How to use the hyperactive.base_positioner.BasePositioner function in hyperactive

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github SimonBlanke / Hyperactive / hyperactive / base_optimizer.py View on Github external
def _init_base_positioner(self, _cand_, positioner=None):
        if positioner:
            _p_ = positioner(self._opt_args_)
        else:
            _p_ = BasePositioner(self._opt_args_)

        _p_.pos_new = _cand_.pos_best
        _p_.score_new = _cand_.score_best

        return _p_
github SimonBlanke / Hyperactive / hyperactive / optimizers / local / hill_climbing_optimizer.py View on Github external
self._update_pos(_cand_, self.p_list[0])

    def _iterate(self, i, _cand_):
        self._hill_climb_iter(i, _cand_)

    def _init_iteration(self, _cand_):
        p = super()._init_base_positioner(_cand_, positioner=HillClimbingPositioner)

        self._optimizer_eval(_cand_, p)
        self._update_pos(_cand_, p)

        return p


class HillClimbingPositioner(BasePositioner):
    def __init__(self, _opt_args_):
        super().__init__(_opt_args_)

        self.epsilon = _opt_args_.epsilon
        self.distribution = _opt_args_.distribution
github SimonBlanke / Hyperactive / hyperactive / optimizers / sequence_model / sbom.py View on Github external
self._optimizer_eval(_cand_, p)
        self._update_pos(_cand_, p)

        self._all_possible_pos(_cand_)

        if self._opt_args_.warm_start_smbo:
            self.X_sample = _cand_.mem._get_para()
            self.Y_sample = _cand_.mem._get_score()
        else:
            self.X_sample = _cand_.pos_best.reshape(1, -1)
            self.Y_sample = np.array(_cand_.score_best).reshape(1, -1)

        return p


class SbomPositioner(BasePositioner):
    def __init__(self, _opt_args_):
        super().__init__(_opt_args_)
github SimonBlanke / Hyperactive / hyperactive / optimizers / population / particle_swarm_optimization.py View on Github external
self._optimizer_eval(_cand_, _p_current)
        self._update_pos(_cand_, _p_current)

        return _cand_

    def _init_iteration(self, _cand_):
        p = self._init_particle(_cand_)

        self._optimizer_eval(_cand_, p)
        self._update_pos(_cand_, p)

        return p


class Particle(BasePositioner):
    def __init__(self):
        super().__init__(self)
        self.velo = None

    def move_part(self, _cand_, pos):
        pos_new = (pos + self.velo).astype(int)
        # limit movement
        n_zeros = [0] * len(_cand_._space_.dim)
        self.pos_new = np.clip(pos_new, n_zeros, _cand_._space_.dim)