physbo.gp.lik.gauss module

class physbo.gp.lik.gauss.gauss(std=1, max_params=1000000.0, min_params=1e-06)[source]

Bases: object

Gaussian likelihood function

get_cand_params(t)[source]

Getting candidate parameters.

Parameters: t (numpy.ndarray) – N dimensional array. The negative energy of each search candidate (value of the objective function to be optimized).
Returns: log[ standard deviation of t] - log 10.0
Return type: numpy.ndarray

get_cov(num_data, params=None)[source]

Get a covariance matrix

Parameters

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

Returns

Diagonal element matrix of exp(2.0*params)

Return type

numpy.ndarray

get_grad(num_data, params=None)[source]

Get a gradient matrix

Parameters

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

Returns

Diagonal element matrix of 2.0 * exp(2.0*params)

Return type

numpy.ndarray

get_params_bound()[source]

Get boundary array.

Returns: bound – A num_params-dimensional array with the tuple (min_params, max_params).
Return type: list

sampling(fmean)[source]

Sampling by adding noise

set_params(params)[source]

Set parameters.

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

supp_params(params=None)[source]

Set maximum (minimum) values for parameters when the parameter is greater(less) than this value.

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

trans_params(params=None)[source]

Get exp[params].

Parameters: params (numpy.ndarray) – Parameters for optimization. Array of real elements of size (n,), where ‘n’ is the number of independent variables.
Returns: std
Return type: numpy.ndarray