// [[Rcpp::depends(BH)]]
//===========================================================================
/*!
 * 
 *
 * \brief       concatenation of two models, with type erasure
 * 
 * 
 *
 * \author      O. Krause
 * \date        2010-2011
 *
 *
 * \par Copyright 1995-2015 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <http://image.diku.dk/shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
//===========================================================================

#ifndef SHARK_MODEL_CONCATENATEDMODEL_H
#define SHARK_MODEL_CONCATENATEDMODEL_H

#include <shark/Models/AbstractModel.h>
#include <boost/scoped_ptr.hpp>

namespace shark {

namespace detail{


///\brief Baseclass for the wrapper which is used to hide the matrix type. 
///
///Additional to the requirement of a Model, a clone() method must be implemented which is used to
///copy a wrapper
template<class InputType, class OutputType>
class ConcatenatedModelWrapperBase:public AbstractModel<InputType,OutputType>{
public:
        ConcatenatedModelWrapperBase():m_optimizeFirst(true), m_optimizeSecond(true){}
        virtual ConcatenatedModelWrapperBase<InputType,OutputType>* clone() const = 0;

        bool optimizeFirstModelParameters()const{
                return m_optimizeFirst;
        }
        
        bool& optimizeFirstModelParameters(){
                return m_optimizeFirst;
        }
        
        bool optimizeSecondModelParameters()const{
                return m_optimizeSecond;
        }
        
        bool& optimizeSecondModelParameters(){
                return m_optimizeSecond;
        }
protected:
        bool m_optimizeFirst;
        bool m_optimizeSecond;
};

///\brief Internal Wrappertype to connect the output of the first model with the input of the second model.
///
///This model is also created when concatenating two models with operator>> (firstModel>>secondModel)
template<class InputType, class IntermediateType, class OutputType>
class ConcatenatedModelWrapper : public ConcatenatedModelWrapperBase<InputType, OutputType> {
protected:
        typedef typename AbstractModel<InputType,IntermediateType>::BatchOutputType BatchIntermediateType;
        AbstractModel<InputType,IntermediateType>* m_firstModel;
        AbstractModel<IntermediateType,OutputType>* m_secondModel;

        typedef ConcatenatedModelWrapperBase<InputType, OutputType> base_type;
        using base_type::m_optimizeFirst;
        using base_type::m_optimizeSecond;

        struct InternalState: public State{
                BatchIntermediateType intermediateResult;
                boost::shared_ptr<State> firstModelState;
                boost::shared_ptr<State> secondModelState;
        };
public:
        typedef typename base_type::BatchInputType BatchInputType;
        
        typedef typename base_type::BatchOutputType BatchOutputType;
        ConcatenatedModelWrapper(
                AbstractModel<InputType, IntermediateType>* firstModel,
                AbstractModel<IntermediateType, OutputType>* secondModel)
        : m_firstModel(firstModel), m_secondModel(secondModel)
        {
                if (firstModel->hasFirstParameterDerivative()
                        && secondModel->hasFirstParameterDerivative()
                        && secondModel ->hasFirstInputDerivative())
                { 
                        this->m_features |= base_type::HAS_FIRST_PARAMETER_DERIVATIVE;
                }

                if (firstModel->hasFirstInputDerivative()
                        && secondModel->hasFirstInputDerivative())
                { 
                        this->m_features |= base_type::HAS_FIRST_INPUT_DERIVATIVE;
                }
        }

        /// \brief From INameable: return the class name.
        std::string name() const
        { return "Concatenation<" + m_firstModel->name() + "," + m_secondModel->name() + ">"; }

        ConcatenatedModelWrapperBase<InputType, OutputType>* clone()const{
                return new ConcatenatedModelWrapper<InputType, IntermediateType, OutputType>(*this);
        }

        RealVector parameterVector() const {
                RealVector params(numberOfParameters());
                if(m_optimizeFirst && m_optimizeSecond)
                        init(params) << parameters(*m_firstModel), parameters(*m_secondModel);
                else if (m_optimizeFirst)
                        params = m_firstModel->parameterVector();
                else if (m_optimizeSecond)
                        params = m_secondModel->parameterVector();
                return params;
        }

        void setParameterVector(RealVector const& newParameters) {
                if(m_optimizeFirst && m_optimizeSecond)
                        init(newParameters) >> parameters(*m_firstModel), parameters(*m_secondModel);
                else if (m_optimizeFirst)
                        m_firstModel->setParameterVector(newParameters);
                else if (m_optimizeSecond)
                        m_secondModel->setParameterVector(newParameters);
                
        }
        
        boost::shared_ptr<State> createState()const{
                InternalState* state = new InternalState();
                boost::shared_ptr<State> ptrState(state);
                state->firstModelState = m_firstModel->createState();
                state->secondModelState = m_secondModel->createState();
                return ptrState;
        }

        std::size_t numberOfParameters() const {
                std::size_t numParams = 0;
                if(m_optimizeFirst)
                        numParams += m_firstModel->numberOfParameters();
                if (m_optimizeSecond)
                        numParams += m_secondModel->numberOfParameters();
                return numParams;
                        
        }

        void eval( BatchInputType const& patterns, BatchOutputType& outputs)const{
                m_secondModel->eval(
                        (*m_firstModel)(patterns), 
                        outputs
                );
        }

        void eval( BatchInputType const& patterns, BatchOutputType& outputs, State& state)const{
                InternalState& s = state.toState<InternalState>();
                m_firstModel->eval(patterns, s.intermediateResult,*s.firstModelState);
                m_secondModel->eval(s.intermediateResult, outputs,*s.secondModelState);
        }

        void weightedParameterDerivative(
                BatchInputType const& patterns, BatchOutputType const& coefficients, State const& state, RealVector& gradient
        )const{
                InternalState const& s = state.toState<InternalState>();

                //don't compute the derivative of the first model if it does not have parameters.
                std::size_t numParamsFirst = m_firstModel->numberOfParameters();
                if(m_optimizeFirst && m_optimizeSecond && numParamsFirst != 0){
                        RealVector firstParameterDerivative;
                        BatchIntermediateType secondInputDerivative;
                        RealVector secondParameterDerivative;
                        
                        m_secondModel->weightedDerivatives(
                                s.intermediateResult,coefficients,*s.secondModelState,
                                secondParameterDerivative,secondInputDerivative
                        );
                        m_firstModel->weightedParameterDerivative(patterns,secondInputDerivative,*s.firstModelState,firstParameterDerivative);
                        
                        gradient.resize(numberOfParameters());
                        init(gradient)<<firstParameterDerivative,secondParameterDerivative;
                }else if(m_optimizeFirst && numParamsFirst != 0){
                        RealVector firstParameterDerivative;
                        BatchIntermediateType secondInputDerivative;
                        
                        m_secondModel->weightedInputDerivative(
                                s.intermediateResult,coefficients,*s.secondModelState,secondInputDerivative
                        );
                        m_firstModel->weightedParameterDerivative(patterns,secondInputDerivative,*s.firstModelState,gradient);
                }else if(m_optimizeSecond){
                        m_secondModel->weightedParameterDerivative(
                                s.intermediateResult,coefficients,*s.secondModelState,
                                gradient
                        );
                }else {
                        gradient.resize(0);
                }
        }

        void weightedInputDerivative(
                BatchInputType const& patterns, BatchOutputType const& coefficients, State const& state, BatchOutputType& gradient
        )const{
                InternalState const& s = state.toState<InternalState>();
                BatchIntermediateType secondInputDerivative;
                m_secondModel->weightedInputDerivative(s.intermediateResult, coefficients, *s.secondModelState, secondInputDerivative);
                m_firstModel->weightedInputDerivative(patterns, secondInputDerivative, *s.firstModelState, gradient);
        }
        
        //special implementation, because we can reuse the input derivative of the second model for the calculation of both derivatives of the first
        virtual void weightedDerivatives(
                BatchInputType const & patterns, 
                BatchOutputType const & coefficients, 
                State const& state,
                RealVector& parameterDerivative,
                BatchInputType& inputDerivative
        )const{
                InternalState const& s = state.toState<InternalState>();
                std::size_t firstParam=m_firstModel->numberOfParameters();
                std::size_t secondParam=m_secondModel->numberOfParameters();
                parameterDerivative.resize(firstParam+secondParam);

                RealVector firstParameterDerivative;
                BatchIntermediateType secondInputDerivative;
                RealVector secondParameterDerivative;
                if(m_optimizeSecond){
                        m_secondModel->weightedDerivatives(
                                s.intermediateResult, coefficients, *s.firstModelState, secondParameterDerivative, secondInputDerivative
                        );
                }else{
                        m_secondModel->weightedInputDerivative(
                                s.intermediateResult, coefficients, *s.firstModelState, secondInputDerivative
                        );
                }
                if(m_optimizeFirst){
                        m_firstModel->weightedDerivatives(
                                patterns, secondInputDerivative, *s.secondModelState, parameterDerivative, inputDerivative
                        );
                }else{
                        m_firstModel->weightedInputDerivative(
                                patterns, secondInputDerivative, *s.secondModelState, inputDerivative
                        );
                }

                parameterDerivative.resize(firstParam+secondParam);
                init(parameterDerivative)<<firstParameterDerivative,secondParameterDerivative;
        }
        /// From ISerializable
        /// From ISerializable
        };

///\brief When using operator>> to connect more than two models, this type is created.
///
///When concatenating two models, the ConcatenatedModelWrapper is created. But it is only a temporary object. 
///Thus when concatenating it with another model, it must be made persistent. We do that by simply calling clone() and saving the now
///persistens pointer. Note, that the right-hand-side is not allowed to be a ConcatenatedModelWrapperBase. This is not checked.
template<class InputType, class IntermediateType, class OutputType>
class ConcatenatedModelList:public ConcatenatedModelWrapper<InputType,IntermediateType,OutputType>{
private:
        typedef ConcatenatedModelWrapper<InputType,IntermediateType,OutputType> base_type;
        typedef ConcatenatedModelWrapperBase<InputType,IntermediateType> FirstModelType;
public: 

        ConcatenatedModelList(
                const FirstModelType& firstModel,
                AbstractModel<IntermediateType, OutputType>* secondModel
        ):base_type(firstModel.clone(),secondModel){
                if (base_type::m_firstModel->hasFirstParameterDerivative()
                        && secondModel->hasFirstParameterDerivative()
                        && secondModel ->hasFirstInputDerivative())
                { 
                        this->m_features |= base_type::HAS_FIRST_PARAMETER_DERIVATIVE;
                }

                if (base_type::m_firstModel->hasFirstInputDerivative()
                        && secondModel->hasFirstInputDerivative())
                { 
                        this->m_features |= base_type::HAS_FIRST_INPUT_DERIVATIVE;
                }
        }

        ~ConcatenatedModelList(){
                delete base_type::m_firstModel;
        }

        /// \brief From INameable: return the class name.
        std::string name() const
        { return "Concatenation<" + base_type::m_firstModel->name() + "," + base_type::m_secondModel->name() + ">"; }

        ConcatenatedModelWrapperBase<InputType, OutputType>* clone()const{
                return new ConcatenatedModelList<InputType, IntermediateType, OutputType>(
                        *static_cast<FirstModelType*>(base_type::m_firstModel),//get the type information back
                        base_type::m_secondModel
                );
        }
};

}

///\brief Connects two AbstractModels so that the output of the first model is the input of the second.
///
///The type of the output of the first model must match the type of the input of the second model exactly.
template<class InputT,class IntermediateT,class OutputT>
detail::ConcatenatedModelWrapper<InputT,IntermediateT,OutputT> 
operator>>(AbstractModel<InputT,IntermediateT>& firstModel,AbstractModel<IntermediateT,OutputT>& secondModel){
        return detail::ConcatenatedModelWrapper<InputT,IntermediateT,OutputT> (&firstModel,&secondModel);
}

///\brief Connects another AbstractModel two a previously created connection of models
template<class InputT,class IntermediateT,class OutputT>
detail::ConcatenatedModelList<InputT,IntermediateT,OutputT> 
operator>>(
        const detail::ConcatenatedModelWrapperBase<InputT,IntermediateT>& firstModel,
        AbstractModel<IntermediateT,OutputT>& secondModel
){
        return detail::ConcatenatedModelList<InputT,IntermediateT,OutputT> (firstModel,&secondModel);
}



///\brief ConcatenatedModel concatenates two models such that the output of the first model is input to the second.
///
///Sometimes a series of models is needed to generate the desired output. For example when input data needs to be 
///normalized before it can be put into the trained model. In this case, the ConcatenatedModel can be used to 
///represent this series as one model. 
///The easiest way to do is is using the operator >> of AbstractModel:
///ConcatenatedModel<InputType,OutputType> model = model1>>model2;
///InputType must be the type of input model1 receives and model2 the output of model2. The output of model1 and input
///of model2 must match. Another way of construction is calling the constructor of ConcatenatedModel using the constructor:
/// ConcatenatedModel<InputType,OutputType> model (&modell,&model2);
///warning: model1 and model2 must outlive model. When they are destroyed first, behavior is undefined.
template<class InputType, class OutputType>
class ConcatenatedModel: public AbstractModel<InputType,OutputType> {
private:
        boost::scoped_ptr<detail::ConcatenatedModelWrapperBase<InputType, OutputType> > m_wrapper;
        typedef AbstractModel<InputType, OutputType> base_type;

public:
        typedef typename base_type::BatchInputType BatchInputType;
        typedef typename base_type::BatchOutputType BatchOutputType;


        ///creates a concatenated model using two base model. this is equivalent to concModel = *firstModel >> *secondModel;
        template<class T>
        ConcatenatedModel(AbstractModel<InputType, T>* firstModel, AbstractModel<T, OutputType>* secondModel) {
                m_wrapper.reset(new detail::ConcatenatedModelWrapper<InputType, T, OutputType>(firstModel, secondModel));
                if (m_wrapper->hasFirstParameterDerivative()){ 
                        this->m_features |= base_type::HAS_FIRST_PARAMETER_DERIVATIVE; 
                }

                if (m_wrapper->hasFirstInputDerivative()){ 
                        this->m_features |= base_type::HAS_FIRST_INPUT_DERIVATIVE; 
                }
        }
        ///copy constructor to allow ConcatenatedModel concModel = model1 >> model2 >> model3;
        ConcatenatedModel(const detail::ConcatenatedModelWrapperBase<InputType,OutputType>& wrapper) {
                m_wrapper.reset(wrapper.clone());
                if (m_wrapper->hasFirstParameterDerivative()){ 
                        this->m_features |= base_type::HAS_FIRST_PARAMETER_DERIVATIVE; 
                }

                if (m_wrapper->hasFirstInputDerivative()){ 
                        this->m_features |= base_type::HAS_FIRST_INPUT_DERIVATIVE; 
                }
        }
        ///operator =  to allow concModel = model1 >> model2 >> model3; for a previously declared concatenadel model
        ConcatenatedModel<InputType,OutputType>& operator = ( detail::ConcatenatedModelWrapperBase<InputType,OutputType>& wrapper ){
                m_wrapper.reset(wrapper.clone());
                if (m_wrapper->hasFirstParameterDerivative()){ 
                        this->m_features |= base_type::HAS_FIRST_PARAMETER_DERIVATIVE; 
                }

                if (m_wrapper->hasFirstInputDerivative()){ 
                        this->m_features |= base_type::HAS_FIRST_INPUT_DERIVATIVE; 
                }

                return *this;
        }
        
        /// \brief Returns whether the parameters of the first model are going to be optimized
        ///
        /// Remember that concatModel = first >> second, so it is the lower layer.
        bool optimizeFirstModelParameters()const{
                return m_wrapper->optimizeFirstModelParameters();
        }
        
        /// \brief Returns a variable indicting whether the parameters of the first model are going to be optimized
        ///
        /// Remember that concatModel = first >> second, so it is the lower layer.
        bool& optimizeFirstModelParameters(){
                return m_wrapper->optimizeFirstModelParameters();
        }
        
        /// \brief Returns whether the parameters of the second model are going to be optimized
        ///
        /// Remember that concatModel = first >> second, so it is the upper layer.
        bool optimizeSecondModelParameters()const{
                return m_wrapper->optimizeSecondModelParameters();
        }
        
        /// \brief Returns a variable indicting whether the parameters of the second model are going to be optimized
        ///
        /// Remember that concatModel = first >> second, so it is the upper layer.
        bool& optimizeSecondModelParameters(){
                return m_wrapper->optimizeSecondModelParameters();
        }

        ConcatenatedModel(const ConcatenatedModel<InputType, OutputType>& src)
        :m_wrapper(src.m_wrapper->clone()) {
                this->m_features = src.m_features;
        }

        /// \brief From INameable: return the class name.
        std::string name() const
        { return m_wrapper->name(); }

        const ConcatenatedModel<InputType,OutputType>& operator = (const ConcatenatedModel<InputType, OutputType>& src) {
                ConcatenatedModel<InputType,OutputType> copy(src);
                swap(m_wrapper,copy.m_wrapper);
                std::swap(base_type::m_features,copy.m_features);
                return *this;
        }

        RealVector parameterVector() const {
                return m_wrapper->parameterVector();
        }

        void setParameterVector(RealVector const& newParameters) {
                m_wrapper->setParameterVector(newParameters);
        }

        size_t numberOfParameters() const {
                return m_wrapper->numberOfParameters();
        }
        
        boost::shared_ptr<State> createState()const{
                return m_wrapper->createState();
        }

        using base_type::eval;
        void eval(BatchInputType const& patterns, BatchOutputType& outputs)const {
                m_wrapper->eval(patterns, outputs);
        }
        void eval(BatchInputType const& patterns, BatchOutputType& outputs, State& state)const {
                m_wrapper->eval(patterns, outputs, state);
        }
        
        void weightedParameterDerivative(
                BatchInputType const& patterns, BatchOutputType const& coefficients, State const& state, RealVector& gradient
        )const{
                m_wrapper->weightedParameterDerivative(patterns, coefficients, state, gradient);
        }

        void weightedInputDerivative(
                BatchInputType const& patterns, BatchOutputType const& coefficients, State const& state, BatchOutputType& derivatives
        )const{
                m_wrapper->weightedInputDerivative(patterns, coefficients, state, derivatives);
        }

        virtual void weightedDerivatives(
                BatchInputType const & patterns,
                BatchOutputType const & coefficients,
                State const& state,
                RealVector& parameterDerivative,
                BatchInputType& inputDerivative
        )const{
                m_wrapper->weightedDerivatives(patterns, coefficients, state, parameterDerivative,inputDerivative);
        }

        /// From ISerializable
        /// From ISerializable
        };


}
#endif

