def forecast(self, ndata, **kwargs):
ret = []
l = len(ndata)
if l <= self.max_lag:
return ndata
for k in np.arange(self.max_lag, l + 1):
flrgs = self.generate_lhs_flrg(ndata[k - self.max_lag:k])
tmp = []
for flrg in flrgs:
if flrg.get_key() not in self.flrgs:
if len(flrg.LHS) > 0:
tmp.append(self.sets[flrg.LHS[-1]].centroid)
else:
flrg = self.flrgs[flrg.get_key()]
tmp.append(flrg.get_midpoint(self.sets))
ret.append(np.nanmean(tmp))
return ret
def forecast(self, data, **kwargs):
ret = []
main_factor = data.ix[:, 0].values
main_key = data.columns[0]
main_fs = list(self.fuzzySetsDict[main_key].values())
l = len(main_factor)
if l <= self.order:
return data
# ndata = self.doTransformations(data)
for k in np.arange(self.order, l + 1):
lags = []
print("Forecasting: instance " + str(k) + " of " + str(l))
for o in range(k - self.order, k):
lhs = []
lhs.append([
set for set in main_fs
if set.membership(main_factor[o]) > 0.0
])
#lhs.append([FuzzySet.getMaxMembershipFuzzySet(main_factor[o], main_fs)])
for c in range(1, len(data.columns)):
sec_key = data.columns[c]
sec_factor = data.ix[:, sec_key].values
sec_fs = list(self.fuzzySetsDict[sec_key].values())
lhs.append([
set for set in sec_fs
if set.membership(sec_factor[o]) > 0.0
])
#lhs.append([FuzzySet.getMaxMembershipFuzzySet(sec_factor[o], sec_fs)])
lags.append(lhs)
tmpflrg = MultivariateHighOrderFLRG(self.order)
tmpflrg.LHS = lags
if tmpflrg.strLHS() not in self.flrgs:
ret.append(tmpflrg.LHS[-1][0][0].centroid)
else:
flrg = self.flrgs[tmpflrg.strLHS()]
ret.append(flrg.get_midpoint())
#ret = self.doInverseTransformations(ret, params=[data[self.order-1:]])
return ret
def forecast(self, ndata, **kwargs):
explain = kwargs.get('explain', False)
ret = []
l = len(ndata) if not explain else self.max_lag + 1
if l < self.max_lag:
return ndata
for k in np.arange(self.max_lag, l+1):
if explain:
print("Fuzzyfication \n")
if not kwargs.get('fuzzyfied', False):
flrgs = self.generate_lhs_flrg(ndata[k - self.max_lag: k], explain)
else:
flrgs = self.generate_lhs_flrg_fuzzyfied(ndata[k - self.max_lag: k], explain)
if explain:
print("Rules:\n")
tmp = []
for flrg in flrgs:
if flrg.get_key() not in self.flrgs:
if len(flrg.LHS) > 0:
mp = self.partitioner.sets[flrg.LHS[-1]].centroid
tmp.append(mp)
if explain:
print("\t {} -> {} (Naïve)\t Midpoint: {}\n".format(str(flrg.LHS), flrg.LHS[-1],
mp))
else:
flrg = self.flrgs[flrg.get_key()]
mp = flrg.get_midpoint(self.partitioner.sets)
tmp.append(mp)
if explain:
print("\t {} \t Midpoint: {}\n".format(str(flrg), mp))
final = np.nanmean(tmp)
ret.append(final)
if explain:
print("Deffuzyfied value: {} \n".format(final))
return ret
def forecast(self, ndata, **kwargs):
explain = kwargs.get('explain', False)
fuzzyfied = kwargs.get('fuzzyfied', False)
mode = kwargs.get('mode', 'mean')
ret = []
l = len(ndata) if not explain else self.max_lag + 1
if l < self.max_lag:
return ndata
elif l == self.max_lag:
l += 1
for k in np.arange(self.max_lag, l):
sample = ndata[k - self.max_lag:k]
if not fuzzyfied:
flrgs = self.generate_lhs_flrg(sample, explain)
else:
flrgs = self.generate_lhs_flrg_fuzzyfied(sample, explain)
midpoints = []
memberships = []
for flrg in flrgs:
if flrg.get_key() not in self.flrgs:
if len(flrg.LHS) > 0:
mp = self.partitioner.sets[flrg.LHS[-1]].centroid
mv = self.partitioner.sets[flrg.LHS[-1]].membership(
sample[-1]) if not fuzzyfied else None
midpoints.append(mp)
memberships.append(mv)
if explain:
self.append_log(
"Rule Matching",
"{} -> {} (Naïve) Midpoint: {}".format(
str(flrg.LHS), flrg.LHS[-1], mp))
else:
flrg = self.flrgs[flrg.get_key()]
mp = flrg.get_midpoint(self.partitioner.sets)
mv = flrg.get_membership(
sample,
self.partitioner.sets) if not fuzzyfied else None
midpoints.append(mp)
memberships.append(mv)
if explain:
self.append_log(
"Rule Matching",
"{}, Midpoint: {} Membership: {}".format(
flrg.get_key(), mp, mv))
if mode == "mean" or fuzzyfied:
final = np.nanmean(midpoints)
if explain:
self.append_log("Deffuzyfication",
"By Mean: {}".format(final))
else:
final = np.dot(midpoints, memberships) / np.nansum(memberships)
if explain:
self.append_log("Deffuzyfication",
"By Memberships: {}".format(final))
ret.append(final)
return ret