jijzepttools.blackbox_optimization.sparse_bayesian_linear_reg

Classes

Trace
SparseBayesianLinearRegression

Functions

efficient_multivariate_normal(phi, d, alpha)
sample_from_inv_gamma(shape, scale)
sample_theta(x, y, lamb2, tau2, sigma2)
sample_sigma2(x, y, theta, lamb2, tau2)
sample_lamb2(theta, sigma2, tau2, nu)
sample_tau2(theta, sigma2, lamb2, xi)
sample_nu(lamb2)
sample_xi(tau2)
horseshoe_gibbs_sampling(x, y, theta, sigma2, lamb2, ...)
remove_duplicates_and_average(x, y[, logger])
get_nonzero_column_indices(x[, logger])

Module Contents

class Trace

coef: numpy.ndarray

bias: numpy.ndarray

lambda2: numpy.ndarray

sigma2: float

tau2: float

nu: numpy.ndarray

xi: float

property num_samples

class SparseBayesianLinearRegression(random_seed: int | None = None)

rs

trace: list[Trace] = []

fit(x: numpy.ndarray, y: numpy.ndarray, draws: int = 10, tune: int = 100)

efficient_multivariate_normal(phi: numpy.ndarray, d: numpy.ndarray, alpha: numpy.ndarray)

sample_from_inv_gamma(shape, scale)

sample_theta(x, y, lamb2, tau2, sigma2)

sample_sigma2(x, y, theta, lamb2, tau2)

sample_lamb2(theta, sigma2, tau2, nu)

sample_tau2(theta, sigma2, lamb2, xi)

sample_nu(lamb2)

sample_xi(tau2)

horseshoe_gibbs_sampling(x, y, theta, sigma2, lamb2, tau2, nu, xi, max_iter)

remove_duplicates_and_average(x, y, logger=None)

get_nonzero_column_indices(x, logger=None)