139 lines
4.0 KiB
Python
139 lines
4.0 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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from pyFTS.probabilistic import kde
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class ProbabilityDistribution(object):
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"""
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Represents a discrete or continous probability distribution
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If type is histogram, the PDF is discrete
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If type is KDE the PDF is continuous
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"""
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def __init__(self,type = "KDE", **kwargs):
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self.uod = kwargs.get("uod", None)
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self.type = type
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if self.type == "KDE":
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self.kde = kde.KernelSmoothing(kwargs.get("h", 10), kwargs.get("method", "epanechnikov"))
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self.nbins = kwargs.get("num_bins", 100)
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self.bins = kwargs.get("bins", None)
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self.labels = kwargs.get("bins_labels", None)
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if self.bins is None:
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self.bins = np.linspace(int(self.uod[0]), int(self.uod[1]), int(self.nbins)).tolist()
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self.labels = [str(k) for k in self.bins]
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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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self.data = []
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data = kwargs.get("data",None)
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if data is not None:
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self.append(data)
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self.name = kwargs.get("name", "")
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def append(self, values):
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if self.type == "histogram":
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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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else:
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self.data.extend(values)
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self.distribution = {}
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dens = self.density(self.bins)
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for v,d in enumerate(dens):
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self.distribution[self.bins[v]] = d
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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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if self.type == "histogram":
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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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else:
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v = self.kde.probability(k, self.data)
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ret.append(v)
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return ret
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def cummulative(self, values):
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pass
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def quantile(self, qt):
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pass
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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 crossentropy(self,q):
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h = -sum([self.distribution[k] * np.log(q.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 kullbackleiblerdivergence(self,q):
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h = sum([self.distribution[k] * np.log(self.distribution[k]/q.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 averageloglikelihood(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 / len(data)
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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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if self.type == "histogram":
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ys = [self.distribution[k]/self.count for k in self.bins]
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else:
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ys = [self.distribution[k] 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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def __str__(self):
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head = '|'
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body = '|'
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for k in sorted(self.distribution.keys()):
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head += str(round(k,2)) + '\t|'
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body += str(round(self.distribution[k] / self.count,3)) + '\t|'
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return head + '\n' + body
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