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Renaming PFTS to PWFTS

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Petrônio Cândido de Lima e Silva 2017-02-22 16:18:15 -03:00
parent ef84d50346
commit c708430313
3 changed files with 24 additions and 24 deletions
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26
benchmarks/benchmarks.py
View File

@ -12,7 +12,7 @@ from mpl_toolkits.mplot3d import Axes3D
from pyFTS.benchmarks import Measures, naive, ResidualAnalysis, ProbabilityDistribution
from pyFTS.partitioners import Grid
from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang, pfts, ifts
from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang, pwfts, ifts
colors = ['grey', 'rosybrown', 'maroon', 'red','orange', 'yellow', 'olive', 'green',
'cyan', 'blue', 'darkblue', 'purple', 'darkviolet']
@ -29,7 +29,7 @@ def allPointForecasters(data_train, data_test, partitions, max_order=3, statisti
if models is None:
models = [naive.Naive, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, pfts.ProbabilisticFTS]
sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]
objs = []
@ -76,16 +76,16 @@ def allPointForecasters(data_train, data_test, partitions, max_order=3, statisti
lcolors.insert(0,'black')
pmfs = []
pmfs.append(
ProbabilityDistribution.ProbabilityDistribution("Original", 100, [min(data_train), max(data_train)], data=data_train) )
ProbabilityDistribution.ProbabilityDistribution("Original", 100, [min(data_test), max(data_test)], data=data_test) )
for m in objs:
forecasts = m.forecast(data_train)
forecasts = m.forecast(data_test)
pmfs.append(
ProbabilityDistribution.ProbabilityDistribution(m.shortname, 100, [min(data_train), max(data_train)],
ProbabilityDistribution.ProbabilityDistribution(m.shortname, 100, [min(data_test), max(data_test)],
data=forecasts))
print(getProbabilityDistributionStatistics(pmfs,data_train))
print(getProbabilityDistributionStatistics(pmfs,data_test))
plotProbabilityDistributions(pmfs, lcolors)
plotProbabilityDistributions(pmfs, lcolors,tam=tam)
def getPointStatistics(data, models, externalmodels = None, externalforecasts = None, indexers=None):
@ -138,7 +138,7 @@ def getProbabilityDistributionStatistics(pmfs, data):
def allIntervalForecasters(data_train, data_test, partitions, max_order=3,save=False, file=None, tam=[20, 5],
models=None, transformation=None):
if models is None:
models = [ifts.IntervalFTS, pfts.ProbabilisticFTS]
models = [ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS]
objs = []
@ -243,8 +243,8 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False,
Util.showAndSaveImage(fig, file, save, lgd=legends)
def plotProbabilityDistributions(pmfs,lcolors):
fig = plt.figure(figsize=[15, 7])
def plotProbabilityDistributions(pmfs,lcolors,tam=[15, 7]):
fig = plt.figure(figsize=tam)
ax = fig.add_subplot(111)
for k,m in enumerate(pmfs,start=0):
@ -257,7 +257,7 @@ def plotProbabilityDistributions(pmfs,lcolors):
def allAheadForecasters(data_train, data_test, partitions, start, steps, resolution = None, max_order=3,save=False, file=None, tam=[20, 5],
models=None, transformation=None, option=2):
if models is None:
models = [pfts.ProbabilisticFTS]
models = [pwfts.ProbabilisticWeightedFTS]
if resolution is None: resolution = (max(data_train) - min(data_train)) / 100
@ -775,7 +775,7 @@ def pftsExploreOrderAndPartitions(data,save=False, file=None):
axes[2].set_title('Interval Forecasts by Order')
for order in np.arange(1, 6):
fts = pfts.ProbabilisticFTS("")
fts = pwfts.ProbabilisticWeightedFTS("")
fts.shortname = "n = " + str(order)
fts.train(data, data_fs1, order=order)
point_forecasts = fts.forecast(data)
@ -797,7 +797,7 @@ def pftsExploreOrderAndPartitions(data,save=False, file=None):
for partitions in np.arange(5, 11):
data_fs = Grid.GridPartitionerTrimf(data, partitions)
fts = pfts.ProbabilisticFTS("")
fts = pwfts.ProbabilisticWeightedFTS("")
fts.shortname = "q = " + str(partitions)
fts.train(data, data_fs, 1)
point_forecasts = fts.forecast(data)

14
pfts.py → pwfts.py
View File

@ -9,9 +9,9 @@ from pyFTS.common import FLR, FuzzySet, SortedCollection
from pyFTS import hofts, ifts, tree
class ProbabilisticFLRG(hofts.HighOrderFLRG):
class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
def __init__(self, order):
super(ProbabilisticFLRG, self).__init__(order)
super(ProbabilisticWeightedFLRG, self).__init__(order)
self.RHS = {}
self.frequencyCount = 0.0
@ -41,9 +41,9 @@ class ProbabilisticFLRG(hofts.HighOrderFLRG):
return self.strLHS() + " -> " + tmp2
class ProbabilisticFTS(ifts.IntervalFTS):
class ProbabilisticWeightedFTS(ifts.IntervalFTS):
def __init__(self, name):
super(ProbabilisticFTS, self).__init__("PFTS")
super(ProbabilisticWeightedFTS, self).__init__("PFTS")
self.shortname = "PFTS " + name
self.name = "Probabilistic FTS"
self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
@ -71,7 +71,7 @@ class ProbabilisticFTS(ifts.IntervalFTS):
l = len(data)
for k in np.arange(self.order, l):
if self.dump: print("FLR: " + str(k))
flrg = ProbabilisticFLRG(self.order)
flrg = ProbabilisticWeightedFLRG(self.order)
sample = data[k - self.order: k]
@ -116,7 +116,7 @@ class ProbabilisticFTS(ifts.IntervalFTS):
l = len(flrs)
for k in np.arange(self.order, l+1):
if self.dump: print("FLR: " + str(k))
flrg = ProbabilisticFLRG(self.order)
flrg = ProbabilisticWeightedFLRG(self.order)
for kk in np.arange(k - self.order, k):
flrg.appendLHS(flrs[kk].LHS)
@ -134,7 +134,7 @@ class ProbabilisticFTS(ifts.IntervalFTS):
def addNewPFLGR(self,flrg):
if flrg.strLHS() not in self.flrgs:
tmp = ProbabilisticFLRG(self.order)
tmp = ProbabilisticWeightedFLRG(self.order)
for fs in flrg.LHS: tmp.appendLHS(fs)
tmp.appendRHS(flrg.LHS[-1])
self.flrgs[tmp.strLHS()] = tmp;

8
tests/pfts.py → tests/pwfts.py
View File

@ -11,7 +11,7 @@ from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
from pyFTS.partitioners import Grid
from pyFTS.common import FLR,FuzzySet,Membership,Transformations
from pyFTS import fts,hofts,ifts,pfts,tree, chen
from pyFTS import fts,hofts,ifts,pwfts,tree, chen
from pyFTS.benchmarks import benchmarks as bchmk
from pyFTS.benchmarks import Measures
from numpy import random
@ -39,7 +39,7 @@ fs = Grid.GridPartitionerTrimf(taiex_treino,10)
#tmp = chen.ConventionalFTS("")
pfts1 = pfts.ProbabilisticFTS("1")
pfts1 = pwfts.ProbabilisticWeightedFTS("1")
#pfts1.appendTransformation(diff)
pfts1.train(taiex_treino,fs,1)
@ -63,11 +63,11 @@ print(pmf1.averageloglikelihood(taiex_treino))
print(pmf2.averageloglikelihood(taiex_treino))
#pfts2 = pfts.ProbabilisticFTS("n = 2")
#pfts2 = pfts.ProbabilisticWeightedFTS("n = 2")
#pfts2.appendTransformation(diff)
#pfts2.train(gauss_treino,fs,2)
#pfts3 = pfts.ProbabilisticFTS("n = 3")
#pfts3 = pfts.ProbabilisticWeightedFTS("n = 3")
#pfts3.appendTransformation(diff)
#pfts3.train(gauss_treino,fs,3)