def run_perceptron(N, alpha, p_pos):
model = glm_generative(N=N,
alpha=alpha,
ensemble_type="gaussian",
prior_type="binary",
output_type="sgn",
prior_p_pos=p_pos)
scenario = BayesOptimalScenario(model, x_ids=["x"])
early = EarlyStopping()
records = scenario.run_all(max_iter=200, callback=early)
return records
def run_cs(N, alpha, ensemble_type, prior_rho):
model = glm_generative(
N=N, alpha=alpha, ensemble_type=ensemble_type,
prior_type="gauss_bernoulli", output_type="gaussian",
prior_rho=prior_rho, output_var=1e-11
)
scenario = BayesOptimalScenario(model, x_ids=["x"])
early = EarlyStopping()
records = scenario.run_all(
metrics=["mse"], max_iter=200, callback=early
)
return records
def run_phase_retrieval(N, alpha, prior_mean):
model = glm_generative(N=N,
alpha=alpha,
ensemble_type="complex_gaussian",
prior_type="gauss_bernouilli",
output_type="modulus",
prior_mean=prior_mean,
prior_rho=0.5)
scenario = BayesOptimalScenario(model, x_ids=["x"])
early = EarlyStopping(wait_increase=10)
records = scenario.run_all(metrics=["mse", "phase_mse"],
max_iter=200,
damping=0.3,
callback=early)
return records