physbo.gp.lik.gauss module

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

Bases: object

Gaussian likelihood function

get_cand_params(t)

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)

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)

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()

Get boundary array.

Returns:

bound – A num_params-dimensional array with the tuple (min_params, max_params).

Return type:

list

sampling(fmean)

Sampling by adding noise

Parameters:

fmean (numpy.ndarray)

set_params(params)

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)

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)

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