gaussian_process.kernels.Hyperparameter

class sklearn.gaussian_process.kernels.Hyperparameter [source]

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

New in version 0.18.

Attributes:

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.

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.

Methods

`count`(...)
`index`((value, [start, ...)	Raises ValueError if the value is not present.

__init__(): x.__init__(...) initializes x; see help(type(x)) for signature

bounds: Alias for field number 2

count(value) ? integer -- return number of occurrences of value

fixed: Alias for field number 4

index(value[, start[, stop]]) ? integer -- return first index of value.: Raises ValueError if the value is not present.

n_elements: Alias for field number 3

name: Alias for field number 0

value_type: Alias for field number 1

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Hyperparameter.html

doc_scikit_learn

2017-01-15 04:22:36

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