physbo.gp.core.model module¶

class physbo.gp.core.model.model(lik, mean, cov, inf='exact')[source]¶

Bases: object

cat_params(lik_params, prior_params)[source]¶

Concatinate the likelihood and prior parameters

Parameters

lik_params (numpy.ndarray) – Parameters for likelihood
prior_params (numpy.ndarray) – Parameters for prior

Returns

params – parameters about likelihood and prior

Return type

numpy.ndarray

decomp_params(params=None)[source]¶

decomposing the parameters to those of likelifood and priors

Parameters

params (numpy.ndarray) – parameters

Returns

lik_params (numpy.ndarray)
prior_params (numpy.ndarray)

eval_marlik(params, X, t, N=None)[source]¶

Evaluating marginal likelihood.

Parameters

params (numpy.ndarray) – Parameters.
X (numpy.ndarray) – N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
t (numpy.ndarray) – N dimensional array. The negative energy of each search candidate (value of the objective function to be optimized).
N (int) – Total number of subset data (if not specified, all dataset is used)

Returns

marlik (float)
Marginal likelihood.

export_blm(num_basis)[source]¶

Exporting the blm(Baysean linear model) predictor

Parameters: num_basis (int) – Total number of basis
Returns
Return type: physbo.blm.core.model

fit(X, t, config)[source]¶

Fitting function (update parameters)

Parameters

X (numpy.ndarray) – N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
t (numpy.ndarray) – N dimensional array. The negative energy of each search candidate (value of the objective function to be optimized).
config (physbo.misc.set_config object) –

get_cand_params(X, t)[source]¶

Getting candidate for parameters

Parameters

X (numpy.ndarray) – N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
t (numpy.ndarray) – N dimensional array. The negative energy of each search candidate (value of the objective function to be optimized).

Returns

params – Parameters

Return type

numpy.ndarray

get_grad_marlik(params, X, t, N=None)[source]¶

Evaluating gradiant of marginal likelihood.

Parameters

params (numpy.ndarray) – Parameters.
X (numpy.ndarray) – N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
t (numpy.ndarray) – N dimensional array. The negative energy of each search candidate (value of the objective function to be optimized).
N (int) – Total number of subset data (if not specified, all dataset is used)

Returns

grad_marlik – Gradiant of marginal likelihood.

Return type

numpy.ndarray

get_params_bound()[source]¶

Getting boundary of the parameters.

Returns: bound – An array with the tuple (min_params, max_params).
Return type: list

get_post_fcov(X, Z, params=None, diag=True)[source]¶

Calculating posterior covariance matrix of model (function)

Parameters

X (numpy.ndarray) – inputs
Z (numpy.ndarray) – feature maps
params (numpy.ndarray) – Parameters
diag (bool) – If X is the diagonalization matrix, true.

Returns

Return type

physbo.gp.inf.exact.get_post_fcov

get_post_fmean(X, Z, params=None)[source]¶

Calculating posterior mean of model (function)

Parameters

X (numpy.ndarray) – inputs
Z (numpy.ndarray) – feature maps
params (numpy.ndarray) – Parameters