64 lines
1.5 KiB
Python
64 lines
1.5 KiB
Python
|
class WeightedFLRG(FTS):
|
||
|
|
def __init__(self,premiss):
|
||
|
|
self.premiss = premiss
|
||
|
|
self.consequent = []
|
||
|
|
self.count = 1.0
|
||
|
|
|
||
|
|
def append(self,c):
|
||
|
|
self.consequent.append(c)
|
||
|
|
self.count = self.count + 1.0
|
||
|
|
|
||
|
|
def weights(self):
|
||
|
|
tot = sum( np.arange(1.0,self.count,1.0) )
|
||
|
|
return np.array([ k/tot for k in np.arange(1.0,self.count,1.0) ])
|
||
|
|
|
||
|
|
def __str__(self):
|
||
|
|
tmp = self.premiss + " -> "
|
||
|
|
tmp2 = ""
|
||
|
|
cc = 1.0
|
||
|
|
tot = sum( np.arange(1.0,self.count,1.0) )
|
||
|
|
for c in self.consequent:
|
||
|
|
if len(tmp2) > 0:
|
||
|
|
tmp2 = tmp2 + ","
|
||
|
|
tmp2 = tmp2 + c + "(" + str(round(cc/tot,3)) + ")"
|
||
|
|
cc = cc + 1.0
|
||
|
|
return tmp + tmp2
|
||
|
|
|
||
|
|
|
||
|
|
class WeightedFTS(FTS):
|
||
|
|
def __init__(self,name):
|
||
|
|
super(WeightedFTS, self).__init__(1,name)
|
||
|
|
|
||
|
|
def defuzzy(self,data):
|
||
|
|
|
||
|
|
actual = self.fuzzy(data)
|
||
|
|
|
||
|
|
if actual["fuzzyset"] not in self.flrgs:
|
||
|
|
return self.sets[actual["fuzzyset"]].centroid
|
||
|
|
|
||
|
|
flrg = self.flrgs[actual["fuzzyset"]]
|
||
|
|
|
||
|
|
mi = np.array([self.sets[s].centroid for s in flrg.consequent])
|
||
|
|
|
||
|
|
return mi.dot( flrg.weights() )
|
||
|
|
|
||
|
|
def learn(self, data, sets):
|
||
|
|
last = {"fuzzyset":"", "membership":0.0}
|
||
|
|
actual = {"fuzzyset":"", "membership":0.0}
|
||
|
|
|
||
|
|
for s in sets:
|
||
|
|
self.sets[s.name] = s
|
||
|
|
|
||
|
|
self.flrgs = {}
|
||
|
|
count = 1
|
||
|
|
for inst in data:
|
||
|
|
actual = self.fuzzy(inst)
|
||
|
|
|
||
|
|
if count > self.order:
|
||
|
|
if last["fuzzyset"] not in self.flrgs:
|
||
|
|
self.flrgs[last["fuzzyset"]] = WeightedFLRG(last["fuzzyset"])
|
||
|
|
|
||
|
|
self.flrgs[last["fuzzyset"]].append(actual["fuzzyset"])
|
||
|
|
count = count + 1
|
||
|
|
last = actual
|