def set_state(self, results):
N, self.pdf = LLRcalc.results_to_pdf(results)
if self.elo_model == "normalized":
mu, var = LLRcalc.stats(self.pdf) # code duplication with LLRcalc
if len(results) == 5:
self.sigma_pg = (2 * var)**0.5
elif len(results) == 3:
self.sigma_pg = var**0.5
else:
assert False
self.s0, self.s1 = [
self.elo_to_score(elo) for elo in (self.elo0, self.elo1)
]
mu_LLR, var_LLR = self.LLR_drift_variance(self.pdf, self.s0, self.s1,
None)
# llr estimate
self.llr = N * mu_LLR
self.T = N
# now normalize llr (if llr is not legal then the implications
# of this are unclear)
slope = self.llr / N
if self.llr > 1.03 * self.b or self.llr < 1.03 * self.a:
self.clamped = True
if self.llr < self.a:
self.T = self.a / slope
self.llr = self.a
elif self.llr > self.b:
self.T = self.b / slope
self.llr = self.b
def set_state(self, results):
N, self.pdf = LLRcalc.results_to_pdf(results)
mu_LLR, var_LLR = self.LLR_drift_variance(self.pdf, self.s0, self.s1, None)
# llr estimate
self.llr = N * mu_LLR
self.T = N
# now normalize llr (if llr is not legal then the implications
# of this are unclear)
slope = self.llr / N
if self.llr > 1.03 * self.b or self.llr < 1.03 * self.a:
self.clamped = True
if self.llr < self.a:
self.T = self.a / slope
self.llr = self.a
elif self.llr > self.b:
self.T = self.b / slope
self.llr = self.b