physbo.gp.core.model module

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

ベースクラス: object

cat_params(lik_params, prior_params)

Concatinate the likelihood and prior parameters

パラメータ

• lik_params (numpy.ndarray) -- Parameters for likelihood

• prior_params (numpy.ndarray) -- Parameters for prior

戻り値

params -- parameters about likelihood and prior

戻り値の型

numpy.ndarray

decomp_params(params=None)

decomposing the parameters to those of likelifood and priors

パラメータ

params (numpy.ndarray) -- parameters

戻り値

• lik_params (numpy.ndarray)

• prior_params (numpy.ndarray)

eval_marlik(params, X, t, N=None)

Evaluating marginal likelihood.

パラメータ

• 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)

戻り値

• marlik (float)

• Marginal likelihood.

export_blm(num_basis)

Exporting the blm(Baysean linear model) predictor

パラメータ

num_basis (int) -- Total number of basis

戻り値

戻り値の型

physbo.blm.core.model

fit(X, t, config)

Fitting function (update parameters)

パラメータ

• 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)

Getting candidate for parameters

パラメータ

• 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).

戻り値

params -- Parameters

戻り値の型

numpy.ndarray

get_grad_marlik(params, X, t, N=None)

Evaluating gradiant of marginal likelihood.

パラメータ

• 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)

戻り値

grad_marlik -- Gradiant of marginal likelihood.

戻り値の型

numpy.ndarray

get_params_bound()

Getting boundary of the parameters.

戻り値

bound -- An array with the tuple (min_params, max_params).

戻り値の型

list

get_post_fcov(X, Z, params=None, diag=True)

Calculating posterior covariance matrix of model (function)

パラメータ

• X (numpy.ndarray) -- inputs

• Z (numpy.ndarray) -- feature maps

• params (numpy.ndarray) -- Parameters

• diag (bool) -- If X is the diagonalization matrix, true.

戻り値

戻り値の型

physbo.gp.inf.exact.get_post_fcov

get_post_fmean(X, Z, params=None)

Calculating posterior mean of model (function)

パラメータ

• X (numpy.ndarray) -- inputs

• Z (numpy.ndarray) -- feature maps

• params (numpy.ndarray) -- Parameters

参考

physbo.gp.inf.exact.get_post_fmean

post_sampling(X, Z, params=None, N=1, alpha=1)

draws samples of mean value of model

パラメータ

• X (numpy.ndarray) -- inputs

• Z (numpy.ndarray) -- feature maps

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

• alpha (float) -- noise for sampling source

戻り値

戻り値の型

numpy.ndarray

predict_sampling(X, Z, params=None, N=1)

パラメータ

• X (numpy.ndarray) -- inputs

• Z (numpy.ndarray) -- feature maps

• params (numpy.ndarray) -- Parameters

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

戻り値

戻り値の型

numpy.ndarray

prepare(X, t, params=None)

パラメータ

• 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).

• params (numpy.ndarray) -- Parameters.

print_params()

Printing parameters

set_params(params)

Setting parameters

パラメータ

params (numpy.ndarray) -- Parameters.

sub_sampling(X, t, N)

Make subset for sampling

パラメータ

• X (numpy.ndarray) -- Each row of X denotes the d-dimensional feature vector of search candidate.

• t (numpy.ndarray) -- The negative energy of each search candidate (value of the objective function to be optimized).

• N (int) -- Total number of data in subset

戻り値

• subX (numpy.ndarray)

• subt (numpy.ndarray)