pyFTS/hwang.py

import numpy as np
from pyFTS import *

class HighOrderFTS(fts.FTS):
	def __init__(self,order,name):
		super(HighOrderFTS, self).__init__(order,name)
        
	def forecast(self,data,t):
		cn = np.array([0.0 for k in range(len(self.sets))])
		ow = np.array([[0.0 for k in range(len(self.sets))] for z in range(self.order-1)])
		rn = np.array([[0.0 for k in range(len(self.sets))] for z in range(self.order-1)])
		ft = np.array([0.0 for k in range(len(self.sets))])

		for s in range(len(self.sets)):
			cn[s] = self.sets[s].membership(data[t]) 
			for w in range(self.order-1):
				ow[w,s] = self.sets[s].membership(data[t-w]) 
				rn[w,s] = ow[w,s] * cn[s]
				ft[s] = max(ft[s],rn[w,s])
		mft =  max(ft)
		out = 0.0
		count = 0.0
		for s in range(len(self.sets)):
			if ft[s] == mft:
				out = out + self.sets[s].centroid
				count = count + 1.0
		return out / count


	def train(self, data, sets):
		self.sets = sets
    
	def predict(self,data,t):
		return self.forecast(data,t)

	def predictDiff(self,data,t):
		return data[t] + self.forecast(common.differential(data),t)