Source code for pyFTS.partitioners.partitioner

from pyFTS.common import FuzzySet, Membership
import numpy as np
import matplotlib.pylab as plt


[docs]class Partitioner(object): """ Universe of Discourse partitioner. Split data on several fuzzy sets """ def __init__(self, **kwargs): """ Universe of Discourse partitioner scheme. Split data on several fuzzy sets """ self.name = kwargs.get('name',"") """partitioner name""" self.partitions = kwargs.get('npart', 10) """The number of universe of discourse partitions, i.e., the number of fuzzy sets that will be created""" self.sets = {} self.membership_function = kwargs.get('func', Membership.trimf) """Fuzzy membership function (pyFTS.common.Membership)""" self.setnames = kwargs.get('names', None) """list of partitions names. If None is given the partitions will be auto named with prefix""" self.prefix = kwargs.get('prefix', 'A') """prefix of auto generated partition names""" self.transformation = kwargs.get('transformation', None) """data transformation to be applied on data""" self.indexer = kwargs.get('indexer', None) self.variable = kwargs.get('variable', None) self.type = kwargs.get('type', 'common') self.ordered_sets = None if kwargs.get('preprocess',True): data = kwargs.get('data',[None]) if self.indexer is not None: ndata = self.indexer.get_data(data) else: ndata = data if self.transformation is not None: ndata = self.transformation.apply(ndata) else: ndata = data if self.indexer is not None: ndata = self.indexer.get_data(ndata) _min = np.nanmin(ndata) if _min == -np.inf: ndata[ndata == -np.inf] = 0 _min = np.nanmin(ndata) self.min = float(_min * 1.1 if _min < 0 else _min * 0.9) _max = np.nanmax(ndata) self.max = float(_max * 1.1 if _max > 0 else _max * 0.9) self.sets = self.build(ndata) if self.ordered_sets is None and self.setnames is not None: self.ordered_sets = self.setnames else: self.ordered_sets = FuzzySet.set_ordered(self.sets) del(ndata)
[docs] def build(self, data): """ Perform the partitioning of the Universe of Discourse :param data: training data :return: """ pass
[docs] def get_name(self, counter): """ Find the name of the fuzzy set given its counter id. :param counter: The number of the fuzzy set :return: String """ return self.prefix + str(counter) if self.setnames is None else self.setnames[counter]
[docs] def lower_set(self): """ Return the fuzzy set on lower bound of the universe of discourse. :return: Fuzzy Set """ return self.sets[self.ordered_sets[0]]
[docs] def upper_set(self): """ Return the fuzzy set on upper bound of the universe of discourse. :return: Fuzzy Set """ return self.sets[self.ordered_sets[-1]]
[docs] def plot(self, ax): """ Plot the partitioning using the Matplotlib axis ax :param ax: Matplotlib axis """ ax.set_title(self.name) ax.set_ylim([0, 1]) ax.set_xlim([self.min, self.max]) ticks = [] x = [] for key in self.sets.keys(): s = self.sets[key] if s.type == 'common': self.plot_set(ax, s) elif s.type == 'composite': for ss in s.sets: self.plot_set(ax, ss) ticks.append(str(round(s.centroid,0))+'\n'+s.name) x.append(s.centroid) ax.xaxis.set_ticklabels(ticks) ax.xaxis.set_ticks(x)
[docs] def plot_set(self, ax, s): """ Plot an isolate fuzzy set on Matplotlib axis :param ax: Matplotlib axis :param s: Fuzzy Set """ if s.mf == Membership.trimf: ax.plot([s.parameters[0], s.parameters[1], s.parameters[2]], [0, s.alpha, 0]) elif s.mf == Membership.gaussmf: tmpx = [kk for kk in np.arange(s.lower, s.upper)] tmpy = [s.membership(kk) for kk in np.arange(s.lower, s.upper)] ax.plot(tmpx, tmpy) elif s.mf == Membership.trapmf: ax.plot(s.parameters, [0, s.alpha, s.alpha, 0])
def __str__(self): """ Return a string representation of the partitioner, the list of fuzzy sets and their parameters :return: """ tmp = self.name + ":\n" for key in self.sets.keys(): tmp += str(self.sets[key])+ "\n" return tmp def __len__(self): """ Return the number of partitions :return: number of partitions """ return self.partitions