77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
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import numpy as np
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import math
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import random as rnd
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import functools, operator
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from pyFTS.common import FuzzySet, Membership
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# 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,”
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# Technol. Forecast. Social Change, vol. 73, no. 5, pp. 524–542, Jun. 2006.
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def splitBelow(data,threshold):
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return [k for k in data if k <= threshold]
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def splitAbove(data,threshold):
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return [k for k in data if k > threshold]
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def PMF(data, threshold):
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a = sum([1.0 for k in splitBelow(data,threshold)])
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b = sum([1.0 for k in splitAbove(data, threshold)])
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l = len(data)
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return [a / l, b / l]
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def entropy(data, threshold):
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pmf = PMF(data, threshold)
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return - sum([pmf[0] * math.log(pmf[0]), pmf[1] * math.log(pmf[1])])
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def informationGain(data, thres1, thres2):
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return entropy(data, thres1) - entropy(data, thres2)
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def bestSplit(data, npart):
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if len(data) < 2:
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return None
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count = 2
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ndata = list(set(data))
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ndata.sort()
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threshold = 0
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while informationGain(data, ndata[count - 1], ndata[count]) <= 0:
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threshold = ndata[count]
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count += 1
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rem = npart % 2
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if (npart - rem)/2 > 1:
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p1 = splitBelow(data,threshold)
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p2 = splitAbove(data,threshold)
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if len(p1) > len(p2):
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np1 = (npart - rem)/2 + rem
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np2 = (npart - rem)/2
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else:
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np1 = (npart - rem) / 2
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np2 = (npart - rem) / 2 + rem
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return [ threshold, bestSplit(p1, np1 ), bestSplit(p2, np2 ) ]
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else:
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return threshold
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def EntropyPartitionerTrimf(data, npart, prefix="A"):
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dmax = max(data)
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dmax += dmax * 0.10
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dmin = min(data)
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dmin -= dmin * 0.10
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sets = [dmin, bestSplit(data, npart), dmax]
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sets.sort()
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for c in np.arange(1, len(sets) - 1):
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sets.append(FuzzySet.FuzzySet(prefix + str(c), Membership.trimf,
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[round(sets[c - 1], 3), round(sets[c], 3),
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round(sets[c + 1], 3)],round(sets[c], 3)))
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return sets
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