[mlpack-svn] r16707 - mlpack/trunk/src/mlpack/methods/decision_stump

fastlab-svn at coffeetalk-1.cc.gatech.edu fastlab-svn at coffeetalk-1.cc.gatech.edu
Tue Jun 24 19:29:09 EDT 2014


Author: rcurtin
Date: Tue Jun 24 19:29:08 2014
New Revision: 16707

Log:
Use const double where possible instead of having a variable used throughout the
function.  This is only for clarity and consistency and is not likely to make
any runtime difference.


Modified:
   mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.hpp

Modified: mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.hpp
==============================================================================
--- mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.hpp	(original)
+++ mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.hpp	Tue Jun 24 19:29:08 2014
@@ -38,7 +38,7 @@
   numClass = classes;
   bucketSize = inpBucketSize;
 
-  /* Check whether the input labels are not all identical. */
+  // Check whether the input labels are not all identical.
   if (!isDistinct<size_t>(classLabels))
   {
     // If the classLabels are all identical, the default class is the only
@@ -46,7 +46,6 @@
     oneClass = true;
     defaultClass = classLabels(0);
   }
-
   else
   {
     // If classLabels are not all identical, proceed with training.
@@ -96,34 +95,31 @@
 void DecisionStump<MatType>::Classify(const MatType& test,
                                       arma::Row<size_t>& predictedLabels)
 {
-  bool flag;
-  double val;
   if (!oneClass)
   {
     for (int i = 0; i < test.n_cols; i++)
     {
       int j = 0;
-      flag = false;
 
-      val = test(splitCol,i);
-      while ((j < split.n_rows) && (!flag))
+      const double val = test(splitCol, i);
+      while (j < split.n_rows)
       {
         if (val < split(j, 0) && (!j))
         {
           predictedLabels(i) = split(0, 1);
-          flag = true;
+          break;
         }
         else if (val >= split(j, 0))
         {
           if (j == split.n_rows - 1)
           {
             predictedLabels(i) = split(split.n_rows - 1, 1);
-            flag = true;
+            break;
           }
           else if (val < split(j + 1, 0))
           {
             predictedLabels(i) = split(j, 1);
-            flag = true;
+            break;
           }
         }
         j++;
@@ -392,7 +388,7 @@
 }
 
 /**
- * Calculating Entropy of attribute.
+ * Calculate entropy of attribute.
  *
  * @param attribute The attribute for which we calculate the entropy.
  * @param labels Corresponding labels of the attribute.



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