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)
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
