_abs_scaler.hpp
+++ b/src/mlpack/core/data/scaler_methods/max_abs_scaler.hpp
@@ -74,7 +74,7 @@ class MaxAbsScaler
     if (scale.is_empty())
     {
       throw std::runtime_error("Call Fit() before Transform(), please"
-        " refer documentation.");
+        " refer to the documentation.");
     }
     output.copy_size(input);
     output = input.each_col() / scale;
diff --git a/src/mlpack/core/data/scaler_methods/mean_normalization.hpp b/src/mlpack/core/data/scaler_methods/mean_normalization.hpp
index e46e1d41a7..cb4693d553 100644
--- a/src/mlpack/core/data/scaler_methods/mean_normalization.hpp
+++ b/src/mlpack/core/data/scaler_methods/mean_normalization.hpp
@@ -75,7 +75,7 @@ class MeanNormalization
     if (itemMean.is_empty() || scale.is_empty())
     {
       throw std::runtime_error("Call Fit() before Transform(), please"
-        " refer documentation.");
+        " refer to the documentation.");
     }
     output.copy_size(input);
     output = (input.each_col() - itemMean).each_col() / scale;
diff --git a/src/mlpack/core/data/scaler_methods/min_max_scaler.hpp b/src/mlpack/core/data/scaler_methods/min_max_scaler.hpp
index b8b1c6203a..bcecc2950b 100644
--- a/src/mlpack/core/data/scaler_methods/min_max_scaler.hpp
+++ b/src/mlpack/core/data/scaler_methods/min_max_scaler.hpp
@@ -97,7 +97,7 @@ class MinMaxScaler
     if (scalerowmin.is_empty() || scale.is_empty())
     {
       throw std::runtime_error("Call Fit() before Transform(), please"
-          " refer documentation.");
+          " refer to the documentation.");
     }
     output.copy_size(input);
     output = (input.each_col() % scale).each_col() + scalerowmin;
diff --git a/src/mlpack/core/data/scaler_methods/pca_whitening.hpp b/src/mlpack/core/data/scaler_methods/pca_whitening.hpp
index d12a51f049..97cde370ce 100644
--- a/src/mlpack/core/data/scaler_methods/pca_whitening.hpp
+++ b/src/mlpack/core/data/scaler_methods/pca_whitening.hpp
@@ -88,7 +88,7 @@ class PCAWhitening
     if (eigenValues.is_empty() || eigenVectors.is_empty())
     {
       throw std::runtime_error("Call Fit() before Transform(), please"
-          " refer documentation.");
+          " refer to the documentation.");
     }
     output.copy_size(input);
     output = (input.each_col() - itemMean);
diff --git a/src/mlpack/core/data/scaler_methods/standard_scaler.hpp b/src/mlpack/core/data/scaler_methods/standard_scaler.hpp
index d199d7a316..cd34d3c90a 100644
--- a/src/mlpack/core/data/scaler_methods/standard_scaler.hpp
+++ b/src/mlpack/core/data/scaler_methods/standard_scaler.hpp
@@ -74,7 +74,7 @@ class StandardScaler
     if (itemMean.is_empty() || itemStdDev.is_empty())
     {
       throw std::runtime_error("Call Fit() before Transform(), please"
-        " refer documentation.");
+        " refer to the documentation.");
     }
     output.copy_size(input);
     output = (input.each_col() - itemMean).each_col() / itemStdDev;
diff --git a/src/mlpack/tests/augmented_rnns_tasks_test.cpp b/src/mlpack/tests/augmented_rnns_tasks_test.cpp
index cee38cab64..cef5c514a2 100644
--- a/src/mlpack/tests/augmented_rnns_tasks_test.cpp
+++ b/src/mlpack/tests/augmented_rnns_tasks_test.cpp
@@ -95,8 +95,7 @@ class HardCodedSortModel
   void Train(arma::field<arma::mat>& predictors,
              arma::field<arma::mat>& labels)
   {
-    const bool check = predictors.n_elem == labels.n_elem;
-    assert(check == true);
+    Log::Assert(check = predictors.n_elem == labels.n_elem);
   }
 
   void Predict(arma::mat& predictors,