added new functionality to set constant variables

setup.py

@@ -8,7 +8,7 @@
 setup(
   name = 'simpful',
   packages = ['simpful'], # this must be the same as the name above
-  version = '2.3.6',
+  version = '2.4.0',
   description = 'A user-friendly Python library for fuzzy logic',
   author = 'Marco S. Nobile',
   author_email = 'm.s.nobile@tue.nl',

simpful/simpful.py

@@ -188,9 +188,9 @@ class FuzzySystem(object):
 		Creates a new fuzzy system.
 
 		Args:
-			operators: a list of strings, specifying fuzzy operators to be used instead of defaults.
-			Currently supported operators: 'AND_PRODUCT'.
+			operators: a list of strings, specifying fuzzy operators to be used instead of defaults. Currently supported operators: 'AND_PRODUCT'.
 			show_banner: True/False, toggles display of banner.
+			sanitize_input: sanitize variables' names to eliminate non-accepted characters (under development).
 			verbose: True/False, toggles verbose mode.
 	"""
 
@@ -202,7 +202,9 @@ def __init__(self, operators=None, show_banner=True, sanitize_input=False, verbo
 		self._crispvalues = {}
 		self._outputfunctions = {}
 		self._outputfuzzysets = {}
-		if show_banner: self._banner()
+
+		self._constants = []
+
 		self._operators = operators
 
 		self._detected_type = None
@@ -211,6 +213,8 @@ def __init__(self, operators=None, show_banner=True, sanitize_input=False, verbo
 		if sanitize_input and verbose:
 			print (" * Warning: Simpful rules sanitization is enabled, please pay attention to possible collisions of symbols.")
 
+		if show_banner: self._banner()
+
 	def _banner(self):
 		import pkg_resources
 		vrs = pkg_resources.get_distribution('simpful').version 
@@ -241,6 +245,23 @@ def set_variable(self, name, value, verbose=False):
 		except ValueError:
 			raise Exception("ERROR: specified value for "+name+" is not an integer or float: "+value)
 
+	def set_constant(self, name, value, verbose=False):
+		"""
+		Sets the numerical value of a linguistic variable to a constant value (i.e. ignore fuzzy inference).
+
+		Args:
+			name: name of the linguistic variables to be set to a constant value.
+			value: numerical value to be set.
+			verbose: True/False, toggles verbose mode.
+		"""
+		if self._sanitize_input: name = self._sanitize(name)
+		try: 
+			value = float(value)
+			self._variables[name] = value
+			self._constants.append(name)
+			if verbose: print(" * Variable %s set to a constant value %f" % (name, value))
+		except ValueError:
+			raise Exception("ERROR: specified value for "+name+" is not an integer or float: "+value)
 
 	def add_rules_from_file(self, path, verbose=False):
 		"""
@@ -487,15 +508,26 @@ def Sugeno_inference(self, terms=None, ignore_errors=False, verbose=False):
 		Returns:
 			a dictionary, containing as keys the variables' names and as values their numerical inferred values.
 		"""
-		# default: inference on ALL rules/terms
 		if self._sanitize and terms is not None: 
 			terms = [self._sanitize(term) for term in terms]
+
+		# default: inference on ALL rules/terms
 		if terms == None:
 			temp = [rule[1][0] for rule in self._rules] 
 			terms= list(set(temp))
 
 		array_rules = array(self._rules, dtype='object')
-		result = self.mediate(terms, array_rules.T[0], array_rules.T[1], ignore_errors=ignore_errors)
+		if len(self._constants)==0:
+			result = self.mediate(terms, array_rules.T[0], array_rules.T[1], ignore_errors=ignore_errors)
+		else:
+			#remove constant variables from list of variables to infer
+			ncost_terms = [t for t in terms if t not in self._constants]
+			result = self.mediate(ncost_terms, array_rules.T[0], array_rules.T[1], ignore_errors=ignore_errors)
+			#add values of constant variables
+			cost_terms = [t for t in terms if t in self._constants]
+			for name in cost_terms:
+				result[name] = self._variables[name]
+
 		return result
 
 
@@ -512,22 +544,33 @@ def Mamdani_inference(self, terms=None, ignore_errors=False, verbose=False, subd
 		Returns:
 			a dictionary, containing as keys the variables' names and as values their numerical inferred values.
 		"""
-
-		# default: inference on ALL rules/terms
 		if self._sanitize and terms is not None: 
 			terms = [self._sanitize(term) for term in terms]
+
+		# default: inference on ALL rules/terms
 		if terms == None:
 			temp = [rule[1][0] for rule in self._rules] 
 			terms= list(set(temp))
 
 		array_rules = array(self._rules, dtype=object)
-		result = self.mediate_Mamdani( terms, array_rules.T[0], array_rules.T[1], ignore_errors=ignore_errors, verbose=verbose , subdivisions=subdivisions)
+		if len(self._constants)==0:
+			result = self.mediate_Mamdani(terms, array_rules.T[0], array_rules.T[1], ignore_errors=ignore_errors, verbose=verbose , subdivisions=subdivisions)
+		else:
+			#remove constant variables from list of variables to infer
+			ncost_terms = [t for t in terms if t not in self._constants]
+			result = self.mediate_Mamdani(ncost_terms, array_rules.T[0], array_rules.T[1], ignore_errors=ignore_errors, verbose=verbose , subdivisions=subdivisions)
+			#add values of constant variables
+			cost_terms = [t for t in terms if t in self._constants]
+			for name in cost_terms:
+				result[name] = self._variables[name]
+
 		return result
 
 
 	def probabilistic_inference(self, terms=None, ignore_errors=False, verbose=False):
 		raise NotImplementedError()
 
+
 	def inference(self, terms=None, ignore_errors=False, verbose=False, subdivisions=1000):
 		"""
 		Performs the fuzzy inference, trying to automatically choose the correct inference engine.
@@ -551,7 +594,6 @@ def inference(self, terms=None, ignore_errors=False, verbose=False, subdivisions
 			raise Exception("ERROR: simpful could not detect the model type, please use either Sugeno_inference() or Mamdani_inference() methods.")
 
 
-
 	def produce_figure(self, outputfile='output.pdf'):
 		"""
 		Plots the membership functions of each linguistic variable contained in the fuzzy system.
@@ -593,7 +635,16 @@ def produce_figure(self, outputfile='output.pdf'):
 
 
 	def aggregate(self, list_variables, function):
-		# TEST
+		"""
+		Performs a fuzzy aggregation of linguistic variables contained in a FuzzySystem object.
+
+		Args:
+			list_variables: list of linguistic variables names in the FuzzySystem object to aggregate.
+			function: pointer to an aggregation function. The function must accept as an argument a list of membership values.
+
+		Returns:
+			the aggregated membership values.
+		""" 
 		memberships = []
 		for variable, fuzzyset in list_variables.items():
 			value = self._variables[variable]