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;