74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from pyFTS.common import FuzzySet,SortedCollection
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class ProbabilityDistribution(object):
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def __init__(self,name,nbins,uod,bins=None,labels=None, data=None):
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self.name = name
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self.nbins = nbins
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self.uod = uod
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if bins is None:
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#range = (uod[1] - uod[0])/nbins
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#self.bins = np.arange(uod[0],uod[1],range).tolist()
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self.bins = np.linspace(uod[0], uod[1], nbins).tolist()
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self.labels = [str(k) for k in self.bins]
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else:
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self.bins = bins
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self.labels = labels
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self.index = SortedCollection.SortedCollection(iterable=sorted(self.bins))
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self.distribution = {}
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self.count = 0
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for k in self.bins: self.distribution[k] = 0
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if data is not None: self.append(data)
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def append(self, values):
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for k in values:
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v = self.index.find_ge(k)
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self.distribution[v] += 1
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self.count += 1
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def density(self, values):
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ret = []
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for k in values:
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v = self.index.find_ge(k)
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ret.append(self.distribution[v] / self.count)
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return ret
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def entropy(self):
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h = -sum([self.distribution[k] * np.log(self.distribution[k]) if self.distribution[k] > 0 else 0
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for k in self.bins])
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return h
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def empiricalloglikelihood(self):
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_s = 0
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for k in self.bins:
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if self.distribution[k] > 0:
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_s += np.log(self.distribution[k])
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return _s
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def pseudologlikelihood(self, data):
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densities = self.density(data)
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_s = 0
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for k in densities:
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if k > 0:
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_s += np.log(k)
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return _s
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def plot(self,axis=None,color="black",tam=[10, 6]):
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if axis is None:
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fig = plt.figure(figsize=tam)
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axis = fig.add_subplot(111)
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ys = [self.distribution[k]/self.count for k in self.bins]
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axis.plot(self.bins, ys,c=color, label=self.name)
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axis.set_xlabel('Universe of Discourse')
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axis.set_ylabel('Probability')
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