Probability density function at FTS

This commit is contained in:
Petrônio Cândido de Lima e Silva 2017-02-14 23:01:44 -02:00
parent 279679b3a4
commit 8787d749ff
5 changed files with 148 additions and 19 deletions

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@ -2,6 +2,7 @@
import numpy as np
import pandas as pd
from pyFTS.common import FuzzySet,SortedCollection
# Autocorrelation function estimative
@ -144,3 +145,44 @@ def crps(targets, densities):
_crps += sum([ (Ff[col][k]-Fa[col][k])**2 for col in densities.columns])
return _crps / float(l * n)
def pdf(data, bins=100):
_mx = max(data)
_mn = min(data)
_pdf = {}
percentiles = np.linspace(_mn, _mx, bins).tolist()
print (percentiles)
index_percentiles = SortedCollection.SortedCollection(iterable=percentiles)
for k in percentiles: _pdf[k] = 0
for k in data:
v = index_percentiles.find_ge(k)
_pdf[v] += 1
norm = sum(list(_pdf.values()))
for k in _pdf: _pdf[k] /= norm
return _pdf
def pdf_fuzzysets(data,sets):
_pdf = {}
for k in sets: _pdf[k.name] = 0
for k in data:
memberships = FuzzySet.fuzzyInstance(k, sets)
for c, fs in enumerate(sets, start=0):
_pdf[fs.name] += memberships[c]
norm = sum(list(_pdf.values()))
for k in _pdf: _pdf[k] /= norm
return _pdf
def entropy(pdf):
h = -sum([pdf[k] * np.log(pdf[k]) for k in pdf])
return h

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@ -32,6 +32,10 @@ def getMaxMembershipFuzzySet(inst, fuzzySets):
mv = fuzzyInstance(inst, fuzzySets)
return fuzzySets[np.argwhere(mv == max(mv))[0, 0]]
def getMaxMembershipFuzzySetIndex(inst, fuzzySets):
mv = fuzzyInstance(inst, fuzzySets)
return np.argwhere(mv == max(mv))[0, 0]
def fuzzySeries(data, fuzzySets):
fts = []

80
fts.py
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@ -1,5 +1,8 @@
import numpy as np
from pyFTS import *
import pandas as pd
from pyFTS import tree
from pyFTS.common import FuzzySet, SortedCollection
from pyFTS.benchmarks import Measures
class FTS(object):
@ -100,3 +103,78 @@ class FTS(object):
for r in sorted(self.flrgs):
tmp = tmp + str(self.flrgs[r]) + "\n"
return tmp
def buildTreeWithoutOrder(self, node, lags, level):
if level not in lags:
return
for s in lags[level]:
node.appendChild(tree.FLRGTreeNode(s))
for child in node.getChildren():
self.buildTreeWithoutOrder(child, lags, level + 1)
def generate_data(self,bins=100):
dim_uod = tuple([bins for k in range(0,self.order)])
dim_fs = tuple([ len(self.sets) for k in range(0, self.order)])
simulation_uod = np.zeros(shape=dim_uod, dtype=float)
simulation_fs = np.zeros(shape=dim_fs, dtype=float)
percentiles = np.linspace(self.sets[0].lower, self.sets[-1].upper, bins).tolist()
pdf_uod = {}
for k in percentiles:
pdf_uod[k] = 0
pdf_fs = {}
for k in self.sets:
pdf_fs[k.name] = 0
index_percentiles = SortedCollection.SortedCollection(iterable=percentiles)
lags = {}
for o in np.arange(0, self.order):
lags[o] = percentiles
# Build the tree with all possible paths
root = tree.FLRGTreeNode(None)
self.buildTreeWithoutOrder(root, lags, 0)
# Trace the possible paths
for p in root.paths():
path = list(reversed(list(filter(None.__ne__, p))))
index_uod = tuple([percentiles.index(k) for k in path])
index_fs = tuple([ FuzzySet.getMaxMembershipFuzzySetIndex(k, self.sets) for k in path])
forecast = self.forecast(path)[0]
simulation_uod[index_uod] = forecast
simulation_fs[index_fs] = forecast
pdf_fs = Measures.pdf_fuzzysets(np.ravel(simulation_fs),self.sets)
pdf_uod = Measures.pdf(np.ravel(simulation_fs), bins=bins)
#tmp_pdf_fs = pd.DataFrame( [[pdf_fs[k] for k in sorted(pdf_fs)]], columns=[k for k in sorted(pdf_fs)])
#tmp_pdf_uod = pd.DataFrame([[pdf_uod[k] for k in sorted(pdf_uod)]], columns=[k for k in sorted(pdf_uod)])
return [pdf_fs, pdf_uod, simulation_fs, simulation_uod ]

11
pfts.py
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@ -378,17 +378,6 @@ class ProbabilisticFTS(ifts.IntervalFTS):
grid[k] += 1
return grid
def buildTreeWithoutOrder(self, node, lags, level):
if level not in lags:
return
for s in lags[level]:
node.appendChild(tree.FLRGTreeNode(s))
for child in node.getChildren():
self.buildTreeWithoutOrder(child, lags, level + 1)
def forecastAheadDistribution(self, data, steps, resolution, parameters=2):
ret = []

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@ -35,24 +35,40 @@ os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSe
diff = Transformations.Differential(1)
fs = Grid.GridPartitionerTrimf(gauss_treino,7)
fs = Grid.GridPartitionerTrimf(gauss_treino,10)
#tmp = chen.ConventionalFTS("")
pfts1 = pfts.ProbabilisticFTS("1")
#pfts1.appendTransformation(diff)
pfts1.train(gauss_treino,fs,1)
pfts2 = pfts.ProbabilisticFTS("n = 2")
pfts1.train(gauss_treino,fs,2)
#pfts2 = pfts.ProbabilisticFTS("n = 2")
#pfts2.appendTransformation(diff)
pfts2.train(gauss_treino,fs,2)
#pfts2.train(gauss_treino,fs,2)
pfts3 = pfts.ProbabilisticFTS("n = 3")
#pfts3 = pfts.ProbabilisticFTS("n = 3")
#pfts3.appendTransformation(diff)
pfts3.train(gauss_treino,fs,3)
#pfts3.train(gauss_treino,fs,3)
densities1 = pfts1.forecastAheadDistribution(gauss_teste[:50],2,1.50, parameters=2)
#densities1 = pfts1.forecastAheadDistribution(gauss_teste[:50],2,1.50, parameters=2)
#print(bchmk.getDistributionStatistics(gauss_teste[:50], [pfts1,pfts2,pfts3], 20, 1.50))
sim_fs, sim_uod =pfts1.generate_data(bins=10)
#print(pdf_fs)
#print(sim_fs)
print(sim_uod)
print(np.ravel(sim_uod))
#print(sim_uod)
#print (Measures.pdf(gauss_treino,bins=10))