sam/pyFTS
1
0
Fork 0
Code Issues 3 Pull Requests Actions Packages Projects Releases Wiki Activity

Replacing the binary_search for a KDTree on fuzzyfication and other minor improvements on partitioner

Browse Source
This commit is contained in:
Petrônio Cândido 2019-04-09 15:17:36 -03:00
parent 6b8607cf3c
commit d3fa1ea534
3 changed files with 139 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
pyFTS/common/FuzzySet.py
View File

@ -7,6 +7,7 @@ class FuzzySet(object):
"""
Fuzzy Set
"""
def __init__(self, name, mf, parameters, centroid, alpha=1.0, **kwargs):
"""
Create a Fuzzy Set
@ -23,15 +24,15 @@ class FuzzySet(object):
"""The alpha cut value"""
self.type = kwargs.get('type', 'common')
"""The fuzzy set type (common, composite, nonstationary, etc)"""
self.variable = kwargs.get('variable',None)
self.variable = kwargs.get('variable', None)
"""In multivariate time series, indicate for which variable this fuzzy set belogs"""
self.Z = None
"""Partition function in respect to the membership function"""
if parameters is not None:
if self.mf == Membership.gaussmf:
self.lower = parameters[0] - parameters[1]*3
self.upper = parameters[0] + parameters[1]*3
self.lower = parameters[0] - parameters[1] * 3
self.upper = parameters[0] + parameters[1] * 3
elif self.mf == Membership.sigmf:
k = (parameters[1] / (2 * parameters[0]))
self.lower = parameters[1] - k
@ -61,7 +62,7 @@ class FuzzySet(object):
"""
return self.mf(self.transform(x), self.parameters) * self.alpha
def partition_function(self,uod=None, nbins=100):
def partition_function(self, uod=None, nbins=100):
"""
Calculate the partition function over the membership function.
@ -101,7 +102,7 @@ def __binary_search(x, fuzzy_sets, ordered_sets):
fs2 = ordered_sets[midpoint + 1] if midpoint < max_len else ordered_sets[max_len]
if fuzzy_sets[fs1].centroid <= fuzzy_sets[fs].transform(x) <= fuzzy_sets[fs2].centroid:
return (midpoint-1, midpoint, midpoint+1)
return (midpoint - 1, midpoint, midpoint + 1)
elif midpoint <= 1:
return [0]
elif midpoint >= max_len:
@ -125,7 +126,7 @@ def fuzzyfy(data, partitioner, **kwargs):
:keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
:keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership
values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )
:returns a list with the fuzzyfied values, depending on the mode
"""
alpha_cut = kwargs.get('alpha_cut', 0.)
@ -295,6 +296,7 @@ def grant_bounds(data, fuzzy_sets, ordered_sets):
else:
return data
def check_bounds(data, fuzzy_sets, ordered_sets):
if data < fuzzy_sets[ordered_sets[0]].lower:
return fuzzy_sets[ordered_sets[0]]

121
pyFTS/partitioners/partitioner.py
View File

@ -1,5 +1,6 @@
from pyFTS.common import FuzzySet, Membership
import numpy as np
from scipy.spatial import KDTree
import matplotlib.pylab as plt
@ -34,6 +35,8 @@ class Partitioner(object):
"""Anonymous function used to extract a single primitive type from an object instance"""
self.ordered_sets = None
"""A ordered list of the fuzzy sets names, sorted by their middle point"""
self.kdtree = None
"""A spatial index to help in fuzzyfication"""
if kwargs.get('preprocess',True):
@ -105,8 +108,95 @@ class Partitioner(object):
"""
return self.sets[self.ordered_sets[-1]]
def build_index(self):
points = []
#self.index = {}
for ct, key in enumerate(self.ordered_sets):
fset = self.sets[key]
points.append([fset.lower, fset.centroid, fset.upper])
#self.index[ct] = fset.name
import sys
sys.setrecursionlimit(100000)
self.kdtree = KDTree(points)
sys.setrecursionlimit(1000)
def fuzzyfy(self, data, **kwargs):
return FuzzySet.fuzzyfy(data, self, **kwargs)
"""
A general method for fuzzyfication.
:param data: input value to be fuzzyfied
:keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode='sets')
:keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
:keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership
values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )
:returns a list with the fuzzyfied values, depending on the mode
"""
if isinstance(data, (list, np.ndarray)):
ret = []
for inst in data:
mv = self.fuzzyfy(inst, **kwargs)
return ret
alpha_cut = kwargs.get('alpha_cut', 0.)
mode = kwargs.get('mode', 'sets')
method = kwargs.get('method', 'fuzzy')
nearest = self.search(data, type='index')
mv = np.zeros(self.partitions)
for ix in nearest:
tmp = self[ix].membership(data)
mv[ix] = tmp if tmp >= alpha_cut else 0.
ix = np.ravel(np.argwhere(mv > 0.))
if ix.size == 0:
mv[self.check_bounds(data)] = 1.
if method == 'fuzzy' and mode == 'vector':
return mv
elif method == 'fuzzy' and mode == 'sets':
ix = np.ravel(np.argwhere(mv > 0.))
sets = [self.ordered_sets[i] for i in ix]
return sets
elif method == 'maximum' and mode == 'sets':
mx = max(mv)
ix = np.ravel(np.argwhere(mv == mx))
return self.ordered_sets[ix[0]]
def check_bounds(self, data):
if data < self.min:
return 0
elif data > self.max:
return self.partitions-1
def search(self, data, type='index', results=3):
'''
Perform a search for the nearest fuzzy sets of the point 'data'. This function were designed to work with several
overlapped fuzzy sets.
:param data: the value to search for the nearest fuzzy sets
:param type: the return type: 'index' for the fuzzy set indexes or 'name' for fuzzy set names.
:param results: the number of nearest fuzzy sets to return
:return: a list with the nearest fuzzy sets
'''
if self.kdtree is None:
self.build_index()
_, ix = self.kdtree.query([data, data, data], results)
if type == 'name':
return [self.ordered_sets[k] for k in sorted(ix)]
else:
return sorted(ix)
def plot(self, ax, rounding=0):
"""
@ -167,3 +257,32 @@ class Partitioner(object):
:return: number of partitions
"""
return self.partitions
def __getitem__(self, item):
"""
Return a fuzzy set by its order or its name.
:param item: If item is an integer then it represents the fuzzy set index (order), if it was a string then
it represents the fuzzy set name.
:return: the fuzzy set
"""
if isinstance(item, (int, np.int, np.int8, np.int16, np.int32, np.int64)):
if item < 0 or item >= self.partitions:
raise ValueError("The fuzzy set index must be between 0 and {}.".format(self.partitions))
return self.sets[self.ordered_sets[item]]
elif isinstance(item, str):
if item not in self.sets:
raise ValueError("The fuzzy set with name {} does not exist.".format(item))
return self.sets[item]
else:
raise ValueError("The parameter 'item' must be an integer or a string and the value informed was {} of type {}!".format(item, type(item)))
def __iter__(self):
"""
Iterate over the fuzzy sets, ordered by its midpoints.
:return: An iterator over the fuzzy sets.
"""
for key in self.ordered_sets:
yield self.sets[key]

25
pyFTS/tests/general.py
View File

@ -14,19 +14,16 @@ from pyFTS.benchmarks import benchmarks as bchmk, Measures
from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts
from pyFTS.common import Transformations, Membership
from pyFTS.data import artificial
from pyFTS.data import TAIEX
fs = Grid.GridPartitioner(data=TAIEX.get_data(), npart=23)
print(fs.min, fs.max)
tmp = fs.search(5500)
print(tmp)
tmp = fs.fuzzyfy(5500, method='fuzzy', alpha_cut=0.3)
print(tmp)
"""
cd = artificial.SignalEmulator()\
.stationary_gaussian(0,.2,length=10, it=1)\
.incremental_gaussian(0.5, 0,start=5,length=5)\
.blip()\
.stationary_gaussian(3,.2,length=10, it=1, additive=False)
print([round(k,3) for k in cd.run()])
"""
signal = artificial.SignalEmulator()\
.stationary_gaussian(1,0.2,length=130,it=10)\
.periodic_gaussian('sinoidal',100, 0.5,0.5,10,1,start=100,length=2000)\
.blip()\
.run()