def get_conversions(n, cvr):
cvr = NormalNoiseGenerator(noise_type).generate_value_with_noise(
cvr, noise_level, self.rng)
p = np.clip(cvr, 0.0, 1.0)
return self.rng.binomial(n, p)
def get_revenue(num_conversions):
rpv = NormalNoiseGenerator(
params.noise_type).generate_value_with_noise(
params.avg_rpv, params.noise_level, self.rng)
self.last_rpv = np.maximum(0.0, rpv)
return self.last_rpv * num_conversions
def sample(num_auctions, cp):
cp_vec = []
for prob in cp:
p = NormalNoiseGenerator(noise_type).generate_value_with_noise(
prob, noise_level, self.rng)
p = np.clip(p, 0.0, 1.0)
cp_vec.append(p)
return (self.rng.binomial(num_auctions, cp_vec))
def get_average_position(cp):
avg_pos = min(params.max_cp / max(cp, 0.0001), self.max_position)
avg_pos = NormalNoiseGenerator(
params.noise_type).generate_value_with_noise(
avg_pos, params.noise_level, self.rng)
return max(round(avg_pos, 2), 1.0)
def get_cvr(bid):
conversion_probability = params.cvr(bid)
cvr = NormalNoiseGenerator(
params.noise_type).generate_value_with_noise(
conversion_probability, params.noise_level, self.rng)
return max(min(cvr, 1.0), 0.0)
def get_cvr(bid):
del bid # Added so that PyCharm doesn't complain
cvr = NormalNoiseGenerator(
params.noise_type).generate_value_with_noise(
params.cvr, params.noise_level, self.rng)
return max(min(cvr, 1.0), 0.0)
def get_cpc(bid):
cpc = bid - avg_cpc_diff
cpc = NormalNoiseGenerator(params.noise_type).generate_value_with_noise(
cpc, params.noise_level, self.rng)
return np.clip(cpc, 0.0, bid)