Source code for pyFTS.common.flrg

import numpy as np


[docs]class FLRG(object): """ Fuzzy Logical Relationship Group Group a set of FLR's with the same LHS. Represents the temporal patterns for time t+1 (the RHS fuzzy sets) when the LHS pattern is identified on time t. """ def __init__(self, order, **kwargs): self.LHS = None """Left Hand Side of the rule""" self.RHS = None """Right Hand Side of the rule""" self.order = order """Number of lags on LHS""" self.midpoint = None self.lower = None self.upper = None self.key = None
[docs] def append_rhs(self, set, **kwargs): pass
[docs] def get_key(self): """Returns a unique identifier for this FLRG""" if self.key is None: if isinstance(self.LHS, (list, set)): names = [c for c in self.LHS] elif isinstance(self.LHS, dict): names = [self.LHS[k] for k in self.LHS.keys()] else: names = [self.LHS] self.key = "" for n in names: if len(self.key) > 0: self.key += "," self.key = self.key + n return self.key
[docs] def get_membership(self, data, sets): """ Returns the membership value of the FLRG for the input data :param data: input data :param sets: fuzzy sets :return: the membership value """ ret = 0.0 if isinstance(self.LHS, (list, set)): if len(self.LHS) == len(data): ret = np.nanmin([sets[self.LHS[ct]].membership(dat) for ct, dat in enumerate(data)]) else: ret = sets[self.LHS].membership(data) return ret
[docs] def get_midpoint(self, sets): """ Returns the midpoint value for the RHS fuzzy sets :param sets: fuzzy sets :return: the midpoint value """ if self.midpoint is None: self.midpoint = np.nanmean(self.get_midpoints(sets)) return self.midpoint
[docs] def get_midpoints(self, sets): if isinstance(self.RHS, (list, set)): return np.array([sets[s].centroid for s in self.RHS]) elif isinstance(self.RHS, dict): return np.array([sets[s].centroid for s in self.RHS.keys()])
[docs] def get_lower(self, sets): """ Returns the lower bound value for the RHS fuzzy sets :param sets: fuzzy sets :return: lower bound value """ if self.lower is None: if isinstance(self.RHS, list): self.lower = min([sets[rhs].lower for rhs in self.RHS]) elif isinstance(self.RHS, dict): self.lower = min([sets[self.RHS[s]].lower for s in self.RHS.keys()]) return self.lower
[docs] def get_upper(self, sets): """ Returns the upper bound value for the RHS fuzzy sets :param sets: fuzzy sets :return: upper bound value """ if self.upper is None: if isinstance(self.RHS, list): self.upper = max([sets[rhs].upper for rhs in self.RHS]) elif isinstance(self.RHS, dict): self.upper = max([sets[self.RHS[s]].upper for s in self.RHS.keys()]) return self.upper
def __len__(self): return len(self.RHS)
[docs] def reset_calculated_values(self): self.midpoint = None self.upper = None self.lower = None