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Improving ProbabilityDistribution indexes

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This commit is contained in:
Petrônio Cândido 2019-05-27 16:01:03 -03:00
parent 8e4b9b4121
commit e3f5faee2b
1 changed files with 28 additions and 15 deletions
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43
pyFTS/probabilistic/ProbabilityDistribution.py
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@ -1,7 +1,8 @@
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyFTS.common import FuzzySet,SortedCollection,tree
from pyFTS.common import FuzzySet
from scipy.spatial import KDTree
from pyFTS.probabilistic import kde
@ -41,13 +42,13 @@ class ProbabilityDistribution(object):
self.nbins = kwargs.get("num_bins", 100)
if self.bins is None:
self.bins = np.linspace(int(self.uod[0]), int(self.uod[1]), int(self.nbins)).tolist()
self.bins = np.linspace(self.uod[0], self.uod[1], int(self.nbins)).tolist()
self.labels = [str(k) for k in self.bins]
if self.uod is not None:
self.resolution = (self.uod[1] - self.uod[0]) / self.nbins
self.bin_index = SortedCollection.SortedCollection(iterable=sorted(self.bins))
self.bin_index = KDTree(self.bins)
self.quantile_index = None
self.distribution = {}
self.cdf = None
@ -67,7 +68,8 @@ class ProbabilityDistribution(object):
:param value: A value in the universe of discourse from the distribution
:param density: The probability density to assign to the value
"""
k = self.bin_index.find_ge(value)
_, ix = self.bin_index.query([value], 1)
k = self.labels[ix]
self.distribution[k] = density
def append(self, values):
@ -78,7 +80,8 @@ class ProbabilityDistribution(object):
"""
if self.type == "histogram":
for k in values:
v = self.bin_index.find_ge(k)
_, ix = self.bin_index.query([k], 1)
v = self.labels[ix]
self.distribution[v] += 1
self.count += 1
else:
@ -96,8 +99,12 @@ class ProbabilityDistribution(object):
"""
if self.type == "histogram":
for interval in intervals:
for k in self.bin_index.inside(interval[0], interval[1]):
self.distribution[k] += 1
_, ix1 = self.bin_index.query([interval[0]], 1)
_, ix2 = self.bin_index.query([interval[1]], 1)
for k in range(ix1, ix2):
v = self.labels[k]
self.distribution[v] += 1
self.count += 1
def density(self, values):
@ -116,13 +123,15 @@ class ProbabilityDistribution(object):
for k in values:
if self.type == "histogram":
v = self.bin_index.find_ge(k)
_, ix = self.bin_index.query([k], 1)
v = self.labels[ix]
ret.append(self.distribution[v] / (self.count + 1e-5))
elif self.type == "KDE":
v = self.kde.probability(k, self.data)
ret.append(v)
else:
v = self.bin_index.find_ge(k)
_, ix = self.bin_index.query([k], 1)
v = self.labels[ix]
ret.append(self.distribution[v])
if scalar:
@ -149,7 +158,7 @@ class ProbabilityDistribution(object):
self.distribution = dist
self.labels = [str(k) for k in self.bins]
self.bin_index = SortedCollection.SortedCollection(iterable=sorted(self.bins))
self.bin_index = KDTree(self.bins)
self.quantile_index = None
self.cdf = None
self.qtl = None
@ -178,7 +187,7 @@ class ProbabilityDistribution(object):
_keys = [float(k) for k in sorted(self.qtl.keys())]
self.quantile_index = SortedCollection.SortedCollection(iterable=_keys)
self.quantile_index = KDTree(_keys)
def cumulative(self, values):
"""
@ -194,10 +203,12 @@ class ProbabilityDistribution(object):
if isinstance(values, list):
ret = []
for val in values:
k = self.bin_index.find_ge(val)
_, ix = self.bin_index.query([val], 1)
k = self.labels[ix]
ret.append(self.cdf[k])
else:
k = self.bin_index.find_ge(values)
_, ix = self.bin_index.query([values], 1)
k = self.labels[ix]
return self.cdf[values]
def quantile(self, values):
@ -214,10 +225,12 @@ class ProbabilityDistribution(object):
if isinstance(values, list):
ret = []
for val in values:
k = self.quantile_index.find_ge(val)
_, ix = self.quantile_index.query([val], 1)
k = self.labels[ix]
ret.append(self.qtl[str(k)][0])
else:
k = self.quantile_index.find_ge(values)
_, ix = self.quantile_index.query([values], 1)
k = self.labels[ix]
ret = self.qtl[str(k)]
return ret