Add Clone() function which handles polymorphism correctly.

src/mlpack/methods/ann/ffn.hpp

@@ -296,7 +296,7 @@ class FFN
    * network or parameters for layers, its state may become invalid, so be sure
    * to call ResetParameters() afterwards.
    */
-  std::vector<Layer<InputType, OutputType>* >& Model() { return network; }
+  std::vector<Layer<InputType, OutputType>*>& Model() { return network; }
 
   //! Return the number of separable functions (the number of predictor points).
   size_t NumFunctions() const { return numFunctions; }

src/mlpack/methods/ann/ffn_impl.hpp

@@ -48,6 +48,7 @@ template<typename OutputLayerType,
          typename OutputType>
 FFN<OutputLayerType, InitializationRuleType, InputType, OutputType>::~FFN()
 {
+  //network.clear();
   for (size_t i = 0; i < network.size(); ++i)
     delete network[i];
 }
@@ -234,8 +235,8 @@ void FFN<OutputLayerType, InitializationRuleType, InputType, OutputType>::
 
   OutputType resultsTemp;
   Forward(arma::mat(predictors.colptr(0), predictors.n_rows, 1, false, true));
-  resultsTemp = network.back()->OutputParameter().col(0);
 
+  resultsTemp = network.back()->OutputParameter().col(0);
   results = arma::mat(resultsTemp.n_elem, predictors.n_cols);
   results.col(0) = resultsTemp.col(0);
 
@@ -629,7 +630,7 @@ FFN<OutputLayerType, InitializationRuleType, InputType, OutputType>::FFN(
   // Build new layers according to source network
   for (size_t i = 0; i < network.network.size(); ++i)
   {
-    this->network.push_back(network.network[i]);
+    this->network.push_back(network.network[i]->Clone());
     ResetUpdate(this->network.back());
   }
 };
@@ -665,7 +666,7 @@ template<typename OutputLayerType,
          typename OutputType>
 FFN<OutputLayerType, InitializationRuleType, InputType, OutputType>&
 FFN<OutputLayerType, InitializationRuleType, InputType, OutputType>::
-operator = (FFN network)
+operator =(FFN network)
 {
   Swap(network);
   return *this;

src/mlpack/methods/ann/layer/base_layer.hpp

@@ -73,6 +73,9 @@ class BaseLayer : public Layer<InputType, OutputType>
     // Nothing to do here.
   }
 
+  //! Clone the BaseLayer object. This handles polymorphism correctly.
+	BaseLayer* Clone() const { return new BaseLayer(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.
@@ -141,6 +144,7 @@ class BaseLayer : public Layer<InputType, OutputType>
 //     typename OutputType = arma::mat
 // >
 // using SigmoidLayer = BaseLayer<ActivationFunction, InputType, OutputType>;
+typedef BaseLayer<LogisticFunction, arma::mat, arma::mat> Sigmoid;
 typedef BaseLayer<LogisticFunction, arma::mat, arma::mat> SigmoidLayer;
 
 /**

src/mlpack/methods/ann/layer/c_relu.hpp

@@ -53,6 +53,9 @@ class CReLUType : public Layer<InputType, OutputType>
   //! Create the CReLU object.
   CReLUType();
 
+	//! Clone the CReLUType object. This handles polymorphism correctly.
+	CReLUType* Clone() const { return new CReLUType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/celu.hpp

@@ -69,6 +69,9 @@ class CELUType : public Layer<InputType, OutputType>
    */
   CELUType(const double alpha = 1.0);
 
+  //! Clone the CELUType object. This handles polymorphism correctly.
+	CELUType* Clone() const { return new CELUType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/concat.hpp

@@ -64,6 +64,9 @@ class ConcatType : public Layer<InputType, OutputType>
    */
   ~ConcatType();
 
+	//! Clone the ConcatType object. This handles polymorphism correctly.
+	ConcatType* Clone() const { return new ConcatType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/concatenate.hpp

@@ -53,6 +53,9 @@ class ConcatenateType : public Layer<InputType, OutputType>
   //! Operator= move constructor.
   ConcatenateType& operator=(ConcatenateType&& layer);
 
+  //! Clone the ConcatenateType object. This handles polymorphism correctly.
+	ConcatenateType* Clone() const { return new ConcatenateType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/constant.hpp

@@ -43,6 +43,8 @@ class ConstantType : public Layer<InputType, OutputType>
    */
   ConstantType(const size_t outSize, const double scalar = 0);
 
+  //! Clone the ConstantType object. This handles polymorphism correctly.
+  ConstantType* Clone() const { return new ConstantType(*this); }
   /**
    * Ordinary feed forward pass of a neural network. The forward pass fills the
    * output with the specified constant parameter.

src/mlpack/methods/ann/layer/dropconnect.hpp

@@ -73,6 +73,9 @@ class DropConnectType : public Layer<InputType, OutputType>
                   const size_t outSize,
                   const double ratio = 0.5);
 
+  //! Clone the DropConnectType object. This handles polymorphism correctly.
+  DropConnectType* Clone() const { return new DropConnectType(*this); }
+
   /**
    * Ordinary feed forward pass of the DropConnect layer.
    *

src/mlpack/methods/ann/layer/dropout.hpp

@@ -73,6 +73,9 @@ class DropoutType : public Layer<InputType, OutputType>
   //! Move assignment operator.
   DropoutType& operator=(DropoutType&& layer);
 
+  //! Clone the DropoutType object. This handles polymorphism correctly.
+  DropoutType* Clone() const { return new DropoutType(*this); }
+
   /**
    * Ordinary feed forward pass of the dropout layer.
    *

src/mlpack/methods/ann/layer/elu.hpp

@@ -127,6 +127,8 @@ class ELUType : public Layer<InputType, OutputType>
    */
   ELUType(const double alpha);
 
+  //! Clone the ELUType object. This handles polymorphism correctly.
+  ELUType* Clone() const { return new ELUType(*this); }
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/fast_lstm.hpp

@@ -97,6 +97,9 @@ class FastLSTMType : public Layer<InputType, OutputType>
                const size_t outSize,
                const size_t rho = std::numeric_limits<size_t>::max());
 
+  //! Clone the FastLSTMType object. This handles polymorphism correctly.
+	FastLSTMType* Clone() const { return new FastLSTMType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/flexible_relu.hpp

@@ -67,6 +67,9 @@ class FlexibleReLUType : public Layer<InputType, OutputType>
    */
   FlexibleReLUType(const double alpha = 0);
 
+  //! Clone the FlexibleReLUType object. This handles polymorphism correctly.
+  FlexibleReLUType* Clone() const { return new FlexibleReLUType(*this); }
+
   /**
    * Reset the layer parameter (alpha). The method is called to
    * assign the allocated memory to the learnable layer parameter.

src/mlpack/methods/ann/layer/hard_tanh.hpp

@@ -59,6 +59,9 @@ class HardTanHType : public Layer<InputType, OutputType>
    */
   HardTanHType(const double maxValue = 1, const double minValue = -1);
 
+  //! Clone the HardTanHType object. This handles polymorphism correctly.
+  HardTanHType* Clone() const { return new HardTanHType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/hardshrink.hpp

@@ -59,6 +59,9 @@ class HardShrinkType : public Layer<InputType, OutputType>
    */
   HardShrinkType(const double lambda = 0.5);
 
+  //! Clone the HardShrinkType object. This handles polymorphism correctly.
+  HardShrinkType* Clone() const { return new HardShrinkType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/highway.hpp

@@ -65,6 +65,9 @@ class HighwayType : public Layer<InputType, OutputType>
   //! Destroy the Highway object.
   ~HighwayType();
 
+	//! Clone the HighwayType object. This handles polymorphism correctly.
+	HighwayType* Clone() const { return new HighwayType(*this); }
+
   /**
    * Reset the layer parameter.
    */

src/mlpack/methods/ann/layer/join.hpp

@@ -36,6 +36,9 @@ class JoinType : public Layer<InputType, OutputType>
   //! Create the JoinType object.
   JoinType();
 
+  //! Clone the JoinType object. This handles polymorphism correctly.
+	JoinType* Clone() const { return new JoinType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

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

@@ -57,12 +57,26 @@ class Layer
 {
  public:
    //! Default constructor.
-   Layer() : outputWidth(0), outputHeight(0)
-   { /* Nothing to do here */ }
+   Layer() : outputWidth(0), outputHeight(0) { /* Nothing to do here */ }
 
    //! Default deconstructor.
    virtual ~Layer() = default;
 
+   //! Copy constructor.
+   Layer(const Layer& /* layer */) { /* Nothing to do here */ }
+
+   //! Make a copy of the object.
+   virtual Layer* Clone() const = 0;
+
+   //! Move constructor.
+   Layer(Layer&& /* layer */) { /* Nothing to do here */ }
+
+   //! Copy assignment operator.
+   virtual Layer& operator=(const Layer& /* layer */) { return *this; }
+
+   //! Move assignment operator.
+   virtual Layer& operator=(Layer&& /* layer */) { return *this; }
+
   /**
    * Takes an input object, and computes the corresponding output of the layer.
    * In general input and output are matrices. However, some special layers like

src/mlpack/methods/ann/layer/layer_norm.hpp

@@ -76,6 +76,9 @@ class LayerNormType : public Layer<InputType, OutputType>
    */
   LayerNormType(const size_t size, const double eps = 1e-8);
 
+  //! Clone the LayerNormType object. This handles polymorphism correctly.
+	LayerNormType* Clone() const { return new LayerNormType(*this); }
+
   /**
    * Reset the layer parameters.
    */

src/mlpack/methods/ann/layer/leaky_relu.hpp

@@ -53,6 +53,9 @@ class LeakyReLUType : public Layer<InputType, OutputType>
    */
   LeakyReLUType(const double alpha = 0.03);
 
+  //! Clone the LeakyReLUType object. This handles polymorphism correctly.
+  LeakyReLUType* Clone() const { return new LeakyReLUType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/linear.hpp

@@ -73,6 +73,9 @@ class LinearType: public Layer<InputType, OutputType>
   //! Move assignment operator.
   LinearType& operator=(LinearType&& layer);
 
+  //! Clone the LinearType object. This handles polymorphism correctly.
+  LinearType* Clone() const { return new LinearType(*this); }
+
   /**
    * Reset the layer parameter (weights and bias). The method is called to
    * assign the allocated memory to the internal learnable parameters.

src/mlpack/methods/ann/layer/linear3d.hpp

@@ -67,6 +67,9 @@ class Linear3DType : public Layer<InputType, OutputType>
   //! Move assignment operator.
   Linear3DType& operator=(Linear3DType&& layer);
 
+	//! Clone the Linear3DType object. This handles polymorphism correctly.
+	Linear3DType* Clone() const { return new Linear3DType(*this); }
+
   /*
    * Reset the layer parameter.
    */

src/mlpack/methods/ann/layer/linear_no_bias.hpp

@@ -55,6 +55,9 @@ class LinearNoBiasType : public Layer<InputType, OutputType>
                    const size_t outSize,
                    RegularizerType regularizer = RegularizerType());
 
+  //! Clone the LinearNoBiasType object. This handles polymorphism correctly.
+  LinearNoBiasType* Clone() const { return new LinearNoBiasType(*this); }
+
   //! Reset the layer parameter.
   void Reset();
 

src/mlpack/methods/ann/layer/log_softmax.hpp

@@ -41,6 +41,9 @@ class LogSoftMaxType : public Layer<InputType, OutputType>
    */
   LogSoftMaxType();
 
+  //! Clone the LogSoftMaxType object. This handles polymorphism correctly.
+  LogSoftMaxType* Clone() const { return new LogSoftMaxType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/lookup.hpp

@@ -49,6 +49,9 @@ class LookupType : public Layer<InputType, OutputType>
    */
   LookupType(const size_t vocabSize = 0, const size_t embeddingSize = 0);
 
+	//! Clone the LookupType object. This handles polymorphism correctly.
+	LookupType* Clone() const { return new LookupType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/multihead_attention.hpp

@@ -84,6 +84,12 @@ class MultiheadAttentionType : public Layer<InputType, OutputType>
                          const InputType& attnmask = InputType(),
                          const InputType& keyPaddingMask = InputType());
 
+	//! Clone the MultiheadAttentionType object. This handles polymorphism correctly.
+	MultiheadAttentionType* Clone() const
+  {
+    return new MultiheadAttentionType(*this);
+  }
+
   /**
    * Reset the layer parameters.
    */

src/mlpack/methods/ann/layer/multiply_constant.hpp

@@ -37,6 +37,12 @@ class MultiplyConstantType : public Layer<InputType, OutputType>
   //! Create the MultiplyConstant object.
   MultiplyConstantType(const double scalar = 1.0);
 
+	//! Clone the MultiplyConstantType object. This handles polymorphism correctly.
+	MultiplyConstantType* Clone() const
+  {
+    return new MultiplyConstantType(*this);
+  }
+
   /**
    * Ordinary feed forward pass of a neural network. Multiply the input with the
    * specified constant scalar value.

src/mlpack/methods/ann/layer/multiply_merge.hpp

@@ -51,6 +51,9 @@ class MultiplyMergeType : public Layer<InputType, OutputType>
   //! Destructor to release allocated memory.
   ~MultiplyMergeType();
 
+	//! Clone the MultiplyMergeType object. This handles polymorphism correctly.
+	MultiplyMergeType* Clone() const { return new MultiplyMergeType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/noisylinear.hpp

@@ -57,6 +57,9 @@ class NoisyLinearType : public Layer<InputType, OutputType>
   //! Operator= move constructor.
   NoisyLinearType& operator=(NoisyLinearType&& layer);
 
+  //! Clone the NoisyLinearType object. This handles polymorphism correctly.
+  NoisyLinearType* Clone() const { return new NoisyLinearType(*this); }
+
   //! Reset the layer parameter.
   void Reset();
 

src/mlpack/methods/ann/layer/padding.hpp

@@ -47,6 +47,9 @@ class PaddingType : public Layer<InputType, OutputType>
               const size_t padHTop = 0,
               const size_t padHBottom = 0);
 
+	//! Clone the PaddingType object. This handles polymorphism correctly.
+	PaddingType* Clone() const { return new PaddingType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/parametric_relu.hpp

@@ -55,6 +55,9 @@ class PReLUType : public Layer<InputType, OutputType>
    */
   PReLUType(const double userAlpha = 0.03);
 
+	//! Clone the PReLUType object. This handles polymorphism correctly.
+	PReLUType* Clone() const { return new PReLUType(*this); }
+
   //! Reset the layer parameter.
   void Reset();
 

src/mlpack/methods/ann/layer/positional_encoding.hpp

@@ -51,6 +51,8 @@ class PositionalEncodingType : public Layer<InputType, OutputType>
   PositionalEncodingType(const size_t embedDim,
                          const size_t maxSequenceLength);
 
+  //! Clone the PositionalEncodingType object. This handles polymorphism correctly.
+	PositionalEncodingType* Clone() const { return new PositionalEncodingType(*this); }
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/reinforce_normal.hpp

@@ -42,6 +42,9 @@ class ReinforceNormalType : public Layer<InputType, OutputType>
    */
   ReinforceNormalType(const double stdev = 1.0);
 
+  //! Clone the ReinforceNormalType object. This handles polymorphism correctly.
+	ReinforceNormalType* Clone() const { return new ReinforceNormalType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/reparametrization.hpp

@@ -72,6 +72,15 @@ class ReparametrizationType : public Layer<InputType, OutputType>
                          const bool includeKl = true,
                          const double beta = 1);
 
+  /**
+   * Clone the ReparametrizationType object. This handles polymorphism
+   * correctly.
+   */
+	ReparametrizationType* Clone() const
+  {
+    return new ReparametrizationType(*this);
+  }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/select.hpp

@@ -42,6 +42,9 @@ class SelectType : public Layer<InputType, OutputType>
    */
   SelectType(const size_t index = 0, const size_t elements = 0);
 
+  //! Clone the SelectType object. This handles polymorphism correctly.
+	SelectType* Clone() const { return new SelectType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/sequential.hpp

@@ -88,6 +88,9 @@ class SequentialType : public Layer<InputType, OutputType>
   //! Destroy the Sequential object.
   ~SequentialType();
 
+  //! Clone the SequentialType object. This handles polymorphism correctly.
+	SequentialType* Clone() const { return new SequentialType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/softmax.hpp

@@ -41,6 +41,9 @@ class SoftmaxType : public Layer<InputType, OutputType>
   //! Create the Softmax object.
   SoftmaxType();
 
+  //! Clone the SoftmaxType object. This handles polymorphism correctly.
+  SoftmaxType* Clone() const { return new SoftmaxType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/softmin.hpp

@@ -38,6 +38,9 @@ class SoftminType : public Layer<InputType, OutputType>
   //! Create the Softmin object.
   SoftminType();
 
+  //! Clone the SoftminType object. This handles polymorphism correctly.
+  SoftminType* Clone() const { return new SoftminType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/softshrink.hpp

@@ -62,6 +62,9 @@ class SoftShrinkType : public Layer<InputType, OutputType>
    */
   SoftShrinkType(const double lambda = 0.5);
 
+  //! Clone the SoftShrinkType object. This handles polymorphism correctly.
+  SoftShrinkType* Clone() const { return new SoftShrinkType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/spatial_dropout.hpp

@@ -58,6 +58,9 @@ class SpatialDropoutType : public Layer<InputType, OutputType>
    */
   SpatialDropoutType(const size_t size, const double ratio = 0.5);
 
+  //! Clone the SpatialDropoutType object. This handles polymorphism correctly.
+  SpatialDropoutType* Clone() const { return new SpatialDropoutType(*this); }
+
   /**
    * Ordinary feed forward pass of the SpatialDropout layer.
    *

src/mlpack/methods/ann/layer/subview.hpp

@@ -59,6 +59,9 @@ class SubviewType : public Layer<InputType, OutputType>
     /* Nothing to do here */
   }
 
+	//! Clone the SubviewType object. This handles polymorphism correctly.
+	SubviewType* Clone() const { return new SubviewType(*this); }
+
   /**
    * Ordinary feed forward pass of a neural network, evaluating the function
    * f(x) by propagating the activity forward through f.

src/mlpack/methods/ann/layer/virtual_batch_norm.hpp

@@ -62,6 +62,12 @@ class VirtualBatchNormType : public Layer<InputType, OutputType>
                        const size_t size,
                        const double eps = 1e-8);
 
+  //! Clone the VirtualBatchNormType object. This handles polymorphism correctly.
+  VirtualBatchNormType* Clone() const
+  {
+    return new VirtualBatchNormType(*this);
+  }
+
   /**
    * Reset the layer parameters.
    */

src/mlpack/methods/ann/layer/weight_norm.hpp

@@ -62,6 +62,9 @@ class WeightNormType : public Layer<InputType, OutputType>
   //! Destructor to release allocated memory.
   ~WeightNormType();
 
+  //! Clone the WeightNormType object. This handles polymorphism correctly.
+  WeightNormType* Clone() const { return new WeightNormType(*this); }
+
   /**
    * Reset the layer parameters.
    */