Hi Anselm, This link is an explaination of the inference systems Mamdani of Sugeno https://www.mathworks.com/help/fuzzy/types-of-fuzzy-inference-systems.html Sugeno doesn't need a step of defuzzyfication and doesn't need only few computing ressources. It has been designed for being embarqued for process control in very small microcontrolers. It's exactly what I need. In fact, Sugeno can be considered as a Mamdani inference if consequents are singletons. With fuzzylogic, singleton is not a single point but a triangle. Then the result of the inference is not the value of the singleton but can produce intermediate values. For instance, pattern-recognition can use a Domain of only integers the corresponding Sets being only singletons. Many thanks for your interest. Jean Le mercredi 17 septembre 2025 à 07:44 -0700, Anselm Kiefner a écrit :

Hi! I'm glad you like it! It wasn't specifically designed for pattern recognition, but I did use parts of it for that purpose before. I admit I never heard of Sugeno-like fuzzy logic before, so I can't say for sure if it's possible what you imagine, but I can't see why not. The rules work with a dictionary, so it shouldn't be too hard to adapt and return arbitrary objects like functions instead. Can you give an example of what you have in mind? — Reply to this email directly, view it on GitHub [1], or unsubscribe [2]. You are receiving this because you authored the thread.Message ID: <Python-Fuzzylogic/fuzzylogic/repo- ***@***.***>

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Hi Anselm, Using fuzzylogic as a base I've developped: . somes domains with their fussy sets . a method for an inference for pattern-recognition,: a set of rules which mimics the dictionnary of rules of fussylogic IF xxxx AND xxxxx THEN (<symbol>, <credence>) a dictionnary with <symbol> as key of conditions to decide is the candidate category can be accepted. <symbol> IF <minimal credence> The second dictionnary act as an aggregator. Follow the piece of corresponding code : class FuzzyLogicEngine: # From module fuzzylogic # def _domain_info(self, dic): assert type(dic) is dict # key of the Set whose membership is maximal # key = max(dic, key=dic.get) # value of that maximal membership # val = dic[key] return (str(key), val) def _rules_builder(self, text_rules): # a very simple parsing method # rules_set = {} for rule in text_rules: # # rtx = 'IF <domaine> IS <fussy- set> AND <domaine> IS <fussy-set> THEN <catégorie>' # rtx = rule.split() premices = (rtx[1]+'.'+rtx[3], rtx[5]+'.'+rtx[7]) consequence = rtx[9] rules_set[premices] = str(consequence) return rules_set def _activations_builder(self, text_rules): # a very simple parsing method # activations_set = {} for rule in text_rules: # # rtx = '<catégorie> AS <annotation> IF <credence> WITH <ac tion>' # rtx = rule.split() categorie = rtx[0] # category annotation = rtx[2] # annotation credence = rtx[4] # minimal credence action = rtx[6] # action activations_set[categorie] = { 'credence' : float(credence), 'annotation': annotation, 'action' : action } return activations_set def __init__(self, cfg, prm): self.energ_domain = prm.fle_energ_domain self.energ_res = prm.fle_energ_res self.energ_sets = prm.fle_energ_sets self.freqy_domain = prm.fle_freqy_domain self.freqy_res = prm.fle_freqy_res self.freqy_sets = prm.fle_freqy_sets # Domain "Energ" # self.Energ = Domain( self.energ_domain[0], self.energ_domain[1], self.energ_domain[2], res=self.energ_res ) # Fuzzy-sets for "Energ" # self.Energ.low = sigmoid( 1.0, self.energ_sets['low'][0], self.energ_sets['low'][1] ) self.Energ.hig = sigmoid( 1.0, self.energ_sets['hig'][0], self.energ_sets['hig'][1] ) self.Energ.mid = ~(self.Energ.low | self.Energ.hig) # Domain "Freqy" # self.Freqy = Domain( self.freqy_domain[0], self.freqy_domain[1], self.freqy_domain[2], res=self.freqy_res ) # Fuzzy-sets for "Freqy" # self.Freqy.low = sigmoid( 1.0, self.freqy_sets['low'][0], self.freqy_sets['low'][1] ) self.Freqy.hig = sigmoid( 1.0, self.freqy_sets['hig'][0], self.freqy_sets['hig'][1] ) self.Freqy.mid = ~(self.Freqy.low | self.Freqy.hig) # Building the rules # text_rules = prm.fle_categorization_rules self.rules = self._rules_builder(text_rules) # Building the set of the contitions for activation # text_activations = prm.fle_activation_conditions self.activations = self._activations_builder(text_activations) def domains(self): return (self.Energ, self.Freqy) # Fuzzyfication # def energ_info(self, vre): return self._domain_info(self.Energ(vre)) def freqy_info(self, w): return self._domain_info(self.Freqy(w)) # Inference # def inference(self, vre, w): # Getting the key of the best result # ... for the different domains # (ekey, eval) = self._domain_info(self.Energ(vre)) (fkey, fval) = self._domain_info(self.Freqy(w)) # Inference # try: catkey = (str(ekey), str(fkey)) # key to search the can didate category categ = self.rules[catkey] # candidate category credence = min(eval, fval) # Fuzzy "and" to get th e credence except: # no candidate category in the categorization rules # categ = None credence = 0 return (categ, credence) # Validation of the result of the inference # def is_inference_validated(self, categ, credence): if categ and (categ in self.activations) and (credence > self.a ctivations[categ]['credence']): return True else: return False def get_annotation(self, categ): return self.activations[categ]['annotation'] def get_action(self, categ): return self.activations[categ]['action'] The main drawback in that implementation of the inference method is that I have not really implement a Sugeno-like inference because I use the function max() on the domain instead iterating on the dictionnary to access at all the memberships of concern. I am on digging more deep in the API of fuzzylogic. It could be, surely, in fuzzylogic to be capable to choose the inference method to use. A parenthesis: I've experimented with : - sci-kit-fuzzy : very complicated to use - fuzzy-expert : is always in alpha state fuzzylogic is, in comparaison, very great ! Jean -- Jean DEMARTINI ***@***.***>