mcmodels.regressors.nonparametric.kernels.Uniform

class mcmodels.regressors.nonparametric.kernels.Uniform(support=1.0, support_bounds=(0, 100000.0))

Uniform kernel.

Identical to Polynomial(shape=0)

Parameters:

support : float, optional, default: 1.0

The support (symmetric) of the kernel such that the kernel is equal to exactly zero where d(x, y) > support.

support_bounds : pair of floats >= 0, optional, default: (1e-5, 1e5)

The lower and upper bound on support.

See also

Polynomial

Attributes:

anisotropic

bounds

Returns the log-transformed bounds on the theta.

coefficient

Coefficient to scale the kernel to have int_D K(u)du == 1

hyperparameter_support

hyperparameters

Returns a list of all hyperparameter specifications.

n_dims

Returns the number of non-fixed hyperparameters of the kernel.

theta

Returns the (flattened, log-transformed) non-fixed hyperparameters.

Methods

__call__(self, X[, Y, eval_gradient])	Return the kernel k(X, Y) and optionally its gradient.
clone_with_theta(self, theta)	Returns a clone of self with given hyperparameters theta.
diag(self, X)	Returns the diagonal of the kernel k(X, X).
get_params(self[, deep])	Get parameters of this kernel.
is_stationary(self)	Returns whether the kernel is stationary.
set_params(self, \*\*params)	Set the parameters of this kernel.

__init__(self, support=1.0, support_bounds=(0, 100000.0))

Methods

__init__(self[, support, support_bounds])
clone_with_theta(self, theta)	Returns a clone of self with given hyperparameters theta.
diag(self, X)	Returns the diagonal of the kernel k(X, X).
get_params(self[, deep])	Get parameters of this kernel.
is_stationary(self)	Returns whether the kernel is stationary.
set_params(self, \*\*params)	Set the parameters of this kernel.