import numpy as np
from pyFTS.models import hofts
from pyFTS.models.nonstationary import common,nsfts
from pyFTS.common import FLR, flrg, tree
[docs]class HighOrderNonstationaryFLRG(hofts.HighOrderFTS):
"""Conventional High Order Fuzzy Logical Relationship Group"""
def __init__(self, order, **kwargs):
super(HighOrderNonstationaryFLRG, self).__init__(order, **kwargs)
self.LHS = []
self.RHS = {}
self.strlhs = ""
[docs] def append_rhs(self, c, **kwargs):
if c not in self.RHS:
self.RHS[c] = c
[docs] def append_lhs(self, c):
self.LHS.append(c)
def __str__(self):
tmp = ""
for c in sorted(self.RHS):
if len(tmp) > 0:
tmp = tmp + ","
tmp = tmp + c
return self.get_key() + " -> " + tmp
def __len__(self):
return len(self.RHS)
[docs]class ConditionalVarianceFTS(hofts.HighOrderFTS):
def __init__(self, **kwargs):
super(ConditionalVarianceFTS, self).__init__(**kwargs)
self.name = "Conditional Variance FTS"
self.shortname = "CVFTS "
self.detail = ""
self.flrgs = {}
self.is_high_order = False
if self.partitioner is not None:
self.append_transformation(self.partitioner.transformation)
self.min_stack = [0,0,0]
self.max_stack = [0,0,0]
self.uod_clip = False
self.order = 1
self.min_order = 1
self.max_lag = 1
self.inputs = []
self.forecasts = []
self.residuals = []
self.variance_residual = 0.
self.mean_residual = 0.
self.memory_window = kwargs.get("memory_window",5)
self.sets = {}
[docs] def train(self, ndata, **kwargs):
tmpdata = common.fuzzySeries(ndata, self.sets,
self.partitioner.ordered_sets,
method='fuzzy', const_t=0)
flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.generate_flrg(flrs)
self.forecasts = self.forecast(ndata, no_update=True)
self.residuals = np.array(ndata[1:]) - np.array(self.forecasts[:-1])
self.variance_residual = np.var(self.residuals) # np.max(self.residuals
self.mean_residual = np.mean(self.residuals)
self.residuals = self.residuals[-self.memory_window:].tolist()
self.forecasts = self.forecasts[-self.memory_window:]
self.inputs = np.array(ndata[-self.memory_window:]).tolist()
[docs] def generate_flrg(self, flrs, **kwargs):
for flr in flrs:
if flr.LHS.name in self.flrgs:
self.flrgs[flr.LHS.name].append_rhs(flr.RHS.name)
else:
self.flrgs[flr.LHS.name] = nsfts.ConventionalNonStationaryFLRG(flr.LHS.name)
self.flrgs[flr.LHS.name].append_rhs(flr.RHS.name)
def _smooth(self, a):
return .1 * a[0] + .3 * a[1] + .6 * a[2]
[docs] def perturbation_factors(self, data, **kwargs):
npart = len(self.partitioner.sets)
_max = 0
_min = 0
if data < self.original_min:
_min = data - self.original_min if data < 0 else self.original_min - data
elif data > self.original_max:
_max = data - self.original_max if data > 0 else self.original_max - data
self.min_stack.pop(2)
self.min_stack.insert(0, _min)
_min = min(self.min_stack)
self.max_stack.pop(2)
self.max_stack.insert(0, _max)
_max = max(self.max_stack)
_range = (_max - _min)/2
translate = np.linspace(_min, _max, npart)
var = np.std(self.residuals)
var = 0 if var < 1 else var
loc = (self.mean_residual + np.mean(self.residuals))
location = [_range + w + loc + k for k in np.linspace(-var,var, npart) for w in translate]
scale = [abs(location[0] - location[2])]
scale.extend([abs(location[k - 1] - location[k + 1]) for k in np.arange(1, npart)])
scale.append(abs(location[-1] - location[-3]))
perturb = [[location[k], scale[k]] for k in np.arange(npart)]
return perturb
[docs] def perturbation_factors__old(self, data):
npart = len(self.partitioner.sets)
_max = 0
_min = 0
if data < self.original_min:
_min = data - self.original_min if data < 0 else self.original_min - data
elif data > self.original_max:
_max = data - self.original_max if data > 0 else self.original_max - data
self.min_stack.pop(2)
self.min_stack.insert(0,_min)
_min = min(self.min_stack)
self.max_stack.pop(2)
self.max_stack.insert(0, _max)
_max = max(self.max_stack)
location = np.linspace(_min, _max, npart)
scale = [abs(location[0] - location[2])]
scale.extend([abs(location[k-1] - location[k+1]) for k in np.arange(1, npart)])
scale.append(abs(location[-1] - location[-3]))
perturb = [[location[k], scale[k]] for k in np.arange(0, npart)]
return perturb
def _fsset_key(self, ix):
return self.partitioner.ordered_sets[ix]
def _affected_sets(self, sample, perturb):
affected_sets = [[ct, self.sets[self._fsset_key(ct)].membership(sample, perturb[ct])]
for ct in np.arange(len(self.partitioner.sets))
if self.sets[self._fsset_key(ct)].membership(sample, perturb[ct]) > 0.0]
if len(affected_sets) == 0:
if sample < self.partitioner.lower_set().get_lower(perturb[0]):
affected_sets.append([0, 1])
elif sample > self.partitioner.upper_set().get_upper(perturb[-1]):
affected_sets.append([len(self.sets) - 1, 1])
return affected_sets
[docs] def forecast(self, ndata, **kwargs):
l = len(ndata)
ret = []
no_update = kwargs.get("no_update",False)
for k in np.arange(0, l):
sample = ndata[k]
if not no_update:
perturb = self.perturbation_factors(sample)
else:
perturb = [[0, 1] for k in np.arange(len(self.partitioner.sets))]
affected_sets = self._affected_sets(sample, perturb)
numerator = []
denominator = []
if len(affected_sets) == 1:
ix = affected_sets[0][0]
aset = self.partitioner.ordered_sets[ix]
if aset in self.flrgs:
numerator.append(self.flrgs[aset].get_midpoint(self.sets, perturb[ix]))
else:
fuzzy_set = self.sets[aset]
numerator.append(fuzzy_set.get_midpoint(perturb[ix]))
denominator.append(1)
else:
for aset in affected_sets:
ix = aset[0]
fs = self.partitioner.ordered_sets[ix]
tdisp = perturb[ix]
if fs in self.flrgs:
numerator.append(self.flrgs[fs].get_midpoint(self.sets, tdisp) * aset[1])
else:
fuzzy_set = self.sets[fs]
numerator.append(fuzzy_set.get_midpoint(tdisp) * aset[1])
denominator.append(aset[1])
if sum(denominator) > 0:
pto = sum(numerator) /sum(denominator)
else:
pto = sum(numerator)
ret.append(pto)
if not no_update:
self.forecasts.append(pto)
self.residuals.append(self.inputs[-1] - self.forecasts[-1])
self.inputs.append(sample)
self.inputs.pop(0)
self.forecasts.pop(0)
self.residuals.pop(0)
return ret
[docs] def forecast_interval(self, ndata, **kwargs):
l = len(ndata)
ret = []
for k in np.arange(0, l):
sample = ndata[k]
perturb = self.perturbation_factors(sample)
affected_sets = self._affected_sets(sample, perturb)
upper = []
lower = []
if len(affected_sets) == 1:
ix = affected_sets[0][0]
aset = self.partitioner.ordered_sets[ix]
if aset in self.flrgs:
lower.append(self.flrgs[aset].get_lower(perturb[ix]))
upper.append(self.flrgs[aset].get_upper(perturb[ix]))
else:
fuzzy_set = self.sets[aset]
lower.append(fuzzy_set.get_lower(perturb[ix]))
upper.append(fuzzy_set.get_upper(perturb[ix]))
else:
for aset in affected_sets:
ix = aset[0]
fs = self.partitioner.ordered_sets[ix]
tdisp = perturb[ix]
if fs in self.flrgs:
lower.append(self.flrgs[fs].get_lower(tdisp) * aset[1])
upper.append(self.flrgs[fs].get_upper(tdisp) * aset[1])
else:
fuzzy_set = self.sets[fs]
lower.append(fuzzy_set.get_lower(tdisp) * aset[1])
upper.append(fuzzy_set.get_upper(tdisp) * aset[1])
itvl = [sum(lower), sum(upper)]
ret.append(itvl)
return ret