[mlpack-git] master: The transform() function isn't available on armadillo 3.6 so we move back to a standard for loop. (f601eae)

[gitdub at big.cc.gt.atl.ga.us]

Repository : https://github.com/mlpack/mlpack

On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/be6fe1f5cebdce38935cf2a5b628fb3196578dc7...f601eaebcd70f940b3cc52b29802cbe6a88a7c37

>---------------------------------------------------------------

commit f601eaebcd70f940b3cc52b29802cbe6a88a7c37
Author: Marcus Edel <marcus.edel at fu-berlin.de>
Date:   Sat Aug 29 16:07:55 2015 +0200

    The transform() function isn't available on armadillo 3.6 so we move back to a standard for loop.


>---------------------------------------------------------------

f601eaebcd70f940b3cc52b29802cbe6a88a7c37
 src/mlpack/methods/ann/activation_functions/logistic_function.hpp | 4 +++-
 src/mlpack/methods/ann/layer/binary_classification_layer.hpp      | 4 +++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/src/mlpack/methods/ann/activation_functions/logistic_function.hpp b/src/mlpack/methods/ann/activation_functions/logistic_function.hpp
index e8b5d60..b388c3f 100644
--- a/src/mlpack/methods/ann/activation_functions/logistic_function.hpp
+++ b/src/mlpack/methods/ann/activation_functions/logistic_function.hpp
@@ -54,7 +54,9 @@ class LogisticFunction
   static void fn(const InputVecType& x, OutputVecType& y)
   {
     y = x;
-    y.transform( [](double x) { return fn(x); } );
+
+    for (size_t i = 0; i < x.n_elem; i++)
+      y(i) = fn(x(i));
   }
 
   /**
diff --git a/src/mlpack/methods/ann/layer/binary_classification_layer.hpp b/src/mlpack/methods/ann/layer/binary_classification_layer.hpp
index bd44a89..ad5d30a 100644
--- a/src/mlpack/methods/ann/layer/binary_classification_layer.hpp
+++ b/src/mlpack/methods/ann/layer/binary_classification_layer.hpp
@@ -56,7 +56,9 @@ class BinaryClassificationLayer
   void OutputClass(const DataType& inputActivations, DataType& output)
   {
     output = inputActivations;
-    output.transform( [](double value) { return (value > 0.5 ? 1 : 0); } );
+
+    for (size_t i = 0; i < output.n_elem; i++)
+      output(i) = output(i) > 0.5 ? 1 : 0;
   }
 }; // class BinaryClassificationLayer