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)
"""In a multivariate context, the variable that contains this partitioner"""
self.type = kwargs.get('type', 'common')
"""The type of fuzzy sets that are generated by this partitioner"""
self.extractor = kwargs.get('extractor', lambda x: x)
"""Anonymous function used to extract a single primitive type from an object instance"""
self.ordered_sets = None
"""A ordered list of the fuzzy sets names, sorted by their middle point"""
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[:len(self.sets)]
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 fuzzyfy(self, data, **kwargs):
return FuzzySet.fuzzyfy(data, self, **kwargs)
[docs] def plot(self, ax, rounding=0):
"""
Plot the partitioning using the Matplotlib axis ax
:param ax: Matplotlib axis
"""
ax.set_title(self.name)
ax.set_ylim([0, 1.1])
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,rounding))+'\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 in (Membership.gaussmf, Membership.bellmf, Membership.sigmf):
tmpx = np.linspace(s.lower, s.upper, 100)
tmpy = [s.membership(kk) for kk in tmpx]
ax.plot(tmpx, tmpy)
elif s.mf == Membership.trapmf:
ax.plot(s.parameters, [0, s.alpha, s.alpha, 0])
elif s.mf == Membership.singleton:
ax.plot([s.parameters[0],s.parameters[0]], [0, s.alpha])
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
