SignalState.hpp
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	//===-- 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.
	///
	//===----------------------------------------------------------------------===//

	// TODO: change to signal state

	#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 PROCDATATYPE type 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),
	"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>>;

	/// StateInfo is a struct StateInformation that contains information about the
	/// current state.
	StateInformation<CONFDATATYPE> 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 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.
	///
	State(unsigned int StateID, PartFuncPointer FuzzyFunctionSampleMatches,
	PartFuncPointer FuzzyFunctionSampleMismatches,
	StepFuncPointer FuzzyFunctionNumOfSamplesMatches,
	StepFuncPointer FuzzyFunctionNumOfSamplesMismatches,
	PartFuncPointer FuzzyFunctionSignalIsDrifting,
	PartFuncPointer FuzzyFunctionSignalIsStable,
	unsigned int SampleHistorySize, unsigned int DABSize,
	unsigned int DABHistorySize) noexcept
	: StateInfo(StateID, 0, VariableStateCondition::UNKNOWN, false, false),
	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) noexcept {
	DAB.clear();
	StateIsValidAfterReentrance = false;
	}

	StateInformation<CONFDATATYPE> insertSample(INDATATYPE Sample) noexcept {
	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

	return 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) noexcept {

	CONFDATATYPE ConfidenceOfBestCase = 0;

	DynamicLengthHistory<PROCDATATYPE, HistoryPolicy::FIFO>
	RelativeDistanceHistory;

	// calculate distances to all history samples
	for (auto &HistorySample : SampleHistory) {
	PROCDATATYPE RelativeDistance = relativeDistance(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 (unsigned int 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
	//@benedikt: check if your partialfunctions can take infinity as
	// argument
	ConfidenceFromRelativeDistance = 0;
	} else {
	ConfidenceFromRelativeDistance =
	FuzzyFunctionSampleMatches(RelativeDistanceHistory[Case]);
	}

	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 + 1)));
	}

	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) noexcept {

	float ConfidenceOfWorstCase = 1;

	DynamicLengthHistory<PROCDATATYPE, HistoryPolicy::FIFO>
	RelativeDistanceHistory;

	// calculate distances to all history samples
	for (auto &HistorySample : SampleHistory) {
	RelativeDistanceHistory.addEntry(relativeDistance(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;
	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 Case = 0;
	Case < RelativeDistanceHistory.numberOfEntries(); Case++) {

	CONFDATATYPE ConfidenceFromRelativeDistance;

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

	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 + 1)));
	}

	return ConfidenceOfWorstCase;
	}

	/// Gives information about the current state.
	///
	/// \return a struct StateInformation that contains information about the
	/// current state.
	StateInformation<CONFDATATYPE> stateInformation(void) noexcept {
	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, ...) noexcept {
	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, ...) noexcept {
	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) noexcept {
	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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