[mlpack] 107/149: Minor code cleanups.

[Barak A. Pearlmutter]

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bap pushed a commit to branch svn-trunk
in repository mlpack.

commit 556c4eb6698cb73d031f6cdac153c9f9f32601e7
Author: rcurtin <rcurtin at 9d5b8971-822b-0410-80eb-d18c1038ef23>
Date:   Wed Nov 19 17:09:01 2014 +0000

    Minor code cleanups.
    
    
    git-svn-id: http://svn.cc.gatech.edu/fastlab/mlpack/trunk@17388 9d5b8971-822b-0410-80eb-d18c1038ef23
---
 .../amf/update_rules/svd_batch_learning.hpp        | 33 ++++++++++------------
 1 file changed, 15 insertions(+), 18 deletions(-)

diff --git a/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp b/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp
index aca613e..3b9308c 100644
--- a/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp
+++ b/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp
@@ -9,25 +9,23 @@
 
 #include <mlpack/core.hpp>
 
-namespace mlpack
-{
-namespace amf
-{
+namespace mlpack {
+namespace amf {
 
 /**
- * This class implements SVD batch learning with momentum. This procedure is 
- * described in the paper 'A Guide to singular Value Decomposition' 
- * by Chih-Chao Ma. Class implements 'Algorithm 4' given in the paper. 
+ * This class implements SVD batch learning with momentum. This procedure is
+ * described in the paper 'A Guide to singular Value Decomposition'
+ * by Chih-Chao Ma. Class implements 'Algorithm 4' given in the paper.
  * This factorizer decomposes the matrix V into two matrices W and H such that
  * sum of sum of squared error between V and W*H is minimum. This optimization is
- * performed with gradient descent. To make gradient descent faster momentum is 
- * added. 
+ * performed with gradient descent. To make gradient descent faster momentum is
+ * added.
  */
 class SVDBatchLearning
 {
  public:
   /**
-   * SVD Batch learning constructor. 
+   * SVD Batch learning constructor.
    *
    * @param u step value used in batch learning
    * @param kw regularization constant for W matrix
@@ -91,7 +89,7 @@ class SVDBatchLearning
       {
         double val;
         if((val = V(i, j)) != 0)
-          deltaW.row(i) += (val - arma::dot(W.row(i), H.col(j))) * 
+          deltaW.row(i) += (val - arma::dot(W.row(i), H.col(j))) *
                                                   arma::trans(H.col(j));
       }
       // add regularization
@@ -135,7 +133,7 @@ class SVDBatchLearning
       {
         double val;
         if((val = V(i, j)) != 0)
-          deltaH.col(j) += (val - arma::dot(W.row(i), H.col(j))) * 
+          deltaH.col(j) += (val - arma::dot(W.row(i), H.col(j))) *
                                                     arma::trans(W.row(i));
       }
       // add regularization
@@ -147,7 +145,7 @@ class SVDBatchLearning
     // add momentum to H
     H += mH;
   }
-  
+
  private:
   //! step size of the algorithm
   double u;
@@ -164,13 +162,13 @@ class SVDBatchLearning
   arma::mat mH;
 }; // class SBDBatchLearning
 
-//! TODO : Merge this template specialized function for sparse matrix using 
+//! TODO : Merge this template specialized function for sparse matrix using
 //!        common row_col_iterator
 
 /**
  * WUpdate function specialization for sparse matrix
  */
-template<> 
+template<>
 inline void SVDBatchLearning::WUpdate<arma::sp_mat>(const arma::sp_mat& V,
                                                     arma::mat& W,
                                                     const arma::mat& H)
@@ -188,7 +186,7 @@ inline void SVDBatchLearning::WUpdate<arma::sp_mat>(const arma::sp_mat& V,
   {
     size_t row = it.row();
     size_t col = it.col();
-    deltaW.row(it.row()) += (*it - arma::dot(W.row(row), H.col(col))) * 
+    deltaW.row(it.row()) += (*it - arma::dot(W.row(row), H.col(col))) *
                                                   arma::trans(H.col(col));
   }
 
@@ -219,7 +217,7 @@ inline void SVDBatchLearning::HUpdate<arma::sp_mat>(const arma::sp_mat& V,
   {
     size_t row = it.row();
     size_t col = it.col();
-    deltaH.col(col) += (*it - arma::dot(W.row(row), H.col(col))) * 
+    deltaH.col(col) += (*it - arma::dot(W.row(row), H.col(col))) *
                                                 arma::trans(W.row(row));
   }
 
@@ -235,7 +233,6 @@ inline void SVDBatchLearning::HUpdate<arma::sp_mat>(const arma::sp_mat& V,
 } // namespace amf
 } // namespace mlpack
 
-
 #endif // __MLPACK_METHODS_AMF_UPDATE_RULES_SVD_BATCHLEARNING_HPP
 
 

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