val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
param_distributions:Py.Object.t ->
n_iter:int ->
unit ->
tGenerator on parameters sampled from given distributions.
Non-deterministic iterable over random candidate combinations for hyper- parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.
Read more in the :ref:`User Guide <search>`.
Parameters ---------- param_distributions : dict Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a ``rvs`` method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.
n_iter : integer Number of parameter settings that are produced.
random_state : int, RandomState instance or None, optional (default=None) Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.
Returns ------- params : dict of string to any **Yields** dictionaries mapping each estimator parameter to as sampled value.
Examples -------- >>> from sklearn.model_selection import ParameterSampler >>> from scipy.stats.distributions import expon >>> import numpy as np >>> rng = np.random.RandomState(0) >>> param_grid = 'a':[1, 2], 'b': expon() >>> param_list = list(ParameterSampler(param_grid, n_iter=4, ... random_state=rng)) >>> rounded_list = dict((k, round(v, 6)) for (k, v) in d.items())
... for d in param_list >>> rounded_list == {'b': 0.89856, 'a': 1},
... {'b': 0.923223, 'a': 1},
... {'b': 1.878964, 'a': 2},
... {'b': 1.038159, 'a': 2} True
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.