physbo.blm.core.model module

class physbo.blm.core.model.model(lik, prior, options={})[source]

Bases: object

Baysean Linear Model

prior

prior distribution of weights

Type

physbo.blm.prior.gauss

lik

kernel

Type

physbo.blm.lik.gauss

nbasis

number of features in random feature map

Type

int

stats

auxially parameters for sampling

Type

Tuple

method

sampling method

Type

str

get_post_fcov(X, Psi=None, diag=True)[source]

calculates posterior covariance of model

Parameters

  • 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)

Returns

Return type

numpy.ndarray

See also

physbo.blm.inf.exact.get_post_fcov

get_post_fmean(X, Psi=None, w=None)[source]

calculates posterior mean of model (function)

Parameters

  • X (numpy.ndarray) – inputs

  • Psi (numpy.ndarray) – feature maps

  • w (numpy.ndarray) – weight

See also

physbo.blm.inf.exact.get_post_fmean

get_post_params_mean()[source]

calculates posterior mean of weights

Returns

Return type

numpy.ndarray

See also

physbo.blm.inf.exact.get_post_params_mean

post_sampling(Xtest, Psi=None, N=1, alpha=1.0)[source]

draws samples of mean value of model

Parameters

  • Xtest (numpy.ndarray) – inputs

  • Psi (numpy.ndarray) – feature maps (default: blm.lik.get_basis(Xtest))

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

  • alpha (float) – noise for sampling source

Returns

Return type

numpy.ndarray

predict_sampling(Xtest, Psi=None, N=1)[source]

draws samples from model

Parameters

  • Xtest (numpy.ndarray) – inputs

  • Psi (numpy.ndarray) – feature map (default: blm.lik.get_basis(Xtest))

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

Returns

Return type

numpy.ndarray

prepare(X, t, Psi=None)[source]

initializes model by using the first training dataset

Parameters

  • X (numpy.ndarray) – inputs

  • t (numpy.ndarray) – target (label)

  • Psi (numpy.ndarray) – feature maps

See also

physbo.blm.inf.exact.prepare

sampling(w_mu=None, N=1, alpha=1.0)[source]

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

See also

physbo.blm.inf.exact.sampling

update_stats(x, t, psi=None)[source]

updates model by using another training data

Parameters

  • x (numpy.ndarray) – input

  • t (float) – target (label)

  • psi (numpy.ndarray) – feature map

See also

physbo.blm.inf.exact.update_stats