physbo.gp.inf.exact module
- physbo.gp.inf.exact.eval_marlik(gp, X, t, params=None)[ソース]
Evaluating marginal likelihood.
- パラメータ:
gp (physbo.gp.core.model) --
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.
- 戻り値:
marlik -- Marginal likelihood.
- 戻り値の型:
float
- physbo.gp.inf.exact.get_grad_marlik(gp, X, t, params=None)[ソース]
Evaluating gradiant of marginal likelihood.
- パラメータ:
gp (physbo.gp.core.model) --
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.
- 戻り値:
grad_marlik -- Gradiant of marginal likelihood.
- 戻り値の型:
numpy.ndarray
- physbo.gp.inf.exact.get_post_fcov(gp, X, Z, params=None, diag=True)[ソース]
Calculating the covariance of posterior
- パラメータ:
gp (physbo.gp.core.model) --
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
- physbo.gp.inf.exact.get_post_fmean(gp, X, Z, params=None)[ソース]
Calculating the mean of posterior
- パラメータ:
gp (physbo.gp.core.model) --
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.
- 戻り値の型:
numpy.ndarray
- physbo.gp.inf.exact.prepare(gp, X, t, params=None)[ソース]
- パラメータ:
gp (physbo.gp.core.model) --
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.
- 戻り値:
stats
- 戻り値の型:
tupple