physbo.blm.prior.gauss module¶

class physbo.blm.prior.gauss.cov_const(params=None)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

sets params

Parameters: params (float) – half of log of variance

class physbo.blm.prior.gauss.gauss(nbasis, cov=None)[source]¶

Bases: object

Gaussian prior

nbasis¶

number of components

Type: int

cov¶

covariance

Type: cov_const

get_cov(params=None)[source]¶

calculates the variance-covariance matrix of priors

Parameters: params (float) – half of log of variance (default: self.cov.params)
Returns
Return type: numpy.ndarray

get_mean(params=None)[source]¶

calculates the mean value of priors

Parameters: params (float) – half of log of variance (not used)
Returns
Return type: numpy.ndarray

get_prec(params=None)[source]¶

calculates the precise matrix of priors

Parameters: params (float) – half of log of variance (default: self.cov.params)
Returns
Return type: numpy.ndarray

set_params(params)[source]¶

sets params

Parameters: params (float) – half of log of variance