diff --git a/include/rosa/agent/SignalState.hpp b/include/rosa/agent/SignalState.hpp
index 63530a9..ccad719 100644
--- a/include/rosa/agent/SignalState.hpp
+++ b/include/rosa/agent/SignalState.hpp
@@ -1,681 +1,684 @@
 //===-- rosa/agent/SignalState.hpp ------------------------------*- C++ -*-===//
 //
 //                                 The RoSA Framework
 //
 //===----------------------------------------------------------------------===//
 ///
 /// \file rosa/agent/SignalState.hpp
 ///
 /// \author Maximilian Götzinger (maximilian.goetzinger@tuwien.ac.at)
 ///
 /// \date 2019
 ///
 /// \brief Definition of *signal state* *functionality*.
 ///
 //===----------------------------------------------------------------------===//
 
 #ifndef ROSA_AGENT_SIGNALSTATE_HPP
 #define ROSA_AGENT_SIGNALSTATE_HPP
 
 #include "rosa/agent/DistanceMetrics.hpp"
 #include "rosa/agent/FunctionAbstractions.hpp"
 #include "rosa/agent/Functionality.h"
 #include "rosa/agent/History.hpp"
 #include "rosa/agent/State.hpp"
 
 #include "rosa/support/math.hpp"
 
 namespace rosa {
 namespace agent {
 
 /// Signal properties defining the properties of the signal which is monitored
 /// by \c rosa::agent::SignalStateDetector and is saved in \c
 /// rosa::agent::SignalStateInformation.
 enum SignalProperties : uint8_t {
   INPUT = 0, ///< The signal is an input signal
   OUTPUT = 1 ///< The signal is an output signal
 };
 
 /// TODO: write description
 template <typename CONFDATATYPE>
 struct SignalStateInformation : StateInformation<CONFDATATYPE> {
   // Make sure the actual type arguments are matching our expectations.
   STATIC_ASSERT((std::is_arithmetic<CONFDATATYPE>::value),
                 "confidence type is not to arithmetic");
 
   /// ConfidenceOfMatchingState is the confidence how good the new sample
   /// matches the state.
   CONFDATATYPE ConfidenceOfMatchingState;
 
   /// ConfidenceOfMatchingState is the confidence how bad the new sample
   /// matches the state.
   CONFDATATYPE ConfidenceOfMismatchingState;
 
   /// The SignalProperty saves whether the monitored signal is an input our
   /// output signal.
   SignalProperties SignalProperty;
 
   /// The SignalStateIsValid saves the number of samples which have been
   /// inserted into the state after entering it.
   uint32_t NumberOfInsertedSamplesAfterEntrance;
 
 public:
   SignalStateInformation(unsigned int SignalStateID,
                          SignalProperties _SignalProperty) {
     this->StateID = SignalStateID;
     this->SignalProperty = _SignalProperty;
     this->StateCondition = StateConditions::UNKNOWN;
     this->NumberOfInsertedSamplesAfterEntrance = 0;
     this->StateIsValid = false;
     this->StateJustGotValid = false;
     this->StateIsValidAfterReentrance = false;
     this->ConfidenceStateIsValid = 0;
     this->ConfidenceStateIsInvalid = 0;
     this->ConfidenceStateIsStable = 0;
     this->ConfidenceStateIsDrifting = 0;
   }
 
   SignalStateInformation() = default;
 };
 
 /// \tparam INDATATYPE type of input data, \tparam CONFDATATYPE type of
 /// data in that the confidence values are given, \tparam PROCDATATYPE type of
 /// the relative distance and the type of data in which DABs are saved.
 template <typename INDATATYPE, typename CONFDATATYPE, typename PROCDATATYPE>
 class SignalState : public Functionality {
 
   // Make sure the actual type arguments are matching our expectations.
   STATIC_ASSERT((std::is_arithmetic<INDATATYPE>::value),
                 "input data type not arithmetic");
   STATIC_ASSERT((std::is_arithmetic<CONFDATATYPE>::value),
                 "confidence data type is not to arithmetic");
   STATIC_ASSERT(
       (std::is_arithmetic<PROCDATATYPE>::value),
       "process data type (DAB and Relative Distance) is not to arithmetic");
 
 public:
   // The metric to calculate the distance between two points
   using DistanceMetricAbstraction =
       Abstraction<std::pair<INDATATYPE, INDATATYPE>, PROCDATATYPE> &;
   // For the convinience to write a shorter data type name
   using PartFuncReference = PartialFunction<INDATATYPE, CONFDATATYPE> &;
   // using PartFuncReference2 = ;
   using StepFuncReference = StepFunction<INDATATYPE, CONFDATATYPE> &;
 
 private:
   /// SignalStateInfo is a struct of SignalStateInformation that contains
   /// information about the current signal state.
   SignalStateInformation<CONFDATATYPE> SignalStateInfo;
 
   /// The metric to calculate the distance between two points
   DistanceMetricAbstraction DistanceMetric;
 
   /// The FuzzyFunctionSampleMatches is the fuzzy function that gives the
   /// confidence how good the new sample matches another sample in the sample
   /// history.
   PartFuncReference FuzzyFunctionSampleMatches;
 
   /// The FuzzyFunctionSampleMismatches is the fuzzy function that gives the
   /// confidence how bad the new sample matches another sample in the sample
   /// history.
   PartFuncReference FuzzyFunctionSampleMismatches;
 
   /// The FuzzyFunctionNumOfSamplesMatches is the fuzzy function that gives the
   /// confidence how many samples from the sampe history match the new sample.
   StepFuncReference FuzzyFunctionNumOfSamplesMatches;
 
   /// The FuzzyFunctionNumOfSamplesMismatches is the fuzzy function that gives
   /// the confidence how many samples from the sampe history mismatch the new
   /// sample.
   StepFuncReference FuzzyFunctionNumOfSamplesMismatches;
 
   /// The FuzzyFunctionSampleValid is the fuzzy function that gives the
   /// confidence how good one matches another sample in the sample
   /// history. This is done to evaluate whether a state is valid.
   PartFuncReference FuzzyFunctionSampleValid;
 
   /// The FuzzyFunctionSampleInvalid is the fuzzy function that gives the
   /// confidence how bad one sample matches another sample in the sample
   /// history. This is done to evaluate whether a state is invalid.
   PartFuncReference FuzzyFunctionSampleInvalid;
 
   /// The FuzzyFunctionNumOfSamplesValid is the fuzzy function that gives the
   /// confidence how many samples from the sample history match another sample.
   /// This is done to evaluate whether a state is valid.
   StepFuncReference FuzzyFunctionNumOfSamplesValid;
 
   /// The FuzzyFunctionNumOfSamplesInvalid is the fuzzy function that gives
   /// the confidence how many samples from the sample history mismatch another
   /// sample. This is done to evaluate whether a state is invalid.
   StepFuncReference FuzzyFunctionNumOfSamplesInvalid;
 
   /// The 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;
 
   /// TODO: description
   PartialFunction<uint32_t, float> &FuzzyFunctionSignalConditionLookBack;
   /// TODO: description
   PartialFunction<uint32_t, float> &FuzzyFunctionSignalConditionHistoryDesicion;
   /// TODO: description
   uint32_t DriftLookbackRange;
 
   /// SampleHistory is a history in that the last sample values are stored.
   DynamicLengthHistory<INDATATYPE, HistoryPolicy::FIFO> SampleHistory;
   /// DAB is a (usually) small history of the last sample values of which a
   /// average is calculated if the DAB is full.
   DynamicLengthHistory<INDATATYPE, HistoryPolicy::SRWF> DAB;
   /// DABHistory is a history in that the last DABs (to be exact, the averages
   /// of the last DABs) are stored.
   DynamicLengthHistory<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;
 
   /// TempConfidenceMatching is the confidence how good a sample matches the
   /// state. However, the value of this variable is only needed temporarly.
   CONFDATATYPE TempConfidenceMatching = 0;
 
   /// TempConfidenceMatching is the confidence how bad a sample matches the
   /// state. However, the value of this variable is only needed temporarly.
   CONFDATATYPE TempConfidenceMismatching = 0;
 
 public:
   /// Creates an instance by setting all parameters
   /// \param SignalStateID The Id of the SignalStateinfo \c
   /// SignalStateInformation.
   ///
   /// \param DistanceMetric the distance metric to calculate the distance
   /// between two points
   ///
   /// \param FuzzyFunctionSampleMatches The FuzzyFunctionSampleMatches is the
   /// fuzzy function that gives the confidence how good the new sample matches
   /// another sample in the sample history.
   ///
   /// \param FuzzyFunctionSampleMismatches The FuzzyFunctionSampleMismatches is
   /// the fuzzy function that gives the confidence how bad the new sample
   /// matches another sample in the sample history.
   ///
   /// \param FuzzyFunctionNumOfSamplesMatches The
   /// FuzzyFunctionNumOfSamplesMatches is the fuzzy function that gives the
   /// confidence how many samples from the sampe history match the new sample.
   ///
   /// \param FuzzyFunctionNumOfSamplesMismatches The
   /// FuzzyFunctionNumOfSamplesMismatches is the fuzzy function that gives the
   /// confidence how many samples from the sampe history mismatch the new
   /// sample.
   ///
   /// \param 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,
       DistanceMetricAbstraction DistanceMetric,
       PartFuncReference FuzzyFunctionSampleMatches,
       PartFuncReference FuzzyFunctionSampleMismatches,
       StepFuncReference FuzzyFunctionNumOfSamplesMatches,
       StepFuncReference FuzzyFunctionNumOfSamplesMismatches,
       PartFuncReference FuzzyFunctionSampleValid,
       PartFuncReference FuzzyFunctionSampleInvalid,
       StepFuncReference FuzzyFunctionNumOfSamplesValid,
       StepFuncReference FuzzyFunctionNumOfSamplesInvalid,
       // SAVE CHANGES
       PartFuncReference FuzzyFunctionSignalIsDrifting,
       PartFuncReference FuzzyFunctionSignalIsStable,
       PartialFunction<uint32_t, float> &FuzzyFunctionSignalConditionLookBack,
       // - SAVE CHANGES
       PartialFunction<uint32_t, float>
           &FuzzyFunctionSignalConditionHistoryDesicion,
       uint32_t DriftLookbackRange) noexcept
       : SignalStateInfo{SignalStateID, SignalProperty},
         DistanceMetric(DistanceMetric),
         FuzzyFunctionSampleMatches(FuzzyFunctionSampleMatches),
         FuzzyFunctionSampleMismatches(FuzzyFunctionSampleMismatches),
         FuzzyFunctionNumOfSamplesMatches(FuzzyFunctionNumOfSamplesMatches),
         FuzzyFunctionNumOfSamplesMismatches(
             FuzzyFunctionNumOfSamplesMismatches),
         FuzzyFunctionSampleValid(FuzzyFunctionSampleValid),
         FuzzyFunctionSampleInvalid(FuzzyFunctionSampleInvalid),
         FuzzyFunctionNumOfSamplesValid(FuzzyFunctionNumOfSamplesValid),
         FuzzyFunctionNumOfSamplesInvalid(FuzzyFunctionNumOfSamplesInvalid),
         FuzzyFunctionSignalIsDrifting(FuzzyFunctionSignalIsDrifting),
         FuzzyFunctionSignalIsStable(FuzzyFunctionSignalIsStable),
         // SAVE CHANGES
         FuzzyFunctionSignalConditionLookBack(
             FuzzyFunctionSignalConditionLookBack),
         FuzzyFunctionSignalConditionHistoryDesicion(
             FuzzyFunctionSignalConditionHistoryDesicion),
         DriftLookbackRange(DriftLookbackRange),
         // - SAVE CHANGES
         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()) {
 
       // TODO: make soring inside of median
       // TODO: make better outlier removal!
       // std::sort(DAB.begin(), DAB.end());
       // DAB.erase(DAB.begin(), DAB.begin() + 1);
       // DAB.erase(DAB.end() - 1, DAB.end());
       // PROCDATATYPE AvgOfDAB = DAB.template median<PROCDATATYPE>();
       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();
 
     SignalStateInfo.ConfidenceOfMatchingState = TempConfidenceMatching;
     SignalStateInfo.ConfidenceOfMismatchingState = TempConfidenceMismatching;
 
     return SignalStateInfo;
   }
 
   /// Gives the confidence how likely the new sample matches the signal state.
   ///
   /// \param Sample is the actual sample of the observed signal.
   ///
   /// \return the confidence of the new sample is matching the signal state.
   CONFDATATYPE
   confidenceSampleMatchesSignalState(INDATATYPE Sample) noexcept {
 
     CONFDATATYPE ConfidenceOfBestCase = 0;
 
     DynamicLengthHistory<PROCDATATYPE, HistoryPolicy::FIFO>
         RelativeDistanceHistory(SampleHistory.maxLength());
 
     // Calculate distances to all history samples.
     for (auto &HistorySample : SampleHistory) {
       PROCDATATYPE RelativeDistance =
           DistanceMetric(std::make_pair(Sample, HistorySample));
       RelativeDistanceHistory.addEntry(RelativeDistance);
     }
 
     // Sort all calculated distances so that the lowest distance (will get the
     // highest confidence) is at the beginning.
     RelativeDistanceHistory.sortAscending();
 
     CONFDATATYPE ConfidenceOfWorstFittingSample = 1;
 
     // Case 1 means that one (the best fitting) sample of the history is
     // compared with the new sample. Case 2 means the two best history samples
     // are compared with the new sample. And so on.
     // TODO (future): to accelerate . don't start with 1 start with some higher
     // number because a low number (i guess lower than 5) will definetely lead
     // to a low confidence. except the history is not full.
 
     // Case 1 means that one (the best fitting) sample of the history is
     // compared with the new sample. Case 2 means the two best history samples
     // are compared with the new sample. And so on.
     for (uint32_t Case = 0; Case < RelativeDistanceHistory.numberOfEntries();
          Case++) {
 
       CONFDATATYPE ConfidenceFromRelativeDistance;
 
       if (std::isinf(RelativeDistanceHistory[Case])) {
         // TODO (future): if fuzzy is defined in a way that infinity is not 0 it
         // would be a problem.
         ConfidenceFromRelativeDistance = 0;
       } else {
         ConfidenceFromRelativeDistance =
             FuzzyFunctionSampleMatches(RelativeDistanceHistory[Case]);
       }
       ConfidenceOfWorstFittingSample = fuzzyAND(ConfidenceOfWorstFittingSample,
                                                 ConfidenceFromRelativeDistance);
 
       ConfidenceOfBestCase =
           fuzzyOR(ConfidenceOfBestCase,
                   fuzzyAND(ConfidenceOfWorstFittingSample,
                            FuzzyFunctionNumOfSamplesMatches(
                                static_cast<CONFDATATYPE>(Case) + 1)));
     }
 
     TempConfidenceMatching = ConfidenceOfBestCase;
 
     return ConfidenceOfBestCase;
   }
 
   /// Gives the confidence how likely the new sample mismatches the signal
   /// state.
   ///
   /// \param Sample is the actual sample of the observed signal.
   ///
   /// \return the confidence of the new sample is mismatching the signal state.
   CONFDATATYPE
   confidenceSampleMismatchesSignalState(INDATATYPE Sample) noexcept {
 
     float ConfidenceOfWorstCase = 1;
 
     DynamicLengthHistory<PROCDATATYPE, HistoryPolicy::FIFO>
         RelativeDistanceHistory(SampleHistory.maxLength());
 
     // Calculate distances to all history samples.
     for (auto &HistorySample : SampleHistory) {
       RelativeDistanceHistory.addEntry(
           DistanceMetric(std::make_pair(Sample, HistorySample)));
     }
 
     // Sort all calculated distances so that the highest distance (will get the
     // lowest confidence) is at the beginning.
     RelativeDistanceHistory.sortDescending();
 
     CONFDATATYPE ConfidenceOfBestFittingSample = 0;
 
     // TODO (future): to accelerate -> don't go until end. Confidences will only
     // get higher. See comment in "CONFDATATYPE
     // confidenceSampleMatchesSignalState(INDATATYPE Sample)".
 
     // Case 1 means that one (the worst fitting) sample of the history is
     // compared with the new sample. Case 2 means the two worst history samples
     // are compared with the new sample. And so on.
     for (uint32_t Case = 0; Case < RelativeDistanceHistory.numberOfEntries();
          Case++) {
 
       CONFDATATYPE ConfidenceFromRelativeDistance;
 
       if (std::isinf(RelativeDistanceHistory[Case])) {
         ConfidenceFromRelativeDistance = 1;
       } else {
         ConfidenceFromRelativeDistance =
             FuzzyFunctionSampleMismatches(RelativeDistanceHistory[Case]);
       }
 
       ConfidenceOfBestFittingSample = fuzzyOR(ConfidenceOfBestFittingSample,
                                               ConfidenceFromRelativeDistance);
 
       ConfidenceOfWorstCase =
           fuzzyAND(ConfidenceOfWorstCase,
                    fuzzyOR(ConfidenceOfBestFittingSample,
                            FuzzyFunctionNumOfSamplesMismatches(
                                static_cast<CONFDATATYPE>(Case) + 1)));
     }
 
     TempConfidenceMismatching = ConfidenceOfWorstCase;
 
     return ConfidenceOfWorstCase;
   }
 
   /// Gives information about the current signal state.
   ///
   /// \return a struct SignalStateInformation that contains information about
   /// the current signal state.
   SignalStateInformation<CONFDATATYPE> signalStateInformation(void) noexcept {
     return SignalStateInfo;
   }
 
 private:
   void validateSignalState(INDATATYPE Sample) {
     // TODO (future): WorstConfidenceDistance and BestConfidenceDistance could
     // be set already in "CONFDATATYPE
     // confidenceSampleMatchesSignalState(INDATATYPE Sample)" and "CONFDATATYPE
     // confidenceSampleMismatchesSignalState(INDATATYPE Sample)" when the new
     // sample is compared to all history samples. This would save a lot time
     // because the comparisons are done only once. However, it has to be asured
     // that the these two functions are called before the insertation, and the
     // FuzzyFunctions for validation and matching have to be the same!
     CONFDATATYPE LowestConfidenceMatching = 1;
     CONFDATATYPE HighestConfidenceMismatching = 0;
 
     for (auto &HistorySample : SampleHistory) {
       // TODO (future): think about using different fuzzy functions for
       // validation and matching.
       LowestConfidenceMatching =
           fuzzyAND(LowestConfidenceMatching,
                    FuzzyFunctionSampleMatches(
                        DistanceMetric(std::make_pair(Sample, HistorySample))));
 
       HighestConfidenceMismatching =
           fuzzyOR(HighestConfidenceMismatching,
                   FuzzyFunctionSampleMismatches(
                       DistanceMetric(std::make_pair(Sample, HistorySample))));
     }
     LowestConfidenceMatchingHistory.addEntry(LowestConfidenceMatching);
     HighestConfidenceMismatchingHistory.addEntry(HighestConfidenceMismatching);
 
     LowestConfidenceMatching = LowestConfidenceMatchingHistory.lowestEntry();
     HighestConfidenceMismatching =
         HighestConfidenceMismatchingHistory.highestEntry();
 
     SignalStateInfo.ConfidenceStateIsValid =
         fuzzyAND(LowestConfidenceMatching,
                  FuzzyFunctionNumOfSamplesValid(static_cast<INDATATYPE>(
                      SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));
 
     SignalStateInfo.ConfidenceStateIsInvalid =
         fuzzyOR(HighestConfidenceMismatching,
                 FuzzyFunctionNumOfSamplesInvalid(static_cast<INDATATYPE>(
                     SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));
 
     if (SignalStateInfo.ConfidenceStateIsValid >
         SignalStateInfo.ConfidenceStateIsInvalid) {
 
       if (SignalStateInfo.StateIsValid) {
         SignalStateInfo.StateJustGotValid = false;
       } else {
         SignalStateInfo.StateJustGotValid = true;
       }
 
       SignalStateInfo.StateIsValid = true;
       SignalStateInfo.StateIsValidAfterReentrance = true;
     }
   }
 
   void checkSignalStability(void) {
 
     /*
   std::cout << "LookbackTest: " << std::endl;
   for (unsigned int t = 1; t <= DriftLookbackRange + 5; t++) {
     std::cout << "t=" << t
               << " -> c=" << FuzzyFunctionSignalConditionLookBack(t)
               << std::endl;
 
     //(*FuzzyFunctionTimeSystemFunctioning)(
     // static_cast<INDATATYPE>(TimeOfDisparity));
   }
   getchar();
 */
     SignalStateInfo.ConfidenceStateIsStable = 0;
     SignalStateInfo.ConfidenceStateIsDrifting = 0;
 
     /*
     std::cout << "ConfidenceStateIsStable (before): "
               << SignalStateInfo.ConfidenceStateIsStable << std::endl;
     std::cout << "ConfidenceStateIsDrifting (before): "
               << SignalStateInfo.ConfidenceStateIsDrifting << std::endl;
 */
 
     bool DriftDirectionIsUp = true;
 
     if (DABHistory.numberOfEntries() >= 2) {
 
       // SAVE CHANGES
       INDATATYPE CurrentDAB = DABHistory[DABHistory.numberOfEntries() - 1];
       INDATATYPE DAB2Compare = DABHistory[0];
 
       // ########### TODO HERE: calculating up_down
 
       uint32_t DriftDnCounter = 0;
       uint32_t DriftUpCounter = 0;
 
       // EXPERIMENTING
       for (unsigned int t = 1;
            t <= DriftLookbackRange && t < DABHistory.numberOfEntries(); t++) {
 
         DAB2Compare = DABHistory[DABHistory.numberOfEntries() - (t + 1)];
 
         // TODO: make the following also for distance measurement when comparing
         // sample with state and validate state
         /*
         // sigma correction
         if (NormalizedDistanceMetric<INDATATYPE, PROCDATATYPE>
                 *NormalizableDistanceMetric = dynamic_cast<
                     NormalizedDistanceMetric<INDATATYPE, PROCDATATYPE> *>(
                     DistanceMetric)) {
           // old was safely casted to NewType
           NormalizableDistanceMetric->setNorm(
               // TODO: (1) Sigma von Sample
               // History(!) abholen, (2) irgendwas mit Sigma hier reinschreiben,
               // und (3) überlegen wegen zweiter History (länger) für
               // Sigmaberechnung
           );
         }
        */
 
         float dist = DistanceMetric(std::make_pair(CurrentDAB, DAB2Compare));
 
         // AVG
         SignalStateInfo.ConfidenceStateIsStable +=
             FuzzyFunctionSignalIsStable(dist);
         SignalStateInfo.ConfidenceStateIsDrifting +=
             FuzzyFunctionSignalIsDrifting(dist);
         // TODO: move following outside of the loop with
         // "if(DriftLookbackRange<=DABHistory.numberOfEntries())"
         if (t == DriftLookbackRange) {
           SignalStateInfo.ConfidenceStateIsStable /= DriftLookbackRange;
+          SignalStateInfo.ConfidenceStateIsDrifting /= DriftLookbackRange;
         } else if (t == DABHistory.numberOfEntries() - 1) {
           SignalStateInfo.ConfidenceStateIsStable /=
               DABHistory.numberOfEntries();
+          SignalStateInfo.ConfidenceStateIsDrifting /=
+              DABHistory.numberOfEntries();
         }
 
         /*
         // AND
         SignalStateInfo.ConfidenceStateIsStable =
             fuzzyOR(SignalStateInfo.ConfidenceStateIsStable,
                     fuzzyAND(FuzzyFunctionSignalIsStable(dist),
                              FuzzyFunctionSignalConditionLookBack(t)));
 
         SignalStateInfo.ConfidenceStateIsDrifting =
             fuzzyOR(SignalStateInfo.ConfidenceStateIsDrifting,
                     fuzzyAND(FuzzyFunctionSignalIsDrifting(dist),
                              FuzzyFunctionSignalConditionLookBack(t)));
         */
         /*
         // MULTI
         SignalStateInfo.ConfidenceStateIsStable =
             fuzzyOR(SignalStateInfo.ConfidenceStateIsStable,
                     FuzzyFunctionSignalIsStable(
                         relativeDistance<INDATATYPE, PROCDATATYPE>(
                             CurrentDAB, DAB2Compare)) *
                         FuzzyFunctionSignalConditionLookBack(t));
 
         SignalStateInfo.ConfidenceStateIsDrifting =
             fuzzyOR(SignalStateInfo.ConfidenceStateIsDrifting,
                     FuzzyFunctionSignalIsDrifting(
                         relativeDistance<INDATATYPE, PROCDATATYPE>(
                             CurrentDAB, DAB2Compare)) *
                         FuzzyFunctionSignalConditionLookBack(t));
         */
 
         if (CurrentDAB > DAB2Compare)
           DriftUpCounter++;
         else if (CurrentDAB < DAB2Compare)
           DriftDnCounter++;
       }
       // TODO: change something because it is biased if the are equal
       DriftDirectionIsUp = DriftUpCounter > DriftDnCounter;
 
       // following outcommented block was the published code
       /*
           SignalStateInfo.ConfidenceStateIsStable =
           FuzzyFunctionSignalIsStable(
               relativeDistance<INDATATYPE, PROCDATATYPE>(CurrentDAB,
                                                          DAB2Compare));
       SignalStateInfo.ConfidenceStateIsDrifting =
           FuzzyFunctionSignalIsDrifting(
               relativeDistance<INDATATYPE, PROCDATATYPE>(CurrentDAB,
                                                          DAB2Compare));
       // TODO: think about a better solution with different confidences
       // (stable, up, down, ...)
       DriftDirectionIsUp = CurrentDAB > DAB2Compare;
       */
       // - SAVE CHANGES
     }
 
     /*
     std::cout << "ConfidenceStateIsStable (after): "
               << SignalStateInfo.ConfidenceStateIsStable << std::endl;
     std::cout << "ConfidenceStateIsDrifting (after): "
               << SignalStateInfo.ConfidenceStateIsDrifting << std::endl;
   */
 
     if (SignalStateInfo.ConfidenceStateIsStable >
         SignalStateInfo.ConfidenceStateIsDrifting) {
       SignalStateInfo.StateCondition = StateConditions::STABLE;
     } else if (SignalStateInfo.ConfidenceStateIsStable <
                SignalStateInfo.ConfidenceStateIsDrifting) {
 
       if (DriftDirectionIsUp) {
         SignalStateInfo.StateCondition = StateConditions::DRIFTING_UP;
       } else {
         SignalStateInfo.StateCondition = StateConditions::DRIFTING_DN;
       }
       /*
         SignalStateInfo.StateCondition = StateConditions::DRIFTING;
         */
     } else {
       SignalStateInfo.StateCondition = StateConditions::UNKNOWN;
     }
   }
 }; // namespace agent
 
 } // namespace agent
 } // End namespace rosa
 
 #endif // ROSA_AGENT_SIGNALSTATE_HPP