pyFTS/partitioners/Entropy.py
Petrônio Cândido de Lima e Silva a95b806a73 - Adding gaussmf and trapmf support on partitioners
- Parallel util for partitioners
2017-03-31 20:34:12 -03:00

105 lines
3.2 KiB
Python
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

import numpy as np
import math
import random as rnd
import functools, operator
from pyFTS.common import FuzzySet, Membership
from pyFTS.partitioners import partitioner
# C. H. Cheng, R. J. Chang, and C. A. Yeh, “Entropy-based and trapezoidal fuzzification-based fuzzy time series approach for forecasting IT project cost,”
# Technol. Forecast. Social Change, vol. 73, no. 5, pp. 524542, Jun. 2006.
def splitBelow(data,threshold):
return [k for k in data if k <= threshold]
def splitAbove(data,threshold):
return [k for k in data if k > threshold]
def PMF(data, threshold):
a = sum([1.0 for k in splitBelow(data,threshold)])
b = sum([1.0 for k in splitAbove(data, threshold)])
l = len(data)
return [a / l, b / l]
def entropy(data, threshold):
pmf = PMF(data, threshold)
if pmf[0] == 0 or pmf[1] == 0:
return 1
else:
return - sum([pmf[0] * math.log(pmf[0]), pmf[1] * math.log(pmf[1])])
def informationGain(data, thres1, thres2):
return entropy(data, thres1) - entropy(data, thres2)
def bestSplit(data, npart):
if len(data) < 2:
return None
count = 1
ndata = list(set(data))
ndata.sort()
l = len(ndata)
threshold = 0
try:
while count < l and informationGain(data, ndata[count - 1], ndata[count]) <= 0:
threshold = ndata[count]
count += 1
except IndexError:
print(threshold)
print (ndata)
print (count)
rem = npart % 2
if (npart - rem)/2 > 1:
p1 = splitBelow(data,threshold)
p2 = splitAbove(data,threshold)
if len(p1) > len(p2):
np1 = (npart - rem)/2 + rem
np2 = (npart - rem)/2
else:
np1 = (npart - rem) / 2
np2 = (npart - rem) / 2 + rem
tmp = [threshold]
for k in bestSplit(p1, np1 ): tmp.append(k)
for k in bestSplit(p2, np2 ): tmp.append(k)
return tmp
else:
return [threshold]
class EntropyPartitioner(partitioner.Partitioner):
def __init__(self, data, npart, func = Membership.trimf, transformation=None):
super(EntropyPartitioner, self).__init__("Entropy", data, npart, func=func, transformation=transformation)
def build(self, data):
sets = []
partitions = bestSplit(data, self.partitions)
partitions.append(self.min)
partitions.append(self.max)
partitions = list(set(partitions))
partitions.sort()
for c in np.arange(1, len(partitions) - 1):
if self.membership_function == Membership.trimf:
sets.append(FuzzySet.FuzzySet(self.prefix + str(c), Membership.trimf,
[partitions[c - 1], partitions[c], partitions[c + 1]],partitions[c]))
elif self.membership_function == Membership.trapmf:
b1 = (partitions[c] - partitions[c - 1])/2
b2 = (partitions[c + 1] - partitions[c]) / 2
sets.append(FuzzySet.FuzzySet(self.prefix + str(c), Membership.trapmf,
[partitions[c - 1], partitions[c] - b1,
partitions[c] - b2, partitions[c + 1]],
partitions[c]))
return sets