[mlpack-svn] r16701 - 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 01:23:01 EDT 2014
Author: rcurtin
Date: Tue Jun 24 01:23:01 2014
New Revision: 16701
Log:
Use bool instead of int.
Modified:
mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump.hpp
mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump_impl.hpp
Modified: mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump.hpp
==============================================================================
--- mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump.hpp (original)
+++ mlpack/trunk/src/mlpack/methods/decision_stump/decision_stump.hpp Tue Jun 24 01:23:01 2014
@@ -56,7 +56,7 @@
int splitCol;
//! Flag value for distinct input class labels.
- int oneClass;
+ bool oneClass;
//! Size of bucket while determining splitting criterion.
size_t bucketSize;
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 01:23:01 2014
@@ -43,14 +43,14 @@
{
// If the classLabels are all identical, the default class is the only
// class.
- oneClass = 1;
+ oneClass = true;
defaultClass = classLabels(0);
}
else
{
// If classLabels are not all identical, proceed with training.
- oneClass = 0;
+ oneClass = false;
int bestAtt = -1;
double entropy;
double bestEntropy = DBL_MAX;
@@ -96,14 +96,14 @@
void DecisionStump<MatType>::Classify(const MatType& test,
arma::Row<size_t>& predictedLabels)
{
- int flag;
+ bool flag;
double val;
if (!oneClass)
{
for (int i = 0; i < test.n_cols; i++)
{
int j = 0;
- flag = 0;
+ flag = false;
val = test(splitCol,i);
while ((j < split.n_rows) && (!flag))
@@ -111,19 +111,19 @@
if (val < split(j, 0) && (!j))
{
predictedLabels(i) = split(0, 1);
- flag = 1;
+ flag = true;
}
else if (val >= split(j, 0))
{
if (j == split.n_rows - 1)
{
predictedLabels(i) = split(split.n_rows - 1, 1);
- flag = 1;
+ flag = true;
}
else if (val < split(j + 1, 0))
{
predictedLabels(i) = split(j, 1);
- flag = 1;
+ flag = true;
}
}
j++;
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