Fix PSO bound type

include/ensmallen_bits/pso/pso.hpp

@@ -40,19 +40,17 @@ namespace ens {
  * For more information, refer to:
  *
  * @inproceedings{Kennedy,
- *    doi = {10.1109/icnn.1995.488968},
- *    url = {https://doi.org/10.1109/icnn.1995.488968},
- *    publisher = {{IEEE}},
- *    author = {J. Kennedy and R. Eberhart},
  *    title = {Particle swarm optimization},
- *    booktitle = {Proceedings of {ICNN}{\textquotesingle}95 - 
- *                 International Conference on Neural Networks}
+ *    author = {J. Kennedy and R. Eberhart},
+ *    booktitle = {Proceedings of {ICNN}{\textquotesingle}95 -
+ *                 International Conference on Neural Networks},
+ *    publisher = {{IEEE}}
  * }
  *
  * PSO can optimize arbitrary functions. For more details, see the documentation
  * on function types included with this distribution or on the ensmallen
  * website.
- * 
+ *
  * For PSO to work, the function being optimized must implement an
  * ArbitraryFunctionType template parameter. The respective class must implement
  * the following function:
@@ -159,16 +157,16 @@ class PSOType
   size_t& NumParticles() { return numParticles; }
 
   //! Retrieve value of lowerBound.
-  size_t LowerBound() const { return lowerBound; }
+  const arma::vec& LowerBound() const { return lowerBound; }
 
   //! Modify value of lowerBound.
-  size_t& LowerBound() { return lowerBound; }
+  arma::vec& LowerBound() { return lowerBound; }
 
   //! Retrieve value of upperBound.
-  size_t UpperBound() const { return upperBound; }
+  const arma::vec& UpperBound() const { return upperBound; }
 
   //! Modify value of upperBound.
-  size_t& UpperBound() { return upperBound; }
+  arma::vec& UpperBound() { return upperBound; }
 
   //! Retrieve value of maxIterations.
   size_t MaxIterations() const { return maxIterations; }

include/ensmallen_bits/pso/pso_impl.hpp

@@ -20,7 +20,7 @@
 namespace ens {
 /* After the velocity of each particle is updated at the end of each iteration
  * in PSO, the position of particle i (in iteration j) is updated as:
- * 
+ *
  * \f[
  *     p_{i, j + 1} = p_{i, j} + v_{i, j}
  * \f]
@@ -42,7 +42,7 @@ double PSOType<VelocityUpdatePolicy, InitPolicy>::Optimize(
    */
   typedef Function<FunctionType> ArbitraryFunctionType;
   ArbitraryFunctionType& f(static_cast<ArbitraryFunctionType&>(function));
-  
+
   // Make sure we have the methods that we need.
   traits::CheckEvaluate<ArbitraryFunctionType>();
 
@@ -70,14 +70,14 @@ double PSOType<VelocityUpdatePolicy, InitPolicy>::Optimize(
     particleFitnesses(i) = f.Evaluate(particlePositions.slice(i));
     particleBestFitnesses(i) = particleFitnesses(i);
   }
-  
+
   // Declare queue to keep track of improvements over a number of iterations.
   queue <double> performanceHorizon;
   // Variable to store the position of the best particle.
   size_t bestParticle = 0;
   // Find the best fitness.
   double bestFitness = std::numeric_limits<double>::max();
-  
+
   // Run PSO for horizonSize number of iterations.
   // This will allow the performanceHorizon to be updated.
   // With some initial values in this, we may proceed with the remaining steps
@@ -128,7 +128,7 @@ double PSOType<VelocityUpdatePolicy, InitPolicy>::Optimize(
     // If there is no significant improvement, terminate.
     if (performanceHorizon.front() - performanceHorizon.back() < impTolerance)
       break;
-    
+
     // Calculate fitness and evaluate personal best.
     for (size_t j = 0; j < numParticles; j++)
     {