"""
antecedent_consequent.py : Contains Antecedent and Consequent classes.
"""
import numpy as np
import networkx as nx
from .state import StatefulProperty
from .fuzzyvariable import FuzzyVariable
def _accu_max(*args):
"""
Wrapper for fuzzy accumulation method.
"""
return np.max(args)
[docs]class Antecedent(FuzzyVariable):
"""
Antecedent (input/sensor) variable for a fuzzy control system.
Parameters
----------
universe : array-like
Universe variable. Must be 1-dimensional and convertible to a NumPy
array.
label : string
Name of the universe variable.
"""
# Customized subclass of `FuzzyVariable`
input = StatefulProperty(None)
[docs] def __init__(self, universe, label):
"""""" + Antecedent.__doc__
super(Antecedent, self).__init__(universe, label)
self.__name__ = 'Antecedent'
@property
def graph(self):
"""
NetworkX graph which connects this Antecedent with its Term(s).
"""
g = nx.DiGraph()
for t in self.terms.values():
g.add_path([self, t])
return g
[docs]class Consequent(FuzzyVariable):
"""
Consequent (output/control) variable for a fuzzy control system.
Parameters
----------
universe : array-like
Universe variable. Must be 1-dimensional and convertible to a NumPy
array.
label : string
Name of the universe variable.
Notes
-----
The ``label`` string chosen must be unique among Antecedents and
Consequents in the ``ControlSystem``.
"""
# Customized subclass of `FuzzyVariable`
output = StatefulProperty(None)
[docs] def __init__(self, universe, label):
"""""" + Consequent.__doc__
super(Consequent, self).__init__(universe, label)
self.__name__ = 'Consequent'
# Default accumulation method is to take the max of any cut
self.accumulation_method = _accu_max
@property
def graph(self):
"""
NetworkX graph which connects this Consequent with its Term(s).
"""
g = nx.DiGraph()
for t in self.terms.values():
g.add_path([t, self])
return g
Source