physbo.gp.core.model module¶

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

cat_params(lik_params, prior_params)[ソース]¶

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)[ソース]¶

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)[ソース]¶

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)[ソース]¶

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)[ソース]¶

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)[ソース]¶

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)[ソース]¶

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()[ソース]¶

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)[ソース]¶

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)[ソース]¶

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)[ソース]¶

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)[ソース]¶

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)[ソース]¶

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()[ソース]¶: Printing parameters

set_params(params)[ソース]¶

Setting parameters

パラメータ:	params (numpy.ndarray) -- Parameters.

sub_sampling(X, t, N)[ソース]¶

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)