physbo.gp.core.model module
- class physbo.gp.core.model.model(lik, mean, cov, inf='exact')[ソース]
ベースクラス:
object- 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
- 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
- 戻り値の型:
numpy.ndarray
- predict_sampling(X, Z, params=None, N=1)[ソース]
- パラメータ:
X (numpy.ndarray) -- training datasets
Z (numpy.ndarray) -- input for sampling objective values
params (numpy.ndarray) -- Parameters
N (int) -- number of samples (default: 1)
- 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.
- 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)