CrossReliability.h
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	//===-- rosa/delux/CrossReliability.h ---------------------------- C++ --===//
	//
	// The RoSA Framework
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file rosa/delux/CrossReliability.h
	///
	/// \author Daniel Schnoell
	///
	/// \date 2019
	///
	/// \brief
	///
	/// \todo there is 1 exception that needs to be handled correctly.
	/// \note the default search function is extremely slow maby this could be done
	/// via template for storage class and the functions/methods to efficiently find
	/// the correct LinearFunction
	//===----------------------------------------------------------------------===//
	#ifndef ROSA_AGENT_CROSSRELIABILITY_H
	#define ROSA_AGENT_CROSSRELIABILITY_H

	#include "rosa/agent/Abstraction.hpp"
	#include "rosa/agent/Functionality.h"
	#include "rosa/core/forward_declarations.h" // needed for id_t
	#include "rosa/support/log.h" // needed for error "handling"
	#include "rosa/agent/ReliabilityConfidenceCombinator.h"

	// nedded headers

	#include <string>
	#include <type_traits> //assert
	#include <vector>
	// for static methods
	#include <algorithm>
	#include <numeric>

	namespace rosa {
	namespace agent {




	/// Calculates the Cross Reliability
	/// \brief it uses the state represented by a numerical value and calculates the
	/// Reliability of a given agent( represented by there id ) in connection to all
	/// other given agents
	///
	/// \note all combination of agents and there coresponding Cross Reliability
	/// function have to be specified
	template <typename StateType, typename Type>
	class CrossReliability : public Abstraction<StateType, Type> {

	static_assert(
	std::is_arithmetic<Type>::value,
	"CrossReliability: <Type> has to be arithmetic type\n"); // sanitny check
	static_assert(
	std::is_arithmetic<StateType>::value,
	"CrossReliability: <StateType> has to be arithmetic type\n"); // sanitny
	// check

	using Abstraction = typename rosa::agent::Abstraction<StateType, Type>;

	struct Functionblock {
	bool exists = false;
	id_t A;
	id_t B;
	Abstraction *Funct;
	};

	/// From Maxi in his code defined as 1 can be changed by set
	Type crossReliabilityParameter = 1;

	/// Stored Cross Reliability Functions
	std::vector<Functionblock> Functions;

	/// Method which is used to combine the generated values
	Type (*Method)(std::vector<Type> values) = AVERAGE;

	//--------------------------------------------------------------------------------
	// helper function
	/// evalues the absolute distance between two values
	/// \note this is actually the absolute distance but to ceep it somewhat
	/// conform with maxis code
	template <typename Type_t> Type_t AbsuluteValue(Type_t A, Type_t B) {
	return ((A - B) < 0) ? B - A : A - B;
	}

	/// verry inefficient searchFunction
	Functionblock (*searchFunction)(std::vector<Functionblock> vect,
	const id_t nameA, const id_t nameB) =
	[](std::vector<Functionblock> vect, const id_t nameA,
	const id_t nameB) -> Functionblock {
	for (Functionblock tmp : vect) {
	if (tmp.A == nameA && tmp.B == nameB)
	return tmp;
	if (tmp.A == nameB && tmp.B == nameA)
	return tmp;
	}
	return Functionblock();
	};

	/// evaluest the corisponding LinearFunction thith the score difference
	/// \param nameA these two parameters are the unique identifiers for the
	/// LinerFunction \param nameB these two parameters are the unique identifiers
	/// for the LinerFunction
	///
	/// \note If the block nameA nameB doesn't exist it logs the error and returns
	/// 0
	/// \note it doesn't matter if they are swapped
	Type getCrossReliabilityFromProfile(id_t nameA, id_t nameB,
	StateType scoreDifference) {
	Functionblock block = searchFunction(Functions, nameA, nameB);
	if (!block.exists) {
	LOG_ERROR(("CrossReliability: Block:" + std::to_string(nameA) + "," +
	std::to_string(nameB) + "doesn't exist returning 0"));
	return 0;
	}
	return block.Funct->operator()(scoreDifference);
	}

	public:
	/// adds a Cross Reliability Profile used to get the Reliability of the state
	/// difference
	/// \param idA The id of the one \c Agent ( idealy the id of \c Unit to make
	/// it absolutly unique )
	///
	/// \param idB The id of the other \c Agent
	///
	/// \param Function A unique pointer to an \c Abstraction it would use the
	/// difference in score for its input
	void addCrossReliabilityProfile(id_t idA, id_t idB,
	std::unique_ptr<Abstraction> &Function) {
	Abstraction *ptr = Function.release();
	Functions.push_back({true, idA, idB, ptr});
	}

	/// sets the cross reliability parameter
	void setCrossReliabilityParameter(Type val) {
	crossReliabilityParameter = val;
	}
	/// sets the used method to combine the values
	/// \param Meth The Function which defines the combination method.
	/// \note Inside \c CrossReliability there are static methods defined which
	/// can be used.
	void setCrossReliabilityMethod(Type (*Meth)(std::vector<Type> values)) {
	Method = Meth;
	}

	CrossReliability() : Abstraction(0) {}

	~CrossReliability() {
	for (auto tmp : Functions)
	delete tmp.Funct;
	Functions.clear();
	}

	/// Calculets the CrossReliability
	/// \note both Main and Slaveagents are represented by there data and an
	/// unique identifier
	///
	/// \param MainAgent defines the value pair around which the Cross Reliability
	/// is calculated
	/// \param SlaveAgents defines all value pairs of the connected Agents it
	/// doesn't matter if Main agent exists inside this vector
	Type operator()(std::pair<id_t, StateType> &&MainAgent,
	std::vector<std::pair<id_t, StateType>> &SlaveAgents);

	/// predefined combination method
	static Type CONJUNCTION(std::vector<Type> values) {
	return *std::min_element(values.begin(), values.end());
	}

	/// predefined combination method
	static Type AVERAGE(std::vector<Type> values) {
	return std::accumulate(values.begin(), values.end(), 0.0) / values.size();
	}

	/// predefined combination method
	static Type DISJUNCTION(std::vector<Type> values) {
	return *std::max_element(values.begin(), values.end());
	}
	};

	template <typename StateType, typename Type>
	inline Type CrossReliability<StateType, Type>::
	operator()(std::pair<id_t, StateType> &&MainAgent,
	std::vector<std::pair<id_t, StateType>> &SlaveAgents) {

	Type crossReliabiability;
	std::vector<Type> values;

	for (std::pair<id_t, StateType> SlaveAgent : SlaveAgents) {

	if (SlaveAgent.first == MainAgent.first)
	continue;

	if (MainAgent.second == SlaveAgent.second)
	crossReliabiability = 1;
	else
	crossReliabiability =
	1 / (crossReliabilityParameter *
	AbsuluteValue(MainAgent.second, SlaveAgent.second));

	// profile reliability
	Type crossReliabilityFromProfile = getCrossReliabilityFromProfile(
	MainAgent.first, SlaveAgent.first,
	AbsuluteValue(MainAgent.second, SlaveAgent.second));
	values.push_back(
	std::max(crossReliabiability, crossReliabilityFromProfile));
	}
	return Method(values);
	}

	/// Calculates the \c CrossConfidence
	/// \brief It uses the a theoretical state represented by a numerical value and
	/// calculates the Reliability of a given agent[ represented by there id ] in
	/// connection to all other given agents this can be used to get a Confidence of
	/// the current state
	///
	/// \note all combination of agents and there coresponding \c CrossReliability
	/// function have to be specified
	template <typename StateType, typename Type>
	class CrossConfidence : public Abstraction<StateType, Type> {

	static_assert(std::is_arithmetic<Type>::value,
	"CrossConfidence: <Type> has to be an arithmetic type\n");
	static_assert(std::is_arithmetic<StateType>::value,
	"CrossConfidence: <StateType> has to be an arithmetic type\n");

	using Abstraction = typename rosa::agent::Abstraction<StateType, Type>;

	struct Functionblock {
	bool exists = false;
	id_t A;
	id_t B;
	Abstraction *Funct;
	};

	/// From Maxi in his code defined as 1 can be changed by set
	Type crossReliabilityParameter = 1;

	/// Stored Cross Reliability Functions
	std::vector<Functionblock> Functions;

	/// Method which is used to combine the generated values
	Type (*Method)(std::vector<Type> values) = AVERAGE;

	//--------------------------------------------------------------------------------
	// helper function
	/// evalues the absolute distance between two values
	/// \note this is actually the absolute distance but to ceep it somewhat
	/// conform with maxis code
	template <typename Type_t> Type_t AbsuluteValue(Type_t A, Type_t B) {
	return ((A - B) < 0) ? B - A : A - B;
	}

	/// verry inefficient searchFunction
	Functionblock (*searchFunction)(std::vector<Functionblock> vect,
	const id_t nameA, const id_t nameB) =
	[](std::vector<Functionblock> vect, const id_t nameA,
	const id_t nameB) -> Functionblock {
	for (Functionblock tmp : vect) {
	if (tmp.A == nameA && tmp.B == nameB)
	return tmp;
	if (tmp.A == nameB && tmp.B == nameA)
	return tmp;
	}
	return Functionblock();
	};

	/// evaluest the corisponding LinearFunction thith the score difference
	/// \param nameA these two parameters are the unique identifiers
	/// \param nameB these two parameters are the unique identifiers
	/// for the LinerFunction
	///
	/// \note it doesn't matter if they are swapped
	Type getCrossReliabilityFromProfile(id_t nameA, id_t nameB,
	StateType scoreDifference) {
	Functionblock block = searchFunction(Functions, nameA, nameB);
	if (!block.exists) {
	LOG_ERROR(("CrossReliability: Block:" + std::to_string(nameA) + "," +
	std::to_string(nameB) + "doesn't exist returning 0"));
	return 0;
	}
	return block.Funct->operator()(scoreDifference);
	}

	public:
	/// adds a Cross Reliability Profile used to get the Reliability of the state
	/// difference
	/// \param idA The id of the one \c Agent ( idealy the id of \c Unit to make
	/// it absolutly unique )
	///
	/// \param idB The id of the other \c Agent
	///
	/// \param Function A unique pointer to an \c Abstraction it would use the
	/// difference in score for its input
	void addCrossReliabilityProfile(id_t idA, id_t idB,
	std::unique_ptr<Abstraction> &Function) {
	Abstraction *ptr = Function.release();
	Functions.push_back({true, idA, idB, ptr});
	}

	/// sets the cross reliability parameter
	void setCrossReliabilityParameter(Type val) {
	crossReliabilityParameter = val;
	}
	/// sets the used method to combine the values
	/// \param Meth The Function which defines the combination method.
	/// \note Inside \c CrossReliability there are static methods defined which
	/// can be used.
	void setCrossReliabilityMethod(Type (*Meth)(std::vector<Type> values)) {
	Method = Meth;
	}

	CrossConfidence() : Abstraction(0) {}

	~CrossConfidence() {
	for (auto tmp : Functions)
	delete tmp.Funct;
	Functions.clear();
	}

	Type operator()(id_t MainAgent, StateType TheoreticalValue,
	std::vector<std::pair<id_t, StateType>> &SlaveAgents);

	/// predefined combination method
	static Type CONJUNCTION(std::vector<Type> values) {
	return *std::min_element(values.begin(), values.end());
	}

	/// predefined combination method
	static Type AVERAGE(std::vector<Type> values) {
	return std::accumulate(values.begin(), values.end(), 0.0) / values.size();
	}

	/// predefined combination method
	static Type DISJUNCTION(std::vector<Type> values) {
	return *std::max_element(values.begin(), values.end());
	}
	};

	/// Calculats the CrossConfidence of the main agent compared to all other Agents
	/// \param MainAgent The id of the Main agent
	/// \param TheoreticalValue The throretical value it should use for calculation
	/// \param SlaveAgents The numerical Representation of all other Slave Agents
	template <typename StateType, typename Type>
	inline Type CrossConfidence<StateType, Type>::
	operator()(id_t MainAgent, StateType TheoreticalValue,
	std::vector<std::pair<id_t, StateType>> &SlaveAgents) {

	Type crossReliabiability;

	std::vector<Type> values;

	for (std::pair<id_t, StateType> SlaveAgent : SlaveAgents) {

	if (SlaveAgent.first == MainAgent)
	continue;

	if (TheoreticalValue == SlaveAgent.second)
	crossReliabiability = 1;
	else
	crossReliabiability =
	1 / (crossReliabilityParameter *
	AbsuluteValue(TheoreticalValue, SlaveAgent.second));

	// profile reliability
	Type crossReliabilityFromProfile = getCrossReliabilityFromProfile(
	MainAgent, SlaveAgent.first,
	AbsuluteValue(TheoreticalValue, SlaveAgent.second));
	values.push_back(
	std::max(crossReliabiability, crossReliabilityFromProfile));
	}
	return Method(values);
	}


	/// This is the Reliability Functionality for the highlevel Agent.
	/// \brief It takes the scores and reliabilities of all connected lowlevel
	/// Agents and calculates the Reliability of them together. Also it creates the
	/// feedback that is needed by the \c ReliabilityForLowLevelAgents, which is a
	/// kind of confidence.
	///
	/// \tparam StateType Datatype of the State ( Typically double or float)
	/// \tparam ReliabilityType Datatype of the Reliability (
	/// Typically long or int)
	///
	/// \note A highlevel Agent is commonly in a master slave relationship with the
	/// lowlevel Agents as the master. It combines the Reliability of all connected
	/// Slaves and uses that as its own Reliability.
	///
	/// \note more information about how the Reliability and feedback is
	/// created at \c operator()()
	// State Type rename
	// merge cross rel/conf darein
	template <typename StateType, typename ReliabilityType> class CrossCombinator {
	public:
	static_assert(std::is_arithmetic<StateType>::value,
	"HighLevel: StateType has to be an arithmetic type\n");
	static_assert(std::is_arithmetic<ReliabilityType>::value,
	"HighLevel: ReliabilityType has to be an arithmetic type\n");

	/// typedef To shorten the writing.
	/// \c ConfOrRel
	typedef ConfOrRel<StateType, ReliabilityType> ConfOrRel;

	/// typedef of the input type for the operator() defined explicitly to
	/// simplify interaction
	///
	typedef std::vector<std::tuple<id_t, StateType, ReliabilityType>> InputType;

	/// The return type for the \c operator()() Method
	struct returnType {
	ReliabilityType CrossReliability;
	std::map<id_t, std::vector<ConfOrRel>> CrossConfidence;
	};

	/// Calculates the Reliability and the Cross Confidences for each lowlevel
	/// Agent for all of there states.
	///
	/// \param Values It gets the States and Reliabilities of
	/// all connected Slaves inside a vector.
	///
	/// \return it returns a struct \c returnType containing the CrossReliability
	/// and all CrossConfidence's
	///
	/// \brief To calculate the Reliability it combines [\c std::min() ] the \c
	/// CrossReliability of all connected Agents. To calculate the feedback it
	/// iterates over all Agents and their states and uses the \c CrossConfidence
	/// Function to play what if with the states.
	returnType operator()(
	std::vector<std::tuple<id_t, StateType, ReliabilityType>> &Values) {

	ReliabilityType combinedInputRel = 1;
	ReliabilityType combinedCrossRel = 1;
	ReliabilityType outputReliability;

	std::vector<std::pair<id_t, StateType>> Agents;
	std::map<id_t, std::vector<ConfOrRel>> output;
	std::vector<ConfOrRel> output_temporary;

	for (auto tmp : Values) {
	std::pair<id_t, StateType> tmp2;
	tmp2.first = std::get<0>(tmp);
	tmp2.second = std::get<1>(tmp);
	Agents.push_back(tmp2);
	}

	for (auto Value : Values) {
	id_t id = std::get<0>(Value);
	StateType sc = std::get<1>(Value);
	ReliabilityType rel = std::get<2>(Value);

	// combination method ([])
	// get input reliability
	combinedInputRel = std::min(combinedInputRel, rel);

	// calculate the cross reliability for this slave agent
	ReliabilityType realCrossReliabilityOfSlaveAgent =
	CrossReliability->operator()(
	{id, sc},
	Agents); // AVERAGE, MULTIPLICATION, CONJUNCTION (best to worst:
	// AVERAGE = CONJUNCTION > MULTIPLICATION >> )

	// get cross confidence
	output_temporary.clear();
	for (StateType thoScore : States[id]) {
	// calculate the cross reliability for this slave agent
	ConfOrRel data;
	data.score = thoScore;
	data.Reliability = CrossConfidence->operator()(id, thoScore, Agents);
	output_temporary.push_back(data);
	}

	output.insert({id, output_temporary});
	// set combination method
	// get combined cross reliability
	combinedCrossRel =
	std::min(combinedCrossRel, realCrossReliabilityOfSlaveAgent);
	}

	// combine cross reliabilites and input reliabilites of all slave agents
	// NOTE: options would be multiply, average, AND (best to worst: )
	// outputReliability = combinedInputRel * combinedCrossRel;
	// outputReliability = (combinedInputRel + combinedCrossRel) / 2;
	// set combination method
	// get output reliability
	outputReliability = std::min(combinedInputRel, combinedCrossRel);
	return {outputReliability, output};
	}

	/// This is the setter for CrossReliability Function
	/// \param CrossReliability A pointer to the Functional for the
	/// CrossReliability
	/// \brief This is needed to calculate the Reliability. It uses this on all
	/// values of all lowlevel Agnets.
	void setCrossReliability(
	std::unique_ptr<CrossReliability<StateType, ReliabilityType>>
	&CrossReliability) {
	this->CrossReliability = std::move(CrossReliability);
	}

	/// This is the setter for CrossConfidence Function
	/// \param CrossConfidence A pointer to the Functional for the \c
	/// CrossConfidence \brief This is needed for the feedback for the \c
	/// ReliabilityForLowLevelAgents.
	void setCrossConfidence(
	std::unique_ptr<CrossConfidence<StateType, ReliabilityType>>
	&CrossConfidence) {
	this->CrossConfidence = std::move(CrossConfidence);
	}

	/// This is the adder for the states
	/// \param id The id of the Agent of the states
	/// \param States id specific states. this will be copied So that if Slaves
	/// have different States they can be used correctly.
	/// \brief The States of all connected lowlevel Agents has to be known to be
	/// able to iterate over them
	void addStates(id_t id, std::vector<StateType> States) {
	this->States.insert({id, States});
	}

	private:
	std::unique_ptr<CrossReliability<StateType, ReliabilityType>>
	CrossReliability;
	std::unique_ptr<CrossConfidence<StateType, ReliabilityType>> CrossConfidence;

	std::map<id_t, std::vector<StateType>> States;
	};



	} // End namespace agent
	} // End namespace rosa

	#endif // ROSA_AGENT_CROSSRELIABILITY_H

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