Python getPStats示例

示例#1

文件： avgPerformance.py 项目： rhartsfield/DataMiningLoL

def averageMatches(participant, matchList):
	length = 5
	statVector = np.zeros(63)
	nonNorm = np.zeros(6)
	pulled = 0
	rank = ""
	for matchRef in matchList:

		try:
			match = matchRef.match(include_timeline=True)
		except:
			print 'Match pull from reference failed.'
			continue
		hold = statVector
		try:
			for p in match.participants:
				if p.summoner.id == participant.summoner.id:
					try:
						rank = p.previous_season_tier.name
					except ValueError:
						rank = "unranked"
					statVector = np.add(statVector, np.array(parseMatch.getPStats(p, match)))
					nonNorm = np.add(nonNorm, np.array(parseMatch.getNonNorm(p, match)))
					pulled += 1
					break
		except AttributeError:
			print 'Match parse failed.'
			statVector = hold
		if pulled > length-1:
			break
	return np.divide(statVector, pulled), np.divide(nonNorm, pulled), rank

示例#2

文件： avgPerformance.py 项目： rhartsfield/DataMiningLoL

def averageMatches(participant, matchList):
	'''
	Process number of games equal to length, average performance
	Returns an averaged feature vector and a non-normalized vector for website
	'''
	length = 5
	statVector = np.zeros(63)	#Length of feature vector
	nonNorm = np.zeros(6)		#Length of stat vector passed to website
	pulled = 0
	rank = ""
	for matchRef in matchList:

		try:
			match = matchRef.match(include_timeline=True)
		except:
			print 'Match pull from reference failed.'
			continue
		hold = statVector
		try:
			for p in match.participants:
				if p.summoner.id == participant.summoner.id:
					try:
						rank = p.previous_season_tier.name
					except ValueError:
						rank = "unranked"
					statVector = np.add(statVector, np.array(parseMatch.getPStats(p, match)))
					nonNorm = np.add(nonNorm, np.array(parseMatch.getNonNorm(p, match)))
					pulled += 1
					break
		except AttributeError:
			print 'Match parse failed.'
			statVector = hold
		if pulled > length-1:
			break
	if pulled == 0:
		return None
	return np.divide(statVector, pulled), np.divide(nonNorm, pulled), rank