# Path: randopt/experiment/experiment.py
# ATTACHMENT_DIR = '_attachments'
# ATTACHMENT_EXT = '.pk'
# class SummaryList(list):
# class JSONSummary(dict):
# class Experiment(object):
#     def __init__(self, results):
#     def __getitem__(self, key):
#     def __getslice__(self, i, j):
#     def __str__(self):
#     def count(self):
#     def filter(self, fn):
#     def values(self, key='result'):
#     def map(self, fn, key='result'):
#     def min(self, key='result'):
#     def max(self, key='result'):
#     def mean(self, key='result'):
#     def variance(self, key='result'):
#     def std(self, key='result'):
#     def median(self, key='result'):
#     def __init__(self, path):
#     def __getattr__(self, attr):
#     def __str__(self):
#     def _load_attachment(self):
#     def __getstate__(self):
#     def __setstate__(self, state):
#     def __init__(self, name, params={}, directory='randopt_results'):
#     def current(self):
#     def _search(self, fn=leq):
#     def top(self, count, fn=leq):
#     def maximum(self):
#     def minimum(self):
#     def all(self):
#     def list(self):
#     def count(self):
#     def seed(self, seed):
#     def set(self, key, value):
#     def sample(self, key):
#     def sample_all_params(self):
#     def add_result(self, result, data=None, attachment=None):
#     def all_results(self):
#     def save_state(self, path):
#     def set_state(self, path):


from random import sample
from .experiment import Experiment, leq

    """
    Selects the best `elite_size` results according to `fitness()` from
    the current experiment. Uniformly samples one of them as a parent, and
    perturbs it using the experiment's samplers.

    Note that in order to change parent, you should call experiment sample_parent()

    Parameters:

    * experiment - (Experiment) experiment to wrap.
    * elite_size - (int) number of results to consider as parents.
    * fitness - (function) function to determine fitness of result.

    Return type: n/a

    Example:

        experiment = Experiment('name', params={'lr': Gaussian()})
        evo = Evolutionary(experiment)
        evo.sample_parent()
        evo.sample_all_params()
        evo.add_result(loss(evo.params))
    """

    def __init__(self, experiment, elite_size=10, fitness=None):
        self.__dict__.update(experiment.__dict__)
        self.experiment = experiment
        self.elite_size = 10
        if fitness is None:

# Path: randopt/experiment/experiment.py
# ATTACHMENT_DIR = '_attachments'
# ATTACHMENT_EXT = '.pk'
# class SummaryList(list):
# class JSONSummary(dict):
# class Experiment(object):
#     def __init__(self, results):
#     def __getitem__(self, key):
#     def __getslice__(self, i, j):
#     def __str__(self):
#     def count(self):
#     def filter(self, fn):
#     def values(self, key='result'):
#     def map(self, fn, key='result'):
#     def min(self, key='result'):
#     def max(self, key='result'):
#     def mean(self, key='result'):
#     def variance(self, key='result'):
#     def std(self, key='result'):
#     def median(self, key='result'):
#     def __init__(self, path):
#     def __getattr__(self, attr):
#     def __str__(self):
#     def _load_attachment(self):
#     def __getstate__(self):
#     def __setstate__(self, state):
#     def __init__(self, name, params={}, directory='randopt_results'):
#     def current(self):
#     def _search(self, fn=leq):
#     def top(self, count, fn=leq):
#     def maximum(self):
#     def minimum(self):
#     def all(self):
#     def list(self):
#     def count(self):
#     def seed(self, seed):
#     def set(self, key, value):
#     def sample(self, key):
#     def sample_all_params(self):
#     def add_result(self, result, data=None, attachment=None):
#     def all_results(self):
#     def save_state(self, path):
#     def set_state(self, path):


# Path: randopt/experiment/evolutionary.py
from random import sample
from .experiment import Experiment, leq


    """
    Selects the best `elite_size` results according to `fitness()` from
    the current experiment. Uniformly samples one of them as a parent, and
    perturbs it using the experiment's samplers.

    Note that in order to change parent, you should call experiment sample_parent()

    Parameters:

    * experiment - (Experiment) experiment to wrap.
    * elite_size - (int) number of results to consider as parents.
    * fitness - (function) function to determine fitness of result.

    Return type: n/a

    Example:

        experiment = Experiment('name', params={'lr': Gaussian()})
        evo = Evolutionary(experiment)
        evo.sample_parent()
        evo.sample_all_params()
        evo.add_result(loss(evo.params))
    """

    def __init__(self, experiment, elite_size=10, fitness=None):
        self.__dict__.update(experiment.__dict__)
        self.experiment = experiment
        self.elite_size = 10
        if fitness is None:

            fitness = leq