diff --git a/include/rosa/agent/SignalState.hpp b/include/rosa/agent/SignalState.hpp
index 0330201..ff09a42 100644
--- a/include/rosa/agent/SignalState.hpp
+++ b/include/rosa/agent/SignalState.hpp
@@ -1,476 +1,479 @@
//===-- 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/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 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");
/// TODO: describe
CONFDATATYPE ConfidenceOfMatchingState;
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->StateIsValid = false;
+ this->StateJustGotValid = false;
+ this->StateIsValidAfterReentrance = false;
+ this->ConfidenceStateIsValid = 0;
+ this->ConfidenceStateIsInvalid = 0;
+ this->ConfidenceStateIsStable = 0;
+ this->ConfidenceStateIsDrifting = 0;
+ }
};
/// \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:
// For the convinience to write a shorter data type name
using PartFuncReference = PartialFunction<INDATATYPE, CONFDATATYPE> &;
using StepFuncReference = StepFunction<INDATATYPE, CONFDATATYPE> &;
private:
/// SignalStateInfo is a struct of SignalStateInformation that contains
/// information about the current signal 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.
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 FuzzyFunctionSignalIsDrifting is the fuzzy function that gives the
/// confidence how likely it is that the signal (resp. the state of a signal)
/// is drifting.
PartFuncReference 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).
PartFuncReference 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;
//@benedikt: neu (passt das so?)
CONFDATATYPE TempConfidenceMatching;
CONFDATATYPE TempConfidenceMismatching;
public:
/// 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, SignalProperties SignalProperty,
uint32_t SampleHistorySize, uint32_t DABSize,
uint32_t DABHistorySize,
PartFuncReference FuzzyFunctionSampleMatches,
PartFuncReference FuzzyFunctionSampleMismatches,
StepFuncReference FuzzyFunctionNumOfSamplesMatches,
StepFuncReference FuzzyFunctionNumOfSamplesMismatches,
PartFuncReference FuzzyFunctionSignalIsDrifting,
PartFuncReference FuzzyFunctionSignalIsStable) noexcept
: //@benedikt/@david: I don't know if i am allowed to initialize it like
// that because the struct is a derivate of another struct
- SignalStateInfo{SignalStateID,
- StateConditions::UNKNOWN,
- false,
- false,
- false,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- SignalProperty,
- 0},
+ SignalStateInfo{SignalStateID, SignalProperty},
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.StateIsValidAfterReentrance = false;
}
SignalStateInformation<CONFDATATYPE>
insertSample(INDATATYPE Sample) noexcept {
SignalStateInfo.NumberOfInsertedSamplesAfterEntrance++;
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();
//@benedikt (das gehört dazu)
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 =
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.
//
// 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)));
}
//@benedikt (das gehört dazu)
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(
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;
// 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)));
}
//@benedikt (das gehört dazu)
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(relativeDistance<INDATATYPE, PROCDATATYPE>(
Sample, HistorySample)));
HighestConfidenceMismatching =
fuzzyOR(HighestConfidenceMismatching,
FuzzyFunctionSampleMismatches(
relativeDistance<INDATATYPE, PROCDATATYPE>(
Sample, HistorySample)));
}
LowestConfidenceMatchingHistory.addEntry(LowestConfidenceMatching);
HighestConfidenceMismatchingHistory.addEntry(HighestConfidenceMismatching);
LowestConfidenceMatching = LowestConfidenceMatchingHistory.lowestEntry();
HighestConfidenceMismatching =
HighestConfidenceMismatchingHistory.highestEntry();
SignalStateInfo.ConfidenceStateIsValid =
fuzzyAND(LowestConfidenceMatching,
FuzzyFunctionNumOfSamplesMatches(static_cast<INDATATYPE>(
SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));
SignalStateInfo.ConfidenceStateIsInvalid =
fuzzyOR(HighestConfidenceMismatching,
FuzzyFunctionNumOfSamplesMismatches(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) {
if (DABHistory.numberOfEntries() >= 2) {
SignalStateInfo.ConfidenceStateIsStable = FuzzyFunctionSignalIsStable(
relativeDistance<INDATATYPE, PROCDATATYPE>(
DABHistory[DABHistory.numberOfEntries() - 1], DABHistory[0]));
SignalStateInfo.ConfidenceStateIsDrifting = FuzzyFunctionSignalIsDrifting(
relativeDistance<INDATATYPE, PROCDATATYPE>(
DABHistory[DABHistory.numberOfEntries() - 1], DABHistory[0]));
} else {
// @benedikt: I do not know if this "initializing" is the best, but I
// think it makes sense because we do not know if it is stable or
// drifting.
SignalStateInfo.ConfidenceStateIsStable = 0;
SignalStateInfo.ConfidenceStateIsDrifting = 0;
}
//@benedikt: before it was "ConfidenceSignalIsStable >=
// ConfidenceSignalIsDrifting" -> stable. However, I think like that it
// makes
// more sense. What do you mean?
if (SignalStateInfo.ConfidenceStateIsStable >
SignalStateInfo.ConfidenceStateIsDrifting) {
SignalStateInfo.StateCondition = StateConditions::STABLE;
} else if (SignalStateInfo.ConfidenceStateIsStable <
SignalStateInfo.ConfidenceStateIsDrifting) {
SignalStateInfo.StateCondition = StateConditions::DRIFTING;
} else {
SignalStateInfo.StateCondition = StateConditions::UNKNOWN;
}
}
};
} // End namespace agent
} // End namespace rosa
#endif // ROSA_AGENT_SIGNALSTATE_HPP