Adapt wording and use Log:Assert instead of assert to fix the unused parameter warning.

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;

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;

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;

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);

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;

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,