Changeset 791 for cpp/frams/neuro/impl/neuroimpl-fuzzy.h

Timestamp:

05/29/18 16:24:39 (6 years ago)

Author:

Maciej Komosinski

Message:

Code formatting

File:

• cpp/frams/neuro/impl/neuroimpl-fuzzy.h (modified) (1 diff)

cpp/frams/neuro/impl/neuroimpl-fuzzy.h

@@ -20 +20 @@
 private:
 
-  double *fuzzySets;    /// list of four digits which represents fuzzy sets: [0]-l, [1]-m, [2]-n, [3]-r, ...  fuzzySet[4*i] = left, fuzzySet[4*i + 1] = midleft, fuzzySet[4*i + 2] = midright, fuzzySet[4*i + 3] = right
+        double *fuzzySets;    /// list of four digits which represents fuzzy sets: [0]-l, [1]-m, [2]-n, [3]-r, ...  fuzzySet[4*i] = left, fuzzySet[4*i + 1] = midleft, fuzzySet[4*i + 2] = midright, fuzzySet[4*i + 3] = right
 
-  /** Determines, which fuzzy set is connected with each input of neuron. For instance third rule:
-  *   'IF input3 = fuzzy set #3 AND input5 = fuzzy set #1 then output2 = fuzzy set #6 AND output7 = fuzzy set #5'
-  *   the variables shoul have values as shown below:
-  *   RulesDef[4]=2; RulesDef[5]=2; //rule 3: 2 inputs, 2 outputs
-  *   Rules[2][0]=3, Rules[2][1]=3, Rules[2][2]=5, Rules[2][3]=1, Rules[2][4]=2, Rules[2][5]=6, Rules[2][6]=7, Rules[2][3]=5
-  */
-  int *rulesDef;    ///list of rules definitions: nr of inputs in rule 1, nr of outputs in rule 1, ... and so on for each rule
-  int **rules;      ///list of rules body: input nr, fuzzy set nr, ... , output nr, fuzzy set nr, ... and so on for each rule
+        /** Determines, which fuzzy set is connected with each input of neuron. For instance third rule:
+        *   'IF input3 = fuzzy set #3 AND input5 = fuzzy set #1 then output2 = fuzzy set #6 AND output7 = fuzzy set #5'
+        *   the variables shoul have values as shown below:
+        *   RulesDef[4]=2; RulesDef[5]=2; //rule 3: 2 inputs, 2 outputs
+        *   Rules[2][0]=3, Rules[2][1]=3, Rules[2][2]=5, Rules[2][3]=1, Rules[2][4]=2, Rules[2][5]=6, Rules[2][6]=7, Rules[2][3]=5
+        */
+        int *rulesDef;    ///list of rules definitions: nr of inputs in rule 1, nr of outputs in rule 1, ... and so on for each rule
+        int **rules;      ///list of rules body: input nr, fuzzy set nr, ... , output nr, fuzzy set nr, ... and so on for each rule
 
-  /**
-  *  Sets defuzzyfication parameters: determines - for each rule - cut level <0;1> (minimum membership function of current rule).
-  *  In fact, defuzzParam remembers the values from 'first layer' - fuzzyfication layer (see neuron at documentation)
-  *  i.e. rule 1: defuzzParam[0] = 0.3522
-  */
-  double *defuzzParam; /// i.e.: defuzParam[5] = 0.455 means that rule #6 has got a minimum membership function (of given inputs set for this rule) at value 0.455 (it's cut level)
+        /**
+        *  Sets defuzzyfication parameters: determines - for each rule - cut level <0;1> (minimum membership function of current rule).
+        *  In fact, defuzzParam remembers the values from 'first layer' - fuzzyfication layer (see neuron at documentation)
+        *  i.e. rule 1: defuzzParam[0] = 0.3522
+        */
+        double *defuzzParam; /// i.e.: defuzParam[5] = 0.455 means that rule #6 has got a minimum membership function (of given inputs set for this rule) at value 0.455 (it's cut level)
 
 protected:
 
-  ///Fuzzy functions
-  double TrapeziumFuzz(int which_fuzzy_set, double input_val);
-  int Fuzzyfication();
-  int Defuzzyfication();
-  int GetFuzzySetParam(int set_nr, double &left, double &midleft, double &midright, double &right);
+        ///Fuzzy functions
+        double TrapeziumFuzz(int which_fuzzy_set, double input_val);
+        int Fuzzyfication();
+        int Defuzzyfication();
+        int GetFuzzySetParam(int set_nr, double &left, double &midleft, double &midright, double &right);
 
 public:
 
-  int fuzzySetsNr;      /// number of fuzzy sets
-  int rulesNr;      ///number of rules
-  SString fuzzySetString; /// strings containing all fuzzy sets given in f0
-  SString fuzzyRulesString; /// strings containing all fuzzy rules given in f0
+        int fuzzySetsNr;      /// number of fuzzy sets
+        int rulesNr;      ///number of rules
+        SString fuzzySetString; /// strings containing all fuzzy sets given in f0
+        SString fuzzyRulesString; /// strings containing all fuzzy rules given in f0
 
-  NI_FuzzyNeuro() {paramentries=NI_FuzzyNeuro_tab; fuzzySets=defuzzParam=NULL; rulesDef=NULL; rules=NULL;}
-  ~NI_FuzzyNeuro();
-  NeuroImpl* makeNew() { return new NI_FuzzyNeuro(); };
-  void go();
-  int lateinit();
-  /** Function build model based on given genotype and conts number of neurons connected with fuzzy neuro,
-    also checks number of fuzzy neuron inputs.
-    \param genotype genotype to be scanned
-    \param inputs number of fuzzy neuron inputs
-    \param output number of fuzzy neuron outputs (= number of neurons connected to fuzzy neuron)
-    @return success or failure
-  **/
-  static int countOuts(const Model *m, const Neuro *fuzzy);
-  
+        NI_FuzzyNeuro() { paramentries = NI_FuzzyNeuro_tab; fuzzySets = defuzzParam = NULL; rulesDef = NULL; rules = NULL; }
+        ~NI_FuzzyNeuro();
+        NeuroImpl* makeNew() { return new NI_FuzzyNeuro(); };
+        void go();
+        int lateinit();
+        /** Function build model based on given genotype and conts number of neurons connected with fuzzy neuro,
+          also checks number of fuzzy neuron inputs.
+          \param genotype genotype to be scanned
+          \param inputs number of fuzzy neuron inputs
+          \param output number of fuzzy neuron outputs (= number of neurons connected to fuzzy neuron)
+          @return success or failure
+          **/
+        static int countOuts(const Model *m, const Neuro *fuzzy);
+
 };