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	diff --git a/include/rosa/agent/SignalState.hpp b/include/rosa/agent/SignalState.hpp
	index 7f8d206..7557729 100644
	--- a/include/rosa/agent/SignalState.hpp
	+++ b/include/rosa/agent/SignalState.hpp
	@@ -1,462 +1,528 @@
	//===-- rosa/agent/SignalState.hpp ------------------------------- C++ --===//
	//
	// The RoSA Framework
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file rosa/agent/SignalState.hpp
	///
	/// \author Maximilian Götzinger (maximilian.goetzinger@tuwien.ac.at)
	///
	/// \date 2019
	///
	/// \brief Definition of signal state functionality.
	///
	//===----------------------------------------------------------------------===//

	#ifndef ROSA_AGENT_SIGNALSTATE_HPP
	#define ROSA_AGENT_SIGNALSTATE_HPP

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

	namespace rosa {
	namespace agent {

	/// Signal state conditions defining how the condition of a \c
	/// rosa::agent::SignalState is saved in \c rosa::agent::SignalStateInformation.
	enum SignalStateCondition : uint8_t {
	STABLE = 0, ///< The signal state is stable
	DRIFTING = 1, ///< The signal state is drifting
	UNKNOWN = 2 ///< The signal state is unknown
	};

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

	/// The signal state ID saved as an uint32_teger number
	uint32_t SignalStateID;
	/// The SignalStateConfidence shows the overall confidence value of the signal
	/// state.
	CONFDATATYPE SignalStateConfidence;
	/// The SignalStateCondition shows the condition of a signal state (stable,
	/// drifting, or unknown)
	SignalStateCondition SignalStateCondition;
	/// The SignalStateIsValid saves the number of samples which have been
	/// inserted into the state after entering it.
	uint32_t NumberOfInsertedSamplesAfterEntrance;
	/// The SignalStateIsValid shows whether a signal state is valid or invalid.
	/// In this context, valid means that enough samples which are in close
	/// proximitry have been inserted into the signal state.
	bool SignalStateIsValid;
	/// The SignalStateJustGotValid shows whether a signal state got valid
	/// (toggled from invalid to valid) during the current inserted sample.
	bool SignalStateJustGotValid;
	/// The SignalStateIsValidAfterReentrance shows whether a signal state is
	/// valid after the variable changed back to it again.
	bool SignalStateIsValidAfterReentrance;
	/// The SignalIsStableNotDrifting shows whether a signal is stable and not
	/// drifting.
	bool SignalIsStable;
	};

	/// \tparam INDATATYPE type of input data, \tparam CONFDATATYPE type of
	/// data in that the confidence values are given, \tparam PROCDATATYPE type of
	/// the relative distance and the type of data in which DABs are saved.
	template <typename INDATATYPE, typename CONFDATATYPE, typename PROCDATATYPE>
	class SignalState : 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),
	"confidence data type is not to arithmetic");
	STATIC_ASSERT(
	(std::is_arithmetic<PROCDATATYPE>::value),
	"process data type (DAB and Relative Distance) 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>>;

	/// SignalStateInfo is a struct SignalStateInformation that contains
	/// information about the current state.
	SignalStateInformation<CONFDATATYPE> SignalStateInfo;

	/// 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;

	/// LowestConfidenceMatchingHistory is a history in that the lowest confidence
	/// for the current sample matches all history samples are saved.
	DynamicLengthHistory<INDATATYPE, HistoryPolicy::FIFO>
	LowestConfidenceMatchingHistory;
	/// HighestConfidenceMatchingHistory is a history in that the highest
	/// confidence for the current sample matches all history samples are saved.
	DynamicLengthHistory<INDATATYPE, HistoryPolicy::FIFO>
	HighestConfidenceMismatchingHistory;

	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 SignalStateID The Id of the SignalStateinfo \c
	/// SignalStateInformation.
	///
	/// \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
	/// 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.
	///
	SignalState(uint32_t SignalStateID, uint32_t SampleHistorySize,
	uint32_t DABSize, uint32_t DABHistorySize,
	PartFuncPointer FuzzyFunctionSampleMatches,
	PartFuncPointer FuzzyFunctionSampleMismatches,
	StepFuncPointer FuzzyFunctionNumOfSamplesMatches,
	StepFuncPointer FuzzyFunctionNumOfSamplesMismatches,
	PartFuncPointer FuzzyFunctionSignalIsDrifting,
	PartFuncPointer FuzzyFunctionSignalIsStable) noexcept
	: SignalStateInfo{SignalStateID, 0, SignalStateCondition::UNKNOWN, 0,
	false, false,
	false, //@maxi added the Signal is stable bool
	true},

	FuzzyFunctionSampleMatches(FuzzyFunctionSampleMatches),
	FuzzyFunctionSampleMismatches(FuzzyFunctionSampleMismatches),
	FuzzyFunctionNumOfSamplesMatches(FuzzyFunctionNumOfSamplesMatches),
	FuzzyFunctionNumOfSamplesMismatches(
	FuzzyFunctionNumOfSamplesMismatches),
	FuzzyFunctionSignalIsDrifting(FuzzyFunctionSignalIsDrifting),
	FuzzyFunctionSignalIsStable(FuzzyFunctionSignalIsStable),
	SampleHistory(SampleHistorySize), DAB(DABSize),
	DABHistory(DABHistorySize),
	LowestConfidenceMatchingHistory(SampleHistorySize),
	HighestConfidenceMismatchingHistory(SampleHistorySize) {}

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

	void leaveSignalState(void) noexcept {
	DAB.clear();
	SignalStateInfo.NumberOfInsertedSamplesAfterEntrance = 0;
	SignalStateInfo.SignalStateIsValidAfterReentrance = false;
	}

	SignalStateInformation<CONFDATATYPE>
	insertSample(INDATATYPE Sample) noexcept {

	validateSignalState(Sample);

	SampleHistory.addEntry(Sample);

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

	FuzzyFunctionNumOfSamplesMatches->setRightLimit(
	static_cast<INDATATYPE>(SampleHistory.numberOfEntries()));
	FuzzyFunctionNumOfSamplesMismatches->setRightLimit(
	static_cast<INDATATYPE>(SampleHistory.numberOfEntries()));

	checkSignalStability();

	return SignalStateInfo;
	}

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

	CONFDATATYPE ConfidenceOfBestCase = 0;

	DynamicLengthHistory<PROCDATATYPE, HistoryPolicy::FIFO>
	RelativeDistanceHistory(SampleHistory.maxLength());

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

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

	CONFDATATYPE ConfidenceOfWorstFittingSample = 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.

	for (uint32_t Case = 0; Case < RelativeDistanceHistory.numberOfEntries();
	Case++) {

	CONFDATATYPE ConfidenceFromRelativeDistance;

	if (std::isinf(RelativeDistanceHistory[Case])) {
	// TODO (future) if fuzzy is defined in a way that infinity is not 0 it
	// would be a problem
	//@David: because we are using these operator functions, here I have to
	// direference in a not (in my opionin) not so beautiful way
	//("(*FuzzyFunctionSampleMatches)"), or I have to write
	//"FuzzyFunctionSampleMatches->operator()(...)". Can we just write
	// functions like "->getBlabla()" or something like that?
	ConfidenceFromRelativeDistance = 0;
	} else {
	ConfidenceFromRelativeDistance =
	(*FuzzyFunctionSampleMatches)(RelativeDistanceHistory[Case]);
	}
	+#if false
	+ //@maxi why the 2?
	+ ConfidenceOfWorstFittingSample =
	+ fuzzyAND((CONFDATATYPE)2, ConfidenceOfWorstFittingSample,
	+ ConfidenceFromRelativeDistance);
	+
	+ //@maxi you could also use it like this then you can define the type and the size
	+ ConfidenceOfWorstFittingSample =
	+ fuzzyAND<CONFDATATYPE,2>({ConfidenceOfWorstFittingSample,
	+ ConfidenceFromRelativeDistance});
	+#else
	+ //@maxi is this what you wanted? you don't need to define the data type
	+ // nor the size
	+ ConfidenceOfWorstFittingSample = fuzzyAND(ConfidenceOfWorstFittingSample,
	+ ConfidenceFromRelativeDistance);
	+#endif

	- ConfidenceOfWorstFittingSample = fuzzyAND<CONFDATATYPE>(
	- 2, ConfidenceOfWorstFittingSample, ConfidenceFromRelativeDistance);
	//@benedikt: same as before with "->operator()"
	+ //@maxi the same as before
	+#if false
	ConfidenceOfBestCase = fuzzyOR<CONFDATATYPE>(
	2, ConfidenceOfBestCase,
	fuzzyAND<CONFDATATYPE>(2, ConfidenceOfWorstFittingSample,
	FuzzyFunctionNumOfSamplesMatches->operator()(
	static_cast<CONFDATATYPE>(Case) + 1)));
	+#else
	+ ConfidenceOfBestCase =
	+ fuzzyOR(ConfidenceOfBestCase,
	+ fuzzyAND(ConfidenceOfWorstFittingSample,
	+ FuzzyFunctionNumOfSamplesMatches->operator()(
	+ static_cast<CONFDATATYPE>(Case) + 1)));
	+
	+#endif
	}

	return ConfidenceOfBestCase;
	}

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

	float ConfidenceOfWorstCase = 1;

	DynamicLengthHistory<PROCDATATYPE, HistoryPolicy::FIFO>
	RelativeDistanceHistory(SampleHistory.maxLength());

	// calculate distances to all history samples
	for (auto &HistorySample : SampleHistory) {
	RelativeDistanceHistory.addEntry(
	relativeDistance<INDATATYPE, PROCDATATYPE>(Sample, HistorySample));
	}

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

	CONFDATATYPE ConfidenceOfBestFittingSample = 0;

	// 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
	// confidenceSampleMatchesSignalState(INDATATYPE Sample)".
	for (uint32_t Case = 0; Case < RelativeDistanceHistory.numberOfEntries();
	Case++) {

	CONFDATATYPE ConfidenceFromRelativeDistance;

	if (std::isinf(RelativeDistanceHistory[Case])) {
	ConfidenceFromRelativeDistance = 1;
	} else {
	//@benedikt: I had to change the following line. The outcommented line
	// was the original one. I think it is ugly like that (new line). Do you
	// have an idea how to make it better/more beautiful?
	ConfidenceFromRelativeDistance =
	FuzzyFunctionSampleMismatches->operator()(
	RelativeDistanceHistory[Case]);
	}

	+//@maxi you don't have to define the data type or the amount
	+#if false
	ConfidenceOfBestFittingSample = fuzzyOR<CONFDATATYPE>(
	2, ConfidenceOfBestFittingSample, ConfidenceFromRelativeDistance);

	//@benedikt: same as before with "->operator()"
	ConfidenceOfWorstCase = fuzzyAND<CONFDATATYPE>(
	2, ConfidenceOfWorstCase,
	fuzzyOR<CONFDATATYPE>(2, ConfidenceOfBestFittingSample,
	FuzzyFunctionNumOfSamplesMismatches->operator()(
	static_cast<CONFDATATYPE>(Case) + 1)));
	+#else
	+ ConfidenceOfBestFittingSample = fuzzyOR(ConfidenceOfBestFittingSample,
	+ ConfidenceFromRelativeDistance);
	+
	+ //@benedikt: same as before with "->operator()"
	+ ConfidenceOfWorstCase =
	+ fuzzyAND(ConfidenceOfWorstCase,
	+ fuzzyOR(ConfidenceOfBestFittingSample,
	+ FuzzyFunctionNumOfSamplesMismatches->operator()(
	+ static_cast<CONFDATATYPE>(Case) + 1)));
	+#endif
	}

	return ConfidenceOfWorstCase;
	}

	/// Gives information about the current signal state.
	///
	/// \return a struct SignalStateInformation that contains information about
	/// the current signal state.
	SignalStateInformation<CONFDATATYPE> signalStateInformation(void) noexcept {
	return SignalStateInfo;
	}

	private:
	void validateSignalState(INDATATYPE Sample) {
	// TODO (future): WorstConfidenceDistance and BestConfidenceDistance could
	// be set already in "CONFDATATYPE
	// confidenceSampleMatchesSignalState(INDATATYPE Sample)" and "CONFDATATYPE
	// confidenceSampleMismatchesSignalState(INDATATYPE Sample)" when the new
	// sample is compared to all history samples. This would save a lot time
	// because the comparisons are done only once. However, it has to be asured
	// that the these two functions are called before the insertation, and the
	// FuzzyFunctions for validation and matching have to be the same!
	CONFDATATYPE LowestConfidenceMatching = 1;
	CONFDATATYPE HighestConfidenceMismatching = 0;
	for (auto &HistorySample : SampleHistory) {
	// TODO (future): think about using different fuzzy functions for
	// validation and matching.
	//@benedikt: same with "->operator()"
	+ //@maxi you don't have to deine the data type nor the amount
	+#if false
	LowestConfidenceMatching =
	fuzzyAND<CONFDATATYPE>(2, LowestConfidenceMatching,
	FuzzyFunctionSampleMatches->operator()(
	relativeDistance<INDATATYPE, PROCDATATYPE>(
	Sample, HistorySample)));
	//@benedikt: same with "->operator()"
	HighestConfidenceMismatching =
	fuzzyOR<CONFDATATYPE>(2, HighestConfidenceMismatching,
	FuzzyFunctionSampleMismatches->operator()(
	relativeDistance<INDATATYPE, PROCDATATYPE>(
	Sample, HistorySample)));
	+#else
	+ LowestConfidenceMatching =
	+ fuzzyAND(LowestConfidenceMatching,
	+ FuzzyFunctionSampleMatches->operator()(
	+ relativeDistance<INDATATYPE, PROCDATATYPE>(
	+ Sample, HistorySample)));
	+ //@benedikt: same with "->operator()"
	+ HighestConfidenceMismatching =
	+ fuzzyOR(HighestConfidenceMismatching,
	+ FuzzyFunctionSampleMismatches->operator()(
	+ relativeDistance<INDATATYPE, PROCDATATYPE>(
	+ Sample, HistorySample)));
	+#endif
	}
	LowestConfidenceMatchingHistory.addEntry(LowestConfidenceMatching);
	HighestConfidenceMismatchingHistory.addEntry(HighestConfidenceMismatching);

	LowestConfidenceMatching = LowestConfidenceMatchingHistory.lowestEntry();
	HighestConfidenceMismatching =
	HighestConfidenceMismatchingHistory.highestEntry();

	+ //@maxi you don't have to define the data type or the amount
	+#if false
	//@benedikt: same with "->operator()"
	CONFDATATYPE ConfidenceSignalStateIsValid = fuzzyAND<CONFDATATYPE>(
	2, LowestConfidenceMatching,
	FuzzyFunctionNumOfSamplesMatches->operator()(static_cast<INDATATYPE>(
	SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));
	//@benedikt: same with "->operator()"
	CONFDATATYPE ConfidenceSignalStateIsInvalid = fuzzyOR<CONFDATATYPE>(
	2, HighestConfidenceMismatching,
	FuzzyFunctionNumOfSamplesMismatches->operator()(static_cast<INDATATYPE>(
	SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));
	+#else
	+ //@benedikt: same with "->operator()"
	+ CONFDATATYPE ConfidenceSignalStateIsValid = fuzzyAND(
	+ LowestConfidenceMatching,
	+ FuzzyFunctionNumOfSamplesMatches->operator()(static_cast<INDATATYPE>(
	+ SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));
	+ //@benedikt: same with "->operator()"
	+ CONFDATATYPE ConfidenceSignalStateIsInvalid = fuzzyOR(
	+ HighestConfidenceMismatching,
	+ FuzzyFunctionNumOfSamplesMismatches->operator()(static_cast<INDATATYPE>(
	+ SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));
	+#endif

	if (ConfidenceSignalStateIsValid > ConfidenceSignalStateIsInvalid) {
	if (SignalStateInfo.SignalStateIsValid) {
	SignalStateInfo.SignalStateJustGotValid = false;
	} else {
	SignalStateInfo.SignalStateJustGotValid = true;
	}
	SignalStateInfo.SignalStateIsValid = true;
	SignalStateInfo.SignalStateIsValidAfterReentrance = true;
	}
	}

	void checkSignalStability(void) {
	CONFDATATYPE ConfidenceSignalIsStable;
	CONFDATATYPE ConfidenceSignalIsDrifting;

	if (DABHistory.numberOfEntries() >= 2) {
	//@benedikt: same "->operator()"
	ConfidenceSignalIsStable = FuzzyFunctionSignalIsStable->operator()(
	relativeDistance<INDATATYPE, PROCDATATYPE>(
	DABHistory[DABHistory.numberOfEntries() - 1], DABHistory[0]));
	//@benedikt: same "->operator()"
	ConfidenceSignalIsDrifting = FuzzyFunctionSignalIsDrifting->operator()(
	relativeDistance<INDATATYPE, PROCDATATYPE>(
	DABHistory[DABHistory.numberOfEntries() - 1], DABHistory[0]));
	} else {
	// TODO: change to enum
	ConfidenceSignalIsStable = 1;
	ConfidenceSignalIsDrifting = 0;
	}

	SignalStateInfo.SignalIsStable =
	ConfidenceSignalIsStable >= ConfidenceSignalIsDrifting;
	}
	};

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

	#endif // ROSA_AGENT_SIGNALSTATE_HPP
	diff --git a/include/rosa/agent/SignalStateDetector.hpp b/include/rosa/agent/SignalStateDetector.hpp
	index 99af791..d7b10e9 100644
	--- a/include/rosa/agent/SignalStateDetector.hpp
	+++ b/include/rosa/agent/SignalStateDetector.hpp
	@@ -1,272 +1,278 @@
	//===-- rosa/agent/SignalStateDetector.hpp ----------------------- C++ --===//
	//
	// The RoSA Framework
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file rosa/agent/SignalStateDetector.hpp
	///
	/// \author Maximilian Götzinger (maximilian.goetzinger@tuwien.ac.at)
	///
	/// \date 2019
	///
	/// \brief Definition of signal state detector functionality.
	///
	//===----------------------------------------------------------------------===//

	#ifndef ROSA_AGENT_SIGNALSTATEDETECTOR_HPP
	#define ROSA_AGENT_SIGNALSTATEDETECTOR_HPP

	#include "rosa/agent/Functionality.h"
	#include "rosa/agent/SignalState.hpp"
	#include "rosa/agent/StateDetector.hpp"

	#include <vector>

	namespace rosa {
	namespace agent {

	/// Implements \c rosa::agent::SignalStateDetector as a functionality that
	/// detects signal states given on input samples.
	///
	/// \note This implementation is supposed to be used for samples of an
	/// arithmetic type.
	///
	/// \tparam INDATATYPE type of input data, \tparam CONFDATATYPE type of
	/// data in that the confidence values are given, \tparam PROCDATATYPE type of
	/// the relative distance and the type of data in which DABs are saved.
	template <typename INDATATYPE, typename CONFDATATYPE, typename PROCDATATYPE,
	HistoryPolicy HP>
	class SignalStateDetector
	: public StateDetector<INDATATYPE, CONFDATATYPE, PROCDATATYPE, HP> {

	+ // @maxi added them so it is compilable is this what you intended?
	+ using StateDetector =
	+ StateDetector<INDATATYPE, CONFDATATYPE, PROCDATATYPE, HP>;
	+ using PartFuncPointer = typename StateDetector::PartFuncPointer;
	+ using StepFuncPointer = typename StateDetector::StepFuncPointer;
	+
	private:
	// For the convinience to write a shorter data type name
	using SignalStatePtr =
	std::shared_ptr<SignalState<INDATATYPE, CONFDATATYPE, PROCDATATYPE>>;

	/// The NextSignalStateID is a counter variable which stores the ID which the
	/// next signal state shall have.
	uint32_t NextSignalStateID;

	/// The SignalStateHasChanged is a flag that show whether a signal has changed
	/// its state.
	bool SignalStateHasChanged;

	/// The CurrentSignalState is a pointer to the (saved) signal state in which
	/// the actual variable (signal) of the observed system is.
	SignalStatePtr CurrentSignalState;

	/// The DetectedSignalStates is a history in that all detected signal states
	/// are saved.
	DynamicLengthHistory<SignalStatePtr, HP> DetectedSignalStates;

	/// 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 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.
	uint32_t SampleHistorySize;
	/// DABSize the size of a DAB (Discrete Average Block).
	uint32_t DABSize;
	/// DABHistorySize is the (maximum) size of the DAB history.
	uint32_t 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
	/// SignalState.
	///
	/// \param DABSize Sets the DAB size which will be used by \c SignalState.
	///
	/// \param DABHistorySize Sets the size which will be used by \c SignalState.
	///
	SignalStateDetector(uint32_t MaximumNumberOfSignalStates,
	PartFuncPointer FuzzyFunctionSampleMatches,
	PartFuncPointer FuzzyFunctionSampleMismatches,
	StepFuncPointer FuzzyFunctionNumOfSamplesMatches,
	StepFuncPointer FuzzyFunctionNumOfSamplesMismatches,
	PartFuncPointer FuzzyFunctionSignalIsDrifting,
	PartFuncPointer FuzzyFunctionSignalIsStable,
	uint32_t SampleHistorySize, uint32_t DABSize,
	uint32_t DABHistorySize) noexcept
	: NextSignalStateID(1), SignalStateHasChanged(false),
	CurrentSignalState(nullptr),
	DetectedSignalStates(MaximumNumberOfSignalStates),
	FuzzyFunctionSampleMatches(FuzzyFunctionSampleMatches),
	FuzzyFunctionSampleMismatches(FuzzyFunctionSampleMismatches),
	FuzzyFunctionNumOfSamplesMatches(FuzzyFunctionNumOfSamplesMatches),
	FuzzyFunctionNumOfSamplesMismatches(
	FuzzyFunctionNumOfSamplesMismatches),
	FuzzyFunctionSignalIsDrifting(FuzzyFunctionSignalIsDrifting),
	FuzzyFunctionSignalIsStable(FuzzyFunctionSignalIsStable),
	SampleHistorySize(SampleHistorySize), DABSize(DABSize),
	DABHistorySize(DABHistorySize) {}

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

	/// Detects the signal state to which the new sample belongs or create a new
	/// signal 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 current signal state (signal state ID and
	/// other parameters).
	// TODO (future): change to operator()
	SignalStateInformation<CONFDATATYPE>
	detectSignalState(INDATATYPE Sample) noexcept {

	if (!CurrentSignalState) {
	ASSERT(DetectedSignalStates.empty());

	SignalStatePtr S = createNewSignalState();
	CurrentSignalState = S;
	} else {
	CONFDATATYPE ConfidenceSampleMatchesSignalState =
	CurrentSignalState->confidenceSampleMatchesSignalState(Sample);
	CONFDATATYPE ConfidenceSampleMismatchesSignalState =
	CurrentSignalState->confidenceSampleMismatchesSignalState(Sample);

	if (ConfidenceSampleMatchesSignalState >
	ConfidenceSampleMismatchesSignalState) {
	SignalStateHasChanged = false;
	} else {
	SignalStateHasChanged = true;

	if (CurrentSignalState->signalStateInformation().SignalStateIsValid) {
	CurrentSignalState->leaveSignalState();
	} else {
	DetectedSignalStates.deleteEntry(CurrentSignalState);
	}

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

	//@benedikt: same question
	for (auto &SavedSignalState : DetectedSignalStates) {
	if (SavedSignalState != CurrentSignalState) {
	ConfidenceSampleMatchesSignalState =
	SavedSignalState->confidenceSampleMatchesSignalState(Sample);
	ConfidenceSampleMismatchesSignalState =
	SavedSignalState->confidenceSampleMismatchesSignalState(Sample);

	if (ConfidenceSampleMatchesSignalState >
	ConfidenceSampleMismatchesSignalState) {
	// TODO (future): maybe it would be better to compare
	// ConfidenceSampleMatchesSignalState of all signal states in the
	// vector in order to find the best matching signal state.
	CurrentSignalState = SavedSignalState;
	break;
	}
	}
	}

	if (!CurrentSignalState) {
	SignalStatePtr S = createNewSignalState();
	CurrentSignalState = S;
	}
	}
	}

	SignalStateInformation<CONFDATATYPE> SignalStateInfo =
	CurrentSignalState->insertSample(Sample);

	if (SignalStateInfo.SignalStateJustGotValid) {
	NextSignalStateID++;
	}

	return SignalStateInfo;
	}

	/// Gives information about the current signal state.
	///
	/// \return a struct SignalStateInformation that contains information about
	/// the current signal state or NULL if no current signal state exists.
	SignalStateInformation<CONFDATATYPE>
	currentSignalStateInformation(void) noexcept {
	if (CurrentSignalState) {
	return CurrentSignalState->signalStateInformation();
	} else {
	return NULL;
	}
	}

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

	private:
	/// Creates a new signal state and adds it to the signal state vector in which
	/// all known states are saved.
	///
	/// \return a pointer to the newly created signal state or NULL if no state
	/// could be created.
	SignalStatePtr createNewSignalState(void) noexcept {
	SignalStatePtr S(new SignalState<INDATATYPE, CONFDATATYPE, PROCDATATYPE>(
	NextSignalStateID, SampleHistorySize, DABSize, DABHistorySize,
	FuzzyFunctionSampleMatches, FuzzyFunctionSampleMismatches,
	FuzzyFunctionNumOfSamplesMatches, FuzzyFunctionNumOfSamplesMismatches,
	FuzzyFunctionSignalIsDrifting, FuzzyFunctionSignalIsStable));

	DetectedSignalStates.addEntry(S);

	return S;
	}
	};

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

	#endif // ROSA_AGENT_SIGNALSTATEDETECTOR_HPP
	diff --git a/include/rosa/agent/StateDetector.hpp b/include/rosa/agent/StateDetector.hpp
	index aa341f6..9bd7430 100644
	--- a/include/rosa/agent/StateDetector.hpp
	+++ b/include/rosa/agent/StateDetector.hpp
	@@ -1,56 +1,57 @@
	//===-- 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.
	///
	//===----------------------------------------------------------------------===//

	#ifndef ROSA_AGENT_STATEDETECTOR_HPP
	#define ROSA_AGENT_STATEDETECTOR_HPP

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

	#include <vector>

	namespace rosa {
	namespace agent {

	template <typename INDATATYPE, typename CONFDATATYPE, typename PROCDATATYPE,
	HistoryPolicy HP>
	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<CONFDATATYPE>::value),
	"confidence abstraction type is not to arithmetic");

	-protected:
	+public:
	using PartFuncPointer =
	std::shared_ptr<PartialFunction<INDATATYPE, CONFDATATYPE>>;
	using StepFuncPointer =
	std::shared_ptr<StepFunction<INDATATYPE, CONFDATATYPE>>;

	+protected:
	/// The NextSignalStateID is a counter variable which stores the ID which the
	/// next signal state shall have.
	unsigned int NextStateID;

	/// The SignalStateHasChanged is a flag that show whether a signal has changed
	/// its state.
	bool StateHasChanged;
	};

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

	#endif // ROSA_AGENT_SIGNALSTATEDETECTOR_HPP
	diff --git a/include/rosa/agent/SystemStateDetector.hpp b/include/rosa/agent/SystemStateDetector.hpp
	index ac9072e..31335bf 100644
	--- a/include/rosa/agent/SystemStateDetector.hpp
	+++ b/include/rosa/agent/SystemStateDetector.hpp
	@@ -1,141 +1,146 @@
	//===-- rosa/agent/SystemStateDetector.hpp ----------------------- C++ --===//
	//
	// The RoSA Framework
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file rosa/agent/SystemStateDetector.hpp
	///
	/// \author Maximilian Götzinger (maximilian.goetzinger@tuwien.ac.at)
	///
	/// \date 2019
	///
	/// \brief Definition of system state detector functionality.
	///
	//===----------------------------------------------------------------------===//

	#ifndef ROSA_AGENT_SYSTEMSTATEDETECTOR_HPP
	#define ROSA_AGENT_SYSTEMSTATEDETECTOR_HPP

	#include "rosa/agent/Functionality.h"
	#include "rosa/agent/StateDetector.hpp"
	#include "rosa/agent/SystemState.hpp"

	#include "rosa/support/debug.hpp"

	namespace rosa {
	namespace agent {

	/// System state conditions defining how the condition of a \c
	/// rosa::agent::SystemState is saved in \c rosa::agent::SystemStateInformation.
	enum class SystemStateCondition {
	STABLE, ///< The system state is stable
	DRIFTING, ///< The system state is drifting
	MALFUNCTIONING, ///< The system state is malfunctioning
	UNKNOWN ///< The system state is unknown
	};

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

	/// The system state ID saved as an uint32_teger number
	uint32_t SystemStateID;
	/// The SystemStateConfidence shows the overall confidence value of the system
	/// state.
	CONFDATATYPE OverallDetectionConfidence;
	/// The SystemStateCondition shows the condition of a system state (stable,
	/// drifting, malfunctioning, or unknown)
	//@David: is it ok to name the variable exactly as the type is named?
	SystemStateCondition SystemStateCondition;
	/// The SystemStateIsValid saves the number of samples which have been
	/// inserted into the state after entering it.
	uint32_t NumberOfInsertedSamplesAfterEntrance;
	/// The SystemStateIsValid 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 SystemStateIsValid;
	/// The SystemStateJustGotValid shows whether a system state got valid
	/// (toggled from invalid to valid) during the current inserted sample.
	bool SystemStateJustGotValid;
	/// The SystemStateIsValidAfterReentrance shows whether a system state is
	/// valid after the variable changed back to it again.
	bool SystemStateIsValidAfterReentrance;
	/// The SystemIsStable shows whether a signa is stable and not
	/// drifting.
	bool SystemIsStable;
	};

	/// TODO: write description
	template <typename INDATATYPE, typename CONFDATATYPE, typename PROCDATATYPE,
	HistoryPolicy HP, std::size_t NUMOFINPUTSIGNALS,
	std::size_t NUMOFOUTPUTSIGNALS>
	class SystemStateDetector
	: public StateDetector<INDATATYPE, CONFDATATYPE, PROCDATATYPE, HP> {

	+ //@maxi added them to make it compilable is this what you wanted?
	+ using StateDetector =
	+ StateDetector<INDATATYPE, CONFDATATYPE, PROCDATATYPE, HP>;
	+ using PartFuncPointer = typename StateDetector::PartFuncPointer;
	+
	private:
	// For the convinience to write a shorter data type name
	using SystemStatePtr =
	std::shared_ptr<SystemState<INDATATYPE, CONFDATATYPE, PROCDATATYPE,
	NUMOFINPUTSIGNALS, NUMOFOUTPUTSIGNALS>>;

	/// The NextSystemStateID is a counter variable which stores the ID which
	/// the
	/// next system state shall have.
	uint32_t NextSystemStateID;

	/// The SystemStateHasChanged is a flag that show whether the observed
	/// system
	/// has changed its state.
	bool SystemStateHasChanged;

	/// The CurrentSystemState is a pointer to the (saved) system state in which
	/// the actual state of the observed system is.
	SystemStatePtr CurrentSystemState;

	/// The DetectedSystemStates is a history in that all detected system states
	/// are saved.
	DynamicLengthHistory<SystemStatePtr, HP> DetectedSystemStates;

	/// The FuzzyFunctionDelayTimeToGetBroken is the fuzzy function that gives
	/// the confidence whether the system is Broken because of an input change
	/// without an output change or vice versa. A small time gap between the two
	/// shall be allowed.
	PartFuncPointer FuzzyFunctionDelayTimeToGetBroken;

	/// The FuzzyFunctionDelayTimeToBeWorking is the fuzzy function that gives
	/// the
	/// confidence whether the system is still OK allthough an input change
	/// without an output change or vice versa.
	PartFuncPointer FuzzyFunctionDelayTimeToBeWorking;

	public:
	/// TODO: write description
	SystemStateDetector(
	uint32_t MaximumNumberOfSystemStates,
	PartFuncPointer FuzzyFunctionDelayTimeToGetBroken,
	PartFuncPointer FuzzyFunctionDelayTimeToBeWorking) noexcept
	: NextSystemStateID(1), SystemStateHasChanged(false),
	CurrentSystemState(nullptr),
	DetectedSystemStates(MaximumNumberOfSystemStates),
	FuzzyFunctionDelayTimeToGetBroken(FuzzyFunctionDelayTimeToGetBroken),
	FuzzyFunctionDelayTimeToBeWorking(FuzzyFunctionDelayTimeToBeWorking) {}

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

	/// TODO: write description
	SystemStateInformation<CONFDATATYPE>
	detectSystemState(INDATATYPE Sample) noexcept {

	// dummy line
	Sample = 1;
	}
	};

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

	#endif // ROSA_AGENT_SYSTEMSTATEDETECTOR_HPP
	diff --git a/include/rosa/support/math.hpp b/include/rosa/support/math.hpp
	index 30e11fb..95a7363 100644
	--- a/include/rosa/support/math.hpp
	+++ b/include/rosa/support/math.hpp
	@@ -1,153 +1,153 @@
	//===-- rosa/support/math.hpp ------------------------------------ C++ --===//
	//
	// The RoSA Framework
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file rosa/support/math.hpp
	///
	/// \author David Juhasz (david.juhasz@tuwien.ac.at)
	///
	/// \date 2017
	///
	/// \brief Math helpers.
	///
	//===----------------------------------------------------------------------===//

	// !!!!!! Please check lines 60 - 180 forward !!!!!!!!!!!!!!

	#ifndef ROSA_SUPPORT_MATH_HPP
	#define ROSA_SUPPORT_MATH_HPP

	#include "debug.hpp"
	#include <algorithm>
	#include <array>
	#include <cmath>
	#include <cstdarg>
	#include <cstdlib>
	#include <limits>
	#include <type_traits>

	namespace rosa {

	/// Computes log base 2 of a number.
	///
	/// \param N the number to compute log base 2 for
	///
	/// \return log base 2 of \p N
	constexpr size_t log2(const size_t N) {
	return ((N < 2) ? 1 : 1 + log2(N / 2));
	}

	/// Tells the next representable floating point value.
	///
	/// \tparam T type to operate on
	///
	/// \note The second type argument enforces \p T being a floating point type,
	/// always use the default value!
	///
	/// \param V value to which find the next representable one
	///
	/// \return the next representable value of type \p T after value \p V
	///
	/// \pre Type \p T must be a floating point type, which is enforced by
	/// `std::enable_if` in the second type argument.
	template <typename T,
	typename = std::enable_if_t<std::is_floating_point<T>::value>>
	T nextRepresentableFloatingPoint(const T V) {
	return std::nextafter(V, std::numeric_limits<T>::infinity());
	}


	/// Conjuncts two or more values with each other.
	///
	/// \param Data an array of the data
	///
	/// \return the conjunction of the values given as parameter.
	template <typename CONFDATATYPE, std::size_t size>
	CONFDATATYPE fuzzyAND(const std::array<CONFDATATYPE, size> & Data) noexcept {
	STATIC_ASSERT(std::is_arithmetic<CONFDATATYPE>::value,
	"Type of FuzzyAnd is not arithmetic");
	STATIC_ASSERT(size > 1, "Number of Arguments is to little");
	for (auto tmp : Data)
	ASSERT(tmp <= 1 && tmp >= 0);
	return *std::min_element(Data.begin(), Data.end());
	}

	/// Conjuncts two or more values with each other. It's a wrapper for \c fuzzyAND() [array]
	///
	/// \param Data first data to get the type explicitly
	///
	/// \param Datan a package of data
	///
	/// \note the types of Datan must be the same type as Data
	///
	/// \return the conjunction of the values given as parameter.
	template <typename CONFDATATYPE, typename... _CONFDATATYPE>
	std::enable_if_t<
	std::conjunction_v<std::is_same<CONFDATATYPE, _CONFDATATYPE>...>,
	CONFDATATYPE>
	-fuzzyAND(CONFDATATYPE & Data, _CONFDATATYPE&... Datan) noexcept {
	+fuzzyAND(const CONFDATATYPE Data, const _CONFDATATYPE... Datan) noexcept {
	return fuzzyAND(
	std::array<CONFDATATYPE, sizeof...(Datan) + 1>{Data, Datan...});
	}




	/// Disjuncts two or more values with each other.
	///
	/// \param Data an array with the data.
	///
	/// \return the disjunction of the values given as parameter.
	template <typename CONFDATATYPE, std::size_t size>
	CONFDATATYPE fuzzyOR(const std::array<CONFDATATYPE, size> & Data) noexcept {
	STATIC_ASSERT(std::is_arithmetic<CONFDATATYPE>::value,
	"Type of FuzzyAnd is not arithmetic");
	STATIC_ASSERT(size > 1, "Number of Arguments is to little");
	ASSERT(std::all_of(Data.begin(), Data.end(),
	[](const auto &v) { return v <= 1 && v >= 0; }));
	return *std::max_element(Data.begin(), Data.end());
	}

	/// Disjuncts two or more values with each other. It's a wrapper for \c fuzzyOR() [array]
	///
	/// \param Data first data to get the type explicitly
	///
	/// \param Datan a package of data
	///
	/// \note the types of Datan must be the same type as Data
	///
	/// \return the disjunction of the values given as parameter.
	template <typename CONFDATATYPE, typename... _CONFDATATYPE>
	std::enable_if_t<
	std::conjunction_v<std::is_same<CONFDATATYPE, _CONFDATATYPE>...>,
	CONFDATATYPE>
	-fuzzyOR(const CONFDATATYPE & Data, const _CONFDATATYPE&... Datan) noexcept {
	+fuzzyOR(const CONFDATATYPE Data, const _CONFDATATYPE... Datan) noexcept {
	return fuzzyOR(
	std::array<CONFDATATYPE, sizeof...(Datan) + 1>{Data, Datan...});
	}


	template <typename INDATATYPE, typename PROCDATATYPE>
	PROCDATATYPE relativeDistance(INDATATYPE NewValue,
	INDATATYPE HistoryValue) noexcept {
	PROCDATATYPE Dist = HistoryValue - NewValue;

	if (Dist == 0) {
	return 0;
	} else {
	Dist = Dist / NewValue;
	if (Dist < 0) {
	// TODO: 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 rosa

	#endif // ROSA_SUPPORT_MATH_HPP

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