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

	#include <cstdarg>

	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 class SignalStateCondition {
	STABLE, ///< The signal state is stable
	DRIFTING, ///< The signal state is drifting
	UNKNOWN ///< The signal state is unknown
	};

	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 unsigned integer number
	unsigned int SignalStateID;
	/// The SignalStateConfidence shows the overall confidence value of the signal
	/// state.
	CONFDATATYPE SignalStateConfidence;
	/// The SignalStateCondition shows the condition of a signal state (stable or
	/// drifting)
	SignalStateCondition SignalStateCondition;
	/// The SignalStateIsValid saves the number of samples which have been
	/// inserted into the state after entering it.
	unsigned int 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 signa 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>>;
	// @Benedikt: are INDATATYPE, CONFDATATYPE right here?
	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(unsigned int SignalStateID, unsigned int SampleHistorySize,
	unsigned int DABSize, unsigned int 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
	false},


	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();
	}

	//@Benedikt: Do I really have to cast here?
	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 (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
	//@benedikt: same as before "->operator()"
	ConfidenceFromRelativeDistance = 0;
	} else {
	ConfidenceFromRelativeDistance = FuzzyFunctionSampleMatches->operator()(
	RelativeDistanceHistory[Case]);
	}

	ConfidenceOfWorstFittingSample = fuzzyAND<CONFDATATYPE>(
	2, ConfidenceOfWorstFittingSample, ConfidenceFromRelativeDistance);
	//@benedikt: do i have to pass the number 2 to tell the function how many
	// arguments are following?
	//@benedikt: same as before with "->operator()"
	ConfidenceOfBestCase = fuzzyOR<CONFDATATYPE>(
	2, ConfidenceOfBestCase,
	fuzzyAND<CONFDATATYPE>(2, ConfidenceOfWorstFittingSample,
	FuzzyFunctionNumOfSamplesMatches->operator()(
	static_cast<CONFDATATYPE>(Case) + 1)));
	}

	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 (unsigned int 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]);
	// FuzzyFunctionSampleMismatches(RelativeDistanceHistory[Case]);
	}

	//@benedikt: do i have to pass the number 2 to tell the function how many
	// arguments are following?
	ConfidenceOfBestFittingSample = fuzzyOR<CONFDATATYPE>(
	2, ConfidenceOfBestFittingSample, ConfidenceFromRelativeDistance);

	//@benedikt: do i have to pass the number 2 to tell the function how many
	// arguments are following?
	//@benedikt: same as before with "->operator()"
	ConfidenceOfWorstCase = fuzzyAND<CONFDATATYPE>(
	2, ConfidenceOfWorstCase,
	fuzzyOR<CONFDATATYPE>(2, ConfidenceOfBestFittingSample,
	FuzzyFunctionNumOfSamplesMismatches->operator()(
	static_cast<CONFDATATYPE>(Case) + 1)));
	}

	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()"
	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)));
	}
	LowestConfidenceMatchingHistory.addEntry(LowestConfidenceMatching);
	HighestConfidenceMismatchingHistory.addEntry(HighestConfidenceMismatching);

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

	//@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)));

	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 {
	// QUESTION: is it ok to say stable = 1 and drift = 0, when I simply don't
	// know because the state is so new. Is there an option for saying don't
	// know?
	ConfidenceSignalIsStable = 1;
	ConfidenceSignalIsDrifting = 0;
	}

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

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

	#endif // ROSA_AGENT_SIGNALSTATE_HPP

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