# encoding=utf8
import logging
from numpy import random as rand
from NiaPy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.other')
logger.setLevel('INFO')
__all__ = ['HillClimbAlgorithm']
def Neighborhood(x, delta, task, rnd=rand):
r"""Get neighbours of point.
Args:
x numpy.ndarray: Point.
delta (float): Standard deviation.
task (Task): Optimization task.
rnd (Optional[mtrand.RandomState]): Random generator.
Returns:
Tuple[numpy.ndarray, float]:
1. New solution.
2. New solutions function/fitness value.
"""
X = x + rnd.normal(0, delta, task.D)
X = task.repair(X, rnd)
Xfit = task.eval(X)
return X, Xfit
[docs]class HillClimbAlgorithm(Algorithm):
r"""Implementation of iterative hill climbing algorithm.
Algorithm:
Hill Climbing Algorithm
Date:
2018
Authors:
Jan Popič
License:
MIT
Reference URL:
Reference paper:
See Also:
* :class:`NiaPy.algorithms.Algorithm`
Attributes:
delta (float): Change for searching in neighborhood.
Neighborhood (Callable[numpy.ndarray, float, Task], Tuple[numpy.ndarray, float]]): Function for getting neighbours.
"""
Name = ['HillClimbAlgorithm', 'BBFA']
[docs] @staticmethod
def algorithmInfo():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
:func:`NiaPy.algorithms.algorithm.Algorithm.algorithmInfo`
"""
return r"""TODO"""
[docs] @staticmethod
def typeParameters():
r"""TODO.
Returns:
Dict[str, Callable]:
* delta (Callable[[Union[int, float]], bool]): TODO
"""
return {'delta': lambda x: isinstance(x, (int, float)) and x > 0}
[docs] def setParameters(self, delta=0.5, Neighborhood=Neighborhood, **ukwargs):
r"""Set the algorithm parameters/arguments.
Args:
* delta (Optional[float]): Change for searching in neighborhood.
* Neighborhood (Optional[Callable[numpy.ndarray, float, Task], Tuple[numpy.ndarray, float]]]): Function for getting neighbours.
"""
Algorithm.setParameters(self, NP=1, **ukwargs)
self.delta, self.Neighborhood = delta, Neighborhood
[docs] def getParameters(self):
d = Algorithm.getParameters(self)
d.update({
'delta': self.delta,
'Neighborhood': self.Neighborhood
})
return d
[docs] def initPopulation(self, task):
r"""Initialize stating point.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, float, Dict[str, Any]]:
1. New individual.
2. New individual function/fitness value.
3. Additional arguments.
"""
x = task.Lower + self.rand(task.D) * task.bRange
return x, task.eval(x), {}
[docs] def runIteration(self, task, x, fx, xb, fxb, **dparams):
r"""Core function of HillClimbAlgorithm algorithm.
Args:
task (Task): Optimization task.
x (numpy.ndarray): Current solution.
fx (float): Current solutions fitness/function value.
xb (numpy.ndarray): Global best solution.
fxb (float): Global best solutions function/fitness value.
**dparams (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, float, numpy.ndarray, float, Dict[str, Any]]:
1. New solution.
2. New solutions function/fitness value.
3. Additional arguments.
"""
lo, xn = False, task.bcLower() + task.bcRange() * self.rand(task.D)
xn_f = task.eval(xn)
while not lo:
yn, yn_f = self.Neighborhood(x, self.delta, task, rnd=self.Rand)
if yn_f < xn_f: xn, xn_f = yn, yn_f
else: lo = True or task.stopCond()
xb, fxb = self.getBest(xn, xn_f, xb, fxb)
return xn, xn_f, xb, fxb, {}
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