physbo.blm.inf.exact module

physbo.blm.inf.exact.get_post_fcov(blm, X, Psi=None, diag=True)

calculates posterior covariance of model

Parameters:

• blm (physbo.blm.core.model)

• X (numpy.ndarray) – inputs

• Psi (numpy.ndarray) – feature maps (default: blm.lik.linear.basis.get_basis(X))

• diag (bool) – if True, returns only variances as a diagonal matrix (default: True)

Return type:

numpy.ndarray

physbo.blm.inf.exact.get_post_fmean(blm, X, Psi=None, w=None)

calculates posterior mean of model

Parameters:

• blm (physbo.blm.core.model)

• X (numpy.ndarray) – inputs

• Psi (numpy.ndarray) – feature maps (default: blm.lik.linear.basis.get_basis(X))

• w (numpy.ndarray) – weights (default: get_post_params_mean(blm))

Return type:

numpy.ndarray

physbo.blm.inf.exact.get_post_params_mean(blm)

calculates mean of weight

Parameters:

blm (physbo.blm.core.model)

Return type:

numpy.ndarray

physbo.blm.inf.exact.prepare(blm, X, t, Psi=None)

initializes auxiaialy parameters for quick sampling

blm.stats will be updated.

Parameters:

• blm (physbo.blm.core.model) – model

• X (numpy.ndarray) – inputs

• t (numpy.ndarray) – target (label)

• Psi – feature maps (default: blm.lik.get_basis(X))

physbo.blm.inf.exact.sampling(blm, w_mu=None, N=1, alpha=1.0)

draws samples of weights

Parameters:

• blm (physbo.blm.core.model) – model

• w_mu (numpy.ndarray) – mean of weight

• N (int) – the number of samples (default: 1)

• alpha (float) – noise for sampling source (default: 1.0)

Returns:

samples of weights

Return type:

numpy.ndarray

physbo.blm.inf.exact.update_stats(blm, x, t, psi=None)

calculates new auxiaialy parameters for quick sampling by fast-update

Parameters:

• blm (physbo.blm.core.model) – model

• x (numpy.ndarray) – input

• t (numpy.ndarray) – target (label)

• psi – feature map (default: blm.lik.get_basis(X))

Returns:

(U, b, alpha) – new auxially parameters

Return type:

Tuple

Notes

blm.stats[0] (U) will be mutated while the others not.