def __init__(self, alpha=0.05, beta=0.05, elo0=0, elo1=5):
self.a = math.log(beta / (1 - alpha))
self.b = math.log((1 - beta) / alpha)
self.elo0 = elo0
self.elo1 = elo1
self.s0 = LLRcalc.L_(elo0)
self.s1 = LLRcalc.L_(elo1)
self.clamped = False
self.LLR_drift_variance = LLRcalc.LLR_drift_variance_alt2
def elo_to_score(self, elo):
"""
"elo" is expressed in our current elo_model."""
if self.elo_model == "normalized":
nt = elo / LLRcalc.nelo_divided_by_nt
return nt * self.sigma_pg + 0.5
else:
return LLRcalc.L_(elo)
def lelo_to_elo(self, lelo):
"""
For external use. "elo" is expressed in our current elo_model.
"lelo" is logistic."""
if self.elo_model == "logistic":
return lelo
score = LLRcalc.L_(lelo)
nt = (score - 0.5) / self.sigma_pg
return nt * LLRcalc.nelo_divided_by_nt
def outcome_prob(self, elo):
"""
The probability of a test with the given elo with worse outcome
(faster fail, slower pass or a pass changed into a fail)."""
s = LLRcalc.L_(elo)
mu_LLR, var_LLR = self.LLR_drift_variance(self.pdf, self.s0, self.s1,
s)
sigma_LLR = math.sqrt(var_LLR)
return Brownian(a=self.a, b=self.b, mu=mu_LLR,
sigma=sigma_LLR).outcome_cdf(T=self.T, y=self.llr)