physbo.gp.core.prior module

class physbo.gp.core.prior.prior(mean, cov)[source]

Bases: object

prior of gaussian process

cat_params(mean_params, cov_params)[source]
Parameters:

  • mean_params (numpy.ndarray) – Mean values of parameters

  • cov_params (numpy.ndarray) – Covariance matrix of parameters

Return type:

numpy.ndarray

decomp_params(params)[source]

decomposing the parameters to those of mean values and covariance matrix for priors

Parameters:

params (numpy.ndarray) – parameters

Returns:

  • mean_params (numpy.ndarray)

  • cov_params (numpy.ndarray)

get_cov(X, Z=None, params=None, diag=False)[source]

Calculating the variance-covariance matrix of priors

Parameters:

  • 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

get_grad_cov(X, params=None)[source]

Calculating the covariance matrix priors

Parameters:

  • 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.

Return type:

numpy.ndarray

get_grad_mean(num_data, params=None)[source]

Calculating the gradiant of mean values of priors

Parameters:

  • num_data (int) – Total number of data

  • params (numpy.ndarray) – Parameters

Return type:

numpy.ndarray

get_mean(num_data, params=None)[source]

Calculating the mean value of priors

Parameters:

  • num_data (int) – Total number of data

  • params (numpy.ndarray) – Parameters

Return type:

float

sampling(X, N=1)[source]

Sampling from GP prior

Parameters:

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

  • N (int) –

Return type:

float

set_cov_params(params)[source]

Setting parameters for covariance matrix of priors

Parameters:

params (numpy.ndarray) – Parameters

set_mean_params(params)[source]

Setting parameters for mean values of priors

Parameters:

params (numpy.ndarray) – Parameters

set_params(params)[source]

Setting parameters

Parameters:

params (numpy.ndarray) – Parameters.