class sklearn.gaussian_process.kernels.Hyperparameter[source]

A kernel hyperparameter’s specification in form of a namedtuple.

New in version 0.18.


The name of the hyperparameter. Note that a kernel using a hyperparameter with name “x” must have the attributes self.x and self.x_bounds


The type of the hyperparameter. Currently, only “numeric” hyperparameters are supported.

boundspair of floats >= 0 or “fixed”

The lower and upper bound on the parameter. If n_elements>1, a pair of 1d array with n_elements each may be given alternatively. If the string “fixed” is passed as bounds, the hyperparameter’s value cannot be changed.

n_elementsint, default=1

The number of elements of the hyperparameter value. Defaults to 1, which corresponds to a scalar hyperparameter. n_elements > 1 corresponds to a hyperparameter which is vector-valued, such as, e.g., anisotropic length-scales.

fixedbool, default=None

Whether the value of this hyperparameter is fixed, i.e., cannot be changed during hyperparameter tuning. If None is passed, the “fixed” is derived based on the given bounds.


count(value, /)

Return number of occurrences of value.

index(value[, start, stop])

Return first index of value.

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

__call__(*args, **kwargs)

Call self as a function.


Alias for field number 2

count(value, /)

Return number of occurrences of value.


Alias for field number 4

index(value, start=0, stop=sys.maxsize, /)

Return first index of value.

Raises ValueError if the value is not present.


Alias for field number 3


Alias for field number 0


Alias for field number 1

Examples using sklearn.gaussian_process.kernels.Hyperparameter