def forecast(self, ndata, **kwargs):
l = 1
if self.partitioner is not None:
ordered_sets = self.partitioner.ordered_sets
else:
ordered_sets = FuzzySet.set_ordered(self.sets)
ndata = np.array(ndata)
l = len(ndata)
ret = []
for k in np.arange(0, l):
actual = FuzzySet.get_maximum_membership_fuzzyset(
ndata[k], self.sets, ordered_sets)
if actual.name not in self.flrgs:
ret.append(actual.centroid)
else:
flrg = self.flrgs[actual.name]
mp = flrg.get_midpoints(self.sets)
ret.append(mp.dot(flrg.weights()))
return ret
def forecast(self, ndata, **kwargs):
explain = kwargs.get('explain',False)
l = len(ndata) if not explain else 1
ret = []
for k in np.arange(0, l):
actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets)
if explain:
print("Fuzzyfication:\n\n {} -> {} \n".format(ndata[k], actual.name))
if actual.name not in self.flrgs:
ret.append(actual.centroid)
if explain:
print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".format(actual.name, actual.name,actual.centroid))
else:
_flrg = self.flrgs[actual.name]
mp = _flrg.get_midpoint(self.sets)
ret.append(mp)
if explain:
print("Rules:\n\n {} \t Midpoint: {} \n".format(str(_flrg), mp))
print("Deffuzyfied value: {} \n".format(mp))
return ret
def generate_indexed_flrs(sets, indexer, data, transformation=None):
"""
Create a season-indexed ordered FLR set from a list of fuzzy sets with recurrence
:param sets: fuzzy sets
:param indexer: seasonality indexer
:param data: original data
:return: ordered list of FLR
"""
flrs = []
index = indexer.get_season_of_data(data)
ndata = indexer.get_data(data)
if transformation is not None:
ndata = transformation.apply(ndata)
for k in np.arange(1, len(ndata)):
lhs = FuzzySet.get_maximum_membership_fuzzyset(ndata[k - 1], sets)
rhs = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], sets)
season = index[k]
flr = IndexedFLR(season, lhs, rhs)
flrs.append(flr)
return flrs
def forecast(self, ndata, **kwargs):
explain = kwargs.get('explain', False)
if self.partitioner is not None:
ordered_sets = self.partitioner.ordered_sets
else:
ordered_sets = FuzzySet.set_ordered(self.sets)
ndata = np.array(ndata)
l = len(ndata) if not explain else 1
ret = []
for k in np.arange(0, l):
actual = FuzzySet.get_maximum_membership_fuzzyset(
ndata[k], self.sets, ordered_sets)
if explain:
print("Fuzzyfication:\n\n {} -> {} \n\n".format(
ndata[k], actual.name))
if actual.name not in self.flrgs:
ret.append(actual.centroid)
if explain:
print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {} \n\n".
format(actual.name, actual.name, actual.centroid))
else:
flrg = self.flrgs[actual.name]
mp = flrg.get_midpoints(self.sets)
final = mp.dot(flrg.weights(self.sets))
ret.append(final)
if explain:
print("Rules:\n\n {} \n\n ".format(str(flrg)))
print("Midpoints: \n\n {}\n\n".format(mp))
print("Deffuzyfied value: {} \n".format(final))
return ret
def forecast(self, data, **kwargs):
ordered_sets = FuzzySet.set_ordered(self.sets)
ret = []
index = self.indexer.get_season_of_data(data)
ndata = self.indexer.get_data(data)
for k in np.arange(0, len(data)):
flrg = self.flrgs[str(index[k])]
d = FuzzySet.get_maximum_membership_fuzzyset(
ndata[k], self.sets, ordered_sets)
mp = self.get_midpoints(flrg, d)
ret.append(sum(mp) / len(mp))
return ret
def forecast(self, ndata, **kwargs):
l = len(ndata)
ret = []
for k in np.arange(0, l):
mv = FuzzySet.fuzzyfy_instance(ndata[k], self.sets)
actual = FuzzySet.get_maximum_membership_fuzzyset(
ndata[k],
self.sets) #self.sets[np.argwhere(mv == max(mv))[0, 0]]
if actual.name not in self.flrgs:
ret.append(actual.centroid)
else:
_flrg = self.flrgs[actual.name]
ret.append(_flrg.get_midpoint(self.sets))
return ret