def extract0to1Gestures(data):
gestures = {0: []} #only create gesture 0
gestureLength = l1 #5 samples / 20Hz = 0.25 seconds
postGestureLength = l2 #time allowed after the gesture was caught in output
for i in range(len(data) - gestureLength - postGestureLength):
#print data[i+gestureLength-1][1][0], data[i + gestureLength][1][0]
if data[i+gestureLength-2][1][0] == 0 and data[i + gestureLength-1][1][0] == 1:
gestureData = [d[0] for d in data[i:i+gestureLength + postGestureLength]] #remove the outputs since we've already associated it with a gesture
normalized = gestureDistanceCalculator.normalizeData(gestureData)
gestures[0].append(normalized)
return gestures
gr = gestureDistanceCalculator.GestureDistanceCalculator(training)
print '\n\n'
fout = open('grOutput.txt', 'w')
allDistances = []
start = time.time()
for i in range(l1 + l2, 30000):
if i >= len(data): break
input = [d[0] for d in data[i-l1-l2:i]]
distance, closest, systemOutput = gr.getOutput(input)
s = '\t'.join([str(i), str([round(i[0], 2) for i in gestureDistanceCalculator.normalizeData(input)]), str(round(distance, 2))])
if distance < DISTANCE_TO_PRINT:
print s
fout.write(s + '\n')
allDistances.append(distance)
#elapsed = time.time() - start
#numPoints = len(data) - l1 - l2
#print 'took %s for %s points' % (elapsed, numPoints)
avDistance = stats.mean(allDistances)
stdDevDistance = stats.stdDev(allDistances)
