pyFTS/partitioners/Entropy.py

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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.
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def splitBelow(data,threshold):
return [k for k in data if k <= threshold]
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def splitAbove(data,threshold):
return [k for k in data if k > threshold]
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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)
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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
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count = 1
ndata = list(set(data))
ndata.sort()
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l = len(ndata)
threshold = 0
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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
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tmp = [threshold]
for k in bestSplit(p1, np1 ): tmp.append(k)
for k in bestSplit(p2, np2 ): tmp.append(k)
return tmp
else:
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return [threshold]
class EntropyPartitioner(partitioner.Partitioner):
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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)
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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,
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partitions[c] + b2, partitions[c + 1]],
partitions[c]))
return sets