# encoding=utf8
import logging
from numpy import random as rand, inf, ndarray, asarray, array_equal, argmin, apply_along_axis
from NiaPy.util import FesException, GenException, TimeException, RefException
from NiaPy.util.utility import objects2array
logging.basicConfig()
logger = logging.getLogger('NiaPy.util.utility')
logger.setLevel('INFO')
__all__ = [
'Algorithm',
'Individual',
'defaultIndividualInit',
'defaultNumPyInit'
]
[docs]def defaultNumPyInit(task, NP, rnd=rand, **kwargs):
r"""Initialize starting population that is represented with `numpy.ndarray` with shape `{NP, task.D}`.
Args:
task (Task): Optimization task.
NP (int): Number of individuals in population.
rnd (Optional[mtrand.RandomState]): Random number generator.
kwargs (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float]]:
1. New population with shape `{NP, task.D}`.
2. New population function/fitness values.
"""
pop = task.Lower + rnd.rand(NP, task.D) * task.bRange
fpop = apply_along_axis(task.eval, 1, pop)
return pop, fpop
[docs]def defaultIndividualInit(task, NP, rnd=rand, itype=None, **kwargs):
r"""Initialize `NP` individuals of type `itype`.
Args:
task (Task): Optimization task.
NP (int): Number of individuals in population.
rnd (Optional[mtrand.RandomState]): Random number generator.
itype (Optional[Individual]): Class of individual in population.
kwargs (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray[Individual], numpy.ndarray[float]:
1. Initialized individuals.
2. Initialized individuals function/fitness values.
"""
pop = objects2array([itype(task=task, rnd=rnd, e=True) for _ in range(NP)])
return pop, asarray([x.f for x in pop])
[docs]class Algorithm:
r"""Class for implementing algorithms.
Date:
2018
Author
Klemen Berkovič
License:
MIT
Attributes:
Name (List[str]): List of names for algorithm.
Rand (mtrand.RandomState): Random generator.
NP (int): Number of inidividuals in populatin.
InitPopFunc (Callable[[int, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Idividual initialization function.
itype (Individual): Type of individuals used in population, default value is None for Numpy arrays.
"""
Name = ['Algorithm', 'AAA']
Rand = rand.RandomState(None)
NP = 50
InitPopFunc = defaultNumPyInit
itype = None
[docs] @staticmethod
def typeParameters():
r"""Return functions for checking values of parameters.
Return:
Dict[str, Callable]:
* NP (Callable[[int], bool]): Check if number of individuals is :math:`\in [0, \infty]`.
"""
return {'NP': lambda x: isinstance(x, int) and x > 0}
[docs] def __init__(self, **kwargs):
r"""Initialize algorithm and create name for an algorithm.
Args:
seed (int): Starting seed for random generator.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
self.Rand, self.exception = rand.RandomState(kwargs.pop('seed', None)), None
self.setParameters(**kwargs)
[docs] @staticmethod
def algorithmInfo():
r"""Get algorithm information.
Returns:
str: Bit item.
"""
return '''Basic algorithm. No implementation!!!'''
[docs] def setParameters(self, NP=50, InitPopFunc=defaultNumPyInit, itype=None, **kwargs):
r"""Set the parameters/arguments of the algorithm.
Args:
NP (Optional[int]): Number of individuals in population :math:`\in [1, \infty]`.
InitPopFunc (Optional[Callable[[int, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]]): Type of individuals used by algorithm.
itype (Optional[Any]): Individual type used in population, default is Numpy array.
**kwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.defaultNumPyInit`
* :func:`NiaPy.algorithms.defaultIndividualInit`
"""
self.NP, self.InitPopFunc, self.itype = NP, InitPopFunc, itype
[docs] def getParameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]:
* Parameter name (str): Represents a parameter name
* Value of parameter (Any): Represents the value of the parameter
"""
return {
'NP': self.NP,
'InitPopFunc': self.InitPopFunc,
'itype': self.itype
}
[docs] def rand(self, D=1):
r"""Get random distribution of shape D in range from 0 to 1.
Args:
D (numpy.ndarray[int]): Shape of returned random distribution.
Returns:
Union[numpy.ndarray[float], float]: Random number or numbers :math:`\in [0, 1]`.
"""
if isinstance(D, (ndarray, list)): return self.Rand.rand(*D)
elif D > 1: return self.Rand.rand(D)
else: return self.Rand.rand()
[docs] def normal(self, loc, scale, D=None):
r"""Get normal random distribution of shape D with mean "loc" and standard deviation "scale".
Args:
loc (float): Mean of the normal random distribution.
scale (float): Standard deviation of the normal random distribution.
D (Union[int, Iterable[int]]): Shape of returned normal random distribution.
Returns:
Union[numpy.ndarray[float], float]: Array of numbers.
"""
return self.Rand.normal(loc, scale, D) if D is not None else self.Rand.normal(loc, scale)
[docs] def randn(self, D=None):
r"""Get standard normal distribution of shape D.
Args:
D (Union[int, Iterable[int]]): Shape of returned standard normal distribution.
Returns:
Union[numpy.ndarray[float], float]: Random generated numbers or one random generated number :math:`\in [0, 1]`.
"""
if D is None: return self.Rand.randn()
elif isinstance(D, int): return self.Rand.randn(D)
return self.Rand.randn(*D)
[docs] def randint(self, Nmax, D=1, Nmin=0, skip=None):
r"""Get discrete uniform (integer) random distribution of D shape in range from "Nmin" to "Nmax".
Args:
Nmin (int): Lower integer bound.
Nmax (int): One above upper integer bound.
D (Union[int, Iterable[int]]): shape of returned discrete uniform random distribution.
skip (Union[int, Iterable[int], numpy.ndarray[int]]): numbers to skip.
Returns:
Union[int, numpy.ndarrayj[int]]: Random generated integer number.
"""
r = None
if isinstance(D, (list, tuple, ndarray)): r = self.Rand.randint(Nmin, Nmax, D)
elif D > 1: r = self.Rand.randint(Nmin, Nmax, D)
else: r = self.Rand.randint(Nmin, Nmax)
return r if skip is None or r not in skip else self.randint(Nmax, D, Nmin, skip)
[docs] def getBest(self, X, X_f, xb=None, xb_f=inf):
r"""Get the best individual for population.
Args:
X (numpy.ndarray): Current population.
X_f (numpy.ndarray): Current populations fitness/function values of aligned individuals.
xb (numpy.ndarray): Best individual.
xb_f (float): Fitness value of best individual.
Returns:
Tuple[numpy.ndarray, float]:
1. Coordinates of best solution.
2. beset fitness/function value.
"""
ib = argmin(X_f)
if isinstance(X_f, (float, int)) and xb_f >= X_f: xb, xb_f = X, X_f
elif isinstance(X_f, (ndarray, list)) and xb_f >= X_f[ib]: xb, xb_f = X[ib], X_f[ib]
return (xb.x.copy() if isinstance(xb, Individual) else xb.copy()), xb_f
[docs] def initPopulation(self, task):
r"""Initialize starting population of optimization algorithm.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
1. New population.
2. New population fitness values.
3. Additional arguments.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
pop, fpop = self.InitPopFunc(task=task, NP=self.NP, rnd=self.Rand, itype=self.itype)
return pop, fpop, {}
[docs] def runIteration(self, task, pop, fpop, xb, fxb, **dparams):
r"""Core functionality of algorithm.
This function is called on every algorithm iteration.
Args:
task (Task): Optimization task.
pop (numpy.ndarray): Current population coordinates.
fpop (numpy.ndarray): Current population fitness value.
xb (numpy.ndarray): Current generation best individuals coordinates.
xb_f (float): current generation best individuals fitness value.
**dparams (Dict[str, Any]): Additional arguments for algorithms.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
1. New populations coordinates.
2. New populations fitness values.
3. New global best position/solution
4. New global best fitness/objective value
5. Additional arguments of the algorithm.
See Also:
* :func:`NiaPy.algorithms.Algorithm.runYield`
"""
return pop, fpop, xb, fxb, dparams
[docs] def runYield(self, task):
r"""Run the algorithm for a single iteration and return the best solution.
Args:
task (Task): Task with bounds and objective function for optimization.
Returns:
Generator[Tuple[numpy.ndarray, float], None, None]: Generator getting new/old optimal global values.
Yield:
Tuple[numpy.ndarray, float]:
1. New population best individuals coordinates.
2. Fitness value of the best solution.
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
* :func:`NiaPy.algorithms.Algorithm.runIteration`
"""
pop, fpop, dparams = self.initPopulation(task)
xb, fxb = self.getBest(pop, fpop)
yield xb, fxb
while True:
pop, fpop, xb, fxb, dparams = self.runIteration(task, pop, fpop, xb, fxb, **dparams)
yield xb, fxb
[docs] def runTask(self, task):
r"""Start the optimization.
Args:
task (Task): Task with bounds and objective function for optimization.
Returns:
Tuple[numpy.ndarray, float]:
1. Best individuals components found in optimization process.
2. Best fitness value found in optimization process.
See Also:
* :func:`NiaPy.algorithms.Algorithm.runYield`
"""
algo, xb, fxb = self.runYield(task), None, inf
while not task.stopCond():
xb, fxb = next(algo)
task.nextIter()
return xb, fxb
[docs] def run(self, task):
r"""Start the optimization.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, float]:
1. Best individuals components found in optimization process.
2. Best fitness value found in optimization process.
See Also:
* :func:`NiaPy.algorithms.Algorithm.runTask`
"""
try:
# task.start()
r = self.runTask(task)
return r[0], r[1] * task.optType.value
except (FesException, GenException, TimeException, RefException): return task.x, task.x_f * task.optType.value
return None, None
[docs] def bad_run(self):
r"""Check if some exeptions where thrown when the algorithm was running.
Returns:
bool: True if some error where detected at runtime of the algorithm, otherwise False
"""
return self.exception is not None
[docs]class Individual:
r"""Class that represents one solution in population of solutions.
Date:
2018
Author:
Klemen Berkovič
License:
MIT
Attributes:
x (numpy.ndarray): Coordinates of individual.
f (float): Function/fitness value of individual.
"""
x = None
f = inf
[docs] def __init__(self, x=None, task=None, e=True, rnd=rand, **kwargs):
r"""Initialize new individual.
Parameters:
task (Optional[Task]): Optimization task.
rand (Optional[mtrand.RandomState]): Random generator.
x (Optional[numpy.ndarray]): Individuals components.
e (Optional[bool]): True to evaluate the individual on initialization. Default value is True.
**kwargs (Dict[str, Any]): Additional arguments.
"""
self.f = task.optType.value * inf if task is not None else inf
if x is not None: self.x = x if isinstance(x, ndarray) else asarray(x)
else: self.generateSolution(task, rnd)
if e and task is not None: self.evaluate(task, rnd)
[docs] def generateSolution(self, task, rnd=rand):
r"""Generate new solution.
Generate new solution for this individual and set it to ``self.x``.
This method uses ``rnd`` for getting random numbers.
For generating random components ``rnd`` and ``task`` is used.
Args:
task (Task): Optimization task.
rnd (Optional[mtrand.RandomState]): Random numbers generator object.
"""
if task is not None: self.x = task.Lower + task.bRange * rnd.rand(task.D)
[docs] def evaluate(self, task, rnd=rand):
r"""Evaluate the solution.
Evaluate solution ``this.x`` with the help of task.
Task is used for reparing the solution and then evaluating it.
Args:
task (Task): Objective function object.
rnd (Optional[mtrand.RandomState]): Random generator.
See Also:
* :func:`NiaPy.util.Task.repair`
"""
self.x = task.repair(self.x, rnd=rnd)
self.f = task.eval(self.x)
[docs] def copy(self):
r"""Return a copy of self.
Method returns copy of ``this`` object so it is safe for editing.
Returns:
Individual: Copy of self.
"""
return Individual(x=self.x.copy(), f=self.f, e=False)
[docs] def __eq__(self, other):
r"""Compare the individuals for equalities.
Args:
other (Union[Any, numpy.ndarray]): Object that we want to compare this object to.
Returns:
bool: `True` if equal or `False` if no equal.
"""
if isinstance(other, ndarray):
for e in other:
if self == e: return True
return False
return array_equal(self.x, other.x) and self.f == other.f
[docs] def __str__(self):
r"""Print the individual with the solution and objective value.
Returns:
str: String representation of self.
"""
return '%s -> %s' % (self.x, self.f)
[docs] def __getitem__(self, i):
r"""Get the value of i-th component of the solution.
Args:
i (int): Position of the solution component.
Returns:
Any: Value of ith component.
"""
return self.x[i]
[docs] def __setitem__(self, i, v):
r"""Set the value of i-th component of the solution to v value.
Args:
i (int): Position of the solution component.
v (Any): Value to set to i-th component.
"""
self.x[i] = v
[docs] def __len__(self):
r"""Get the length of the solution or the number of components.
Returns:
int: Number of components.
"""
return len(self.x)
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