[mlpack-git] master: Minor style changes. (23b52db)

gitdub at big.cc.gt.atl.ga.us gitdub at big.cc.gt.atl.ga.us
Sat Feb 28 17:53:27 EST 2015


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

On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/de0988d27899f88e1b8d87817bad9152ccebf205...23b52dbf5a3db99d0c3094c59b33f6ac3f2131aa

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

commit 23b52dbf5a3db99d0c3094c59b33f6ac3f2131aa
Author: Marcus Edel <marcus.edel at fu-berlin.de>
Date:   Sat Feb 28 23:53:19 2015 +0100

    Minor style changes.


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

23b52dbf5a3db99d0c3094c59b33f6ac3f2131aa
 src/mlpack/methods/ann/ffnn.hpp                                  | 6 +++---
 src/mlpack/methods/ann/layer/binary_classification_layer.hpp     | 4 ++--
 src/mlpack/methods/ann/layer/multiclass_classification_layer.hpp | 4 ++--
 src/mlpack/methods/ann/performance_functions/cee_function.hpp    | 2 +-
 src/mlpack/methods/ann/performance_functions/mse_function.hpp    | 2 +-
 src/mlpack/methods/ann/performance_functions/sse_function.hpp    | 2 +-
 src/mlpack/methods/ann/rnn.hpp                                   | 6 +++---
 src/mlpack/tests/performance_functions_test.cpp                  | 6 +++---
 8 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/src/mlpack/methods/ann/ffnn.hpp b/src/mlpack/methods/ann/ffnn.hpp
index 8cfb347..314f66d 100644
--- a/src/mlpack/methods/ann/ffnn.hpp
+++ b/src/mlpack/methods/ann/ffnn.hpp
@@ -238,13 +238,13 @@ class FFNN
                       VecType& error)
     {
        // Calculate and store the output error.
-      outputLayer.calculateError(std::get<0>(
+      outputLayer.CalculateError(std::get<0>(
           std::get<sizeof...(Tp) - 1>(t)).OutputLayer().InputActivation(),
           target, error);
 
       // Masures the network's performance with the specified performance
       // function.
-      return PerformanceFunction::error(std::get<0>(
+      return PerformanceFunction::Error(std::get<0>(
           std::get<sizeof...(Tp) - 1>(t)).OutputLayer().InputActivation(),
           target);
     }
@@ -256,7 +256,7 @@ class FFNN
     void OutputPrediction(std::tuple<Tp...>& t, VecType& output)
     {
        // Calculate and store the output prediction.
-      outputLayer.outputClass(std::get<0>(
+      outputLayer.OutputClass(std::get<0>(
           std::get<sizeof...(Tp) - 1>(t)).OutputLayer().InputActivation(),
           output);
     }
diff --git a/src/mlpack/methods/ann/layer/binary_classification_layer.hpp b/src/mlpack/methods/ann/layer/binary_classification_layer.hpp
index c928f7d..109895d 100644
--- a/src/mlpack/methods/ann/layer/binary_classification_layer.hpp
+++ b/src/mlpack/methods/ann/layer/binary_classification_layer.hpp
@@ -45,7 +45,7 @@ class BinaryClassificationLayer
    * @param error The calculated error with respect to the input activation and
    * the given target.
    */
-  void calculateError(const VecType& inputActivations,
+  void CalculateError(const VecType& inputActivations,
                       const VecType& target,
                       VecType& error)
   {
@@ -58,7 +58,7 @@ class BinaryClassificationLayer
    * @param inputActivations Input data used to calculate the output class.
    * @param output Output class of the input activation.
    */
-  void outputClass(const VecType& inputActivations, VecType& output)
+  void OutputClass(const VecType& inputActivations, VecType& output)
   {
     output = inputActivations;
     output.transform( [](double value) { return (value > 0.5 ? 1 : 0); } );
diff --git a/src/mlpack/methods/ann/layer/multiclass_classification_layer.hpp b/src/mlpack/methods/ann/layer/multiclass_classification_layer.hpp
index 40ca62b..2c8807f 100644
--- a/src/mlpack/methods/ann/layer/multiclass_classification_layer.hpp
+++ b/src/mlpack/methods/ann/layer/multiclass_classification_layer.hpp
@@ -49,7 +49,7 @@ class MulticlassClassificationLayer
    * @param error The calculated error with respect to the input activation and
    * the given target.
    */
-  void calculateError(const VecType& inputActivations,
+  void CalculateError(const VecType& inputActivations,
                       const VecType& target,
                       VecType& error)
   {
@@ -62,7 +62,7 @@ class MulticlassClassificationLayer
    * @param inputActivations Input data used to calculate the output class.
    * @param output Output class of the input activation.
    */
-  void outputClass(const VecType& inputActivations, VecType& output)
+  void OutputClass(const VecType& inputActivations, VecType& output)
   {
     output = inputActivations;
   }
diff --git a/src/mlpack/methods/ann/performance_functions/cee_function.hpp b/src/mlpack/methods/ann/performance_functions/cee_function.hpp
index 0113694..66b5471 100644
--- a/src/mlpack/methods/ann/performance_functions/cee_function.hpp
+++ b/src/mlpack/methods/ann/performance_functions/cee_function.hpp
@@ -39,7 +39,7 @@ class CrossEntropyErrorFunction
    * @param target Target data.
    * @return cross-entropy error.
    */
-  static double error(const VecType& input, const VecType& target)
+  static double Error(const VecType& input, const VecType& target)
   {
     if (LayerTraits<Layer>::IsBinary)
       return -arma::dot(arma::trunc_log(arma::abs(target - input)), target);
diff --git a/src/mlpack/methods/ann/performance_functions/mse_function.hpp b/src/mlpack/methods/ann/performance_functions/mse_function.hpp
index cd59c3d..ebe29f5 100644
--- a/src/mlpack/methods/ann/performance_functions/mse_function.hpp
+++ b/src/mlpack/methods/ann/performance_functions/mse_function.hpp
@@ -29,7 +29,7 @@ class MeanSquaredErrorFunction
    * @param target Target data.
    * @return mean of squared errors.
    */
-  static double error(const VecType& input, const VecType& target)
+  static double Error(const VecType& input, const VecType& target)
   {
     return arma::mean(arma::square(target - input));
   }
diff --git a/src/mlpack/methods/ann/performance_functions/sse_function.hpp b/src/mlpack/methods/ann/performance_functions/sse_function.hpp
index b88e5a2..01b5418 100644
--- a/src/mlpack/methods/ann/performance_functions/sse_function.hpp
+++ b/src/mlpack/methods/ann/performance_functions/sse_function.hpp
@@ -29,7 +29,7 @@ class SumSquaredErrorFunction
    * @param target Target data.
    * @return sum of squared errors.
    */
-  static double error(const VecType& input, const VecType& target)
+  static double Error(const VecType& input, const VecType& target)
   {
     return arma::sum(arma::square(target - input));
   }
diff --git a/src/mlpack/methods/ann/rnn.hpp b/src/mlpack/methods/ann/rnn.hpp
index a4a085b..a8dcc6d 100644
--- a/src/mlpack/methods/ann/rnn.hpp
+++ b/src/mlpack/methods/ann/rnn.hpp
@@ -356,7 +356,7 @@ class RNN
                      VecType& error)
     {
        // Calculate and store the output error.
-      outputLayer.calculateError(std::get<0>(
+      outputLayer.CalculateError(std::get<0>(
           std::get<sizeof...(Tp) - 1>(t)).OutputLayer().InputActivation(),
           target, error);
 
@@ -366,7 +366,7 @@ class RNN
 
       // Masures the network's performance with the specified performance
       // function.
-      err = PerformanceFunction::error(std::get<0>(
+      err = PerformanceFunction::Error(std::get<0>(
           std::get<sizeof...(Tp) - 1>(t)).OutputLayer().InputActivation(),
           target);
 
@@ -381,7 +381,7 @@ class RNN
     void OutputPrediction(std::tuple<Tp...>& t, VecType& output)
     {
        // Calculate and store the output prediction.
-      outputLayer.outputClass(std::get<0>(
+      outputLayer.OutputClass(std::get<0>(
           std::get<sizeof...(Tp) - 1>(t)).OutputLayer().InputActivation(),
           output);
     }
diff --git a/src/mlpack/tests/performance_functions_test.cpp b/src/mlpack/tests/performance_functions_test.cpp
index 82372b0..31eec7e 100644
--- a/src/mlpack/tests/performance_functions_test.cpp
+++ b/src/mlpack/tests/performance_functions_test.cpp
@@ -24,7 +24,7 @@ BOOST_AUTO_TEST_CASE(MeanSquaredErrorTest)
   arma::colvec input("1.0 0.0 1.0 0.0 -1.0 0.0 -1.0 0.0");
   arma::colvec target = arma::zeros<arma::colvec>(8);
 
-  BOOST_REQUIRE_EQUAL(MeanSquaredErrorFunction<>::error(input, target), 0.5);
+  BOOST_REQUIRE_EQUAL(MeanSquaredErrorFunction<>::Error(input, target), 0.5);
 }
 
 // Test the cross entropy performance function.
@@ -34,7 +34,7 @@ BOOST_AUTO_TEST_CASE(CrossEntropyErrorTest)
   input << std::exp(-2.0) << std::exp(-1.0);
   arma::colvec target = arma::ones<arma::colvec>(2);
 
-  BOOST_REQUIRE_EQUAL(CrossEntropyErrorFunction<>::error(input, target), 3);
+  BOOST_REQUIRE_EQUAL(CrossEntropyErrorFunction<>::Error(input, target), 3);
 }
 
 // Test the sum squared error performance function.
@@ -43,7 +43,7 @@ BOOST_AUTO_TEST_CASE(SumSquaredErrorTest)
   arma::colvec input("1.0 0.0 1.0 0.0 -1.0 0.0 -1.0 0.0");
   arma::colvec target = arma::zeros<arma::colvec>(8);
 
-  BOOST_REQUIRE_EQUAL(SumSquaredErrorFunction<>::error(input, target), 4);
+  BOOST_REQUIRE_EQUAL(SumSquaredErrorFunction<>::Error(input, target), 4);
 }
 
 BOOST_AUTO_TEST_SUITE_END();



More information about the mlpack-git mailing list