package sklearn
Module Kernels.HyperparameterSource
val create :
?n_elements:Py.Object.t ->
?fixed:Py.Object.t ->
name:Py.Object.t ->
value_type:Py.Object.t ->
bounds:Py.Object.t ->
unit ->
tA kernel hyperparameter's specification in form of a namedtuple.
.. versionadded:: 0.18
Attributes ---------- name : string 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
value_type : string The type of the hyperparameter. Currently, only "numeric" hyperparameters are supported.
bounds : pair 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_elements : int, 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.
fixed : bool, 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.
Return selfkey.
Return number of occurrences of value.
Return first index of value.
Raises ValueError if the value is not present.
Attribute bounds: get value or raise Not_found if None.
Attribute bounds: get value as an option.
Pretty-print the object to a formatter.