diff --git a/include/rosa/agent/SignalState.hpp b/include/rosa/agent/SignalState.hpp
index 5e088ba..69402c1 100644
--- a/include/rosa/agent/SignalState.hpp
+++ b/include/rosa/agent/SignalState.hpp
@@ -1,649 +1,651 @@
 //===-- 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");
 
   /// 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:
   // 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 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 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 FuzzyFunctionSampleValid,
       PartFuncReference FuzzyFunctionSampleInvalid,
       StepFuncReference FuzzyFunctionNumOfSamplesValid,
       StepFuncReference FuzzyFunctionNumOfSamplesInvalid,
       PartFuncReference FuzzyFunctionSignalIsDrifting,
       PartFuncReference FuzzyFunctionSignalIsStable //,
       // PartialFunction<uint32_t, float> &FuzzyFunctionSignalConditionLookBack,
       // PartialFunction<uint32_t, float>
       //    &FuzzyFunctionSignalConditionHistoryDesicion,
       // uint32_t DriftLookbackRange
       ) noexcept
       : SignalStateInfo{SignalStateID, SignalProperty},
         FuzzyFunctionSampleMatches(FuzzyFunctionSampleMatches),
         FuzzyFunctionSampleMismatches(FuzzyFunctionSampleMismatches),
         FuzzyFunctionNumOfSamplesMatches(FuzzyFunctionNumOfSamplesMatches),
         FuzzyFunctionNumOfSamplesMismatches(
             FuzzyFunctionNumOfSamplesMismatches),
         FuzzyFunctionSampleValid(FuzzyFunctionSampleValid),
         FuzzyFunctionSampleInvalid(FuzzyFunctionSampleInvalid),
         FuzzyFunctionNumOfSamplesValid(FuzzyFunctionNumOfSamplesValid),
         FuzzyFunctionNumOfSamplesInvalid(FuzzyFunctionNumOfSamplesInvalid),
         FuzzyFunctionSignalIsDrifting(FuzzyFunctionSignalIsDrifting),
         FuzzyFunctionSignalIsStable(FuzzyFunctionSignalIsStable),
         // FuzzyFunctionSignalConditionLookBack(
         //    FuzzyFunctionSignalConditionLookBack),
         // FuzzyFunctionSignalConditionHistoryDesicion(
         //    FuzzyFunctionSignalConditionHistoryDesicion),
         // DriftLookbackRange(DriftLookbackRange),
         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()) {
       // Experiment -> exchanged next line with the folowings
       // PROCDATATYPE AvgOfDAB = DAB.template average<PROCDATATYPE>();
 
       // 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 =
           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)));
     }
 
     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();
+    // EDIT: the highest distance would yield the highest mismatching confidence
+    // To get the lowest confidence at the beginning, distances have to be sorted ascendingly.
+    RelativeDistanceHistory.sortAscending();
 
     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(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,
                  FuzzyFunctionNumOfSamplesValid(static_cast<INDATATYPE>(
                      SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));
 
     SignalStateInfo.ConfidenceStateIsInvalid =
         fuzzyOR(HighestConfidenceMismatching,
                 FuzzyFunctionNumOfSamplesInvalid(static_cast<INDATATYPE>(
                     SignalStateInfo.NumberOfInsertedSamplesAfterEntrance)));
 
     if (SignalStateInfo.StateIsValid)
       SignalStateInfo.StateJustGotValid = false;
 
     if (SignalStateInfo.ConfidenceStateIsValid >
         SignalStateInfo.ConfidenceStateIsInvalid) {
 
       if (!SignalStateInfo.StateIsValid)
         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;
 */
 
     if (DABHistory.numberOfEntries() >= 2) {
       /*
             // EXPERIMENTING
             for (unsigned int t = 1;
                  t <= DriftLookbackRange && t < DABHistory.numberOfEntries();
          t++) {
 
               // AND
 
               SignalStateInfo.ConfidenceStateIsStable = fuzzyOR(
                   SignalStateInfo.ConfidenceStateIsStable,
                   fuzzyAND(
                       FuzzyFunctionSignalIsStable(
                           relativeDistance<INDATATYPE, PROCDATATYPE>(
                               DABHistory[DABHistory.numberOfEntries() - 1],
                               DABHistory[DABHistory.numberOfEntries() - (t +
          1)])), FuzzyFunctionSignalConditionLookBack(t)));
 
               SignalStateInfo.ConfidenceStateIsDrifting = fuzzyOR(
                   SignalStateInfo.ConfidenceStateIsDrifting,
                   fuzzyAND(
                       FuzzyFunctionSignalIsDrifting(
                           relativeDistance<INDATATYPE, PROCDATATYPE>(
                               DABHistory[DABHistory.numberOfEntries() - 1],
                               DABHistory[DABHistory.numberOfEntries() - (t +
          1)])), FuzzyFunctionSignalConditionLookBack(t))); */
 
       /*
               std::cout
                   << "t=" << t
                   << ", DABact=" << DABHistory[DABHistory.numberOfEntries() -
          1]
                   << ", DAB_t-" << t << "="
                   << DABHistory[DABHistory.numberOfEntries() - (t + 1)]
                   << " / FuzzyStb="
                   << FuzzyFunctionSignalIsStable(
                          relativeDistance<INDATATYPE, PROCDATATYPE>(
                              DABHistory[DABHistory.numberOfEntries() - 1],
                              DABHistory[DABHistory.numberOfEntries() - (t +
          1)]))
                   << ", FuzzyDft="
                   << FuzzyFunctionSignalIsDrifting(
                          relativeDistance<INDATATYPE, PROCDATATYPE>(
                              DABHistory[DABHistory.numberOfEntries() - 1],
                              DABHistory[DABHistory.numberOfEntries() - (t +
          1)]))
                   << ", FuzzyLB=" << FuzzyFunctionSignalConditionLookBack(t)
                   << std::endl;
       */
       // MULTI
       /*
               SignalStateInfo.ConfidenceStateIsStable = fuzzyOR(
                   SignalStateInfo.ConfidenceStateIsStable,
                   FuzzyFunctionSignalIsStable(
                       relativeDistance<INDATATYPE, PROCDATATYPE>(
                           DABHistory[DABHistory.numberOfEntries() - 1],
                           DABHistory[DABHistory.numberOfEntries() - (t + 1)]))
          * FuzzyFunctionSignalConditionLookBack(t));
 
               SignalStateInfo.ConfidenceStateIsDrifting = fuzzyOR(
                   SignalStateInfo.ConfidenceStateIsDrifting,
                   FuzzyFunctionSignalIsDrifting(
                       relativeDistance<INDATATYPE, PROCDATATYPE>(
                           DABHistory[DABHistory.numberOfEntries() - 1],
                           DABHistory[DABHistory.numberOfEntries() - (t + 1)]))
          * FuzzyFunctionSignalConditionLookBack(t));
       */
       // std::cout << "t = " << t << ", HistLength = " <<
       // DABHistory.numberOfEntries() << std::endl;
       //}
 
       // EXPERIMENTING -> following outcommented block was the published code
 
       SignalStateInfo.ConfidenceStateIsStable = FuzzyFunctionSignalIsStable(
           relativeDistance<INDATATYPE, PROCDATATYPE>(
               DABHistory[DABHistory.numberOfEntries() - 1], DABHistory[0]));
       SignalStateInfo.ConfidenceStateIsDrifting = FuzzyFunctionSignalIsDrifting(
           relativeDistance<INDATATYPE, PROCDATATYPE>(
               DABHistory[DABHistory.numberOfEntries() - 1], DABHistory[0]));
     }
 
     /*
     std::cout << "ConfidenceStateIsStable (after): "
               << SignalStateInfo.ConfidenceStateIsStable << std::endl;
     std::cout << "ConfidenceStateIsDrifting (after): "
               << SignalStateInfo.ConfidenceStateIsDrifting << std::endl;
 */
 
     /*
     else {
       // Initializing the following variables because (at this moment) we do not
       // know if the signal is stable or drifting.
       SignalStateInfo.ConfidenceStateIsStable = 0;
       SignalStateInfo.ConfidenceStateIsDrifting = 0;
     }
     */
 
     if (SignalStateInfo.ConfidenceStateIsStable >
         SignalStateInfo.ConfidenceStateIsDrifting) {
       SignalStateInfo.StateCondition = StateConditions::STABLE;
     } else if (SignalStateInfo.ConfidenceStateIsStable <
                SignalStateInfo.ConfidenceStateIsDrifting) {
       SignalStateInfo.StateCondition = StateConditions::DRIFTING;
     } else {
       SignalStateInfo.StateCondition = StateConditions::UNKNOWN;
       /*
             if (SignalStateInfo.ConfidenceStateIsStable != 0)
               getchar();
               */
     }
   }
 };
 
 } // End namespace agent
 } // End namespace rosa
 
 #endif // ROSA_AGENT_SIGNALSTATE_HPP