Several bugfixes and improvements in pwfts and ProbabilityDistribution

pyFTS/common/flrg.py

@@ -37,13 +37,9 @@ class FLRG(object):
             self.key = ""
 
             for n in names:
-                try:
-                    if len(self.key) > 0:
-                        self.key += ","
-                    self.key += n
-                except Exception as ex:
-                    print(self.key, n)
-                    raise ex
+                if len(self.key) > 0:
+                    self.key += ","
+                self.key += n
         return self.key
 
     def get_membership(self, data, sets):

pyFTS/models/pwfts.py

@@ -171,7 +171,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         return np.nanprod(vals)
 
     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]
 
         nsample = [self.partitioner.fuzzyfy(k, mode="sets", alpha_cut=self.alpha_cut)

pyFTS/probabilistic/ProbabilityDistribution.py

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

pyFTS/tests/general.py

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