# Path: opentuner/search/technique.py
# class SearchTechnique(SearchPlugin, SearchTechniqueBase):
#     """
#     a search search technique with basic utility functions
#     """
# 
#     def __init__(self, *pargs, **kwargs):
#         super(SearchTechnique, self).__init__(*pargs, **kwargs)
#         self.driver = None
#         self.manipulator = None
#         self.objective = None
#         self.request_count = 0
# 
#     def set_driver(self, driver):
#         super(SearchTechnique, self).set_driver(driver)
#         self.manipulator = driver.manipulator
#         self.objective = driver.objective
#         driver.add_plugin(self)
# 
#     def desired_result(self):
#         """
#         create and return a resultsdb.models.DesiredResult
#         returns None if no desired results and False if waiting for results
#         """
#         cfg = self.desired_configuration()
#         if cfg is None:
#             return None
#         if cfg is False:
#             return False
#         if type(cfg) is Configuration:
#             config = cfg
#         else:
#             config = self.driver.get_configuration(cfg)
#         desired = DesiredResult(configuration=config,
#                                 requestor=self.name,
#                                 generation=self.driver.generation,
#                                 request_date=datetime.now(),
#                                 tuning_run=self.driver.tuning_run)
#         if hasattr(self, 'limit'):
#             desired.limit = self.limit
#         self.driver.register_result_callback(desired, self.handle_requested_result)
#         self.request_count += 1
#         return desired
# 
#     @abc.abstractmethod
#     def desired_configuration(self):
#         """
#         return a cfg that we should test
#         given a ConfigurationManipulator and SearchDriver
#         return None if there are no configurations to test
#         return False if waiting for results
#         """
#         return dict()
# 
#     def handle_requested_result(self, result):
#         """called for each new Result(), regardless of who requested it"""
#         pass
# 
#     def default_generated_name(self):
#         """ The default generated name for this technique """
#         return self.base_name()
# 
#     def use_default_generated_name(self):
#         """ set the name of this technique to the default generated name """
#         self.name = self.default_generated_name()
# 
#     def base_name(self):
#         """
#         Return the base name of this technique with form
#         classname;hyperparam1,v1;hyperparam2,v2 ...
#         where hyperparams are taken in order from get_hyper_parameters()
# 
#         Should only be called after this technique has finished initializing.
#         """
#         out = [self.__class__.__name__]
#         for hyper_parameter in self.get_hyper_parameters():
#             # get hyperparam,v as a string and append
#             try:
#                 out.append(hyper_parameter + ',' + str(getattr(self, hyper_parameter)))
#             except AttributeError:
#                 log.error("Uninitialized hyper-parameter %s for technique %s.",
#                           hyper_parameter, self.__class__.__name__)
# 
#         return ';'.join(out)
# 
#     @classmethod
#     def get_hyper_parameters(cls):
#         """
#         return a list of hyper-parameters names for this technique
# 
#         Name strings must match the corresponding attribute with the hyper-parameter
#         value on technique instances. Names should also match the key word argument
#         used when initializing an instance. Hyperparameters should only take literal
#         values.
# 
#         For example, given hyper parameter "mutation_rate", then the __init__ method
#         should have 'mutation_rate' as a key word argument and later have the line
#         self.mutation_rate = mutation_rate
#         """
#         return []
# 
#     @classmethod
#     def generate_technique(cls, manipulator=None, *args, **kwargs):
#         """ return a new technique based off this instance """
#         t = cls(*args, **kwargs)
#         t.use_default_generated_name()
#         return t
# 
# Path: opentuner/search/technique.py
# def register(t):
#     the_registry.append(t)


import logging
import random
import time
from builtins import map
from builtins import object
from builtins import range
from fn import _
from past.utils import old_div
from .technique import SearchTechnique
from .technique import register
        use_f = old_div(random.random(), 2.0) + 0.5

        params = self.manipulator.param_names(cfg, x1, x2, x3)
        random.shuffle(params)
        for i, k in enumerate(params):
            if i < self.n_cross or random.random() < self.cr:
                # cfg = x1 + use_f*(x2 - x3)
                cfg_params[k].op4_set_linear(x1, x2, x3, 1.0, use_f, -use_f)

        return cfg

    def handle_requested_result(self, result):
        """called when new results are added"""
        for p in self.population:
            if p.candidate_replacement == result.configuration:
                if self.objective.lt(p.candidate_replacement, p.config):
                    # candidate replacement was better, replace it!
                    p.config = p.candidate_replacement
                    log.info('better point')
                p.candidate_replacement = None


class DifferentialEvolutionAlt(DifferentialEvolution):
    def __init__(self, cr=0.2, **kwargs):
        kwargs['cr'] = cr
        super(DifferentialEvolutionAlt, self).__init__(**kwargs)


register(DifferentialEvolution())
register(DifferentialEvolutionAlt())

# Path: opentuner/search/technique.py
# class SearchTechnique(SearchPlugin, SearchTechniqueBase):
#     """
#     a search search technique with basic utility functions
#     """
# 
#     def __init__(self, *pargs, **kwargs):
#         super(SearchTechnique, self).__init__(*pargs, **kwargs)
#         self.driver = None
#         self.manipulator = None
#         self.objective = None
#         self.request_count = 0
# 
#     def set_driver(self, driver):
#         super(SearchTechnique, self).set_driver(driver)
#         self.manipulator = driver.manipulator
#         self.objective = driver.objective
#         driver.add_plugin(self)
# 
#     def desired_result(self):
#         """
#         create and return a resultsdb.models.DesiredResult
#         returns None if no desired results and False if waiting for results
#         """
#         cfg = self.desired_configuration()
#         if cfg is None:
#             return None
#         if cfg is False:
#             return False
#         if type(cfg) is Configuration:
#             config = cfg
#         else:
#             config = self.driver.get_configuration(cfg)
#         desired = DesiredResult(configuration=config,
#                                 requestor=self.name,
#                                 generation=self.driver.generation,
#                                 request_date=datetime.now(),
#                                 tuning_run=self.driver.tuning_run)
#         if hasattr(self, 'limit'):
#             desired.limit = self.limit
#         self.driver.register_result_callback(desired, self.handle_requested_result)
#         self.request_count += 1
#         return desired
# 
#     @abc.abstractmethod
#     def desired_configuration(self):
#         """
#         return a cfg that we should test
#         given a ConfigurationManipulator and SearchDriver
#         return None if there are no configurations to test
#         return False if waiting for results
#         """
#         return dict()
# 
#     def handle_requested_result(self, result):
#         """called for each new Result(), regardless of who requested it"""
#         pass
# 
#     def default_generated_name(self):
#         """ The default generated name for this technique """
#         return self.base_name()
# 
#     def use_default_generated_name(self):
#         """ set the name of this technique to the default generated name """
#         self.name = self.default_generated_name()
# 
#     def base_name(self):
#         """
#         Return the base name of this technique with form
#         classname;hyperparam1,v1;hyperparam2,v2 ...
#         where hyperparams are taken in order from get_hyper_parameters()
# 
#         Should only be called after this technique has finished initializing.
#         """
#         out = [self.__class__.__name__]
#         for hyper_parameter in self.get_hyper_parameters():
#             # get hyperparam,v as a string and append
#             try:
#                 out.append(hyper_parameter + ',' + str(getattr(self, hyper_parameter)))
#             except AttributeError:
#                 log.error("Uninitialized hyper-parameter %s for technique %s.",
#                           hyper_parameter, self.__class__.__name__)
# 
#         return ';'.join(out)
# 
#     @classmethod
#     def get_hyper_parameters(cls):
#         """
#         return a list of hyper-parameters names for this technique
# 
#         Name strings must match the corresponding attribute with the hyper-parameter
#         value on technique instances. Names should also match the key word argument
#         used when initializing an instance. Hyperparameters should only take literal
#         values.
# 
#         For example, given hyper parameter "mutation_rate", then the __init__ method
#         should have 'mutation_rate' as a key word argument and later have the line
#         self.mutation_rate = mutation_rate
#         """
#         return []
# 
#     @classmethod
#     def generate_technique(cls, manipulator=None, *args, **kwargs):
#         """ return a new technique based off this instance """
#         t = cls(*args, **kwargs)
#         t.use_default_generated_name()
#         return t
# 
# Path: opentuner/search/technique.py
# def register(t):
#     the_registry.append(t)


# Path: opentuner/search/differentialevolution.py
import logging
import random
import time
from builtins import map
from builtins import object
from builtins import range
from fn import _
from past.utils import old_div
from .technique import SearchTechnique
from .technique import register

        use_f = old_div(random.random(), 2.0) + 0.5

        params = self.manipulator.param_names(cfg, x1, x2, x3)
        random.shuffle(params)
        for i, k in enumerate(params):
            if i < self.n_cross or random.random() < self.cr:
                # cfg = x1 + use_f*(x2 - x3)
                cfg_params[k].op4_set_linear(x1, x2, x3, 1.0, use_f, -use_f)

        return cfg

    def handle_requested_result(self, result):
        """called when new results are added"""
        for p in self.population:
            if p.candidate_replacement == result.configuration:
                if self.objective.lt(p.candidate_replacement, p.config):
                    # candidate replacement was better, replace it!
                    p.config = p.candidate_replacement
                    log.info('better point')
                p.candidate_replacement = None


class DifferentialEvolutionAlt(DifferentialEvolution):
    def __init__(self, cr=0.2, **kwargs):
        kwargs['cr'] = cr
        super(DifferentialEvolutionAlt, self).__init__(**kwargs)


register(DifferentialEvolution())
register(DifferentialEvolutionAlt())

register(DifferentialEvolution(population_size=100, cr=0.2,