Source code for pyFTS.models.multivariate.common

import numpy as np
import pandas as pd
from pyFTS.common import FuzzySet, Composite


class MultivariateFuzzySet(Composite.FuzzySet):
    """
    Multivariate Composite Fuzzy Set
    """
    def __init__(self, **kwargs):
        """
        Create an empty composite fuzzy set
        :param name: fuzzy set name
        """
        super(MultivariateFuzzySet, self).__init__("")
        self.sets = {}
        self.target_variable = kwargs.get('target_variable',None)

    def append_set(self, variable, set):
        """
        Appends a new fuzzy set from a new variable

        :param variable: an multivariate.variable instance
        :param set: an common.FuzzySet instance
        """
        self.sets[variable] = set

        if variable == self.target_variable.name:
            self.centroid = set.centroid
            self.upper = set.upper
            self.lower = set.lower

        self.name += set.name

    def set_target_variable(self, variable):
        self.target_variable = variable
        self.centroid = self.sets[variable.name].centroid
        self.upper = self.sets[variable.name].upper
        self.lower = self.sets[variable.name].lower

    def membership(self, x):
        mv = []
        if isinstance(x, (dict, pd.DataFrame)):
            for var in self.sets.keys():
                data = x[var]
                mv.append(self.sets[var].membership(data))
        else:
            mv = [self.sets[self.target_variable.name].membership(x)]

        return np.nanmin(mv)


def fuzzyfy_instance(data_point, var, tuples=True):
    #try:
    fsets = var.partitioner.fuzzyfy(data_point, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
    if tuples:
        return [(var.name, fs) for fs in fsets]
    else:
        return fsets
    #except Exception as ex:
    # print(data_point)


def fuzzyfy_instance_clustered(data_point, cluster, **kwargs):
    alpha_cut = kwargs.get('alpha_cut', 0.0)
    mode = kwargs.get('mode', 'sets')
    fsets = []
    for fset in cluster.search(data_point, type='name'):
        if cluster.sets[fset].membership(data_point) >= alpha_cut:
            if mode == 'sets':
                fsets.append(fset)
            elif mode =='both':
                fsets.append( (fset, cluster.sets[fset].membership(data_point)) )
    return fsets