"""Evolution Strategies. """ import copy import numpy as np import opytimizer.math.random as r import opytimizer.utils.exception as e import opytimizer.utils.logging as l from opytimizer.core.optimizer import Optimizer logger = l.get_logger(__name__) class ES(Optimizer): """An ES class, inherited from Optimizer. This is the designed class to define ES-related variables and methods. References: T. Bäck and H.–P. Schwefel. An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation (1993). """ def __init__(self, params=None): """Initialization method. Args: params (dict): Contains key-value parameters to the meta-heuristics.
"""Moth-Flame Optimization. """ import copy from typing import Any, Dict, Optional import numpy as np import opytimizer.math.random as rnd import opytimizer.utils.exception as e from opytimizer.core import Optimizer from opytimizer.core.space import Space from opytimizer.utils import logging logger = logging.get_logger(__name__) class MFO(Optimizer): """A MFO class, inherited from Optimizer. This is the designed class to define MFO-related variables and methods. References: S. Mirjalili. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems (2015). """ def __init__(self, params: Optional[Dict[str, Any]] = None) -> None: """Initialization method.
def test_get_logger(): logger = logging.get_logger(__name__) assert logger.name == 'test_logging' assert logger.hasHandlers() == True
def test_logging_to_file(): logger = logging.get_logger(__name__) assert logger.to_file('msg') == None
def test_logging_to_file(): logger = logging.get_logger(__name__) assert logger.to_file("msg") is None
def test_logging_get_logger(): logger = logging.get_logger(__name__) assert logger.name == "test_logging" assert logger.hasHandlers() is True