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	diff --git a/include/rosa/agent/SignalStateDetector.hpp b/include/rosa/agent/SignalStateDetector.hpp
	index 3b19cba..6a94da4 100644
	--- a/include/rosa/agent/SignalStateDetector.hpp
	+++ b/include/rosa/agent/SignalStateDetector.hpp
	@@ -1,281 +1,337 @@
	//===-- rosa/agent/StateDetector.hpp ----------------------------- C++ --===//
	//
	// The RoSA Framework
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file rosa/agent/StateDetector.hpp
	///
	/// \author Maximilian Götzinger (maximilian.goetzinger@tuwien.ac.at)
	///
	/// \date 2019
	///
	/// \brief Definition of state detector functionality.
	///
	//===----------------------------------------------------------------------===//

	-//TODO: noexcept
	+// TODO: noexcept

	#ifndef ROSA_AGENT_STATEDETECTOR_HPP
	#define ROSA_AGENT_STATEDETECTOR_HPP

	#include "rosa/agent/FunctionAbstractions.hpp"
	#include "rosa/agent/Functionality.h"
	#include "rosa/agent/State.hpp"

	#include <vector>

	namespace rosa {
	namespace agent {

	/// Implements \c rosa::agent::StateDetector as a functionality that detects
	/// states given on input samples.
	///
	/// \note This implementation is supposed to be used for samples of an
	/// arithmetic type.
	///
	/// \tparam INDATATYPE is the type of input data, \tparam CONFTYPE is type of
	/// data in that the confidence values are given
	template <typename INDATATYPE, typename CONFTYPE>
	class StateDetector : public Functionality {

	// Make sure the actual type arguments are matching our expectations.
	STATIC_ASSERT((std::is_arithmetic<INDATATYPE>::value),
	"input data type not arithmetic");
	STATIC_ASSERT((std::is_arithmetic<CONFTYPE>::value),
	"confidence abstraction type is not to arithmetic");

	private:
	// For the convinience to write a shorter data type name
	using PartFuncPointer =
	std::shared_ptr<PartialFunction<INDATATYPE, CONFTYPE>>;
	using StepFuncPointer = std::shared_ptr<StepFunction<INDATATYPE, CONFTYPE>>;
	using StatePtr = std::shared_ptr<StateInformation<CONFTYPE>>;
	using StateInfoPtr = std::shared_ptr<StateInformation<CONFTYPE>>;

	/// The NextStateID is a counter variable which stores the ID which the next
	/// state shall have.
	unsigned int NextStateID;

	/// The StateHasChanged is a flag that show whether a state change has
	/// happened.
	bool StateHasChanged;

	/// The CurrentState is a pointer to the (saved) state in which the actual
	/// variable (signal) of the observed system is.
	StatePtr CurrentState;
	+
	/// The DetectedStates is vector in that all detected states are saved.
	std::vector<StatePtr> DetectedStates;

	/// The FuzzyFunctionSampleMatches is the fuzzy function that gives the
	/// confidence how good the new sample matches another sample in the sample
	/// history.
	PartFuncPointer FuzzyFunctionSampleMatches;
	- /// The FuzzyFunctionSampleMatches is the fuzzy function that gives the
	+
	+ /// The FuzzyFunctionSampleMismatches is the fuzzy function that gives the
	/// confidence how bad the new sample matches another sample in the sample
	/// history.
	PartFuncPointer FuzzyFunctionSampleMismatches;
	- /// The FuzzyFunctionSampleMatches is the fuzzy function that gives the
	+
	+ /// The FuzzyFunctionNumOfSamplesMatches is the fuzzy function that gives the
	/// confidence how many samples from the sampe history match the new sample.
	StepFuncPointer FuzzyFunctionNumOfSamplesMatches;
	- /// The FuzzyFunctionSampleMatches is the fuzzy function that gives the
	- /// confidence how many samples from the sampe history mismatch the new
	+
	+ /// The FuzzyFunctionNumOfSamplesMismatches is the fuzzy function that gives
	+ /// the confidence how many samples from the sampe history mismatch the new
	/// sample.
	StepFuncPointer FuzzyFunctionNumOfSamplesMismatches;

	/// The FuzzyFunctionSignalIsDrifting is the fuzzy function that gives the
	/// confidence how likely it is that the signal is drifting.
	PartFuncPointer FuzzyFunctionSignalIsDrifting;
	+
	/// The FuzzyFunctionSignalIsStable is the fuzzy function that gives the
	/// confidence how likely it is that the signal is stable (not drifting).
	PartFuncPointer FuzzyFunctionSignalIsStable;

	/// SampleHistorySize is the (maximum) size of the sample history.
	unsigned int SampleHistorySize;
	/// DABSize the size of a DAB (Discrete Average Block).
	unsigned int DABSize;
	/// DABHistorySize is the (maximum) size of the DAB history.
	unsigned int DABHistorySize;

	public:
	/// Creates an instance by setting all parameters
	+ /// \param FuzzyFunctionSampleMatches The FuzzyFunctionSampleMatches is the
	+ /// fuzzy function that gives the confidence how good the new sample matches
	+ /// another sample in the sample history.
	+ ///
	+ /// \param FuzzyFunctionSampleMismatches The FuzzyFunctionSampleMismatches is
	+ /// the fuzzy function that gives the confidence how bad the new sample
	+ /// matches another sample in the sample history.
	+ ///
	+ /// \param FuzzyFunctionNumOfSamplesMatches The
	+ /// FuzzyFunctionNumOfSamplesMatches is the fuzzy function that gives the
	+ /// confidence how many samples from the sampe history match the new sample.
	+ ///
	+ /// \param FuzzyFunctionNumOfSamplesMismatches The
	+ /// FuzzyFunctionNumOfSamplesMismatches is the fuzzy function that gives the
	+ /// confidence how many samples from the sampe history mismatch the new
	+ /// sample.
	+ ///
	+ /// \param FuzzyFunctionSignalIsDrifting The FuzzyFunctionSignalIsDrifting is
	+ /// the fuzzy function that gives the confidence how likely it is that the
	+ /// signal (resp. the state of a signal) is drifting.
	+ ///
	+ /// \param FuzzyFunctionSignalIsStable The FuzzyFunctionSignalIsStable is the
	+ /// fuzzy function that gives the confidence how likely it is that the signal
	+ /// (resp. the state of a signal) is stable (not drifting).
	+ ///
	+ /// \param SampleHistorySize Sets the History size which will be used by \c
	+ /// State .
	+ ///
	+ /// \param DABSize Sets the DAB size which will be used by \c State .
	+ ///
	+ /// \param DABHistorySize Sets the size which will be used by \c State .
	+ ///
	StateDetector(PartFuncPointer FuzzyFunctionSampleMatches,
	PartFuncPointer FuzzyFunctionSampleMismatches,
	StepFuncPointer FuzzyFunctionNumOfSamplesMatches,
	StepFuncPointer FuzzyFunctionNumOfSamplesMismatches,
	PartFuncPointer FuzzyFunctionSignalIsDrifting,
	PartFuncPointer FuzzyFunctionSignalIsStable,
	unsigned int SampleHistorySize, unsigned int DABSize,
	unsigned int DABHistorySize) noexcept
	: NextStateID(1), StateHasChanged(false), CurrentState(NULL),
	FuzzyFunctionSampleMatches(FuzzyFunctionSampleMatches),
	FuzzyFunctionSampleMismatches(FuzzyFunctionSampleMismatches),
	FuzzyFunctionNumOfSamplesMatches(FuzzyFunctionNumOfSamplesMatches),
	FuzzyFunctionNumOfSamplesMismatches(
	FuzzyFunctionNumOfSamplesMismatches),
	FuzzyFunctionSignalIsDrifting(FuzzyFunctionSignalIsDrifting),
	FuzzyFunctionSignalIsStable(FuzzyFunctionSignalIsStable),
	SampleHistorySize(SampleHistorySize), DABSize(DABSize),
	DABHistorySize(DABHistorySize) {}

	/// Destroys \p this object.
	~StateDetector(void) = default;

	/// Detects the state to which the new sample belongs or create a new state if
	/// the new sample does not match to any of the saved states.
	///
	/// \param Sample is the actual sample of the observed signal.
	///
	- /// \return the state ID as unsigend integer type. State IDs start with number
	+ /// \return the state ID as unsigned integer type. State IDs start with number
	/// 1; that means if there is no current state, the return value is 0.
	unsigned int detectState(INDATATYPE Sample) {
	StateInfoPtr StateInfo = detectState__debug(Sample);
	return StateInfo->StateID;
	}

	/// Gives information about the current state.
	///
	/// \return a the State ID (as unsigned integer type) of the current state.
	/// State IDs start with number 1; that means if there is no current state,
	/// the return value is 0.
	unsigned int currentStateInformation(void) {
	StateInfoPtr StateInfo = currentStateInformation__debug();
	if (StateInfo) {
	return StateInfo->StateID;
	} else {
	return 0;
	}
	}

	/// Gives information whether a state change has happened or not.
	///
	/// \return true if a state change has happened, and false if not.
	bool stateHasChanged(void) { return StateHasChanged; }

	private:
	+ //@maxi \param is there to Document a specific parameter of a method/function
	+ //this method doesn't have any parameters.
	/// Creates a new state and adds this state to the state vector in which all
	/// known states are saved.
	///
	/// \param SampleHistorySize the (maximum) size of the sample history.
	/// \param DABSize the size of a DAB.
	/// \param DABHistorySize the (maximum) size of the DAB history.
	/// \param FuzzyFunctionSampleMatches the
	/// \param FuzzyFunctionSampleMismatches
	/// \param FuzzyFunctionNumOfSamplesMatches
	/// \param FuzzyFunctionNumOfSamplesMismatches
	///
	- /// \return the new created state or NULL if no state could be created.
	+ /// \return a pointer to the newly created state or NULL if no state could be created.
	StatePtr createNewState(void) {

	StatePtr S = new (std::nothrow) State(
	SampleHistorySize, DABSize, DABHistorySize, FuzzyFunctionSampleMatches,
	FuzzyFunctionSampleMismatches, FuzzyFunctionNumOfSamplesMatches,
	FuzzyFunctionNumOfSamplesMismatches);

	// TODO: maybe assert which checks if push_back worked (ask benedikt)
	DetectedStates.push_back(S);

	return S;
	}

	#ifdef STATEDETECTORDEBUGMODE
	public:
	#else
	private:
	#endif // STATEDETECTORDEBUGMODE

	+ // @maxi is this a debug method or is it a method that will be used and
	+ // you simply want to have access to it in debug mode?
	+ // debug -> extend the preprocessor around the function
	+ // access -> remove the __debug from the name ( it is confusing)
	+ // if you want to have it marked as a debug method for auto
	+ // complete you can do something like this :
	+ //
	+ //#ifdef STATEDETECTORDEBUGMODE
	+ // public:
	+ // StateInfoPtr debug_detectState(INDATATYPE Sample) {
	+ // return detectState(Sample);
	+ // }
	+ //#endif // STATEDETECTORDEBUGMODE
	+ // private :
	+ // StateInfoPtr detectState(INDATATYPE Sample) { ...
	+ //
	+
	/// Detects the state to which the new sample belongs or create a new state if
	/// the new sample does not match to any of the saved states.
	///
	/// \param Sample is the actual sample of the observed signal.
	///
	/// \return the information of the actual state (state ID and other
	/// parameters).
	// TODO: return something const.. cannot remember exactly (ask benedikt)
	StateInfoPtr detectState__debug(INDATATYPE Sample) {

	if (!CurrentState) {
	ASSERT(DetectedStates.empty());

	StatePtr S = createNewState();
	CurrentState = S;
	} else {
	CONFTYPE ConfidenceSampleMatchesState =
	CurrentState->confSampleMatchesState(Sample);
	CONFTYPE ConfidenceSampleMismatchesState =
	CurrentState->confSampleMismatchesState(Sample);

	if (ConfidenceSampleMatchesState > ConfidenceSampleMismatchesState) {
	StateHasChanged = false;
	} else {
	StateHasChanged = true;

	if (CurrentState->stateInformation()->StateIsValid) {
	CurrentState->leaveState();
	} else {
	DetectedStates.erase(std::find(DetectedStates.begin(),
	DetectedStates.end(), CurrentState));
	}

	// TODO (future): additionally save averages to enable fast
	// iteration through recorded state vector (maybe sort vector based on
	// these average values)
	CurrentState = nullptr;

	for (auto &SavedState : DetectedStates) {
	if (SavedState != CurrentState) {
	CONFTYPE ConfidenceSampleMatchesState =
	SavedState->confSampleMatchesState(Sample);
	CONFTYPE ConfidenceSampleMismatchesState =
	SavedState->confSampleMismatchesState(Sample);

	if (ConfidenceSampleMatchesState >
	ConfidenceSampleMismatchesState) {
	// TODO (future): maybe it would be better to compare
	// ConfidenceSampleMatchesState of all states in the vector in
	// order to find the best matching state.
	CurrentState = SavedState;
	break;
	}
	}
	}

	if (!CurrentState) {
	StatePtr S = createNewState();
	CurrentState = S;
	}
	}
	}

	StateInfoPtr StateInfo = CurrentState->insertSample(Sample);

	if (StateInfo->StateJustGotValid) {
	NextStateID++;
	}

	return CurrentState->stateInformation();
	}

	#ifdef STATEDETECTORDEBUGMODE
	public:
	#else
	private:
	#endif // STATEDETECTORDEBUGMODE

	/// Gives information about the current state.
	///
	/// \return a struct StateInformation that contains information about the
	/// current state or NULL if no current state exists.
	StateInfoPtr currentStateInformation__debug(void) {
	if (CurrentState) {
	return CurrentState->stateInformation();
	} else {
	return NULL;
	}
	}
	};

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

	#endif // ROSA_AGENT_STATEDETECTOR_HPP
	diff --git a/include/rosa/agent/State.hpp b/include/rosa/agent/State.hpp
	index aee717d..eb17258 100644
	--- a/include/rosa/agent/State.hpp
	+++ b/include/rosa/agent/State.hpp
	@@ -1,415 +1,415 @@
	//===-- rosa/agent/State.hpp ----------------------------- C++ --===//
	//
	// The RoSA Framework
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file rosa/agent/State.hpp
	///
	/// \author Maximilian Götzinger (maximilian.goetzinger@tuwien.ac.at)
	///
	/// \date 2019
	///
	/// \brief Definition of state functionality.
	///
	//===----------------------------------------------------------------------===//

	#ifndef ROSA_AGENT_STATE_HPP
	#define ROSA_AGENT_STATE_HPP

	#include "rosa/agent/FunctionAbstractions.hpp"
	#include "rosa/agent/Functionality.h"
	#include "rosa/agent/History.hpp"

	#include <cstdarg>

	namespace rosa {
	namespace agent {

	/// State conditions defining how the condition of a \c rosa::agent::State is
	/// saved in \c rosa::agent::StateInformation.
	enum class VariableStateCondition {
	STABLE, ///< The state is stable
	DRIFTING, ///< The state is drifting
	UNKNOWN ///< The state is unknown
	};

	template <typename CONFDATATYPE> struct StateInformation {
	// Make sure the actual type arguments are matching our expectations.
	STATIC_ASSERT((std::is_arithmetic<CONFDATATYPE>::value),
	"confidence type is not to arithmetic");

	/// The state ID saved as an unsigned integer number
	unsigned int StateID;
	/// The StateConfidence shows the overall confidence value of the state.
	CONFDATATYPE StateConfidence;
	/// The VariableStateCondition shows the condition of a state (stable or
	/// drifting)
	VariableStateCondition VariableStateCondition;
	/// The StateIsValid shows whether a state is valid or invalid. In this
	/// context, valid means that enough samples which are in close proximitry
	/// have been inserted into the state.
	bool StateIsValid;
	/// The StateJustGotValid shows whether a state got valid (toggled from
	/// invalid to valid) during the current inserted sample.
	bool StateJustGotValid;
	/// The StateIsValidAfterReentrance shows whether a state is valid after the
	/// variable changed back to it again.
	bool StateIsValidAfterReentrance;
	};

	// @Benedikt: now there are 4 datatypes. Do you think we can merge PROCDATATYPE
	// and PROCDATATYPE somehow?
	/// \tparam INDATATYPE type of input data, \tparam CONFDATATYPE type of
	/// data in that the confidence values are given, \param PROCDATATYPEtype of
	/// the relative distance and the type of data in which DABs are saved.
	template <typename INDATATYPE, typename CONFDATATYPE, typename PROCDATATYPE>
	class State : public Functionality {

	// Make sure the actual type arguments are matching our expectations.
	STATIC_ASSERT((std::is_arithmetic<INDATATYPE>::value),
	"input data type not arithmetic");
	STATIC_ASSERT((std::is_arithmetic<CONFDATATYPE>::value),
	"DAB storage type is not to arithmetic");

	private:
	// For the convinience to write a shorter data type name
	using PartFuncPointer =
	std::shared_ptr<PartialFunction<INDATATYPE, CONFDATATYPE>>;
	using StepFuncPointer =
	std::shared_ptr<StepFunction<INDATATYPE, CONFDATATYPE>>;
	using StateInfoPtr = std::shared_ptr<StateInformation<CONFDATATYPE>>;

	/// StateInfo is a struct StateInformation that contains information about the
	/// current state.
	StateInfoPtr StateInfo;

	/// The FuzzyFunctionSampleMatches is the fuzzy function that gives the
	/// confidence how good the new sample matches another sample in the sample
	/// history.
	PartFuncPointer FuzzyFunctionSampleMatches;

	/// The FuzzyFunctionSampleMismatches is the fuzzy function that gives the
	/// confidence how bad the new sample matches another sample in the sample
	/// history.
	PartFuncPointer FuzzyFunctionSampleMismatches;

	/// The FuzzyFunctionNumOfSamplesMatches is the fuzzy function that gives the
	/// confidence how many samples from the sampe history match the new sample.
	StepFuncPointer FuzzyFunctionNumOfSamplesMatches;

	/// The FuzzyFunctionNumOfSamplesMismatches is the fuzzy function that gives
	/// the confidence how many samples from the sampe history mismatch the new
	/// sample.
	StepFuncPointer FuzzyFunctionNumOfSamplesMismatches;

	/// The FuzzyFunctionSignalIsDrifting is the fuzzy function that gives the
	/// confidence how likely it is that the signal (resp. the state of a signal)
	/// is drifting.
	PartFuncPointer FuzzyFunctionSignalIsDrifting;

	/// The FuzzyFunctionSignalIsStable is the fuzzy function that gives the
	/// confidence how likely it is that the signal (resp. the state of a signal)
	/// is stable (not drifting).
	PartFuncPointer FuzzyFunctionSignalIsStable;

	/// SampleHistory is a history in that the last sample values are stored.
	DynamicLengthHistory<INDATATYPE, HistoryPolicy::FIFO> SampleHistory;
	/// DAB is a (usually) small history of the last sample values of which a
	/// average is calculated if the DAB is full.
	DynamicLengthHistory<INDATATYPE, HistoryPolicy::SRWF> DAB;
	/// DABHistory is a history in that the last DABs (to be exact, the averages
	/// of the last DABs) are stored.
	DynamicLengthHistory<PROCDATATYPE, HistoryPolicy::LIFO> DABHistory;

	/// The StateIsValid shows whether a state is valid or invalid. In this
	/// context, valid means that enough samples which are in close proximitry
	/// have been inserted into the state.
	bool StateIsValid;
	/// The StateIsValidAfterReentrance shows whether a state is valid after the
	/// variable changed back to it again.
	bool StateIsValidAfterReentrance;

	public:
	// @Maxi doxygen per default doesn't display private attributes of a class. So
	// I copied them to the constructor. So the user has more information.
	/// Creates an instance by setting all parameters
	- /// \param StateID The Id of the Stateinfo \c StateInformation
	+ /// \param StateID The Id of the Stateinfo \c StateInformation .
	///
	/// \param FuzzyFunctionSampleMatches The FuzzyFunctionSampleMatches is the
	/// fuzzy function that gives the confidence how good the new sample matches
	/// another sample in the sample history.
	///
	/// \param FuzzyFunctionSampleMismatches The FuzzyFunctionSampleMismatches is
	/// the fuzzy function that gives the confidence how bad the new sample
	/// matches another sample in the sample history.
	///
	/// \param FuzzyFunctionNumOfSamplesMatches The
	/// FuzzyFunctionNumOfSamplesMatches is the fuzzy function that gives the
	/// confidence how many samples from the sampe history match the new sample.
	///
	/// \param FuzzyFunctionNumOfSamplesMismatches The
	/// FuzzyFunctionNumOfSamplesMismatches is the fuzzy function that gives the
	/// confidence how many samples from the sampe history mismatch the new
	/// sample.
	///
	/// \param FuzzyFunctionSignalIsDrifting The FuzzyFunctionSignalIsDrifting is
	/// the fuzzy function that gives the confidence how likely it is that the
	/// signal (resp. the state of a signal) is drifting.
	///
	/// \param FuzzyFunctionSignalIsStable The FuzzyFunctionSignalIsStable is the
	/// fuzzy function that gives the confidence how likely it is that the signal
	/// (resp. the state of a signal) is stable (not drifting).
	///
	- /// \param sampleHistorySize Size of the Sample History \c
	+ /// \param SampleHistorySize Size of the Sample History \c
	/// DynamicLengthHistory . SampleHistory is a history in that the last sample
	/// values are stored.
	///
	/// \param DABSize Size of DAB \c DynamicLengthHistory . DAB is a (usually)
	/// small history of the last sample values of which a average is calculated
	/// if the DAB is full.
	///
	/// \param DABHistorySize Size of the DABHistory \c DynamicLengthHistory .
	/// DABHistory is a history in that the last DABs (to be exact, the averages
	/// of the last DABs) are stored.
	///
	State(unsigned int StateID, PartFuncPointer FuzzyFunctionSampleMatches,
	PartFuncPointer FuzzyFunctionSampleMismatches,
	StepFuncPointer FuzzyFunctionNumOfSamplesMatches,
	StepFuncPointer FuzzyFunctionNumOfSamplesMismatches,
	PartFuncPointer FuzzyFunctionSignalIsDrifting,
	PartFuncPointer FuzzyFunctionSignalIsStable,
	- unsigned int sampleHistorySize, unsigned int DABSize,
	+ unsigned int SampleHistorySize, unsigned int DABSize,
	unsigned int DABHistorySize) noexcept
	: StateInfo(StateID, 0, VariableStateCondition::UNKNOWN, false, false),
	- SampleHistory(sampleHistorySize), DAB(DABSize),
	+ SampleHistory(SampleHistorySize), DAB(DABSize),
	DABHistory(DABHistorySize),
	FuzzyFunctionSampleMatches(FuzzyFunctionSampleMatches),
	FuzzyFunctionSampleMismatches(FuzzyFunctionSampleMismatches),
	FuzzyFunctionNumOfSamplesMatches(FuzzyFunctionNumOfSamplesMatches),
	FuzzyFunctionNumOfSamplesMismatches(
	FuzzyFunctionNumOfSamplesMismatches),
	FuzzyFunctionSignalIsDrifting(FuzzyFunctionSignalIsDrifting),
	FuzzyFunctionSignalIsStable(FuzzyFunctionSignalIsStable) {}

	/// Destroys \p this object.
	~State(void) = default;

	void leaveState(void) {
	DAB.clear();
	StateIsValidAfterReentrance = false;
	}

	void insertSample(INDATATYPE Sample) {
	SampleHistory.addEntry(Sample);

	DAB.addEntry(Sample);
	if (DAB.full()) {
	PROCDATATYPE AvgOfDAB = DAB.template average<PROCDATATYPE>();
	DABHistory.addEntry(AvgOfDAB);
	DAB.clear();
	}

	FuzzyFunctionNumOfSamplesMatches->setRightLimit(
	SampleHistory->numberOfEntries());
	FuzzyFunctionNumOfSamplesMismatches->setRightLimit(
	SampleHistory->numberOfEntries());

	// TODO: calculate whether state is valid and properly set StateIsValid,
	// StateJustGotValid, StateIsValidAfterReentrance

	// TODO: check actual state whether it drifts

	// TODO: write in StateInfo
	}

	/// Gives the confidence how likely the new sample matches the state.
	///
	/// \param Sample is the actual sample of the observed signal.
	///
	/// \return the confidence of the new sample is matching the state.
	CONFDATATYPE
	confSampleMatchesState(INDATATYPE Sample) {

	CONFDATATYPE ConfidenceOfBestCase = 0;

	// TODO: history
	std::vector<PROCDATATYPE> RelativeDistanceHistory;

	// calculate distances to all history samples
	for (auto &HistorySample : SampleHistory) {
	PROCDATATYPE RelativeDistance = relativeDistance(Sample, HistorySample);
	RelativeDistanceHistory.push_back(RelativeDistance);
	}

	// sort all calculated distances so that the lowest distance (will get the
	// highest confidence) is at the beginning.
	sort(RelativeDistanceHistory.begin(), RelativeDistanceHistory.end());

	CONFDATATYPE ConfidenceOfWorstFittingSample = 1;
	unsigned int Case = 1;

	// Case 1 means that one (the best fitting) sample of the history is
	// compared with the new sample. Case 2 means the two best history samples
	// are compared with the new sample. And so on.
	// TODO (future): to accelerate -> don't start with 1 start with some higher
	// number because a low number (i guess lower than 5) will definetely lead
	// to a low confidence. except the history is not full.

	// TODO: history
	// TODO: iterate thorugh case and use []
	// TODO:
	for (auto RelativeDistance = RelativeDistanceHistory.cbegin();
	RelativeDistance != RelativeDistanceHistory.cend();
	RelativeDistance++, Case++) {

	CONFDATATYPE ConfidenceFromRelativeDistance;

	// CHECK if I should use * for RelativeDistance
	if (std::isinf(RelativeDistance)) {
	// TODO (future) if fuzzy is defined in a way that infinity is not 0 it
	// would be a problem
	//@benedikt: check if your partialfunctions can take infinity as
	// argument
	ConfidenceFromRelativeDistance = 0;
	} else {
	ConfidenceFromRelativeDistance =
	FuzzyFunctionSampleMatches(RelativeDistance);
	}

	ConfidenceOfWorstFittingSample = fuzzyAND(ConfidenceOfWorstFittingSample,
	ConfidenceFromRelativeDistance);

	// @benedikt: change old-style cast to one of these: reinterpret_cast,
	// static_cast, dynamic_cast or const_cast. Which should I use? Or should
	// the HistSampleCounter variable already be CONFDATATYPE type?
	ConfidenceOfBestCase = fuzzyOR(
	ConfidenceOfBestCase,
	fuzzyAND(ConfidenceOfWorstFittingSample,
	FuzzyFunctionNumOfSamplesMatches((CONFDATATYPE)Case)));
	}

	return ConfidenceOfBestCase;
	}

	/// Gives the confidence how likely the new sample mismatches the state.
	///
	/// \param Sample is the actual sample of the observed signal.
	///
	/// \return the confidence of the new sample is mismatching the state.
	CONFDATATYPE
	confSampleMismatchesState(INDATATYPE Sample) {

	float ConfidenceOfWorstCase = 1;

	// TODO: history!
	std::vector<PROCDATATYPE> RelativeDistanceHistory;

	// calculate distances to all history samples
	for (auto &HistorySample : SampleHistory) {
	RelativeDistanceHistory.push_back(
	relativeDistance(Sample, HistorySample));
	}

	// sort all calculated distances so that the highest distance (will get the
	// lowest confidence) is at the beginning.
	sort(RelativeDistanceHistory.rbegin(), RelativeDistanceHistory.rend());

	CONFDATATYPE ConfidenceOfBestFittingSample = 0;
	unsigned int Case = 1;

	// Case 1 means that one (the worst fitting) sample of the history is
	// compared with the new sample. Case 2 means the two worst history samples
	// are compared with the new sample. And so on.
	// TODO (future): to accelerate -> don't go until end. Confidences will only
	// get higher. See comment in "CONFDATATYPE
	// confSampleMatchesState(INDATATYPE Sample)".
	for (unsigned int RelativeDistance = RelativeDistanceHistory.cbegin();
	RelativeDistance != RelativeDistanceHistory.cend();
	RelativeDistance++, Case++) {

	CONFDATATYPE ConfidenceFromRelativeDistance;

	if (std::isinf(RelativeDistance)) {
	ConfidenceFromRelativeDistance = 1;
	} else {
	ConfidenceFromRelativeDistance =
	FuzzyFunctionSampleMismatches(RelativeDistance);
	}

	ConfidenceOfBestFittingSample = fuzzyOR(ConfidenceOfBestFittingSample,
	ConfidenceFromRelativeDistance);

	// @benedikt: change old-style cast to one of these: reinterpret_cast,
	// static_cast, dynamic_cast or const_cast. Which should I use? Or should
	// the HistSampleCounter variable already be CONFDATATYPE type?
	ConfidenceOfWorstCase = fuzzyAND(
	ConfidenceOfWorstCase,
	fuzzyOR(ConfidenceOfBestFittingSample,
	FuzzyFunctionNumOfSamplesMismatches((CONFDATATYPE)Case)));
	}

	return ConfidenceOfWorstCase;
	}

	/// Gives information about the current state.
	///
	/// \return a struct StateInformation that contains information about the
	/// current state.
	StateInfoPtr stateInformation(void) { return StateInfo; }

	private:
	// @David: Where should these next functions (fuzzyAND, fuzzyOR,
	// relativeDistance) moved to (I guess we will use them also somewhere else)?

	// copied from the internet and adapted
	// (https://stackoverflow.com/questions/1657883/variable-number-of-arguments-in-c)
	CONFDATATYPE fuzzyAND(int n_args, ...) {
	va_list ap;
	va_start(ap, n_args);
	CONFDATATYPE min = va_arg(ap, CONFDATATYPE);
	for (int i = 2; i <= n_args; i++) {
	CONFDATATYPE a = va_arg(ap, CONFDATATYPE);
	min = std::min(a, min);
	}
	va_end(ap);
	return min;
	}

	// copied from the internet
	// (https://stackoverflow.com/questions/1657883/variable-number-of-arguments-in-c)
	CONFDATATYPE fuzzyOR(int n_args, ...) {
	va_list ap;
	va_start(ap, n_args);
	CONFDATATYPE max = va_arg(ap, CONFDATATYPE);
	for (int i = 2; i <= n_args; i++) {
	CONFDATATYPE a = va_arg(ap, CONFDATATYPE);
	std::max(a, max);
	}
	va_end(ap);
	return max;
	}

	PROCDATATYPE relativeDistance(INDATATYPE SampleValue,
	INDATATYPE HistoryValue) {
	PROCDATATYPE Dist = HistoryValue - SampleValue;

	if (Dist == 0) {
	return 0;
	} else {
	Dist = Dist / SampleValue;
	if (Dist < 0) {
	//@benedikt: I guess this multiplication here should not be done because
	// it could be that the distance fuzzy functions are not symetrical
	//(negative and positive side)
	Dist = Dist * (-1);
	}
	return (Dist);
	}
	}
	};

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

	#endif // ROSA_AGENT_STATE_HPP

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