# Path: opentuner/search/technique.py
# class SearchTechniqueBase(with_metaclass(abc.ABCMeta, object)):
# class SearchTechnique(SearchPlugin, SearchTechniqueBase):
# class PureRandom(SearchTechnique):
# class AsyncProceduralSearchTechnique(SearchTechnique):
# class SequentialSearchTechnique(AsyncProceduralSearchTechnique):
#     def __init__(self, name=None):
#     def is_ready(self):
#     def default_name(self):
#     def handle_requested_result(self, result):
#     def set_driver(self, driver):
#     def desired_result(self):
#     def __init__(self, *pargs, **kwargs):
#     def set_driver(self, driver):
#     def desired_result(self):
#     def desired_configuration(self):
#     def handle_requested_result(self, result):
#     def default_generated_name(self):
#     def use_default_generated_name(self):
#     def base_name(self):
#     def get_hyper_parameters(cls):
#     def generate_technique(cls, manipulator=None, *args, **kwargs):
#     def desired_configuration(self):
#     def __init__(self, *pargs, **kwargs):
#     def call_main_generator(self):
#     def desired_configuration(self):
#     def main_generator(self):
#     def is_ready(self):
#     def __init__(self, novelty_threshold=50, reset_threshold=500, *pargs, **kwargs):
#     def yield_nonblocking(self, cfg):
#     def call_main_generator(self):
# def register(t):
# def register_generator(cls, generator_weight=1.0, *args, **kwargs):
# def get_random_generator_technique(generators=None, manipulator=None):
# def weighted_choice(choices):
# def all_techniques():
# def get_enabled(args):
# def get_root(args):


from opentuner.search import technique
from .manipulator import *
        phi_l: influence of the particle's distance to its historial best position, a float in range [0,1]
        phi_g: influence of the particle's distance to the global best position, a float in range [0,1]
        """

        self.manipulator = m
        self.position = self.manipulator.random()
        self.best = self.position
        self.omega = omega
        self.phi_l = phi_l
        self.phi_g = phi_g
        self.crossover_choice = crossover_choice
        self.velocity = {}
        for p in self.manipulator.params:
            # Velocity as a continous value
            self.velocity[p.name] = 0

    def move(self, global_best):
        """
        Update parameter values using corresponding operators.
        TODO: introduce operator choice map
        """
        m = self.manipulator
        for p in m.params:
            self.velocity[p.name] = p.op3_swarm(self.position, global_best, self.best, c=self.omega, c1=self.phi_g,
                                                c2=self.phi_l, xchoice=self.crossover_choice,
                                                velocity=self.velocity[p.name])


technique.register(PSO(crossover='op3_cross_OX3'))
technique.register(PSO(crossover='op3_cross_OX1'))

# Path: opentuner/search/technique.py
# class SearchTechniqueBase(with_metaclass(abc.ABCMeta, object)):
# class SearchTechnique(SearchPlugin, SearchTechniqueBase):
# class PureRandom(SearchTechnique):
# class AsyncProceduralSearchTechnique(SearchTechnique):
# class SequentialSearchTechnique(AsyncProceduralSearchTechnique):
#     def __init__(self, name=None):
#     def is_ready(self):
#     def default_name(self):
#     def handle_requested_result(self, result):
#     def set_driver(self, driver):
#     def desired_result(self):
#     def __init__(self, *pargs, **kwargs):
#     def set_driver(self, driver):
#     def desired_result(self):
#     def desired_configuration(self):
#     def handle_requested_result(self, result):
#     def default_generated_name(self):
#     def use_default_generated_name(self):
#     def base_name(self):
#     def get_hyper_parameters(cls):
#     def generate_technique(cls, manipulator=None, *args, **kwargs):
#     def desired_configuration(self):
#     def __init__(self, *pargs, **kwargs):
#     def call_main_generator(self):
#     def desired_configuration(self):
#     def main_generator(self):
#     def is_ready(self):
#     def __init__(self, novelty_threshold=50, reset_threshold=500, *pargs, **kwargs):
#     def yield_nonblocking(self, cfg):
#     def call_main_generator(self):
# def register(t):
# def register_generator(cls, generator_weight=1.0, *args, **kwargs):
# def get_random_generator_technique(generators=None, manipulator=None):
# def weighted_choice(choices):
# def all_techniques():
# def get_enabled(args):
# def get_root(args):


# Path: opentuner/search/pso.py
from opentuner.search import technique
from .manipulator import *

        phi_l: influence of the particle's distance to its historial best position, a float in range [0,1]
        phi_g: influence of the particle's distance to the global best position, a float in range [0,1]
        """

        self.manipulator = m
        self.position = self.manipulator.random()
        self.best = self.position
        self.omega = omega
        self.phi_l = phi_l
        self.phi_g = phi_g
        self.crossover_choice = crossover_choice
        self.velocity = {}
        for p in self.manipulator.params:
            # Velocity as a continous value
            self.velocity[p.name] = 0

    def move(self, global_best):
        """
        Update parameter values using corresponding operators.
        TODO: introduce operator choice map
        """
        m = self.manipulator
        for p in m.params:
            self.velocity[p.name] = p.op3_swarm(self.position, global_best, self.best, c=self.omega, c1=self.phi_g,
                                                c2=self.phi_l, xchoice=self.crossover_choice,
                                                velocity=self.velocity[p.name])


technique.register(PSO(crossover='op3_cross_OX3'))
technique.register(PSO(crossover='op3_cross_OX1'))

technique.register(PSO(crossover='op3_cross_PMX'))