physbo.gp.core.learning module

class physbo.gp.core.learning.adam(gp, config)[ソース]

ベースクラス: physbo.gp.core.learning.online

default

get_one_update(params, X, t)[ソース]
パラメータ

  • params (numpy.ndarray) -- Parameters for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables.

  • 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 vector that represents the corresponding negative energy of search candidates.

reset()[ソース]
class physbo.gp.core.learning.batch(gp, config)[ソース]

ベースクラス: object

basis class for batch learning

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 vector that represents the corresponding negative energy of search candidates.

戻り値

The parameters which give the minimum marginal likelihood.

戻り値の型

numpy.ndarray

one_run(params, X, t, max_iter=None)[ソース]
パラメータ

  • params (numpy.ndarray) -- Initial guess for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables.

  • 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 vector that represents the corresponding negative energy of search candidates.

  • max_iter (int) -- Maximum number of iterations to perform.

戻り値

The solution of the optimization.

戻り値の型

numpy.ndarray

run(X, t)[ソース]

Performing optimization using the L-BFGS-B algorithm

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 vector that represents the corresponding negative energy of search candidates.

戻り値

The solution of the optimization.

戻り値の型

numpy.ndarray

class physbo.gp.core.learning.online(gp, config)[ソース]

ベースクラス: object

base class for online learning

disp_marlik(params, eval_X, eval_t, num_epoch=None)[ソース]

Displaying marginal likelihood

パラメータ

  • params (numpy.ndarray) -- Parameters for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables.

  • eval_X (numpy.ndarray) -- N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.

  • eval_t (numpy.ndarray) -- N-dimensional vector that represents the corresponding negative energy of search candidates.

  • num_epoch (int) -- Number of epochs

get_one_update(params, X, t)[ソース]

Initial parameter searchs

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 vector that represents the corresponding negative energy of search candidates.

戻り値

The parameter which gives the minimum likelihood.

戻り値の型

numpy.ndarray

one_run(params, X, t, max_epoch=None)[ソース]
パラメータ

  • params (numpy.ndarray) -- Parameters for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables.

  • 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 vector that represents the corresponding negative energy of search candidates.

  • max_epoch (int) -- Maximum candidate epochs

戻り値

The solution of the optimization.

戻り値の型

numpy.ndarray

run(X, t)[ソース]

Run initial search and hyper parameter running.

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 vector that represents the corresponding negative energy of search candidates.

戻り値

The solution of the optimization.

戻り値の型

numpy.ndarray