def __init__(self, match_data, score_weighting=False):
if match_data is None:
raise ValueError("Data cannot be null")
self._trained = False
_, _, team_num_map = process.match_vectors(match_data)
self.team_num_map = team_num_map
homes = pd.unique(match_data.home_team)
aways = pd.unique(match_data.away_team)
teams = np.union1d(homes, aways)
self.team_ratings = {}
for team in teams:
self.team_ratings[team] = Rating()
self.avg_margin = 0
self.num_draws = 0
trueskill.setup(mu=25.0,
sigma=8.333333333333334,
beta=4.1666666666666,
tau=0.08333333333333334,
draw_probability=0.26)
self.train(match_data, score_weighting)
self.team_skills = np.zeros((len(teams), ))
for team in teams:
self.team_skills[self.team_num_map[team]] = \
self.team_ratings[team].mu
return
def __init__(self, data=None, goal_dif=False, n_iter=1000):
if data is None:
raise ValueError("Data cannot be null")
self._trained = False
matches, results, team_num_map = process.match_vectors(data)
self.xs = matches
self.ys = results
self.team_num_map = team_num_map
self.train(n_iter=n_iter)
def __init__(self, data=None, inf_type='Var', goal_dif=False, n_iter=1000):
if data is None:
raise ValueError("Data cannot be null")
self._trained = False
all_vectors = process.match_vectors(data, goals=True, seperate=True)
matches, results, team_num_map = all_vectors
self.xs1 = matches[:, :, 0]
self.xs2 = matches[:, :, 1]
self.ys = results
self.team_num_map = team_num_map
self.inf_type = inf_type
self.train(n_iter=n_iter)