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Several bugfixes and improvements in pwfts and ProbabilityDistribution

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This commit is contained in:
Petrônio Cândido 2019-06-23 11:51:40 -03:00
parent 34ed8a1002
commit a0468d0fed
4 changed files with 9 additions and 13 deletions
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4
pyFTS/common/flrg.py
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@ -37,13 +37,9 @@ class FLRG(object):
self.key = "" self.key = ""
for n in names: for n in names:
try:
if len(self.key) > 0: if len(self.key) > 0:
self.key += "," self.key += ","
self.key += n self.key += n
except Exception as ex:
print(self.key, n)
raise ex
return self.key return self.key
def get_membership(self, data, sets): def get_membership(self, data, sets):

2
pyFTS/models/pwfts.py
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@ -171,7 +171,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
return np.nanprod(vals) return np.nanprod(vals)
def generate_lhs_flrg(self, sample, explain=False): def generate_lhs_flrg(self, sample, explain=False):
if not isinstance(sample, (list, np.ndarray)): if not isinstance(sample, (tuple, list, np.ndarray)):
sample = [sample] sample = [sample]
nsample = [self.partitioner.fuzzyfy(k, mode="sets", alpha_cut=self.alpha_cut) nsample = [self.partitioner.fuzzyfy(k, mode="sets", alpha_cut=self.alpha_cut)

3
pyFTS/probabilistic/ProbabilityDistribution.py
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@ -211,7 +211,8 @@ class ProbabilityDistribution(object):
for val in values: for val in values:
try: try:
k = self.bin_index.find_ge(val) k = self.bin_index.find_ge(val)
ret.append(self.cdf[k]) #ret.append(self.cdf[k])
ret.append(self.cdf[val])
except: except:
ret.append(np.nan) ret.append(np.nan)
else: else:

7
pyFTS/tests/general.py
View File

@ -32,16 +32,15 @@ fs = Grid.GridPartitioner(data=train, npart=45)
model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=1) model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=1)
model.fit(train) model.fit(train)
horizon = 10
'''
forecasts = model.predict(test[9:20], type='point') forecasts = model.predict(test[9:20], type='point')
intervals = model.predict(test[9:20], type='interval') intervals = model.predict(test[9:20], type='interval')
distributions = model.predict(test[9:20], type='distribution') distributions = model.predict(test[9:20], type='distribution')
horizon = 10
forecasts = model.predict(test[9:20], type='point', steps_ahead=horizon) forecasts = model.predict(test[9:20], type='point', steps_ahead=horizon)
intervals = model.predict(test[9:20], type='interval', steps_ahead=horizon) intervals = model.predict(test[9:20], type='interval', steps_ahead=horizon)
'''
distributions = model.predict(test[9:20], type='distribution', steps_ahead=horizon) distributions = model.predict(test[9:20], type='distribution', steps_ahead=horizon)