def getCurrentMatch(summonerName, region="NA"):
riotapi.set_region(region)
summoner = riotapi.get_summoner_by_name(summonerName)
match = riotapi.get_current_game(summoner)
if match is None:
return None
roleMap = allRoles(match)
# for x in roleMap:
# print x.champion.name, x.side.name, roleMap[x]
if len(roleMap.keys()) < 10:
print "Role confusion!"
return None
statMap = {}
rankMap = {}
nonNormMap = {}
for p in match.participants:
role = roleMap[p]
stats, nonNorm, rank = avgPerformance.getAvgPerformance(p, role)
if stats is None:
#currently filling with avg gold?
stats = getAvg()
rank = "unranked"
statMap[p.side.name+role] = list(stats)
rankMap[p] = rank
nonNormMap[p] = nonNorm
print p.summoner_name, p.side.name+role
statVector = (statMap['blueTop']+statMap['blueMid']+statMap['blueJung']+
statMap['blueSup']+statMap['blueADC']+statMap['redTop']+
statMap['redMid']+statMap['redJung']+statMap['redSup']+
statMap['redADC'])
model = fetchModel(match)
results = model.predict_proba(statVector)
return format.prepareReturn(roleMap, rankMap, nonNormMap, results, match)
def getCurrentMatch(summonerName, region="NA"):
riotapi.set_region(region)
try:
summoner = riotapi.get_summoner_by_name(summonerName)
match = riotapi.get_current_game(summoner)
except APIError as e:
print e
return None
if match is None:
return None
if match.mode.name != "classic":
print "Not classic"
return None
roleMap = allRoles(match)
if len(roleMap.keys()) < 10:
roleMap = assignRandom(match)
print "Role confusion!"
statMap = {}
rankMap = {}
nonNormMap = {}
for p in match.participants:
role = roleMap[p]
try:
stats, nonNorm, rank = avgPerformance.getAvgPerformance(p, role)
except:
stats = getAvg(p.side.name+role)
rank = "unranked"
nonNorm = [0, 0, 0, 0, 0, 0]
statMap[p.side.name+role] = list(stats)
rankMap[p] = rank
nonNormMap[p] = nonNorm
print p.summoner_name, p.side.name+role
statVector = (statMap['blueTop']+statMap['blueMid']+statMap['blueJung']+
statMap['blueSup']+statMap['blueADC']+statMap['redTop']+
statMap['redMid']+statMap['redJung']+statMap['redSup']+
statMap['redADC'])
model = fetchModel(match, rankMap)
results = model.predict_proba(statVector)
return format.prepareReturn(roleMap, rankMap, nonNormMap, results, match)
