From 682dd907e66256ea647f2dda91f34f7ad82cbb48 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Petr=C3=B4nio=20C=C3=A2ndido=20de=20Lima=20e=20Silva?=
 <petronio.candido@gmail.com>
Date: Wed, 19 Oct 2016 14:45:11 -0200
Subject: [PATCH] =?UTF-8?q?Refatora=C3=A7=C3=A3o=20dos=20c=C3=B3digos=20pa?=
 =?UTF-8?q?ra=20padronizar=20com=20a=20rfts=20e=20HOFTS?=
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---
 hofts.py | 85 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 85 insertions(+)
 create mode 100644 hofts.py

diff --git a/hofts.py b/hofts.py
new file mode 100644
index 0000000..e2807d6
--- /dev/null
+++ b/hofts.py
@@ -0,0 +1,85 @@
+import numpy as np
+from pyFTS import *
+
+class HighOrderFLRG:
+	def __init__(self,order):
+		self.LHS = []
+		self.RHS = []
+		self.order = order
+		self.strlhs = ""
+
+	def appendRHS(self,c):
+		self.RHS.append(c)
+		
+	def strLHS(self):
+		if len(self.strlhs) == 0:
+			for c in self.LHS:
+				if len(self.strlhs) > 0:
+					self.strlhs = self.strlhs + ","
+				self.strlhs = self.strlhs + c.name
+		return self.strlhs
+	
+	def appendLHS(self,c):
+		self.LHS.append(c)
+
+	def __str__(self):
+		tmp = ""
+		for c in sorted(self.RHS, key=lambda s: s.name):
+			if len(tmp) > 0:
+				tmp = tmp + ","
+			tmp = tmp + c.name
+		return self.strLHS() + " -> " + tmp
+		
+class HighOrderFTS(fts.FTS):
+	def __init__(self,name):
+		super(HighOrderFTS, self).__init__(1,name)
+		self.order = 1
+		
+	def generateFLRG(self, flrs):
+		flrgs = {}
+		l = len(flrs)
+		for k in np.arange(self.order +1, l):
+			flrg = HighOrderFLRG(self.order)
+			
+			for kk in np.arange(k - self.order, k):
+				flrg.appendLHS( flrs[kk].LHS )
+						
+			if flrg.strLHS() in flrgs:
+				flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
+			else:
+				flrgs[flrg.strLHS()] = flrg;
+				flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
+		return (flrgs)
+
+	def train(self, data, sets, order):
+		self.order = order
+		self.sets = sets
+		tmpdata = common.fuzzySeries(data,sets)
+		flrs = common.generateRecurrentFLRs(tmpdata)
+		self.flrgs = self.generateFLRG(flrs)
+        
+	def forecast(self,data):
+		
+		ret = []
+		
+		l = len(data)
+		
+		if l <= self.order:
+			return data
+			
+		for k in np.arange(self.order, l):
+			tmpdata = common.fuzzySeries(data[k-self.order : k],self.sets)
+			tmpflrg = HighOrderFLRG(self.order)
+		
+			for s in tmpdata: tmpflrg.appendLHS(s)
+			
+			if tmpflrg.strLHS() not in self.flrgs:
+				ret.append(tmpdata[-1].centroid)
+			else:
+				flrg = self.flrgs[tmpflrg.strLHS()]
+				mp = self.getMidpoints(flrg)
+				
+				ret.append(sum(mp)/len(mp))
+			
+		return ret
+