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Probability distributions metrics

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Petrônio Cândido de Lima e Silva 2017-02-19 01:02:59 -03:00
parent 47b1cb57c9
commit ef84d50346
3 changed files with 35 additions and 3 deletions
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20
benchmarks/ProbabilityDistribution.py
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@ -43,6 +43,16 @@ class ProbabilityDistribution(object):
for k in self.bins]) for k in self.bins])
return h return h
def crossentropy(self,q):
h = -sum([self.distribution[k] * np.log(q.distribution[k]) if self.distribution[k] > 0 else 0
for k in self.bins])
return h
def kullbackleiblerdivergence(self,q):
h = sum([self.distribution[k] * np.log(self.distribution[k]/q.distribution[k]) if self.distribution[k] > 0 else 0
for k in self.bins])
return h
def empiricalloglikelihood(self): def empiricalloglikelihood(self):
_s = 0 _s = 0
for k in self.bins: for k in self.bins:
@ -60,6 +70,16 @@ class ProbabilityDistribution(object):
_s += np.log(k) _s += np.log(k)
return _s return _s
def averageloglikelihood(self, data):
densities = self.density(data)
_s = 0
for k in densities:
if k > 0:
_s += np.log(k)
return _s / len(data)
def plot(self,axis=None,color="black",tam=[10, 6]): def plot(self,axis=None,color="black",tam=[10, 6]):
if axis is None: if axis is None:
fig = plt.figure(figsize=tam) fig = plt.figure(figsize=tam)

2
benchmarks/benchmarks.py
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@ -119,7 +119,7 @@ def getPointStatistics(data, models, externalmodels = None, externalforecasts =
ret += " 1 & " ret += " 1 & "
ret += str(round(Measures.rmse(data, externalforecasts[k][:-1]), 2)) + " & " ret += str(round(Measures.rmse(data, externalforecasts[k][:-1]), 2)) + " & "
ret += str(round(Measures.smape(data, externalforecasts[k][:-1]), 2))+ " & " ret += str(round(Measures.smape(data, externalforecasts[k][:-1]), 2))+ " & "
ret += str(round(Measures.UStatistic(np.array(data), np.array(forecasts[:-1])), 2)) ret += str(round(Measures.UStatistic(data, externalforecasts[k][:-1]), 2))
ret += " \\\\ \n" ret += " \\\\ \n"
return ret return ret

16
tests/pfts.py
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@ -47,9 +47,21 @@ from pyFTS.benchmarks import ProbabilityDistribution as dist
forecasts = pfts1.forecast(taiex_treino) forecasts = pfts1.forecast(taiex_treino)
pmf1 = dist.ProbabilityDistribution("Original",10,[min(taiex_treino),max(taiex_treino)],data=forecasts) pmf1 = dist.ProbabilityDistribution("Original",100,[min(taiex_treino),max(taiex_treino)],data=taiex_treino)
print(pmf1) #print(pmf1.entropy())
pmf2 = dist.ProbabilityDistribution("Original",100,[min(taiex_treino),max(taiex_treino)],data=forecasts)
#print(pmf2.entropy())
#print(pmf2.kullbackleiblerdivergence(pmf1))
#print(pmf2.crossentropy(pmf1))
print(pmf1.averageloglikelihood(taiex_treino))
print(pmf2.averageloglikelihood(taiex_treino))
#pfts2 = pfts.ProbabilisticFTS("n = 2") #pfts2 = pfts.ProbabilisticFTS("n = 2")
#pfts2.appendTransformation(diff) #pfts2.appendTransformation(diff)