def __init__(self):
self.sensorclassifierlist=list()
self.last_state=str("Nuetral")
self.trainingdata=ClassifyUtils.load_trainingdata("C:/Users/Gaurav/My Documents/GitHub/KineticEEG/Tests/testdata.dat")
self.data=dict()
self.allvotes=list()
self.vote_counts=dict()
self.curr_state=str()
def get_neighbors(self,num):
self.ds=list()
leng=len(self.trainingdata)-1
for i in range(len(self.trainingdata)):
distance=ClassifyUtils.euclideandistance(self.inst, self.trainingdata[i],leng)
self.ds.append((self.trainingdata[i], distance))
self.ds.sort(key=lambda tup:tup[1])
self.neighbors=list()
for i in range(num):
self.neighbors.append(self.ds[i][0])
def __init__(self):
self.sensorclassifierlist = list()
self.last_state = str("Nuetral")
self.trainingdata = ClassifyUtils.load_trainingdata(
"C:/Users/Gaurav/My Documents/GitHub/KineticEEG/Tests/testdata.dat"
)
self.data = dict()
self.allvotes = list()
self.vote_counts = dict()
self.curr_state = str()
def get_neighbors(self,num):
self.ds=list()x
leng=len(self.trainingdata)-1
for i in range(len(self.trainingdata)):
distance=ClassifyUtils.euclideandistance(self.inst, self.trainingdata[i],leng)
self.ds.append((self.trainingdata[i], distance))
self.ds.sort(key=lambda tup:tup[1])
self.neighbors=list()
for i in range(num):
self.neighbors.append(self.ds[i][0])
def run_detection(self):
"""Given a window of data this sets a threshold"""
TC_CONSTANT=2
Null=int()
detected=int()
for i in self.train:
trainset=copy.copy(i)
trainset=list(trainset)
del trainset[-1]
dis=ClassifyUtils.euclideandistance(self.data, trainset, len(trainset))
dis1=tuple((i[-1], dis))
self.alldis.append(dis1)
self.alldis.sort(key=lambda tup:tup[1], reverse=False)
#Now do some vote gettingi
consideration=self.alldis[0:TC_CONSTANT-1]
for i in consideration:
if i[0]=="NULL":
Null+=1
else:
detected+=1
if Null>detected:print("NULL")
else:print("Got a bite!")
