the remaining functions signatures

Signed-off-by: Omar Shrit <omar@avontech.fr>

src/mlpack/methods/ann/layer/not_adapted/pixel_shuffle.hpp

@@ -134,7 +134,7 @@ class PixelShuffle
    * Serialize the layer.
    */
   template<typename Archive>
-  void serialize(Archive& ar, const unsigned int /* version */);
+  void serialize(Archive& ar, const uint32_t /* version */);
 
  private:
   //! Locally-stored delta object.

src/mlpack/methods/ann/layer/not_adapted/pixel_shuffle_impl.hpp

@@ -119,7 +119,7 @@ template<typename InputDataType, typename OutputDataType>
 template<typename Archive>
 void PixelShuffle<InputDataType, OutputDataType>::serialize(
     Archive& ar,
-    const unsigned int /* version */)
+    const uint32_t /* version */)
 {
   ar(CEREAL_NVP(delta));
   ar(CEREAL_NVP(outputParameter));

src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error.hpp

@@ -74,7 +74,7 @@ class MeanAbsolutePercentageErrorType
    * Serialize the layer.
    */
   template<typename Archive>
-  void serialize(Archive& ar, const unsigned int /* version */) { }
+  void serialize(Archive& ar, const uint32_t /* version */) { }
 }; // class MeanAbsolutePercentageErrorType
 
 // Default typedef for typical `arma::mat` usage.

src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss.hpp

@@ -91,7 +91,7 @@ class MultiLabelSoftMarginLossType
    * Serialize the layer.
    */
   template<typename Archive>
-  void serialize(Archive& ar, const unsigned int /* version */);
+  void serialize(Archive& ar, const uint32_t /* version */);
 
  private:
   //! The boolean value that tells if reduction is sum or mean.

src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss_impl.hpp

@@ -71,7 +71,7 @@ template<typename MatType>
 template<typename Archive>
 void MultiLabelSoftMarginLossType<MatType>::serialize(
     Archive& ar,
-    const unsigned int /* version */)
+    const uint32_t /* version */)
 {
   ar(CEREAL_NVP(classWeights));
   ar(CEREAL_NVP(reduction));

src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp

@@ -81,7 +81,7 @@ class TripletMarginLossType
    * Serialize the layer.
    */
   template<typename Archive>
-  void serialize(Archive& ar, const unsigned int /* version */);
+  void serialize(Archive& ar, const uint32_t /* version */);
 
  private:
   //! The margin value used in calculating Triplet Margin Loss.

src/mlpack/methods/ann/loss_functions/triplet_margin_loss_impl.hpp

@@ -55,7 +55,7 @@ template<typename MatType>
 template<typename Archive>
 void TripletMarginLossType<MatType>::serialize(
     Archive& ar,
-    const unsigned int /* version */)
+    const uint32_t /* version */)
 {
   ar(CEREAL_NVP(margin));
 }

src/mlpack/methods/lmnn/lmnn.hpp

@@ -205,7 +205,7 @@ class LMNN
 
   // Serialize the LMNN object.
   template<typename Archive>
-  void serialize(Archive& ar, const unsigned int /* version */);
+  void serialize(Archive& ar, const uint32_t /* version */);
 
  private:
   //! Dataset pointer (will be removed in mlpack 5.0.0).

src/mlpack/methods/lmnn/lmnn_impl.hpp

@@ -115,7 +115,7 @@ void LMNN<DistanceType, DeprecatedOptimizerType>::LearnDistance(
 template<typename DistanceType, typename DeprecatedOptimizerType>
 template<typename Archive>
 void LMNN<DistanceType, DeprecatedOptimizerType>::serialize(
-    Archive& ar, const unsigned int /* version */)
+    Archive& ar, const uint32_t /* version */)
 {
   ar(CEREAL_NVP(k));
   ar(CEREAL_NVP(regularization));

src/mlpack/methods/nca/nca.hpp

@@ -167,7 +167,7 @@ class NCA
   DistanceType& Distance() { return distance; }
 
   template<typename Archive>
-  void serialize(Archive& ar, const unsigned int /* version */);
+  void serialize(Archive& ar, const uint32_t /* version */);
 
  private:
   //! Dataset pointer (will be removed in mlpack 5.0.0).

src/mlpack/methods/nca/nca_impl.hpp

@@ -98,7 +98,7 @@ void NCA<DistanceType, DeprecatedOptimizerType>::LearnDistance(
 template<typename DistanceType, typename DeprecatedOptimizerType>
 template<typename Archive>
 void NCA<DistanceType, DeprecatedOptimizerType>::serialize(
-    Archive& ar, const unsigned int /* version */)
+    Archive& ar, const uint32_t /* version */)
 {
   ar(CEREAL_NVP(distance));
 }

src/mlpack/methods/radical/radical.hpp

@@ -148,7 +148,7 @@ class Radical
 
   // Serialize the Radical object.
   template<typename Archive>
-  void serialize(Archive& ar, const unsigned int /* version */);
+  void serialize(Archive& ar, const uint32_t /* version */);
 
  private:
   //! Standard deviation of the Gaussian noise added to the replicates of

src/mlpack/methods/radical/radical_impl.hpp

@@ -217,7 +217,7 @@ inline void Radical::Apply(const MatType& matXT,
 }
 
 template<typename Archive>
-void Radical::serialize(Archive& ar, const unsigned int /* version */)
+void Radical::serialize(Archive& ar, const uint32_t /* version */)
 {
   ar(CEREAL_NVP(noiseStdDev));
   ar(CEREAL_NVP(replicates));