[mlpack-svn] r17388 - mlpack/trunk/src/mlpack/methods/amf/update_rules

[fastlab-svn at coffeetalk-1.cc.gatech.edu]

Author: rcurtin
Date: Wed Nov 19 12:09:01 2014
New Revision: 17388

Log:
Minor code cleanups.


Modified:
   mlpack/trunk/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp

Modified: mlpack/trunk/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp
==============================================================================
--- mlpack/trunk/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp	(original)
+++ mlpack/trunk/src/mlpack/methods/amf/update_rules/svd_batch_learning.hpp	Wed Nov 19 12:09:01 2014
@@ -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 @@
       {
         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 @@
       {
         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 @@
     // add momentum to H
     H += mH;
   }
-  
+
  private:
   //! step size of the algorithm
   double u;
@@ -164,13 +162,13 @@
   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 @@
   {
     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 @@
   {
     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 @@
 } // namespace amf
 } // namespace mlpack
 
-
 #endif // __MLPACK_METHODS_AMF_UPDATE_RULES_SVD_BATCHLEARNING_HPP