94 lines
2.2 KiB
Python
94 lines
2.2 KiB
Python
#!/usr/bin/python
|
|
# -*- coding: utf8 -*-
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import math
|
|
from operator import itemgetter
|
|
from pyFTS.common import FLR, FuzzySet, SortedCollection
|
|
from pyFTS import fts
|
|
|
|
class EnsembleFTS(fts.FTS):
|
|
def __init__(self, order, name, **kwargs):
|
|
super(EnsembleFTS, self).__init__("Ensemble FTS")
|
|
self.shortname = "Ensemble FTS " + name
|
|
self.name = "Ensemble FTS"
|
|
self.flrgs = {}
|
|
self.hasPointForecasting = True
|
|
self.hasIntervalForecasting = True
|
|
self.hasDistributionForecasting = True
|
|
self.isHighOrder = True
|
|
self.models = []
|
|
self.parameters = []
|
|
|
|
def train(self, data, sets, order=1,parameters=None):
|
|
|
|
pass
|
|
|
|
def forecast(self, data, **kwargs):
|
|
|
|
ndata = np.array(self.doTransformations(data))
|
|
|
|
l = len(ndata)
|
|
|
|
ret = []
|
|
|
|
for k in np.arange(self.order - 1, l):
|
|
pass
|
|
|
|
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
|
|
|
|
return ret
|
|
|
|
def forecastInterval(self, data, **kwargs):
|
|
|
|
ndata = np.array(self.doTransformations(data))
|
|
|
|
l = len(ndata)
|
|
|
|
ret = []
|
|
|
|
for k in np.arange(self.order - 1, l):
|
|
pass
|
|
|
|
return ret
|
|
|
|
def forecastAhead(self, data, steps, **kwargs):
|
|
pass
|
|
|
|
def forecastAheadInterval(self, data, steps, **kwargs):
|
|
pass
|
|
|
|
|
|
def getGridClean(self, resolution):
|
|
grid = {}
|
|
|
|
if len(self.transformations) == 0:
|
|
_min = self.sets[0].lower
|
|
_max = self.sets[-1].upper
|
|
else:
|
|
_min = self.original_min
|
|
_max = self.original_max
|
|
|
|
for sbin in np.arange(_min,_max, resolution):
|
|
grid[sbin] = 0
|
|
|
|
return grid
|
|
|
|
def gridCount(self, grid, resolution, index, interval):
|
|
#print(interval)
|
|
for k in index.inside(interval[0],interval[1]):
|
|
#print(k)
|
|
grid[k] += 1
|
|
return grid
|
|
|
|
def gridCountPoint(self, grid, resolution, index, point):
|
|
k = index.find_ge(point)
|
|
# print(k)
|
|
grid[k] += 1
|
|
return grid
|
|
|
|
def forecastAheadDistribution(self, data, steps, **kwargs):
|
|
pass
|
|
|