import numpy as np
from pyFTS import *

class SeasonalFLRG(fts.FTS):
	def __init__(self,seasonality):
		self.LHS = seasonality
		self.RHS = []

	def append(self,c):
		self.RHS.append(c)

	def __str__(self):
		tmp = str(self.LHS) + " -> "
		tmp2 = ""
		for c in sorted(self.RHS, key=lambda s: s.name):
			if len(tmp2) > 0:
				tmp2 = tmp2 + ","
			tmp2 = tmp2 + c.name 
		return tmp + tmp2
		

class SeasonalFTS(fts.FTS):
	def __init__(self,name):
		super(SeasonalFTS, self).__init__(1,name)
		self.seasonality = 1
        
        
	def generateFLRG(self, flrs):
		flrgs = []
		season = 1
		for flr in flrs:
			if len(flrgs) < self.seasonality:
				flrgs.append(SeasonalFLRG(season))
			
			flrgs[season].append(flr.RHS)
			
			season = (season + 1) % (self.seasonality + 1)
			
			if season == 0: season = 1
			
		return (flrgs)
		
	def train(self, data, sets, seasonality):
		self.sets = sets
		self.seasonality = seasonality
		tmpdata = common.fuzzySeries(data,sets)
		flrs = common.generateRecurrentFLRs(tmpdata)
		self.flrgs = self.generateFLRG(flrs)
        
	def forecast(self,data):
		
		ndata = np.array(data)
		
		l = len(ndata)
		
		ret = []
		
		for k in np.arange(1,l):
			flrg = self.flrgs[ data[k] ]
			
			mp = self.getMidpoints(flrg)
				
			ret.append(sum(mp)/len(mp))
			
		return ret