Applied more style improvements

src/mlpack/core/optimizers/Nadam/nadam.hpp

@@ -2,7 +2,7 @@
  * @file nadam.hpp
  * @author Sourabh Varshney
  *
- * Nadam optimizer. Nadam is an optimizer that combines the effect of Adam and 
+ * Nadam optimizer. Nadam is an optimizer that combines the effect of Adam and
  * NAG to the gradient descent to improve its Performance.
  *
  * mlpack is free software; you may redistribute it and/or modify it under the
@@ -51,11 +51,12 @@ namespace optimization {
  *
  * NumFunctions() should return the number of functions (\f$n\f$), and in the
  * other two functions, the parameter i refers to which individual function (or
- * gradient) is being evaluated.  So, for the case of a data-dependent function,
- * such as NCA (see mlpack::nca::NCA), NumFunctions() should return the number
- * of points in the dataset, and Evaluate(coordinates, 0) will evaluate the
- * objective function on the first point in the dataset (presumably, the dataset
- * is held internally in the DecomposableFunctionType).
+ * gradient) is being evaluated.  So, for the case of a data-dependent 
+ * function, such as NCA (see mlpack::nca::NCA), NumFunctions() should return 
+ * the number of points in the dataset, and Evaluate(coordinates, 0) will 
+ * evaluate the objective function on the first point in the dataset 
+ * (presumably, the dataset is held internally in the 
+ * DecomposableFunctionType).
  *
  * @tparam UpdateRule Nadam optimizer update rule to be used.
  */
@@ -68,8 +69,8 @@ class NadamType
    * defaults here are not necessarily good for the given problem, so it is
    * suggested that the values used be tailored to the task at hand.  The
    * maximum number of iterations refers to the maximum number of points that
-   * are processed (i.e., one iteration equals one point; one iteration does not
-   * equal one pass over the dataset).
+   * are processed (i.e., one iteration equals one point; one iteration does
+   * not equal one pass over the dataset).
    *
    * @param stepSize Step size for each iteration.
    * @param batchSize Number of points to process in a single step.

src/mlpack/core/optimizers/Nadam/nadam_update.hpp

@@ -43,15 +43,15 @@ class NadamUpdate
    *        parameter.
    * @param beta1 The smoothing parameter.
    */
-  NadamUpdate(const double epsilon = 1e-8,const double beta1 = 0.9) 
+  NadamUpdate(const double epsilon = 1e-8,const double beta1 = 0.9)
   :epsilon(epsilon),beta1(beta1),iteration(0)
   {
     // Nothing to do.
   }
 
   /**
-   * The Initialize method is called by SGD Optimizer method before the start of
-   * the iteration update process.
+   * The Initialize method is called by SGD Optimizer method before the start 
+   * of the iteration update process.
    *
    * @param rows Number of rows in the gradient matrix.
    * @param cols Number of columns in the gradient matrix.
@@ -69,7 +69,8 @@ class NadamUpdate
    * @param stepSize Step size to be used for the given iteration.
    * @param gradient The gradient matrix.
    */
-  void Update(arma::mat& iterate,const double stepSize,const arma::mat& gradient)
+  void Update(arma::mat& iterate, const double stepSize,
+  const arma::mat& gradient)
   {
     // Increment the iteration counter variable.
     ++iteration;

src/mlpack/tests/nadam_test.cpp

@@ -52,8 +52,10 @@ BOOST_AUTO_TEST_CASE(SimpleNadamTestFunction)
 BOOST_AUTO_TEST_CASE(NadamLogisticRegressionTest)
 {
   // Generate a two-Gaussian dataset.
-  GaussianDistribution g1(arma::vec("1.0 1.0 1.0"), arma::eye<arma::mat>(3, 3));
-  GaussianDistribution g2(arma::vec("9.0 9.0 9.0"), arma::eye<arma::mat>(3, 3));
+  GaussianDistribution g1(arma::vec("1.0 1.0 1.0"),
+  arma::eye<arma::mat>(3, 3));
+  GaussianDistribution g2(arma::vec("9.0 9.0 9.0"),
+  arma::eye<arma::mat>(3, 3));
 
   arma::mat data(3, 1000);
   arma::Row<size_t> responses(1000);