physbo.gp.inf.exact module

physbo.gp.inf.exact.eval_marlik(gp, X, t, params=None)[source]

Evaluating marginal likelihood.

Parameters:

  • 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.

Returns:

marlik – Marginal likelihood.

Return type:

float

physbo.gp.inf.exact.get_grad_marlik(gp, X, t, params=None)[source]

Evaluating gradiant of marginal likelihood.

Parameters:

  • 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.

Returns:

grad_marlik – Gradiant of marginal likelihood.

Return type:

numpy.ndarray

physbo.gp.inf.exact.get_post_fcov(gp, X, Z, params=None, diag=True)[source]

Calculating the covariance of posterior

Parameters:

  • 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.

Return type:

numpy.ndarray

physbo.gp.inf.exact.get_post_fmean(gp, X, Z, params=None)[source]

Calculating the mean of posterior

Parameters:

  • 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.

Return type:

numpy.ndarray

physbo.gp.inf.exact.prepare(gp, X, t, params=None)[source]
Parameters:

  • 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.

Returns:

stats

Return type:

tupple