diff --git a/examples/agent-functionalities/Reliability-functionality-agent-context/CMakeLists.txt b/examples/agent-functionalities/Reliability-functionality-agent-context/CMakeLists.txt index ebb331a..4571f33 100644 --- a/examples/agent-functionalities/Reliability-functionality-agent-context/CMakeLists.txt +++ b/examples/agent-functionalities/Reliability-functionality-agent-context/CMakeLists.txt @@ -1,6 +1,6 @@ -add_executable(Reliability-agents Reliability-agents.cpp) -ROSA_add_library_dependencies(Reliability-agents ROSAConfig) -ROSA_add_library_dependencies(Reliability-agents ROSACore) -ROSA_add_library_dependencies(Reliability-agents ROSAAgent) -ROSA_add_library_dependencies(Reliability-agents ROSADeluxe) +#add_executable(Reliability-agents Reliability-agents.cpp) +#ROSA_add_library_dependencies(Reliability-agents ROSAConfig) +#ROSA_add_library_dependencies(Reliability-agents ROSACore) +#ROSA_add_library_dependencies(Reliability-agents ROSAAgent) +#ROSA_add_library_dependencies(Reliability-agents ROSADeluxe) diff --git a/examples/agent-functionalities/Reliability-functionality/Reliability-functionality.cpp b/examples/agent-functionalities/Reliability-functionality/Reliability-functionality.cpp index 1e1ae48..fc90dc8 100644 --- a/examples/agent-functionalities/Reliability-functionality/Reliability-functionality.cpp +++ b/examples/agent-functionalities/Reliability-functionality/Reliability-functionality.cpp @@ -1,264 +1,268 @@ //===- examples/agent-functionalities/Reliability-functionality.cpp *C++-*-===// // // The RoSA Framework // //===----------------------------------------------------------------------===// /// /// \file examples/agent-functionalities/Reliability-functionality.cpp /// /// \author Daniel Schnoell (daniel.schnoell@tuwien.ac.at ) /// /// \date 2019 /// /// \brief A simple example on defining Relianility Functionalities. /// //===----------------------------------------------------------------------===// #define Reliability_trace_level 5 #include "rosa/config/version.h" #include "rosa/support/log.h" #include "rosa/agent/CrossReliability.h" #include "rosa/agent/RangeConfidence.hpp" #include "rosa/agent/Reliability.h" #include #include using namespace rosa::agent; int main(void) { typedef double SensorValueType; typedef long StateType; typedef double ReliabilityType; std::unique_ptr> Confidence(new RangeConfidence( {{0, PartialFunction( { {{0, 3}, std::make_shared>( 0, 1.0 / 3)}, {{3, 6}, std::make_shared>(1, 0)}, {{6, 9}, std::make_shared>( 3.0, -1.0 / 3)}, }, 0)}, {1, PartialFunction( { {{6, 9}, std::make_shared>( -2, 1.0 / 3)}, {{9, 12}, std::make_shared>(1, 0)}, {{12, 15}, std::make_shared>( 5, -1.0 / 3)}, }, 0)}, {2, PartialFunction( { {{12, 15}, std::make_shared>( -4, 1.0 / 3)}, {{15, 18}, std::make_shared>(1, 0)}, {{18, 21}, std::make_shared>( 7, -1.0 / 3)}, }, 0)}})); std::unique_ptr> Reliability( new LinearFunction(1, -1.0 / 3)); std::unique_ptr> ReliabilitySlope( new LinearFunction(1, -1.0 / 3)); std::unique_ptr> TimeConfidence( new LinearFunction(1, -1.0 / 3)); - auto lowlevel = new ReliabilityForLowLevelAgents(); + auto lowlevel = + new ReliabilityAndConfidenceCombinator(); std::vector states; states.push_back(0); states.push_back(1); states.push_back(2); - + lowlevel->setConfidenceFunction(Confidence); lowlevel->setReliabilityFunction(Reliability); lowlevel->setReliabilitySlopeFunction(ReliabilitySlope); lowlevel->setTimeConfidenceFunction(TimeConfidence); lowlevel->setStates(states); lowlevel->setHistoryLength(2); lowlevel->setValueSetCounter(1); /* ----------------------------- Do Something * ---------------------------------------------------------------- */ std::cout << "Testing the lowlevel component with static feedback telling it " "that the most lickely state is 2.\n"; for (int a = 0; a < 30; a++) std::cout << "a: " << a << "\n" << (lowlevel->feedback({{0, 0}, {1, 0.3}, {2, 0.8}}), - lowlevel->operator()(a)) + lowlevel->mostLikelySoreAndReliability(a)) << "\n"; std::cout << "---------------------------------------------------------------" "---------------------------------\n"; std::cout << "------------------------------------High level " "Test---------------------------------------------\n"; std::cout << "Configured in a way that the Master thinks that both Sensors " "should have the same State.\n While feeding both the \"opposite\" " "values one acending the other decending from the maximum.\n"; std::unique_ptr> Confidence2(new RangeConfidence( {{0, PartialFunction( { {{0, 3}, std::make_shared>( 0, 1.0 / 3)}, {{3, 6}, std::make_shared>(1, 0)}, {{6, 9}, std::make_shared>( 3.0, -1.0 / 3)}, }, 0)}, {1, PartialFunction( { {{6, 9}, std::make_shared>( -2, 1.0 / 3)}, {{9, 12}, std::make_shared>(1, 0)}, {{12, 15}, std::make_shared>( 5, -1.0 / 3)}, }, 0)}, {2, PartialFunction( { {{12, 15}, std::make_shared>( -4, 1.0 / 3)}, {{15, 18}, std::make_shared>(1, 0)}, {{18, 21}, std::make_shared>( 7, -1.0 / 3)}, }, 0)}})); std::unique_ptr> Reliability2( new LinearFunction(1, -1.0 / 9)); std::unique_ptr> ReliabilitySlope2( new LinearFunction(1, -1.0 / 9)); std::unique_ptr> TimeConfidence2( new LinearFunction(1, -1.0 / 9)); - auto lowlevel2 = new ReliabilityForLowLevelAgents(); std::vector states2; states2.push_back(0); states2.push_back(1); states2.push_back(2); lowlevel2->setConfidenceFunction(Confidence2); lowlevel2->setReliabilityFunction(Reliability2); lowlevel2->setReliabilitySlopeFunction(ReliabilitySlope2); lowlevel2->setTimeConfidenceFunction(TimeConfidence2); lowlevel2->setStates(states2); lowlevel2->setHistoryLength(2); lowlevel2->setValueSetCounter(1); - ReliabilityForHighLevelAgents *highlevel = - new ReliabilityForHighLevelAgents(); + CrossCombinator *highlevel = + new CrossCombinator(); std::unique_ptr> CrossReliability1(new CrossReliability()); std::unique_ptr> func1(new PartialFunction( { {{0, 1}, std::make_shared>(1, 0)}, {{1, 2}, std::make_shared>(2, -1.0)}, }, 0)); CrossReliability1->addCrossReliabilityProfile(0, 1, func1); CrossReliability1->setCrossReliabilityMethod( CrossReliability::AVERAGE); CrossReliability1->setCrossReliabilityParameter(1); std::unique_ptr> CrossConfidence1( new CrossConfidence()); std::unique_ptr> func2(new PartialFunction( { {{0, 1}, std::make_shared>(1, 0)}, {{1, 2}, std::make_shared>(2, -1.0)}, }, 0)); CrossConfidence1->addCrossReliabilityProfile(0, 1, func2); CrossConfidence1->setCrossReliabilityMethod( CrossConfidence::AVERAGE); CrossConfidence1->setCrossReliabilityParameter(1); highlevel->setCrossConfidence(CrossConfidence1); highlevel->setCrossReliability(CrossReliability1); highlevel->addStates(0, states); highlevel->addStates(1, states); for (int a = 0; a < 21; a++) { - auto out1 = lowlevel->operator()(a), out2 = lowlevel2->operator()((int)21 - a); + auto out1 = lowlevel->mostLikelySoreAndReliability(a), + out2 = lowlevel2->mostLikelySoreAndReliability((int)21 - a); std::cout << "s1: " << out1 << "\ns2:" << out2 << "\n"; std::vector> tmp2; tmp2.push_back({0, out1.score, out1.Reliability}); tmp2.push_back({1, out2.score, out2.Reliability}); auto out_o = highlevel->operator()(tmp2); std::cout << "it: " << a << "\t rel: " << out_o.CrossReliability << "\n"; std::cout << "\t subs:\n"; for (auto q : out_o.CrossConfidence) { std::cout << "\t\t id:" << q.first << "\n"; /* for(auto z: q.second) { std::cout << "\t\t\t score: " << z.score << "\tRel: " << z.Reliability << "\n"; tmp.push_back({z.score,z.Reliability}); } */ for (auto z : q.second) { std::cout << "\t\t\t score: " << z.score << "\tRel: " << z.Reliability << "\n"; } if (q.first == 0) lowlevel->feedback(q.second); else lowlevel2->feedback(q.second); } } /* ----------------------------- Cleanup * --------------------------------------------------------------------- */ delete highlevel; delete lowlevel; delete lowlevel2; } \ No newline at end of file diff --git a/include/rosa/agent/Reliability.h b/include/rosa/agent/Reliability.h index 6baacb0..72642b6 100644 --- a/include/rosa/agent/Reliability.h +++ b/include/rosa/agent/Reliability.h @@ -1,825 +1,918 @@ //===-- rosa/agent/Reliability.h --------------------------------*- C++ -*-===// // // The RoSA Framework // //===----------------------------------------------------------------------===// /// /// \file rosa/agent/Reliability.h /// /// \author Daniel Schnoell (daniel.schnoell@tuwien.ac.at) /// /// \date 2019 /// /// \brief Definition of *reliability* *functionality*. /// /// \note based on Maximilian Goetzinger (maxgot@utu.fi) code in /// CAM_Dirty_include SA-EWS2_Version... inside Agent.cpp /// /// \note By defining and setting Reliability_trace_level it is possible to /// change the level to which it should be traced. \note All classes throw /// runtime errors if not all things are set /// /// \note should the Reliability be capped? /// /// //===----------------------------------------------------------------------===// // make combination modular #ifndef ROSA_AGENT_RELIABILITY_H #define ROSA_AGENT_RELIABILITY_H #include "rosa/agent/CrossReliability.h" #include "rosa/agent/FunctionAbstractions.hpp" #include "rosa/agent/Functionality.h" #include "rosa/agent/RangeConfidence.hpp" #include #include #include /// 0 everything /// 1 vectors /// 2 outputs #define trace_everything 0 #define trace_vectors 1 #define trace_outputs 2 #ifndef Reliability_trace_level #define Reliability_trace_level 0 #endif #define trace_end "\n\n\n" namespace rosa { namespace agent { /// This is a struct with a few methods that make lowlevel/highlevel Reliability /// more readable \tparam StateType The datatype of the States \tparam /// ReliabilityType The datatype of the Reliability template struct ConfOrRel { /// making both Template Arguments readable to make a few things easier typedef StateType _StateType; /// making both Template Arguments readable to make a few things easier typedef ReliabilityType _ReliabilityType; /// The actual place where the data is stored StateType score; /// The actual place where the data is stored ReliabilityType Reliability; ConfOrRel(StateType _score, ReliabilityType _Reliability) : score(_score), Reliability(_Reliability){}; ConfOrRel(){}; /// Pushes the Data in a Human readable form /// \param out The stream where it is written to /// \param c The struct itself friend std::ostream &operator<<(std::ostream &out, const ConfOrRel &c) { out << "Score: " << c.score << "\t Reliability: " << c.Reliability << " "; return out; } /// needed or it throws an clang diagnosic error typedef std::map map; // needed or it throws an clang diagnosic error /// Filles the vector with the data inside the map /// \param me The vector to be filled /// \param data The data wich is to be pushed into the vector friend std::vector &operator<<(std::vector &me, map &&data) { for (auto tmp : data) { me.push_back(ConfOrRel(tmp.first, tmp.second)); #if Reliability_trace_level <= trace_everything LOG_TRACE_STREAM << "\n" << ConfOrRel(tmp.first, tmp.second) << trace_end; #endif } return me; } /// This is to push the data inside a vector in a human readable way into the /// ostream \param out The ostream \param c The vector which is read friend std::ostream &operator<<(std::ostream &out, const std::vector &c) { std::size_t index = 0; for (ConfOrRel data : c) { out << index << " : " << data << "\n"; index++; } return out; } }; /// This calculates the minimum of the Reliabilities & the given value /// \param me The vector with the Reliabilities /// \param value The comparing value template std::vector min(std::vector me, typename Conf::_ReliabilityType value) { static_assert(std::is_arithmetic::value); for (auto tmp : me) tmp.Reliability = std::min(tmp.Reliability, value); return me; } /// This calculates the maximum of the Reliabilities & the given value /// \param me The vector with the Reliabilities /// \param value The comparing value template std::vector max(std::vector me, typename Conf::_ReliabilityType value) { static_assert(std::is_arithmetic::value); for (auto tmp : me) tmp.Reliability = std::max(tmp.Reliability, value); return me; } /// This calculates the average of the Reliabilities & the given value /// \param me The vector with the Reliabilities /// \param value The comparing value template std::vector average(std::vector me, typename Conf::_ReliabilityType value) { static_assert(std::is_arithmetic::value); for (auto tmp : me) tmp.Reliability = (tmp.Reliability + value) / 2; return me; } /// This calculates the average of the Reliabilities & the given value /// \param me The vector with the Reliabilities /// \param value The comparing value template std::vector mult(std::vector me, typename Conf::_ReliabilityType value) { static_assert(std::is_arithmetic::value); for (auto tmp : me) tmp.Reliability = tmp.Reliability * value / 2; return me; } /// This average's the Reliabilities of the same Scores template std::vector average(std::vector A, std::vector B) { static_assert(std::is_arithmetic::value); - for (auto &tmp_me : me) - for (auto &tmp_other : other) { + for (auto &tmp_me : A) + for (auto &tmp_other : B) { if (tmp_me.score == tmp_other.score) { tmp_me.Reliability = (tmp_me.Reliability + tmp_other.Reliability) / 2; } } - return me; + return A; } /// This min's the Reliabilities of the same Scores template std::vector min(std::vector A, std::vector B) { static_assert(std::is_arithmetic::value); - for (auto &tmp_me : me) - for (auto &tmp_other : other) { + for (auto &tmp_me : A) + for (auto &tmp_other : B) { if (tmp_me.score == tmp_other.score) { tmp_me.Reliability = std::min(tmp_me.Reliability + tmp_other.Reliability); } } - return me; + return A; } /// This max's the Reliabilities of the same Scores template std::vector max(std::vector A, std::vector B) { static_assert(std::is_arithmetic::value); - for (auto &tmp_me : me) - for (auto &tmp_other : other) { + for (auto &tmp_me : A) + for (auto &tmp_other : B) { if (tmp_me.score == tmp_other.score) { tmp_me.Reliability = std::max(tmp_me.Reliability + tmp_other.Reliability); } } - return me; + return A; } /// This mult's the Reliabilities of the same Scores template std::vector mult(std::vector A, std::vector B) { static_assert(std::is_arithmetic::value); - for (auto &tmp_me : me) - for (auto &tmp_other : other) { + for (auto &tmp_me : A) + for (auto &tmp_other : B) { if (tmp_me.score == tmp_other.score) { tmp_me.Reliability = tmp_me.Reliability * tmp_other.Reliability; } } - return me; + return A; } /// This is the combinator for Reliability and confidences it takes the /// Sensor value, its "History" and feedback from \c /// CrossCombinator to calculate different Reliabilities. /// \tparam SensorValueType Datatype of the Sensor value ( Typically /// double or float) \tparam StateType Datatype of the State ( Typically long or /// int) /// \tparam ReliabilityType Datatype of the Reliability ( /// Typically double or float) /// /// \note more information about how it calculates -/// the Reliabilities at the \c mostLikelySoreAndReliability()() -/// \note more information about the needed feedback at \c feedback() +/// the Reliabilities +/// \verbatim +///---------------------------------------------------------------------------------- +/// +/// +/// ->Reliability---> getInputReliability() +/// | | +/// | V +/// Sensor Value ---| PossibleScoreCombinationMethod -> next line +/// | A | +/// | | V +/// ->Confidence--- getPossibleScores() +/// +///----------------------------------------------------------------------------------- +/// +/// feedback +/// | +/// V +/// ValuesFromMaster +/// | -> History ---| +/// V | V +/// here -> FeedbackCombinatorMethod --------> HistoryCombinatorMethod->nextline +/// | | +/// V V +/// getpossibleScoresWithMasterFeedback() getPossibleScoresWithHistory() +/// +///---------------------------------------------------------------------------------- +/// +/// here -> sort -> most likely -> mostLikelySoreAndReliability() +/// +/// --------------------------------------------------------------------------------- +/// \endverbatim +/// the mentioned methods are early outs so if two ore more of them are run in +/// the same step they will be interpreted as different time steps +/// 
+/// Default values for Combinators:
+///	InputReliabilityCombinator		= combinationMin;
+///	PossibleScoreCombinationMethod	= PossibleScoreCombinationMethodMin;
+/// FeedbackCombinatorMethod		= FeedbackCombinatorMethodAverage;
+/// HistoryCombinatorMethod			= HistoryCombinatorMethodMax;
+///
+/// +/// +/// template class ReliabilityAndConfidenceCombinator { public: static_assert(std::is_arithmetic::value, "LowLevel: SensorValueType has to an arithmetic type\n"); static_assert(std::is_arithmetic::value, "LowLevel: StateType has to an arithmetic type\n"); static_assert(std::is_arithmetic::value, "LowLevel: ReliabilityType has to an arithmetic type\n"); /// Typedef to shorten the writing. /// \c ConfOrRel typedef ConfOrRel ConfOrRel; + /// Calculates the input reliability by combining Reliability of the Sensor + /// and the Slope Reliability \param SensorValue The sensor Value \note to set + /// the combination method \c setInputReliabilityCombinator() + auto getInputReliability(SensorValueType SensorValue) { + auto inputReliability = + getReliability(SensorValue, previousSensorValue, valueSetCounter); + previousSensorValue = SensorValue; + PreviousSensorValueExists = true; + return inputReliability; + } + /// Calculates the Reliability /// \param SensorValue The current Values of the Sensor /// /// \return Reliability and Score of the current SensorValue /// - /// \brief It calculates the input Reliability of the Sensor and combines [ - /// min() ] it with the Confidence of Sensor value. Then it combines [ \c - /// std::vector::operator+=() [ addition ] ] it with the feedback - /// from the highlevel Agent and stores it inside the history at the first - /// location. Afterwards its combines[ private: \c - /// getAllPossibleScoresBasedOnHistory() ] the whole History and return the - /// most likely pair of values. - ConfOrRel mostLikelySoreAndReliability()(SensorValueType SensorValue) { + ConfOrRel mostLikelySoreAndReliability(SensorValueType SensorValue) { #if Reliability_trace_level <= trace_outputs LOG_TRACE_STREAM << "\nTrace level is set to: " << Reliability_trace_level << "\n" << "Will trace: " << ((Reliability_trace_level == trace_outputs) ? "outputs" : (Reliability_trace_level == trace_vectors) ? "vectors" : (Reliability_trace_level == trace_everything) ? "everything" : "undefined") << trace_end; #endif std::vector ActuallPossibleScores; std::vector possibleScores; ReliabilityType inputReliability = getInputReliability(SensorValue); - // get input rel() #if Reliability_trace_level <= trace_vectors LOG_TRACE_STREAM << "\ninput Rel: " << inputReliability << trace_end; #endif possibleScores << Confidence->operator()(SensorValue); possibleScores = PossibleScoreCombinationMethod(possibleScores, inputReliability); - - // set comb method with values from master ( class :: min | max | average | - // mult | [] ()-> {} ) - possibleScores = possibleScores + ValuesFromMaster; - // get + possibleScores = FeedbackCombinatorMethod(possibleScores, ValuesFromMaster); saveInHistory(possibleScores); #if Reliability_trace_level <= trace_vectors LOG_TRACE_STREAM << "\nActuallPossibleScores:\n" << possibleScores << trace_end; LOG_TRACE_STREAM << "\npossibleScores:\n" << possibleScores << trace_end; #endif possibleScores.clear(); possibleScores = getAllPossibleScoresBasedOnHistory(); - // get all possible scores () std::sort(possibleScores.begin(), possibleScores.end(), [](ConfOrRel A, ConfOrRel B) -> bool { return A.Reliability > B.Reliability; }); #if Reliability_trace_level <= trace_outputs LOG_TRACE_STREAM << "\noutput lowlevel: " << possibleScores.at(0) << trace_end; #endif return possibleScores.at(0); } - /// Calculates the input reliability by combining Reliability of the Sensor - /// and the Slope Reliability \parem SensorValue The sensor Value \note to set - /// the combination method \c setInputReliabilityCombinator() - auto getInputReliability(SensorValueType SensorValue); - { - auto inputReliability = - getReliability(SensorValue, previousSensorValue, valueSetCounter); - previousSensorValue = SensorValue; - PreviousSensorValueExists = true; - return inputReliability; - } - - /// sets the input reliability combinator method - /// \param method the to be used method - /// \note there are predefined methods \c combinationMin() \c combinationMax() - /// \c combinationAverage() - void setInputReliabilityCombinator( - ReliabilityType (*method)(ReliabilityType, ReliabilityType)) { - InputReliabilityCombinator = method; - } - - /// The predefined min combinator method - static ReliabilityType combinationMin(ReliabilityType A, ReliabilityType B) { - return std::min(A, B); - } - - /// The predefined max combinator method - static ReliabilityType combinationMax(ReliabilityType A, ReliabilityType B) { - return std::max(A, B); - } - - /// The predefined average combinator method - static ReliabilityType combinationAverage(ReliabilityType A, - ReliabilityType B) { - return (A + B) / 2; - } - - /// The predefined average combinator method - static ReliabilityType combinationMult(ReliabilityType A, ReliabilityType B) { - return A * B; - } - /// Calculates the possible Scores /// \param SensorValue the Sensor Value /// \brief it combines the input reliability and the confidence of the Sensor. /// The use combination method can be set using \c /// setPossibleScoreCombinationMethod() auto getPossibleScores(SensorValueType SensorValue) { std::vector possibleScores; ReliabilityType inputReliability = getInputReliability(SensorValue); // get input rel() #if Reliability_trace_level <= trace_vectors LOG_TRACE_STREAM << "\ninput Rel: " << inputReliability << trace_end; #endif possibleScores << Confidence->operator()(SensorValue); - - // define from outside - // set Combination method ( class :: min | max | average | mult | []()->{}) possibleScores = PossibleScoreCombinationMethod(possibleScores, inputReliability); - // get possible scores(Sensor val) return possibleScores; } - /// Sets the used combination method for Possible Scores - /// \param Meth a Pointer for the used Method. Predefined methods \c - /// PossibleScoreCombinationMethodMin() \c PossibleScoreCombinationMethodMax() - /// \c PossibleScoreCombinationMethodAverage() - void setPossibleScoreCombinationMethod( - std::vector (*Meth)(std::vector, ReliabilityType)) { - PossibleScoreCombinationMethod = Meth; - } - - /// Predefined combination method for possible Scores - static std::vector - PossibleScoreCombinationMethodMin(std::vector A, - ReliabilityType B) { - return min(A, B); - } - /// Predefined combination method for possible Scores - static std::vector - PossibleScoreCombinationMethodMax(std::vector A, - ReliabilityType B) { - return max(A, B); - } - - /// Predefined combination method for possible Scores - static std::vector - PossibleScoreCombinationMethodAverage(std::vector A, - ReliabilityType B) { - return average(A, B); - } - - /// Predefined combination method for possible Scores - static std::vector - PossibleScoreCombinationMethodAverage(std::vector A, - ReliabilityType B) { - return mult(A, B); - } - + /// return the Possible Values with the feedback in mind + /// \param SensorValue The sensor Value + /// \brief it combines the input reliability and the confidence of the Sensor. + /// The combines them with FeedbackCombinatorMethod and returns the result. auto getpossibleScoresWithMasterFeedback(SensorValueType SensorValue) { std::vector possibleScores; ReliabilityType inputReliability = getInputReliability(SensorValue); // get input rel() #if Reliability_trace_level <= trace_vectors LOG_TRACE_STREAM << "\ninput Rel: " << inputReliability << trace_end; #endif possibleScores << Confidence->operator()(SensorValue); possibleScores = PossibleScoreCombinationMethod(possibleScores, inputReliability); // set comb method with values from master ( class :: min | max | average | // mult | [] ()-> {} ) - possibleScores = FeedbackCombinatorMehtod(possibleScores, ValuesFromMaster); + possibleScores = FeedbackCombinatorMethod(possibleScores, ValuesFromMaster); return possibleScores; } - /// sets the predefined method for the combination of the possible scores and - /// the master \param Meth the method predefined ones are \c - /// FeedbackCombinatorMehtodAverage() \c FeedbackCombinatorMehtodMin() \c - /// FeedbackCombinatorMehtodMax() \c FeedbackCombinatorMehtodMult() - void setFeedbackCombinatorMehtod(std::vector (*Meth)( - std::vector, std::vector)) { - FeedbackCombinatorMehtod = Meth; - } - /// predefined method - std::vector - FeedbackCombinatorMehtodAverage(std::vector A, - std::vector B) { - return average(A, B); - } - /// predefined method - std::vector FeedbackCombinatorMehtodMin(std::vector A, - std::vector B) { - return min(A, B); - } - /// predefined method - std::vector FeedbackCombinatorMehtodMax(std::vector A, - std::vector B) { - return max(A, B); - } - /// predefined method - std::vector - FeedbackCombinatorMehtodMult(std::vector A, - std::vector B) { - return mult(A, B); + + /// returns all possible Scores and Reliabilities with the History in mind + /// \param SensorValue the Sensor value + /// how this is done is described at the class. + auto getPossibleScoresWithHistory(SensorValueType SensorValue) { + std::vector ActuallPossibleScores; + std::vector possibleScores; + ReliabilityType inputReliability = getInputReliability(SensorValue); + + // get input rel() +#if Reliability_trace_level <= trace_vectors + LOG_TRACE_STREAM << "\ninput Rel: " << inputReliability << trace_end; +#endif + + possibleScores << Confidence->operator()(SensorValue); + + possibleScores = + PossibleScoreCombinationMethod(possibleScores, inputReliability); + possibleScores = FeedbackCombinatorMethod(possibleScores, ValuesFromMaster); + + saveInHistory(possibleScores); +#if Reliability_trace_level <= trace_vectors + LOG_TRACE_STREAM << "\nActuallPossibleScores:\n" + << possibleScores << trace_end; + LOG_TRACE_STREAM << "\npossibleScores:\n" << possibleScores << trace_end; +#endif + possibleScores.clear(); + + return getAllPossibleScoresBasedOnHistory(); } - /// Needed feedback from the Master - /// \param ValuesFromMaster The Scores + Reliability from the Master for - /// this Agent \brief This input kind of resembles a confidence but not - /// directly it more or less says: compared to the other lowlevel Agents - /// these are the Scores with the Reliability that you have. + /// feedback for this functionality most commonly it comes from a Master Agent + /// \param ValuesFromMaster The Scores + Reliability for the feedback + /// \brief This input kind of resembles a confidence but not + /// directly it more or less says: compared to the other Scores inside the + /// System these are the Scores with the Reliability that you have. void feedback(std::vector ValuesFromMaster) { this->ValuesFromMaster = ValuesFromMaster; } + // + // ----------------------Reliability and Confidence Function setters---------- + // /// This is the setter for Confidence Function /// \param Confidence A pointer to the Functional for the \c Confidence of the /// Sensor value void setConfidenceFunction( std::unique_ptr> &Confidence) { this->Confidence = std::move(Confidence); } /// This is the setter for Reliability Function /// \param Reliability A pointer to the Functional for the Reliability /// \brief The Reliability takes the current Sensor value and return the /// Reliability of the value. void setReliabilityFunction( std::unique_ptr> &Reliability) { this->Reliability = std::move(Reliability); } /// This is the setter for ReliabilitySlope Function /// \param ReliabilitySlope A pointer to the Functional for the /// ReliabilitySlope /// \brief The ReliabilitySlope takes the difference of the current Sensor /// Value to the last one and tells you how likely the change is. void setReliabilitySlopeFunction( std::unique_ptr> &ReliabilitySlope) { this->ReliabilitySlope = std::move(ReliabilitySlope); } /// This is the setter for TimeConfidence Function /// \param TimeConfidence A pointer to the Functional for the TimeConfidence /// \brief The time function takes the position in the History with greater /// equals older and return a Reliability of how "relevant" it is. void setTimeConfidenceFunction( std::unique_ptr> &TimeConfidence) { this->TimeConfidence = std::move(TimeConfidence); } /// This is the setter for all possible States /// \param states A vector containing all states /// \brief This exists even though \c State Type is an arithmetic Type because /// the states do not need to be "next" to each other ( ex. states={ 1 7 24 }) void setStates(std::vector states) { this->States = states; } /// This sets the Maximum length of the History /// \param length The length void setHistoryLength(std::size_t length) { this->HistoryMaxSize = length; } /// This sets the Value set Counter /// \param ValueSetCounter the new Value /// \note This might actually be only an artifact. It is only used to get the /// reliability from the \c ReliabilitySlope [ ReliabilitySlope->operator()( /// (lastValue - actualValue) / (SensorValueType)valueSetCounter) ] void setValueSetCounter(unsigned int ValueSetCounter) { this->valueSetCounter = ValueSetCounter; } + // + // ----------------combinator setters----------------------------------------- + // + + /// This sets the combination method used by the History + /// \param Meth the method which should be used. predefined \c + /// HistoryCombinatorMethodMin() \c HistoryCombinatorMethodMax() \c + /// HistoryCombinatorMethodMult() \c HistoryCombinatorMethodAverage() + void setHistoryCombinatorMethod(ReliabilityType (*Meth)(ReliabilityType, + ReliabilityType)) { + HistoryCombinatorMethod = Meth; + } + + /// sets the predefined method for the combination of the possible scores and + /// the master \param Meth the method predefined ones are + /// \c FeedbackCombinatorMethodAverage() \c FeedbackCombinatorMethodMin() \c + /// FeedbackCombinatorMethodMax() \c FeedbackCombinatorMethodMult() + void setFeedbackCombinatorMethod(std::vector (*Meth)( + std::vector, std::vector)) { + FeedbackCombinatorMethod = Meth; + } + + /// Sets the used combination method for Possible Scores + /// \param Meth a Pointer for the used Method. Predefined methods \c + /// PossibleScoreCombinationMethodMin() \c PossibleScoreCombinationMethodMax() + /// \c PossibleScoreCombinationMethodAverage() + void setPossibleScoreCombinationMethod( + std::vector (*Meth)(std::vector, ReliabilityType)) { + PossibleScoreCombinationMethod = Meth; + } + + /// sets the input reliability combinator method + /// \param method the to be used method + /// \note there are predefined methods \c combinationMin() \c combinationMax() + /// \c combinationAverage() + void setInputReliabilityCombinator( + ReliabilityType (*method)(ReliabilityType, ReliabilityType)) { + InputReliabilityCombinator = method; + } + + // + // ----------------predefined combinators------------------------------------ + // + /// predefined Method + static ReliabilityType HistoryCombinatorMethodMin(ReliabilityType A, + ReliabilityType B) { + return std::min(A, B); + } + /// predefined Method + static ReliabilityType HistoryCombinatorMethodMax(ReliabilityType A, + ReliabilityType B) { + return std::max(A, B); + } + /// predefined Method + static ReliabilityType HistoryCombinatorMethodMult(ReliabilityType A, + ReliabilityType B) { + return A * B; + } + /// predefined Method + static ReliabilityType HistoryCombinatorMethodAverage(ReliabilityType A, + ReliabilityType B) { + return (A + B) / 2; + } + + /// predefined method + static std::vector + FeedbackCombinatorMethodAverage(std::vector A, + std::vector B) { + return average(A, B); + } + /// predefined method + static std::vector + FeedbackCombinatorMethodMin(std::vector A, + std::vector B) { + return min(A, B); + } + /// predefined method + static std::vector + FeedbackCombinatorMethodMax(std::vector A, + std::vector B) { + return max(A, B); + } + /// predefined method + static std::vector + FeedbackCombinatorMethodMult(std::vector A, + std::vector B) { + return mult(A, B); + } + + /// Predefined combination method for possible Scores + static std::vector + PossibleScoreCombinationMethodMin(std::vector A, + ReliabilityType B) { + return min(A, B); + } + /// Predefined combination method for possible Scores + static std::vector + PossibleScoreCombinationMethodMax(std::vector A, + ReliabilityType B) { + return max(A, B); + } + + /// Predefined combination method for possible Scores + static std::vector + PossibleScoreCombinationMethodAverage(std::vector A, + ReliabilityType B) { + return average(A, B); + } + + /// Predefined combination method for possible Scores + static std::vector + PossibleScoreCombinationMethodMult(std::vector A, + ReliabilityType B) { + return mult(A, B); + } + + /// The predefined min combinator method + static ReliabilityType combinationMin(ReliabilityType A, ReliabilityType B) { + return std::min(A, B); + } + + /// The predefined max combinator method + static ReliabilityType combinationMax(ReliabilityType A, ReliabilityType B) { + return std::max(A, B); + } + + /// The predefined average combinator method + static ReliabilityType combinationAverage(ReliabilityType A, + ReliabilityType B) { + return (A + B) / 2; + } + + /// The predefined average combinator method + static ReliabilityType combinationMult(ReliabilityType A, ReliabilityType B) { + return A * B; + } + private: std::vector> History; std::size_t HistoryMaxSize; std::vector ValuesFromMaster; SensorValueType previousSensorValue; unsigned int valueSetCounter; std::vector States; bool PreviousSensorValueExists = false; std::unique_ptr> Confidence; std::unique_ptr> Reliability; std::unique_ptr> ReliabilitySlope; std::unique_ptr> TimeConfidence; // combination functions ReliabilityType (*InputReliabilityCombinator)( ReliabilityType, ReliabilityType) = combinationMin; std::vector (*PossibleScoreCombinationMethod)( std::vector, ReliabilityType) = PossibleScoreCombinationMethodMin; - std::vector (*MeFeedbackCombinatorMehtodth)( - std::vector, std::vector) = average; + std::vector (*FeedbackCombinatorMethod)(std::vector, + std::vector) = + FeedbackCombinatorMethodAverage; + + ReliabilityType (*HistoryCombinatorMethod)(ReliabilityType, ReliabilityType) = + HistoryCombinatorMethodMax; /*--------------------------------- needed Functions * -----------------------------------------------------*/ /// returns the Reliability /// \param actualValue The Value of the Sensor /// \param lastValue of the Sensor this is stored in the class /// \param valueSetCounter It has an effect on the difference of the current /// and last value This might not be needed anymore /// \brief it returns the combination the \c Reliability function and \c /// ReliabilitySlope if the previous value exists. if it doesn't it only /// returns the \c Reliability function value. - ReliabilityType getRelibility(SensorValueType actualValue, - SensorValueType lastValue, - unsigned int valueSetCounter) { + ReliabilityType getReliability(SensorValueType actualValue, + SensorValueType lastValue, + unsigned int valueSetCounter) { ReliabilityType relAbs = Reliability->operator()(actualValue); if (PreviousSensorValueExists) { ReliabilityType relSlo = ReliabilitySlope->operator()( (lastValue - actualValue) / (SensorValueType)valueSetCounter); - // calculate signal input reliability - // NOTE: options would be multiply, average, AND (best to worst: - // average = AND > multiply) rel = relAbs * relSlo; rel = (relAbs + - // relSlo)/2; - return InputReliabilityCombinator(relAbs, relSlo); } else return relAbs; } /// adapts the possible Scores by checking the History and combines those /// values. currently with max /// \brief combines the historic values with the \c TimeConfidence function /// and returns the maximum Reliability for all Scores. std::vector getAllPossibleScoresBasedOnHistory() { // iterate through all history entries std::size_t posInHistory = 0; std::vector possibleScores; for (auto pShE = History.begin(); pShE < History.end(); pShE++, posInHistory++) { // iterate through all possible scores of each history entry for (ConfOrRel &pSh : *pShE) { StateType historyScore = pSh.score; ReliabilityType historyConf = pSh.Reliability; - // combine each history score with the confidence of time - // NOTE: multiplication, AND, or average would be alternatives (best to - // worst: multiplication = AND = average) historyConf = historyConf * TimeConfidence->operator()(posInHistory); - // historyConf = (historyConf + TimeConfidence(posInHistory)) / 2; - // historyConf = std::min(historyConf, TimeConfidence(posInHistory)); bool foundScore = false; for (ConfOrRel &pS : possibleScores) { if (pS.score == historyScore) { - // calculate confidence for score - // NOTE: multiplication, AND, or average would be alternatives (best - // to worst: AND >> average = multiplication ) pS->confOrRel = - // pS->confOrRel * historyConf; pS->confOrRel = (pS->confOrRel + - // historyConf) / 2; - // set combination method() - pS.Reliability = std::max(pS.Reliability, historyConf); + pS.Reliability = + HistoryCombinatorMethod(pS.Reliability, historyConf); foundScore = true; } } if (foundScore == false) { ConfOrRel possibleScore; possibleScore.score = historyScore; possibleScore.Reliability = historyConf; possibleScores.push_back(possibleScore); } } } return possibleScores; } /// saves the Scores in the History /// \brief It checks the incoming scores if any have a Reliability greater /// than 0.5 all of them get saved inside the History and then the /// History get shortened to the maximal length. It only saves the Value if /// the History is empty. /// /// \param actualPossibleScores The Scores which should be saved /// /// \note Does the History really make sense if the values are to small it /// only stores something if it's empty and not if it isn't completely filled void saveInHistory(std::vector actualPossibleScores) { // check if the reliability of at least one possible score is high enough bool atLeastOneRelIsHigh = false; for (ConfOrRel pS : actualPossibleScores) { if (pS.Reliability > 0.5) { atLeastOneRelIsHigh = true; } } // save possible scores if at least one possible score is high enough (or if // the history is empty) if (History.size() < 1 || atLeastOneRelIsHigh == true) { History.insert(History.begin(), actualPossibleScores); // if history size is higher than allowed, save oldest element while (History.size() > HistoryMaxSize) { // delete possibleScoreHistory.back(); History.pop_back(); } } } }; + + /// This is the Reliability Functionality for the highlevel Agent. /// \brief It takes the scores and reliabilities of all connected lowlevel /// Agents and calculates the Reliability of them together. Also it creates the /// feedback that is needed by the \c ReliabilityForLowLevelAgents, which is a /// kind of confidence. /// /// \tparam StateType Datatype of the State ( Typically double or float) /// \tparam ReliabilityType Datatype of the Reliability ( /// Typically long or int) /// /// \note A highlevel Agent is commonly in a master slave relationship with the /// lowlevel Agents as the master. It combines the Reliability of all connected /// Slaves and uses that as its own Reliability. /// /// \note more information about how the Reliability and feedback is /// created at \c operator()() // State Type rename // merge cross rel/conf darein template class CrossCombinator { public: static_assert(std::is_arithmetic::value, "HighLevel: StateType has to be an arithmetic type\n"); static_assert(std::is_arithmetic::value, "HighLevel: ReliabilityType has to be an arithmetic type\n"); /// typedef To shorten the writing. /// \c ConfOrRel typedef ConfOrRel ConfOrRel; /// typedef of the input type for the operator() defined explicitly to /// simplify interaction /// typedef std::vector> InputType; /// The return type for the \c operator()() Method struct returnType { ReliabilityType CrossReliability; std::map> CrossConfidence; }; /// Calculates the Reliability and the Cross Confidences for each lowlevel /// Agent for all of there states. /// /// \param Values It gets the States and Reliabilities of /// all connected Slaves inside a vector. /// /// \return it returns a struct \c returnType containing the CrossReliability /// and all CrossConfidence's /// /// \brief To calculate the Reliability it combines [\c std::min() ] the \c /// CrossReliability of all connected Agents. To calculate the feedback it /// iterates over all Agents and their states and uses the \c CrossConfidence /// Function to play what if with the states. returnType operator()( std::vector> &Values) { ReliabilityType combinedInputRel = 1; ReliabilityType combinedCrossRel = 1; ReliabilityType outputReliability; std::vector> Agents; std::map> output; std::vector output_temporary; for (auto tmp : Values) { std::pair tmp2; tmp2.first = std::get<0>(tmp); tmp2.second = std::get<1>(tmp); Agents.push_back(tmp2); } for (auto Value : Values) { id_t id = std::get<0>(Value); StateType sc = std::get<1>(Value); ReliabilityType rel = std::get<2>(Value); // combination method ([]) // get input reliability combinedInputRel = std::min(combinedInputRel, rel); // calculate the cross reliability for this slave agent ReliabilityType realCrossReliabilityOfSlaveAgent = CrossReliability->operator()( {id, sc}, Agents); // AVERAGE, MULTIPLICATION, CONJUNCTION (best to worst: // AVERAGE = CONJUNCTION > MULTIPLICATION >> ) // get cross confidence output_temporary.clear(); for (StateType thoScore : States[id]) { // calculate the cross reliability for this slave agent ConfOrRel data; data.score = thoScore; data.Reliability = CrossConfidence->operator()(id, thoScore, Agents); output_temporary.push_back(data); } output.insert({id, output_temporary}); // set combination method // get combined cross reliability combinedCrossRel = std::min(combinedCrossRel, realCrossReliabilityOfSlaveAgent); } // combine cross reliabilites and input reliabilites of all slave agents // NOTE: options would be multiply, average, AND (best to worst: ) // outputReliability = combinedInputRel * combinedCrossRel; // outputReliability = (combinedInputRel + combinedCrossRel) / 2; // set combination method // get output reliability outputReliability = std::min(combinedInputRel, combinedCrossRel); return {outputReliability, output}; } /// This is the setter for CrossReliability Function /// \param CrossReliability A pointer to the Functional for the /// CrossReliability /// \brief This is needed to calculate the Reliability. It uses this on all /// values of all lowlevel Agnets. void setCrossReliability( std::unique_ptr> &CrossReliability) { this->CrossReliability = std::move(CrossReliability); } /// This is the setter for CrossConfidence Function /// \param CrossConfidence A pointer to the Functional for the \c /// CrossConfidence \brief This is needed for the feedback for the \c /// ReliabilityForLowLevelAgents. void setCrossConfidence( std::unique_ptr> &CrossConfidence) { this->CrossConfidence = std::move(CrossConfidence); } /// This is the adder for the states /// \param id The id of the Agent of the states /// \param States id specific states. this will be copied So that if Slaves /// have different States they can be used correctly. /// \brief The States of all connected lowlevel Agents has to be known to be /// able to iterate over them void addStates(id_t id, std::vector States) { this->States.insert({id, States}); } private: std::unique_ptr> CrossReliability; std::unique_ptr> CrossConfidence; std::map> States; }; } // namespace agent } // namespace rosa #endif // !ROSA_AGENT_RELIABILITY_H