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