def match(svmdata, modeldata):
y,x = svmutil.svm_read_problem(svmdata)
m = svmutil.svm_load_model(modeldata)
labels, accuracy, values = svmutil.svm_predict(y,x,m)
# We'll see if the labels work, and what they return, and we'll classify from there
import pdb;pdb.set_trace()
return 0
def runmatch(modeldata):
m = svmutil.svm_load_model.model(data)
window = deque()
consume = False
while 1:
output = ser.readline()
data = output.split(',')
y = arange(len(data),1,1)
labels, accuracy, values = svmutil.svm_predict(y,x,m)
window.appendleft(accuracy)
if len(window) > 5:
window.pop()
if sum(window) > 3 and consume == False:
print label
consume = True
if sum(window) < 2 and consume == True:
consume = False
