physbo.gp.core.prior module
- class physbo.gp.core.prior.prior(mean, cov)[ソース]
ベースクラス:
objectprior of gaussian process
- cat_params(mean_params, cov_params)[ソース]
- パラメータ:
mean_params (numpy.ndarray) -- Mean values of parameters
cov_params (numpy.ndarray) -- Covariance matrix of parameters
- 戻り値の型:
numpy.ndarray
- decomp_params(params)[ソース]
decomposing the parameters to those of mean values and covariance matrix for priors
- パラメータ:
params (numpy.ndarray) -- parameters
- 戻り値:
mean_params (numpy.ndarray)
cov_params (numpy.ndarray)
- get_cov(X, Z=None, params=None, diag=False)[ソース]
Calculating the variance-covariance matrix of priors
- 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.
Z (numpy.ndarray) -- N x d dimensional matrix. Each row of Z denotes the d-dimensional feature vector of tests.
params (numpy.ndarray) -- Parameters.
diag (bool) -- If X is the diagonalization matrix, true.
- 戻り値の型:
numpy.ndarray
- get_grad_cov(X, params=None)[ソース]
Calculating the covariance matrix priors
- 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.
params (numpy.ndarray) -- Parameters.
- 戻り値の型:
numpy.ndarray
- get_grad_mean(num_data, params=None)[ソース]
Calculating the gradiant of mean values of priors
- パラメータ:
num_data (int) -- Total number of data
params (numpy.ndarray) -- Parameters
- 戻り値の型:
numpy.ndarray
- get_mean(num_data, params=None)[ソース]
Calculating the mean value of priors
- パラメータ:
num_data (int) -- Total number of data
params (numpy.ndarray) -- Parameters
- 戻り値の型:
float
- sampling(X, N=1)[ソース]
Sampling from GP prior
- 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.
N (int)
- 戻り値の型:
float
- set_cov_params(params)[ソース]
Setting parameters for covariance matrix of priors
- パラメータ:
params (numpy.ndarray) -- Parameters