def getTeamMatDis(team_url, wodd, lodd, ha_now):
content = spider.url_get(team_url + '/teamfixture', "gb2312")
Reses = netdata.get_TeamMatchHistory(content) #1*30
Odds = netdata.get_TeamOddsHistory(content) #1*30
HA = netdata.get_TeamHAHistory(content) #1*30
#PGR = trans.PG_to_R(Reses[0:len(Reses)-1])
(PGR_g, PGR_l) = netdata.get_TeamGoalHistory(content) #1*30
#del PGR_g[0];del PGR_g[1];del PGR_l[0];del PGR_l[1];
if len(PGR_g) < 20:
return -1
PGO = trans.ODD_to_O_29(Odds) #1*29
FOR = trans.PG_to_O_29(np.array(PGR_g) - np.array(PGR_l)) #1*29
del PGR_g[0]
del PGR_l[0]
del HA[0]
Mat = trans.RFOH_to_StaticMat(PGR_g[0:len(PGR_g) - 1],
PGR_l[0:len(PGR_l) - 1], PGO[0:len(PGO) - 1],
HA[0:len(HA) - 1], FOR[0:len(FOR) - 1])
#print Mat
for_now = FOR[-1]
#print for_now
odd_now = PGO[-1]
#ha_now =
#print odd_now
#print for_now
#return np.array(Mat[odd_now][for_now])
rv = Mat[odd_now][for_now][ha_now]
# if rv:
# pass
# print '>>>>>>>>>>>>'
# print '胜赔: ' + str(wodd)
# print '进球期望: ' + str(rv.get_average_goal())
# print '失球期望: ' + str(rv.get_average_lose())
# print " --- "+ str(rv.get_pre_vec()) +" --- 置信度: " + str(rv.num_matches)
return (rv, wodd, int(PGR_g[-1]))
def getTeamMatDis(team_url,wodd,lodd):
content = spider.url_get(team_url+'/teamfixture',"gb2312")
Reses = netdata.get_TeamMatchHistory(content)
Odds = netdata.get_TeamOddsHistory(content)
#PGR = trans.PG_to_R(Reses[0:len(Reses)-1])
(PGR_g,PGR_l) = netdata.get_TeamGoalHistory(content)
#del PGR_g[0];del PGR_g[1];del PGR_l[0];del PGR_l[1];
if len(PGR_g)<20:
return -1
PGO = trans.ODD_to_O(Odds[0:len(Odds)])
FOR = trans.PG_to_O(np.array(PGR_g)-np.array(PGR_l))
del PGR_g[0];del PGR_g[1];del PGR_l[0];del PGR_l[1];
Mat = trans.RFO_to_StaticMat(PGR_g,PGR_l,PGO,FOR)
#print Mat
for_now = trans.FOR(PGR_g[len(PGR_g)-2]-PGR_l[len(PGR_l)-2],PGR_g[len(PGR_g)-1]-PGR_l[len(PGR_l)-1])
odd_now = trans.OD(lodd/wodd)
#print odd_now
#print for_now
#return np.array(Mat[odd_now][for_now])
rv = (Mat[odd_now][for_now],wodd)
# if rv:
# pass
# print '>>>>>>>>>>>>'
# print '胜赔: ' + str(wodd)
# print '进球期望: ' + str(rv.get_average_goal())
# print '失球期望: ' + str(rv.get_average_lose())
# print " --- "+ str(rv.get_pre_vec()) +" --- 置信度: " + str(rv.num_matches)
return rv
def getTeamMatDis(team_url, wodd, lodd, ha_now):
content = spider.url_get(team_url + '/teamfixture', "gb2312")
Reses = netdata.get_TeamMatchHistory(content) #1*30
Odds = netdata.get_TeamOddsHistory(content) #1*30
HA = netdata.get_TeamHAHistory(content) #1*30
#PGR = trans.PG_to_R(Reses[0:len(Reses)-1])
(PGR_g, PGR_l) = netdata.get_TeamGoalHistory(content) #1*30
#del PGR_g[0];del PGR_g[1];del PGR_l[0];del PGR_l[1];
if len(PGR_g) < 20:
return -1
PGO = trans.ODD_to_O_29(Odds[0:len(Odds)]) #1*29
FOR = trans.PG_to_O_29(np.array(PGR_g) - np.array(PGR_l), Odds) #1*29
del PGR_g[0]
del PGR_l[0]
del HA[0]
Mat = trans.RFOH_to_StaticMat(PGR_g, PGR_l, PGO, HA, FOR)
#print Mat
for_now = trans.FOR_29(PGR_g[-1] - PGR_l[-1], Odds[-1])
#print for_now
odd_now = trans.OD(lodd / wodd)
#ha_now =
#print odd_now
#print for_now
#return np.array(Mat[odd_now][for_now])
#rv = (Mat[odd_now][for_now][ha_now],wodd)
# if rv:
# pass
last_3_goals = PGR_g[-3:]
last_3_losts = PGR_l[-3:]
(this_goals, this_loses) = get_This_Goals(PGR_g, PGR_l, PGO, FOR, HA,
odd_now, for_now, ha_now)
return (Mat[odd_now][for_now][ha_now], wodd, last_3_goals, last_3_losts,
this_goals, this_loses)
def getTeamMatTest(team_url):
content = spider.url_get(team_url+'/teamfixture',"gb2312")
Reses = netdata.get_TeamMatchHistory(content)
Odds = netdata.get_TeamOddsHistory(content)
if len(Reses)<20:
return -1
PGR = trans.PG_to_R(Reses[0:len(Reses)-2])
PGO = trans.ODD_to_O(Odds[0:len(Odds)-2])
FOR = trans.PG_to_O(Reses[0:len(Reses)-2])
Mat = trans.RFO_to_StaticMat(PGR,PGO,FOR)
for_test = trans.FOR(Reses[len(Reses)-3],Reses[len(Reses)-2])
odd_test = trans.OD(Odds[len(Reses)-1])
res_test = trans.RS(Reses[len(Reses)-1])
if Mat[odd_test][for_test].sum() == 0:
return -1 # stat lose;No data;
elif Mat[odd_test][for_test][res_test]/Mat[odd_test][for_test].sum() >= 0.5:
return 1 # positive instance;
else:
return 0 # negative instance;
