physbo.blm.prior.gauss module

class physbo.blm.prior.gauss.cov_const(params=None)

Bases: object

isotropic variance-covariance

All elements have the same variance and are independent with each other

params

half of log of covariance

Type:

float

sigma2

covariance

Type:

float

prec

precision (= inv. of covariance)

Type:

float

get_cov(nbasis, params=None)

computes the covariance

Parameters:

• nbasis (int) – the number of components

• params (float) – half of log of variance (default: self.params)

Returns:

nbasis-by-n-basis covariance matrix

Return type:

numpy.ndarray

get_prec(nbasis, params=None)

computes the precision

Parameters:

• nbasis (int) – the number of components

• params (float) – half of log of variance (default: self.params)

Returns:

nbasis-by-n-basis precision matrix

Return type:

numpy.ndarray

set_params(params)

sets params

Parameters:

params (float) – half of log of variance

class physbo.blm.prior.gauss.gauss(nbasis, cov=None)

Bases: object

Gaussian prior

nbasis

number of components

Type:

int

cov

covariance

Type:

cov_const

get_cov(params=None)

calculates the variance-covariance matrix of priors

Parameters:

params (float) – half of log of variance (default: self.cov.params)

Return type:

numpy.ndarray

get_mean(params=None)

calculates the mean value of priors

Parameters:

params (float) – half of log of variance (not used)

Return type:

numpy.ndarray

get_prec(params=None)

calculates the precise matrix of priors

Parameters:

params (float) – half of log of variance (default: self.cov.params)

Return type:

numpy.ndarray

set_params(params)

sets params

Parameters:

params (float) – half of log of variance