def forecast(self, data, **kwargs):
ret = []
ndata = self.apply_transformations(data)
for ix in ndata.index:
data_point = ndata.loc[ix]
flrs = self.generate_lhs_flrs(data_point)
mvs = []
mps = []
for flr in flrs:
flrg = mvflrg.FLRG(lhs=flr.LHS)
if flrg.get_key() not in self.flrgs:
#print('hit')
mvs.append(0.)
mps.append(0.)
else:
mvs.append(self.flrgs[flrg.get_key()].get_membership(self.format_data(data_point), self.explanatory_variables))
mps.append(self.flrgs[flrg.get_key()].get_midpoint(self.target_variable.partitioner.sets))
#print('mv', mvs)
#print('mp', mps)
mv = np.array(mvs)
mp = np.array(mps)
ret.append(np.dot(mv,mp.T)/np.sum(mv))
ret = self.target_variable.apply_inverse_transformations(ret,
params=data[self.target_variable.data_label].values)
return ret
def generate_flrg(self, flrs):
for flr in flrs:
flrg = mvflrg.FLRG(lhs=flr.LHS)
if flrg.get_key() not in self.flrgs:
self.flrgs[flrg.get_key()] = flrg
self.flrgs[flrg.get_key()].append_rhs(flr.RHS)
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.nansum(mv)
lo = np.dot(mv, np.array(los).T) / np.nansum(mv)
ret.append([lo, up])
ret = self.target_variable.apply_inverse_transformations(
ret, params=data[self.target_variable.data_label].values)
return ret