"""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
