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Interval forecasting on MVFTS and WMVFTS

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This commit is contained in:
Petrônio Cândido 2019-03-22 15:07:44 -03:00
parent 838140d0f2
commit 3868bb1c48
3 changed files with 63 additions and 16 deletions
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12
pyFTS/models/hofts.py
View File

@ -73,24 +73,12 @@ class WeightedHighOrderFLRG(flrg.FLRG):
return self.midpoint
def get_lower(self, sets):
"""
Returns the lower bound value for the RHS fuzzy sets
:param sets: fuzzy sets
:return: lower bound value
"""
if self.lower is None:
lw = np.array([sets[s].lower for s in self.RHS.keys()])
self.lower = lw.dot(self.weights())
return self.lower
def get_upper(self, sets):
"""
Returns the upper bound value for the RHS fuzzy sets
:param sets: fuzzy sets
:return: upper bound value
"""
if self.upper is None:
up = np.array([sets[s].upper for s in self.RHS.keys()])
self.upper = up.dot(self.weights())

48
pyFTS/models/multivariate/mvfts.py
View File

@ -145,8 +145,9 @@ class MVFTS(fts.FTS):
mvs.append(0.)
mps.append(0.)
else:
mvs.append(self.flrgs[flrg.get_key()].get_membership(data_point, self.explanatory_variables))
mps.append(self.flrgs[flrg.get_key()].get_midpoint(self.target_variable.partitioner.sets))
_flrg = self.flrgs[flrg.get_key()]
mvs.append(_flrg.get_membership(data_point, self.explanatory_variables))
mps.append(_flrg.get_midpoint(self.target_variable.partitioner.sets))
mv = np.array(mvs)
mp = np.array(mps)
@ -201,6 +202,49 @@ class MVFTS(fts.FTS):
return ret
def forecast_interval(self, data, **kwargs):
ret = []
ndata = self.apply_transformations(data)
c = 0
for index, row in ndata.iterrows() if isinstance(ndata, pd.DataFrame) else enumerate(ndata):
data_point = self.format_data(row)
flrs = self.generate_lhs_flrs(data_point)
mvs = []
ups = []
los = []
for flr in flrs:
flrg = mvflrg.FLRG(lhs=flr.LHS)
if flrg.get_key() not in self.flrgs:
#Naïve approach is applied when no rules were found
if self.target_variable.name in flrg.LHS:
fs = flrg.LHS[self.target_variable.name]
fset = self.target_variable.partitioner.sets[fs]
up = fset.upper
lo = fset.lower
mv = fset.membership(data_point[self.target_variable.name])
mvs.append(mv)
ups.append(up)
los.append(lo)
else:
mvs.append(0.)
ups.append(0.)
los.append(0.)
else:
_flrg = self.flrgs[flrg.get_key()]
mvs.append(_flrg.get_membership(data_point, self.explanatory_variables))
ups.append(_flrg.get_upper(self.target_variable.partitioner.sets))
los.append(_flrg.get_lower(self.target_variable.partitioner.sets))
mv = np.array(mvs)
up = np.dot(mv, np.array(ups).T) / np.sum(mv)
lo = np.dot(mv, np.array(los).T) / np.sum(mv)
ret.append([lo, up])
ret = self.target_variable.apply_inverse_transformations(ret,
params=data[self.target_variable.data_label].values)
return ret
def clone_parameters(self, model):
super(MVFTS, self).clone_parameters(model)

19
pyFTS/models/multivariate/wmvfts.py
View File

@ -33,8 +33,23 @@ class WeightedFLRG(mvflrg.FLRG):
return self.w
def get_midpoint(self, sets):
mp = np.array([sets[c].centroid for c in self.RHS.keys()])
return mp.dot(self.weights())
if self.midpoint is None:
mp = np.array([sets[c].centroid for c in self.RHS.keys()])
self.midpoint = mp.dot(self.weights())
return self.midpoint
def get_lower(self, sets):
if self.lower is None:
lw = np.array([sets[s].lower for s in self.RHS.keys()])
self.lower = lw.dot(self.weights())
return self.lower
def get_upper(self, sets):
if self.upper is None:
up = np.array([sets[s].upper for s in self.RHS.keys()])
self.upper = up.dot(self.weights())
return self.upper
def __str__(self):