pyFTS/fts.py

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import numpy as np
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import pandas as pd
from pyFTS import tree
from pyFTS.common import FuzzySet, SortedCollection
class FTS(object):
"""
Fuzzy Time Series
"""
def __init__(self, order, name, **kwargs):
"""
Create a Fuzzy Time Series model
:param order: model order
:param name: model name
:param kwargs: model specific parameters
"""
self.sets = {}
self.flrgs = {}
self.order = order
self.shortname = name
self.name = name
self.detail = name
self.is_high_order = False
self.min_order = 1
self.has_seasonality = False
self.has_point_forecasting = True
self.has_interval_forecasting = False
self.has_probability_forecasting = False
self.is_multivariate = False
self.dump = False
self.transformations = []
self.transformations_param = []
self.original_max = 0
self.original_min = 0
self.partitioner = None
self.auto_update = False
self.benchmark_only = False
def fuzzy(self, data):
"""
Fuzzify a data point
:param data: data point
:return: maximum membership fuzzy set
"""
best = {"fuzzyset": "", "membership": 0.0}
for f in self.sets:
fset = self.sets[f]
if best["membership"] <= fset.membership(data):
best["fuzzyset"] = fset.name
best["membership"] = fset.membership(data)
return best
def forecast(self, data, **kwargs):
"""
Point forecast one step ahead
:param data: time series with minimal length to the order of the model
:param kwargs:
:return:
"""
pass
def forecastInterval(self, data, **kwargs):
"""
Interval forecast one step ahead
:param data:
:param kwargs:
:return:
"""
pass
def forecastDistribution(self, data, **kwargs):
"""
Probabilistic forecast one step ahead
:param data:
:param kwargs:
:return:
"""
pass
def forecastAhead(self, data, steps, **kwargs):
"""
Point forecast n steps ahead
:param data:
:param steps:
:param kwargs:
:return:
"""
ndata = [k for k in self.doTransformations(data[- self.order:])]
ret = []
for k in np.arange(0,steps):
tmp = self.forecast(ndata[-self.order:], **kwargs)
if isinstance(tmp,(list, np.ndarray)):
tmp = tmp[0]
ret.append(tmp)
ndata.append(tmp)
ret = self.doInverseTransformations(ret, params=[ndata[self.order - 1:]])
return ret
def forecastAheadInterval(self, data, steps, **kwargs):
"""
Interval forecast n steps ahead
:param data:
:param steps:
:param kwargs:
:return:
"""
pass
def forecastAheadDistribution(self, data, steps, **kwargs):
"""
Probabilistic forecast n steps ahead
:param data:
:param steps:
:param kwargs:
:return:
"""
pass
def train(self, data, sets, order=1, parameters=None):
"""
:param data:
:param sets:
:param order:
:param parameters:
:return:
"""
pass
def getMidpoints(self, flrg):
ret = np.array([s.centroid for s in flrg.RHS])
return ret
def appendTransformation(self, transformation):
if transformation is not None:
self.transformations.append(transformation)
def doTransformations(self,data,params=None,updateUoD=False, **kwargs):
ndata = data
if updateUoD:
if min(data) < 0:
self.original_min = min(data) * 1.1
else:
self.original_min = min(data) * 0.9
if max(data) > 0:
self.original_max = max(data) * 1.1
else:
self.original_max = max(data) * 0.9
if len(self.transformations) > 0:
if params is None:
params = [ None for k in self.transformations]
for c, t in enumerate(self.transformations, start=0):
ndata = t.apply(ndata,params[c])
return ndata
def doInverseTransformations(self, data, params=None, **kwargs):
ndata = data
if len(self.transformations) > 0:
if params is None:
params = [None for k in self.transformations]
for c, t in enumerate(reversed(self.transformations), start=0):
ndata = t.inverse(ndata, params[c], **kwargs)
return ndata
def __str__(self):
tmp = self.name + ":\n"
for r in sorted(self.flrgs):
tmp = tmp + str(self.flrgs[r]) + "\n"
return tmp
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def __len__(self):
return len(self.flrgs)
def len_total(self):
return sum([len(k) for k in self.flrgs])
def get_empty_grid(self, _min, _max, resolution):
grid = {}
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for sbin in np.arange(_min,_max, resolution):
grid[sbin] = 0
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return grid
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def getGridClean(self, resolution):
if len(self.transformations) == 0:
_min = self.sets[0].lower
_max = self.sets[-1].upper
else:
_min = self.original_min
_max = self.original_max
return self.get_empty_grid(_min, _max, resolution)
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def gridCount(self, grid, resolution, index, interval):
#print(point_to_interval)
for k in index.inside(interval[0],interval[1]):
grid[k] += 1
return grid
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def gridCountPoint(self, grid, resolution, index, point):
if not isinstance(point, (list, np.ndarray)):
point = [point]
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for p in point:
k = index.find_ge(p)
grid[k] += 1
return grid
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