debounce.py

inference_list = []
DEBOUNCE_THRESHOLD = 9
TIME_DIFF = 450 #ms

for st in inference_step: # looping through the whole length of the dataset
    inference = model.score(data)

    if inference in [1,2,3]:
        inference_list.append((time, inference))
		
	# if there are more or equal than 9 predictions in a list 
	# AND
	# the difference between the first and last predictions is > 450 ms

    if len(inference_list) >= DEBOUNCE_THRESHOLD and (inference_list[-1][0] - inference_list[0][0]) >= TIME_DIFF:
        inferences = [x[1] for x in inferences_list[:DEBOUNCE_THRESHOLD]] # gets the first 8 predictions
        inference_final = max(set(inferences), key=inferences.count) # gets the most frequent value
        inference_list = [] # cleans up the list