physbo.gp.cov.gauss module

class physbo.gp.cov.gauss.gauss(num_dim, width=3, scale=1, ard=False, max_width=1000000.0, min_width=1e-06, max_scale=1000000.0, min_scale=1e-06)[source]

Bases: object

gaussian kernel

cat_params(width, scale)[source]

Taking the logarithm of width and scale parameters and concatinate them into one ndarray

Parameters

  • width (int) –

  • scale (int) –

Returns

params – Parameters

Return type

numpy.ndarray

decomp_params(params)[source]

decompose the parameters defined on the log region into width and scale parameters

Parameters

params (numpy.ndarray) – parameters

Returns

  • width (float)

  • scale (float)

get_cand_params(X, t)[source]

Getting candidate parameters.

Parameters

  • 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).

Returns

params

Return type

numpy.ndarray

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

compute the covariant matrix :param X: N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate. :type X: numpy.ndarray :param Z: N x d dimensional matrix. Each row of Z denotes the d-dimensional feature vector of search candidate. :type Z: numpy.ndarray :param params: Parameters :type params: numpy.ndarray :param diag: If X is the diagonalization matrix, true. :type diag: bool

Returns

G – covariant matrix

Return type

numpy.ndarray

get_grad(X, params=None)[source]

Getting gradiant values of X

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) –

Returns

grad

Return type

numpy.ndarray

get_params_bound()[source]

Getting boundary array.

Returns

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

Return type

list

load(file_name)[source]

Recovering the Gaussian kernel from file :param file_name: file name to load the information of the kernel :type file_name: str

prepare(params=None)[source]

Setting parameters

Parameters

params (numpy.ndarray) – parameters

Returns

  • params (numpy.ndarray)

  • width (int)

  • scale (int)

print_params()[source]

show the current kernel parameters

rand_expans(num_basis, params=None)[source]

Kernel Expansion

Parameters

  • num_basis (int) – total number of basis

  • params (numpy.ndarray) – Parameters

Returns

Return type

tupple (W, b, amp)

save(file_name)[source]

save the gaussian kernel

Parameters

file_name (str) – file name to save the information of the kernel

set_params(params)[source]

set kernel parameters

Parameters

params (numpy.ndarray) – Parameters for optimization.

supp_params(params)[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.

Returns

params

Return type

numpy.ndarray