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	diff --git a/include/rosa/agent/Abstraction.hpp b/include/rosa/agent/Abstraction.hpp
	index 6afa7fd..a39ee4a 100644
	--- a/include/rosa/agent/Abstraction.hpp
	+++ b/include/rosa/agent/Abstraction.hpp
	@@ -1,244 +1,244 @@
	//===-- rosa/agent/Abstraction.hpp ------------------------------- C++ --===//
	//
	// The RoSA Framework
	//
	// Distributed under the terms and conditions of the Boost Software License 1.0.
	// See accompanying file LICENSE.
	//
	// If you did not receive a copy of the license file, see
	// http://www.boost.org/LICENSE_1_0.txt.
	//
	//===----------------------------------------------------------------------===//
	///
	/// \file rosa/agent/Abstraction.hpp
	///
	/// \author David Juhasz (david.juhasz@tuwien.ac.at)
	///
	/// \date 2017
	///
	/// \brief Definition of abstraction functionality.
	///
	//===----------------------------------------------------------------------===//

	#ifndef ROSA_AGENT_ABSTRACTION_HPP
	#define ROSA_AGENT_ABSTRACTION_HPP

	#include "rosa/agent/Functionality.h"

	#include "rosa/support/debug.hpp"

	#include <algorithm>
	#include <map>

	namespace rosa {
	namespace agent {

	/// Abstracts values from a type to another one.
	///
	/// \tparam T type to abstract from
	/// \tparam A type to abstract to
	template <typename T, typename A> class Abstraction : public Functionality {
	protected:
	/// Value to abstract to by default.
	const A Default;
	public:
	/// Creates an instance.
	///
	/// \param Default value to abstract to by default
	Abstraction(const A Default) noexcept : Default(Default) {}

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

	/// Checks wether the Abstraction evaluates to default at the given position
	///
	/// \param V the value at which to check if the function falls back to it's
	/// default value.
	/// \return true, the default implementation always falls back to the default
	/// value
	virtual bool isDefaultAt(const T &V) const noexcept{
	(void)V;
	return true;
	}

	/// Abstracts a value from type \p T to type \p A.
	///
	/// \note The default implementation always returns
	/// \c rosa::agent::Abstraction::Default, hence the actual argument is
	/// ignored.
	///
	/// \return the abstracted value
	virtual A operator()(const T &) const noexcept { return Default; }
	};

	/// Implements \c rosa::agent::Abstraction as a \c std::map from a type to
	/// another one.
	///
	/// \note This implementation is supposed to be used to abstract between
	/// enumeration types, which is statically enforced.
	///
	/// \tparam T type to abstract from
	/// \tparam A type to abstract to
	template <typename T, typename A>
	class MapAbstraction : public Abstraction<T, A>, private std::map<T, A> {
	// Make sure the actual type arguments are enumerations.
	STATIC_ASSERT((std::is_enum<T>::value && std::is_enum<A>::value),
	"mapping not enumerations");

	// Bringing into scope inherited members.
	using Abstraction<T, A>::Default;
	using std::map<T, A>::end;
	using std::map<T, A>::find;

	public:
	/// Creates an instance by initializing the underlying \c std::map.
	///
	/// \param Map the mapping to do abstraction according to
	/// \param Default value to abstract to by default
	MapAbstraction(const std::map<T, A> &Map, const A Default) noexcept
	: Abstraction<T, A>(Default),
	std::map<T, A>(Map) {}

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

	/// Checks wether the Abstraction evaluates to default at the given position
	///
	/// \param V the value at which to check if the function falls back to it's
	/// default value.
	/// \return true if the Abstraction falls back to the default value
	bool isDefaultAt(const T &V) const noexcept override {
	const auto I = find(V);
	return I == end() ? true : false;
	}

	/// Abstracts a value from type \p T to type \p A based on the set mapping.
	///
	/// Results in the value associated by the set mapping to the argument, or
	/// \c rosa::agent::MapAbstraction::Default if the actual argument is not
	/// associated with anything by the set mapping.
	///
	/// \param V value to abstract
	///
	/// \return the abstracted value based on the set mapping
	A operator()(const T &V) const noexcept override {
	const auto I = find(V);
	return I == end() ? Default : *I;
	}
	};

	/// Implements \c rosa::agent::Abstraction as a \c std::map from ranges of a
	/// type to values of another type.
	///
	/// \note This implementation is supposed to be used to abstract ranges of
	/// arithmetic types into enumerations, which is statically enforced.
	///
	/// \invariant The keys in the underlying \c std::map define valid ranges
	/// such that `first <= second` and there are no overlapping ranges defined by
	/// the keys.
	///
	/// \tparam T type to abstract from
	/// \tparam A type to abstract to
	template <typename T, typename A>
	class RangeAbstraction : public Abstraction<T, A>,
	private std::map<std::pair<T, T>, A> {
	// Make sure the actual type arguments are matching our expectations.
	STATIC_ASSERT((std::is_arithmetic<T>::value), "abstracting not arithmetic");
	/// \todo check if this compiles with the definition of abstractions as
	/// self-aware properties
	//STATIC_ASSERT((std::is_enum<A>::value), "abstracting not to enumeration");

	// Bringing into scope inherited members.
	using Abstraction<T, A>::Default;
	using std::map<std::pair<T, T>, A>::begin;
	using std::map<std::pair<T, T>, A>::end;
	using std::map<std::pair<T, T>, A>::find;

	public:
	/// Creates an instance by Initializing the unserlying \c std::map.
	///
	/// \param Map the mapping to do abstraction according to
	/// \param Default value to abstract to by default
	///
	/// \pre Each key defines a valid range such that `first <= second` and
	/// there are no overlapping ranges defined by the keys.
	RangeAbstraction(const std::map<std::pair<T, T>, A> &Map, const A &Default)
	: Abstraction<T, A>(Default), std::map<std::pair<T, T>, A>(Map) {
	// Sanity check.
	ASSERT(std::all_of(
	begin(), end(), [this](const std::pair<std::pair<T, T>, A> &P) {
	return P.first.first <= P.first.second &&
	std::all_of(++find(P.first), end(),
	[&P](const std::pair<std::pair<T, T>, A> &R) {
	// \note Values in \c Map are sorted.
	- return P.first.first < P.first.second &&
	+ return P.first.first <= P.first.second &&
	P.first.second <= R.first.first \|\|
	P.first.first == P.first.second &&
	- P.first.second < R.first.first;
	+ P.first.second <= R.first.first;
	});
	}));
	}

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

	/// Checks wether the Abstraction evaluates to default at the given position
	///
	/// \param V the value at which to check if the function falls back to it's
	/// default value.
	/// \return true if the Abstraction falls back to the default value
	bool isDefaultAt(const T &V) const noexcept override {
	auto I = begin();
	bool Found = false; // Indicates if \c I refers to a matching range.
	bool Failed = false; // Indicates if it is pointless to continue searching.
	while (!Found && !Failed && I != end()) {
	if (V < I->first.first) {
	// No match so far and \p V is below the next range, never will match.
	// \note Keys are sorted in the map.
	return true;
	} else if (I->first.first <= V && V < I->first.second) {
	// Matching range found.
	return false;
	} else {
	// Cannot conclude in this step, move to the next range.
	++I;
	}
	}
	return true;
	}

	/// Abstracts a value from type \p T to type \p A based on the set mapping.
	///
	/// Results in the value associated by the set mapping to the argument, or
	/// \c rosa::agent::RangeAbstraction::Default if the actual argument is not
	/// included in any of the ranges in the set mapping.
	///
	/// \param V value to abstract
	///
	/// \return the abstracted value based on the set mapping
	A operator()(const T &V) const noexcept override {
	auto I = begin();
	bool Found = false; // Indicates if \c I refers to a matching range.
	bool Failed = false; // Indicates if it is pointless to continue searching.
	while (!Found && !Failed && I != end()) {
	if (V < I->first.first) {
	// No match so far and \p V is below the next range, never will match.
	// \note Keys are sorted in the map.
	Failed = true;
	} else if (I->first.first <= V && V < I->first.second) {
	// Matching range found.
	Found = true;
	} else {
	// Cannot conclude in this step, move to the next range.
	++I;
	}
	}
	ASSERT(!Found \|\| I != end());
	return Found ? I->second : Default;
	}
	};

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

	#endif // ROSA_AGENT_ABSTRACTION_HPP
	diff --git a/include/rosa/agent/SignalState.hpp b/include/rosa/agent/SignalState.hpp
	index 2b2595e..1ea6b6a 100644
	--- a/include/rosa/agent/SignalState.hpp
	+++ b/include/rosa/agent/SignalState.hpp
	@@ -1,688 +1,638 @@
	//===-- 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/DistanceMetrics.hpp"
	#include "rosa/agent/FunctionAbstractions.hpp"
	#include "rosa/agent/Functionality.h"
	#include "rosa/agent/History.hpp"
	#include "rosa/agent/State.hpp"

	#include "rosa/support/math.hpp"

	namespace rosa {
	namespace agent {

	/// Signal properties defining the properties of the signal which is monitored
	/// by \c rosa::agent::SignalStateDetector and is saved in \c
	/// rosa::agent::SignalStateInformation.
	enum SignalProperties : uint8_t {
	INPUT = 0, ///< The signal is an input signal
	OUTPUT = 1 ///< The signal is an output signal
	};

	/// TODO: write description
	template <typename PROCDATATYPE, typename CONFDATATYPE> struct DABHistoryEntry {

	/// TODO: write description
	PROCDATATYPE AvgValue;

	/// TODO: write description
	CONFDATATYPE DecisionDABIsStable;

	/// TODO: write description
	CONFDATATYPE DecisionDABIsDriftingDown;

	/// TODO: write description
	CONFDATATYPE DecisionDABIsDriftingUp;

	/// TODO: write description
	bool DABIsCurrent;

	public:
	DABHistoryEntry(PROCDATATYPE AvgValue) {
	this->AvgValue = AvgValue;
	this->DecisionDABIsStable = 1;
	this->DecisionDABIsDriftingDown = 0;
	this->DecisionDABIsDriftingUp = 0;
	this->DABIsCurrent = true;
	}
	};

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

	/// ConfidenceOfMatchingState is the confidence how good the new sample
	/// matches the state.
	CONFDATATYPE ConfidenceOfMatchingState;

	/// ConfidenceOfMatchingState is the confidence how bad the new sample
	/// matches the state.
	CONFDATATYPE ConfidenceOfMismatchingState;

	/// The SignalProperty saves whether the monitored signal is an input our
	/// output signal.
	SignalProperties SignalProperty;

	/// The SignalStateIsValid saves the number of samples which have been
	/// inserted into the state after entering it.
	uint32_t NumberOfInsertedSamplesAfterEntrance;

	public:
	SignalStateInformation(unsigned int SignalStateID,
	SignalProperties _SignalProperty) {
	this->StateID = SignalStateID;
	this->SignalProperty = _SignalProperty;
	this->StateCondition = StateConditions::UNKNOWN;
	this->NumberOfInsertedSamplesAfterEntrance = 0;
	this->StateIsValid = false;
	this->StateJustGotValid = false;
	this->StateIsValidAfterReentrance = false;
	this->ConfidenceStateIsValid = 0;
	this->ConfidenceStateIsInvalid = 0;
	this->ConfidenceStateIsStable = 0;
	this->ConfidenceStateIsDrifting = 0;
	this->ConfidenceStateIsDriftingDown = 0;
	this->ConfidenceStateIsDriftingUp = 0;
	}

	SignalStateInformation() = default;
	};

	/// \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");

	public:
	// The metric to calculate the distance between two points
	using DistanceMetricAbstraction =
	Abstraction<std::pair<INDATATYPE, INDATATYPE>, PROCDATATYPE> &;
	// For the convinience to write a shorter data type name
	using PartFuncReference = PartialFunction<INDATATYPE, CONFDATATYPE> &;
	// using PartFuncReference2 = ;
	using StepFuncReference = StepFunction<INDATATYPE, CONFDATATYPE> &;

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

	/// The metric to calculate the distance between two points
	DistanceMetricAbstraction DistanceMetric;

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

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

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

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

	/// The FuzzyFunctionSampleValid is the fuzzy function that gives the
	/// confidence how good one matches another sample in the sample
	/// history. This is done to evaluate whether a state is valid.
	PartFuncReference FuzzyFunctionSampleValid;

	/// The FuzzyFunctionSampleInvalid is the fuzzy function that gives the
	/// confidence how bad one sample matches another sample in the sample
	/// history. This is done to evaluate whether a state is invalid.
	PartFuncReference FuzzyFunctionSampleInvalid;

	/// The FuzzyFunctionNumOfSamplesValid is the fuzzy function that gives the
	/// confidence how many samples from the sample history match another sample.
	/// This is done to evaluate whether a state is valid.
	StepFuncReference FuzzyFunctionNumOfSamplesValid;

	/// The FuzzyFunctionNumOfSamplesInvalid is the fuzzy function that gives
	/// the confidence how many samples from the sample history mismatch another
	/// sample. This is done to evaluate whether a state is invalid.
	StepFuncReference FuzzyFunctionNumOfSamplesInvalid;

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

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

	/// 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).
	PartFuncReference FuzzyFunctionSignalIsStable;

	/// TODO: description
	PartialFunction<uint32_t, float> &FuzzyFunctionSignalConditionLookBack;
	/// TODO: description
	PartialFunction<uint32_t, float> &FuzzyFunctionSignalConditionHistoryDesicion;
	/// TODO: description
	uint32_t DriftLookbackRange;

	/// 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<DABHistoryEntry<PROCDATATYPE, CONFDATATYPE>,
	HistoryPolicy::FIFO>
	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;

	/// TempConfidenceMatching is the confidence how good a sample matches the
	/// state. However, the value of this variable is only needed temporarly.
	CONFDATATYPE TempConfidenceMatching = 0;

	/// TempConfidenceMatching is the confidence how bad a sample matches the
	/// state. However, the value of this variable is only needed temporarly.
	CONFDATATYPE TempConfidenceMismatching = 0;

	+ /// For the linear regression
	+ PROCDATATYPE MeanX;
	+
	+ /// For the linear regression
	+ PROCDATATYPE RegressionDivisor;
	+
	public:
	/// Creates an instance by setting all parameters
	/// \param SignalStateID The Id of the SignalStateinfo \c
	/// SignalStateInformation.
	///
	/// \param DistanceMetric the distance metric to calculate the distance
	/// between two points
	///
	/// \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 FuzzyFunctionSignalIsDriftingDown The
	/// FuzzyFunctionSignalIsDriftingDown is the fuzzy function that gives the
	/// confidence how likely it is that the signal (resp. the state of a signal)
	/// is drifting down.
	///
	///
	/// \param FuzzyFunctionSignalIsDriftingUp The FuzzyFunctionSignalIsDriftingUp
	/// is the fuzzy function that gives the confidence how likely it is that the
	/// signal (resp. the state of a signal) is drifting down.
	///
	/// \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.
	///
	SignalState(
	uint32_t SignalStateID, SignalProperties SignalProperty,
	uint32_t SampleHistorySize, uint32_t DABSize,
	DistanceMetricAbstraction DistanceMetric,
	PartFuncReference FuzzyFunctionSampleMatches,
	PartFuncReference FuzzyFunctionSampleMismatches,
	StepFuncReference FuzzyFunctionNumOfSamplesMatches,
	StepFuncReference FuzzyFunctionNumOfSamplesMismatches,
	PartFuncReference FuzzyFunctionSampleValid,
	PartFuncReference FuzzyFunctionSampleInvalid,
	StepFuncReference FuzzyFunctionNumOfSamplesValid,
	StepFuncReference FuzzyFunctionNumOfSamplesInvalid,
	PartFuncReference FuzzyFunctionSignalIsDriftingDown,
	PartFuncReference FuzzyFunctionSignalIsDriftingUp,
	PartFuncReference FuzzyFunctionSignalIsStable,
	PartialFunction<uint32_t, float> &FuzzyFunctionSignalConditionLookBack,
	PartialFunction<uint32_t, float>
	&FuzzyFunctionSignalConditionHistoryDesicion,
	uint32_t DriftLookbackRange) noexcept
	: SignalStateInfo{SignalStateID, SignalProperty},
	DistanceMetric(DistanceMetric),
	FuzzyFunctionSampleMatches(FuzzyFunctionSampleMatches),
	FuzzyFunctionSampleMismatches(FuzzyFunctionSampleMismatches),
	FuzzyFunctionNumOfSamplesMatches(FuzzyFunctionNumOfSamplesMatches),
	FuzzyFunctionNumOfSamplesMismatches(
	FuzzyFunctionNumOfSamplesMismatches),
	FuzzyFunctionSampleValid(FuzzyFunctionSampleValid),
	FuzzyFunctionSampleInvalid(FuzzyFunctionSampleInvalid),
	FuzzyFunctionNumOfSamplesValid(FuzzyFunctionNumOfSamplesValid),
	FuzzyFunctionNumOfSamplesInvalid(FuzzyFunctionNumOfSamplesInvalid),
	FuzzyFunctionSignalIsDriftingDown(FuzzyFunctionSignalIsDriftingDown),
	FuzzyFunctionSignalIsDriftingUp(FuzzyFunctionSignalIsDriftingUp),
	FuzzyFunctionSignalIsStable(FuzzyFunctionSignalIsStable),
	FuzzyFunctionSignalConditionLookBack(
	FuzzyFunctionSignalConditionLookBack),
	FuzzyFunctionSignalConditionHistoryDesicion(
	FuzzyFunctionSignalConditionHistoryDesicion),
	DriftLookbackRange(DriftLookbackRange),
	SampleHistory(SampleHistorySize), DAB(DABSize),
	DABHistory(DriftLookbackRange + 1),
	LowestConfidenceMatchingHistory(SampleHistorySize),
	- HighestConfidenceMismatchingHistory(SampleHistorySize) {}
	+ HighestConfidenceMismatchingHistory(SampleHistorySize),
	+ MeanX(DriftLookbackRange/2),
	+ RegressionDivisor(0) {
	+
	+ for (unsigned int i = 0; i <= DriftLookbackRange; i++) {
	+ RegressionDivisor += (i-MeanX)*(i-MeanX);
	+ }
	+ RegressionDivisor *= DABSize;
	+ }

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

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

	SignalStateInformation<CONFDATATYPE>
	insertSample(INDATATYPE Sample) noexcept {

	SignalStateInfo.NumberOfInsertedSamplesAfterEntrance++;

	validateSignalState(Sample);

	SampleHistory.addEntry(Sample);

	DAB.addEntry(Sample);

	if (DAB.full()) {
	// TODO: try median instead of avg
	PROCDATATYPE AvgOfDAB = DAB.template average<PROCDATATYPE>();
	DABHistory.addEntry(
	DABHistoryEntry<PROCDATATYPE, CONFDATATYPE>(AvgOfDAB));
	DAB.clear();
	}

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

	checkSignalStability();

	SignalStateInfo.ConfidenceOfMatchingState = TempConfidenceMatching;
	SignalStateInfo.ConfidenceOfMismatchingState = TempConfidenceMismatching;

	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 =
	DistanceMetric(std::make_pair(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.

	// 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.
	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.
	ConfidenceFromRelativeDistance = 0;
	} else {
	ConfidenceFromRelativeDistance =
	FuzzyFunctionSampleMatches(RelativeDistanceHistory[Case]);
	}
	ConfidenceOfWorstFittingSample = fuzzyAND(ConfidenceOfWorstFittingSample,
	ConfidenceFromRelativeDistance);

	ConfidenceOfBestCase =
	fuzzyOR(ConfidenceOfBestCase,
	fuzzyAND(ConfidenceOfWorstFittingSample,
	FuzzyFunctionNumOfSamplesMatches(
	static_cast<CONFDATATYPE>(Case) + 1)));
	}

	TempConfidenceMatching = ConfidenceOfBestCase;

	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(
	DistanceMetric(std::make_pair(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;

	// TODO (future): to accelerate -> don't go until end. Confidences will only
	// get higher. See comment in "CONFDATATYPE
	// confidenceSampleMatchesSignalState(INDATATYPE Sample)".

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

	CONFDATATYPE ConfidenceFromRelativeDistance;

	if (std::isinf(RelativeDistanceHistory[Case])) {
	ConfidenceFromRelativeDistance = 1;
	} else {
	ConfidenceFromRelativeDistance =
	FuzzyFunctionSampleMismatches(RelativeDistanceHistory[Case]);
	}

	ConfidenceOfBestFittingSample = fuzzyOR(ConfidenceOfBestFittingSample,
	ConfidenceFromRelativeDistance);

	ConfidenceOfWorstCase =
	fuzzyAND(ConfidenceOfWorstCase,
	fuzzyOR(ConfidenceOfBestFittingSample,
	FuzzyFunctionNumOfSamplesMismatches(
	static_cast<CONFDATATYPE>(Case) + 1)));
	}

	TempConfidenceMismatching = ConfidenceOfWorstCase;

	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.
	LowestConfidenceMatching =
	fuzzyAND(LowestConfidenceMatching,
	FuzzyFunctionSampleMatches(
	DistanceMetric(std::make_pair(Sample, HistorySample))));

	HighestConfidenceMismatching =
	fuzzyOR(HighestConfidenceMismatching,
	FuzzyFunctionSampleMismatches(
	DistanceMetric(std::make_pair(Sample, HistorySample))));
	}
	LowestConfidenceMatchingHistory.addEntry(LowestConfidenceMatching);
	HighestConfidenceMismatchingHistory.addEntry(HighestConfidenceMismatching);

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

	SignalStateInfo.ConfidenceStateIsValid =
	fuzzyAND(LowestConfidenceMatching,
	FuzzyFunctionNumOfSamplesValid(static_cast<INDATATYPE>(
	SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));

	SignalStateInfo.ConfidenceStateIsInvalid =
	fuzzyOR(HighestConfidenceMismatching,
	FuzzyFunctionNumOfSamplesInvalid(static_cast<INDATATYPE>(
	SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));

	if (SignalStateInfo.ConfidenceStateIsValid >
	SignalStateInfo.ConfidenceStateIsInvalid) {

	if (SignalStateInfo.StateIsValid) {
	SignalStateInfo.StateJustGotValid = false;
	} else {
	SignalStateInfo.StateJustGotValid = true;
	}

	SignalStateInfo.StateIsValid = true;
	SignalStateInfo.StateIsValidAfterReentrance = true;
	}
	}

	void checkSignalStability(void) {

	- CONFDATATYPE CurrentConfidenceStable = 0;
	- CONFDATATYPE CurrentConfidenceStateDriftingDown = 0;
	- CONFDATATYPE CurrentConfidenceStateDriftingUp = 0;
	-
	- CONFDATATYPE HistoryConfidenceStable = 0;
	- CONFDATATYPE HistoryConfidenceStateDriftingDown = 0;
	- CONFDATATYPE HistoryConfidenceStateDriftingUp = 0;
	-
	- if (DABHistory.numberOfEntries() >= 2) {
	+ if (DABHistory.numberOfEntries() > DriftLookbackRange) {

	DABHistoryEntry<PROCDATATYPE, CONFDATATYPE> CurrentDAB =
	DABHistory[DABHistory.numberOfEntries() - 1];

	if (CurrentDAB.DABIsCurrent == true) {
	+ CurrentDAB.DABIsCurrent = false;
	+
	+ PROCDATATYPE MeanY = 0;
	+ PROCDATATYPE k = 0;

	- // THIS WOULD BE FOR EQUATION NORMALIZATION
	- // TODO: make the following also for distance measurement when comparing
	- // sample with state and validate state
	- /*
	- // sigma correction
	- if (NormalizedDistanceMetric<INDATATYPE, PROCDATATYPE>
	- *NormalizableDistanceMetric = dynamic_cast<
	- NormalizedDistanceMetric<INDATATYPE, PROCDATATYPE> *>(
	- DistanceMetric)) {
	- // old was safely casted to NewType
	- NormalizableDistanceMetric->setNorm(
	- // TODO: (1) Sigma von Sample
	- // History(!) abholen, (2) irgendwas mit Sigma hier reinschreiben,
	- // und (3) überlegen wegen zweiter History (länger) für
	- // Sigmaberechnung
	- );
	- }
	- */

	// Iterate through all history DABs
	- for (unsigned int t = 1;
	- t <= DriftLookbackRange && t < DABHistory.numberOfEntries(); t++) {
	+ for (unsigned int t = 0; t <= DriftLookbackRange; t++) {

	- // Pick one history DAB
	- DABHistoryEntry<PROCDATATYPE, CONFDATATYPE> DAB2Compare =
	+ DABHistoryEntry<PROCDATATYPE, CONFDATATYPE> DAB_T =
	DABHistory[DABHistory.numberOfEntries() - (t + 1)];
	-
	- // Calculate distance between most recent (completed) DAB and the
	- // chosen history DAB
	- PROCDATATYPE dist = DistanceMetric(
	- std::make_pair(CurrentDAB.AvgValue, DAB2Compare.AvgValue));
	-
	- // Add current confidences for Stable, drift down and drift up
	- // together
	- CurrentConfidenceStable += FuzzyFunctionSignalIsStable(dist);
	- CurrentConfidenceStateDriftingDown +=
	- FuzzyFunctionSignalIsDriftingDown(dist);
	- CurrentConfidenceStateDriftingUp +=
	- FuzzyFunctionSignalIsDriftingUp(dist);
	-
	- // Add history confidences for Stable, drift down and drift up
	- // together
	- HistoryConfidenceStable += DAB2Compare.DecisionDABIsStable;
	- HistoryConfidenceStateDriftingDown +=
	- DAB2Compare.DecisionDABIsDriftingDown;
	- HistoryConfidenceStateDriftingUp +=
	- DAB2Compare.DecisionDABIsDriftingUp;
	+ MeanY += DAB_T.AvgValue;
	}
	+ MeanY /= (DriftLookbackRange+1);

	- // chose right divisor for average calculation
	- unsigned int numOfCompares;
	- if (DriftLookbackRange <= DABHistory.numberOfEntries())
	- numOfCompares = DriftLookbackRange;
	- else
	- numOfCompares =
	- static_cast<unsigned int>(DABHistory.numberOfEntries() - 1);
	-
	- // calculate the average of the current confidence decision
	- CurrentConfidenceStable /= numOfCompares;
	- CurrentConfidenceStateDriftingDown /= numOfCompares;
	- CurrentConfidenceStateDriftingUp /= numOfCompares;
	-
	- // store the current confidence decision in current DAB
	- CurrentDAB.DecisionDABIsStable = CurrentConfidenceStable;
	- CurrentDAB.DecisionDABIsDriftingDown =
	- CurrentConfidenceStateDriftingDown;
	- CurrentDAB.DecisionDABIsDriftingUp = CurrentConfidenceStateDriftingUp;
	- CurrentDAB.DABIsCurrent = false;

	- // calculate the confidences for SignalStateInfo (current output)
	- HistoryConfidenceStable =
	- (HistoryConfidenceStable + CurrentConfidenceStable) /
	- (numOfCompares + 1);
	- HistoryConfidenceStateDriftingDown =
	- (HistoryConfidenceStateDriftingDown +
	- CurrentConfidenceStateDriftingDown) /
	- (numOfCompares + 1);
	- HistoryConfidenceStateDriftingUp = (HistoryConfidenceStateDriftingUp +
	- CurrentConfidenceStateDriftingUp) /
	- (numOfCompares + 1);
	-
	- // set SignalStateInfo Confidences
	- SignalStateInfo.ConfidenceStateIsStable = HistoryConfidenceStable;
	- SignalStateInfo.ConfidenceStateIsDrifting =
	- HistoryConfidenceStateDriftingDown >
	- HistoryConfidenceStateDriftingUp
	- ? HistoryConfidenceStateDriftingDown
	- : HistoryConfidenceStateDriftingUp;
	+ for (unsigned int t = 0; t <= DriftLookbackRange; t++) {
	+ DABHistoryEntry<PROCDATATYPE, CONFDATATYPE> DAB_T =
	+ DABHistory[DABHistory.numberOfEntries() - (t + 1)];
	+ k += (DriftLookbackRange - t - MeanX)*(DAB_T.AvgValue - MeanY);
	+ }
	+ k /= RegressionDivisor;
	+
	+ SignalStateInfo.ConfidenceStateIsStable =
	+ FuzzyFunctionSignalIsStable(k);
	SignalStateInfo.ConfidenceStateIsDriftingDown =
	- HistoryConfidenceStateDriftingDown;
	+ FuzzyFunctionSignalIsDriftingDown(k);
	SignalStateInfo.ConfidenceStateIsDriftingUp =
	- HistoryConfidenceStateDriftingUp;
	+ FuzzyFunctionSignalIsDriftingUp(k);
	+ if (SignalStateInfo.ConfidenceStateIsDriftingDown >
	+ SignalStateInfo.ConfidenceStateIsDriftingUp) {
	+ SignalStateInfo.ConfidenceStateIsDrifting =
	+ SignalStateInfo.ConfidenceStateIsDriftingDown;
	+ } else {
	+ SignalStateInfo.ConfidenceStateIsDrifting =
	+ SignalStateInfo.ConfidenceStateIsDriftingUp;
	+ }

	// set SignalStateInfo StateCondition
	if (SignalStateInfo.ConfidenceStateIsStable >
	SignalStateInfo.ConfidenceStateIsDrifting)
	SignalStateInfo.StateCondition = StateConditions::STABLE;
	else if (SignalStateInfo.ConfidenceStateIsStable <
	SignalStateInfo.ConfidenceStateIsDrifting)
	if (SignalStateInfo.ConfidenceStateIsDriftingDown >
	SignalStateInfo.ConfidenceStateIsDriftingUp)
	SignalStateInfo.StateCondition = StateConditions::DRIFTING_DN;
	else if (SignalStateInfo.ConfidenceStateIsDriftingDown <
	SignalStateInfo.ConfidenceStateIsDriftingUp)
	SignalStateInfo.StateCondition = StateConditions::DRIFTING_UP;
	else
	SignalStateInfo.StateCondition = StateConditions::UNKNOWN;
	else
	SignalStateInfo.StateCondition = StateConditions::UNKNOWN;
	}
	} else {
	SignalStateInfo.ConfidenceStateIsStable = 0;
	SignalStateInfo.ConfidenceStateIsDrifting = 0;
	SignalStateInfo.ConfidenceStateIsDriftingDown = 0;
	SignalStateInfo.ConfidenceStateIsDriftingUp = 0;
	SignalStateInfo.StateCondition = StateConditions::UNKNOWN;
	}
	}
	}; // namespace agent

	} // namespace agent
	} // End namespace rosa

	#endif // ROSA_AGENT_SIGNALSTATE_HPP

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