Upgrade ensmallen 1 16 0 (#13)

* Upgrade to ensmallen 1.16.0

* Bump package version

* Add new release notes

* Bump cran comments

* Address return type issue identified in mlpack/ensmallen#123

* Ensure const reference

* Fix another type return mlpack/ensmallen#126

Author: coatless

ChangeLog

@@ -1,3 +1,15 @@
+2019-08-09  James Balamuta  <[email protected]>
+
+	* DESCRIPTION (Version, Date): Release 1.16.0
+
+	* NEWS.md: Update for Ensmallen release 1.16.0
+
+	* inst/include/ensmallen_bits: Upgraded to Ensmallen 1.16.0
+	* inst/include/ensmallen.hpp: ditto
+
+	* inst/include/ensmallen_bits/pso/pso.hpp: Fixed return type issue detected
+	  by gcc 9.
+
 2019-05-20  James Balamuta  <[email protected]>
 
 	* DESCRIPTION (Version, Date): Release 1.15.0

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: RcppEnsmallen
 Title: Header-Only C++ Mathematical Optimization Library for 'Armadillo'
-Version: 0.1.15.0.1
+Version: 0.1.16.0.1
 Authors@R: c(
     person("James Joseph", "Balamuta", email = "[email protected]", 
            role = c("aut", "cre", "cph"), 

NEWS.md

@@ -1,3 +1,14 @@
+# RcppEnsmallen 0.1.16.0.1
+
+- Upgraded to ensmallen release 1.16.0 "Loud Alarm Clock" (2019-08-09)
+  - Add option to avoid computing exact objective at the end of the optimization
+    ([#109](https://github.com/mlpack/ensmallen/pull/109)).
+  - Fix handling of curvature for BigBatchSGD ([#118](https://github.com/mlpack/ensmallen/pull/118)).
+  - Reduce runtime of tests ([#118](https://github.com/mlpack/ensmallen/pull/118)).
+  - Introduce local-best particle swarm optimization, `LBestPSO`, for
+    unconstrained optimization problems ([#86](https://github.com/mlpack/ensmallen/pull/86)).
+- Fix return type error in `PSO` ([#123](https://github.com/mlpack/ensmallen/pull/123))
+
 # RcppEnsmallen 0.1.15.0.1
 
 - Upgraded to ensmallen release 1.15.0 "Wrong Side Of The Road" (2019-05-14)

cran-comments.md

@@ -1,7 +1,7 @@
 ## Test environments
 
-* local OS X install, R 3.6.0
-* ubuntu 14.04 (on travis-ci), R 3.6.0
+* local OS X install, R 3.6.1
+* ubuntu 14.04 (on travis-ci), R 3.6.1
 * win-builder (devel and release)
 
 ## R CMD check results

inst/include/ensmallen.hpp

@@ -83,6 +83,7 @@
 #include "ensmallen_bits/lbfgs/lbfgs.hpp"
 #include "ensmallen_bits/padam/padam.hpp"
 #include "ensmallen_bits/parallel_sgd/parallel_sgd.hpp"
+#include "ensmallen_bits/pso/pso.hpp"
 #include "ensmallen_bits/rmsprop/rmsprop.hpp"
 
 #include "ensmallen_bits/sa/sa.hpp"

inst/include/ensmallen_bits/ada_delta/ada_delta.hpp

@@ -66,6 +66,8 @@ class AdaDelta
    *        function is visited in linear order.
    * @param resetPolicy If true, parameters are reset before every Optimize
    *        call; otherwise, their values are retained.
+   * @param exactObjective Calculate the exact objective (Default: estimate the
+   *        final objective obtained on the last pass over the data).
    */
   AdaDelta(const double stepSize = 1.0,
            const size_t batchSize = 32,
@@ -74,7 +76,8 @@ class AdaDelta
            const size_t maxIterations = 100000,
            const double tolerance = 1e-5,
            const bool shuffle = true,
-           const bool resetPolicy = true);
+           const bool resetPolicy = true,
+           const bool exactObjective = false);
 
   /**
    * Optimize the given function using AdaDelta. The given starting point will
@@ -128,6 +131,11 @@ class AdaDelta
   //! Modify whether or not the individual functions are shuffled.
   bool& Shuffle() { return optimizer.Shuffle(); }
 
+  //! Get whether or not the actual objective is calculated.
+  bool ExactObjective() const { return optimizer.ExactObjective(); }
+  //! Modify whether or not the actual objective is calculated.
+  bool& ExactObjective() { return optimizer.ExactObjective(); }
+
   //! Get whether or not the update policy parameters
   //! are reset before Optimize call.
   bool ResetPolicy() const { return optimizer.ResetPolicy(); }

inst/include/ensmallen_bits/ada_delta/ada_delta_impl.hpp

@@ -26,15 +26,17 @@ inline AdaDelta::AdaDelta(const double stepSize,
                           const size_t maxIterations,
                           const double tolerance,
                           const bool shuffle,
-                          const bool resetPolicy) :
+                          const bool resetPolicy,
+                          const bool exactObjective) :
     optimizer(stepSize,
               batchSize,
               maxIterations,
               tolerance,
               shuffle,
               AdaDeltaUpdate(rho, epsilon),
               NoDecay(),
-              resetPolicy)
+              resetPolicy,
+              exactObjective)
 { /* Nothing to do. */ }
 
 } // namespace ens

inst/include/ensmallen_bits/ada_grad/ada_grad.hpp

@@ -64,14 +64,17 @@ class AdaGrad
    *        function is visited in linear order.
    * @param resetPolicy If true, parameters are reset before every Optimize
    *        call; otherwise, their values are retained.
+   * @param exactObjective Calculate the exact objective (Default: estimate the
+   *        final objective obtained on the last pass over the data).
    */
   AdaGrad(const double stepSize = 0.01,
           const size_t batchSize = 32,
           const double epsilon = 1e-8,
           const size_t maxIterations = 100000,
           const double tolerance = 1e-5,
           const bool shuffle = true,
-          const bool resetPolicy = true);
+          const bool resetPolicy = true,
+          const bool exactObjective = false);
 
   /**
    * Optimize the given function using AdaGrad. The given starting point will
@@ -119,6 +122,11 @@ class AdaGrad
   //! Modify whether or not the individual functions are shuffled.
   bool& Shuffle() { return optimizer.Shuffle(); }
 
+  //! Get whether or not the actual objective is calculated.
+  bool ExactObjective() const { return optimizer.ExactObjective(); }
+  //! Modify whether or not the actual objective is calculated.
+  bool& ExactObjective() { return optimizer.ExactObjective(); }
+
   //! Get whether or not the update policy parameters
   //! are reset before Optimize call.
   bool ResetPolicy() const { return optimizer.ResetPolicy(); }

inst/include/ensmallen_bits/ada_grad/ada_grad_impl.hpp

@@ -23,15 +23,17 @@ inline AdaGrad::AdaGrad(const double stepSize,
                         const size_t maxIterations,
                         const double tolerance,
                         const bool shuffle,
-                        const bool resetPolicy) :
+                        const bool resetPolicy,
+                        const bool exactObjective) :
     optimizer(stepSize,
               batchSize,
               maxIterations,
               tolerance,
               shuffle,
               AdaGradUpdate(epsilon),
               NoDecay(),
-              resetPolicy)
+              resetPolicy,
+              exactObjective)
 { /* Nothing to do. */ }
 
 } // namespace ens

inst/include/ensmallen_bits/adam/adam.hpp

@@ -88,6 +88,8 @@ class AdamType
    *        function is visited in linear order.
    * @param resetPolicy If true, parameters are reset before every Optimize
    *        call; otherwise, their values are retained.
+   * @param exactObjective Calculate the exact objective (Default: estimate the
+   *        final objective obtained on the last pass over the data).
    */
   AdamType(const double stepSize = 0.001,
            const size_t batchSize = 32,
@@ -97,7 +99,8 @@ class AdamType
            const size_t maxIterations = 100000,
            const double tolerance = 1e-5,
            const bool shuffle = true,
-           const bool resetPolicy = true);
+           const bool resetPolicy = true,
+           const bool exactObjective = false);
 
   /**
    * Optimize the given function using Adam. The given starting point will be
@@ -155,6 +158,11 @@ class AdamType
   //! Modify whether or not the individual functions are shuffled.
   bool& Shuffle() { return optimizer.Shuffle(); }
 
+  //! Get whether or not the actual objective is calculated.
+  bool ExactObjective() const { return optimizer.ExactObjective(); }
+  //! Modify whether or not the actual objective is calculated.
+  bool& ExactObjective() { return optimizer.ExactObjective(); }
+
   //! Get whether or not the update policy parameters
   //! are reset before Optimize call.
   bool ResetPolicy() const { return optimizer.ResetPolicy(); }