def set_parameters(self,
n=50,
F=1,
CR=0.8,
CrossMutt=cross_rand1,
**ukwargs):
r"""Set the algorithm parameters.
Args:
n (Optional[int]): Population size.
F (Optional[float]): Scaling factor.
CR (Optional[float]): Crossover rate.
CrossMutt (Optional[Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, list], numpy.ndarray]]): Crossover and mutation strategy.
ukwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`WeOptPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self,
n=n,
init_pop_func=ukwargs.pop(
'init_pop_func', default_individual_init),
itype=ukwargs.pop('itype', Individual),
**ukwargs)
self.F, self.CR, self.CrossMutt = F, CR, CrossMutt
def set_parameters(self,
n=100,
a=0.5,
epsilon=0.001,
alpha=1.0,
r=0.5,
qmin=0.0,
qmax=2.0,
**ukwargs):
r"""Set the parameters of the algorithm.
Args:
n (int): Number of individuals in population.
a (Optional[float]): Starting loudness.
epsilon (Optional[float]): Scaling factor.
alpha (Optional[float]): Constant for updating loudness.
r (Optional[float]): Pulse rate.
qmin (Optional[float]): Minimum frequency.
qmax (Optional[float]): Maximum frequency.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self, n=n, **ukwargs)
self.A, self.epsilon, self.alpha, self.r, self.Qmin, self.Qmax = a, epsilon, alpha, r, qmin, qmax
def set_parameters(self,
n=None,
alpha=0.1,
gamma=0.3,
rho=-0.2,
sigma=-0.2,
**ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
n (Optional[int]): Number of individuals.
alpha (Optional[float]): Reflection coefficient parameter
gamma (Optional[float]): Expansion coefficient parameter
rho (Optional[float]): Contraction coefficient parameter
sigma (Optional[float]): Shrink coefficient parameter
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self,
n=n,
init_pop_func=ukwargs.pop(
'init_pop_func', self.init_pop),
**ukwargs)
self.alpha, self.gamma, self.rho, self.sigma = alpha, gamma, rho, sigma
def set_parameters(self,
M=40,
NoLsTests=5,
NoLs=5,
NoLsBest=5,
NoEnabled=17,
BONUS1=10,
BONUS2=1,
LSs=(MTS_LS1, MTS_LS2, MTS_LS3),
**ukwargs):
r"""Set the arguments of the algorithm.
Arguments:
M (int): Number of individuals in population.
NoLsTests (int): Number of test runs on local search algorithms.
NoLs (int): Number of local search algorithm runs.
NoLsBest (int): Number of locals search algorithm runs on best solution.
NoEnabled (int): Number of best solution for testing.
BONUS1 (int): Bonus for improving global best solution.
BONUS2 (int): Bonus for improving self.
LSs (Iterable[Callable[[numpy.ndarray, float, numpy.ndarray, float, bool, numpy.ndarray, Task, Dict[str, Any]], Tuple[numpy.ndarray, float, numpy.ndarray, float, bool, int, numpy.ndarray]]]): Local searches to use.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self, n=ukwargs.pop('n', M), **ukwargs)
self.NoLsTests, self.NoLs, self.NoLsBest, self.NoEnabled, self.BONUS1, self.BONUS2 = NoLsTests, NoLs, NoLsBest, NoEnabled, BONUS1, BONUS2
self.LSs = LSs
def set_parameters(self, **ukwargs): r"""Set the algorithm parameters/arguments. Args: ukwargs (dict): Additional keyword arguments. """ Algorithm.set_parameters(self, **ukwargs)
def set_parameters(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.set_parameters(self, n=1, **ukwargs)
self.delta, self.Neighborhood = delta, Neighborhood
def set_parameters(self, epsilon=1e-20, **ukwargs):
r"""Set core parameters of CovarianceMatrixAdaptionEvolutionStrategy algorithm.
Args:
epsilon (float): Small number.
ukwargs (Dict[str, Any]): Additional arguments.
"""
Algorithm.set_parameters(self, **ukwargs)
self.epsilon = epsilon
def set_parameters(self, n=25, a=3, Rmin=0, Rmax=2, **ukwargs):
r"""Set the arguments of an algorithm.
Args:
n (Optional[int]): Number of individual in population
a (Optional[float]): Parameter for control in :math:`r_1` value
Rmin (Optional[float]): Minimu value for :math:`r_3` value
Rmax (Optional[float]): Maximum value for :math:`r_3` value
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.setParameters`
"""
Algorithm.set_parameters(self, n=n, **ukwargs)
self.a, self.Rmin, self.Rmax = a, Rmin, Rmax
def set_parameters(self, n=25, p=0.35, beta=1.5, **ukwargs):
r"""Set core parameters of FlowerPollinationAlgorithm algorithm.
Args:
n (int): Population size.
p (float): Probability switch.
beta (float): Shape of the gamma distribution (should be greater than zero).
See Also:
* :func:`WeOptPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self, n=n, **ukwargs)
self.p, self.beta = p, beta
self.S = np.zeros((n, 10))
def set_parameters(self, N=50, pa=0.2, alpha=0.5, **ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
N (int): Population size :math:`\in [1, \infty)`
pa (float): factor :math:`\in [0, 1]`
alpah (float): TODO
ukwargs (Dict[str, Any]): Additional arguments
See Also:
* :func:`WeOptPy.algorithms.Algorithm.setParameters`
"""
ukwargs.pop('n', None)
Algorithm.set_parameters(self, n=N, **ukwargs)
self.pa, self.alpha = pa, alpha
def set_parameters(self, n=20, alpha=1, betamin=1, gamma=2, **ukwargs): r"""Set the parameters of the algorithm. Args: n (Optional[int]): Population size. alpha (Optional[float]): Alpha parameter. betamin (Optional[float]): Betamin parameter. gamma (Optional[flaot]): Gamma parameter. ukwargs (Dict[str, Any]): Additional arguments. See Also: * :func:`WeOptPy.algorithms.Algorithm.setParameters` """ Algorithm.set_parameters(self, n=n, **ukwargs) self.alpha, self.betamin, self.gamma = alpha, betamin, gamma
def set_parameters(self, n=10, limit=100, **ukwargs):
r"""Set the parameters of Artificial Bee Colony Algorithm.
Args:
n (int): Number of individuals in population.
limit (Optional[Union[float, numpy.ndarray[float]]]): Limit.
ukwargs (dict): Additional arguments.
See Also:
* :func:`WeOptPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self,
n=n,
init_pop_func=default_individual_init,
itype=SolutionABC,
**ukwargs)
self.FoodNumber, self.Limit = int(self.NP / 2), limit
def set_parameters(self,
n=40,
A=0.5,
r=0.5,
Qmin=0.0,
Qmax=2.0,
**ukwargs):
r"""Set the parameters of the algorithm.
Args:
A (Optional[float]): Loudness.
r (Optional[float]): Pulse rate.
Qmin (Optional[float]): Minimum frequency.
Qmax (Optional[float]): Maximum frequency.
See Also:
* :func:`WeOptPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self, n=n, **ukwargs)
self.A, self.r, self.Qmin, self.Qmax = A, r, Qmin, Qmax
def set_parameters(self,
delta=0.5,
T=2000,
deltaT=0.8,
coolingMethod=coolDelta,
epsilon=1e-23,
**ukwargs):
r"""Set the algorithm parameters/arguments.
Arguments:
delta (Optional[float]): Movement for neighbour search.
T (Optional[float]); Starting temperature.
deltaT (Optional[float]): Change in temperature.
coolingMethod (Optional[Callable]): Neighbourhood function.
epsilon (Optional[float]): Error value.
See Also
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
ukwargs.pop('n', None)
Algorithm.set_parameters(self, n=1, **ukwargs)
self.delta, self.T, self.deltaT, self.cool, self.epsilon = delta, T, deltaT, coolingMethod, epsilon
def set_parameters(self,
mu=1,
k=10,
c_a=1.1,
c_r=0.5,
epsilon=1e-20,
**ukwargs):
r"""Set the arguments of an algorithm.
Args:
mu (Optional[int]): Number of parents
k (Optional[int]): Number of iterations before checking and fixing rho
c_a (Optional[float]): Search range amplification factor
c_r (Optional[float]): Search range reduction factor
See Also:
* :func:`WeOptPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self,
n=mu,
itype=ukwargs.pop('itype', IndividualES),
**ukwargs)
self.mu, self.k, self.c_a, self.c_r, self.epsilon = mu, k, c_a, c_r, epsilon
def set_parameters(self, n=43, alpha=(1, 0.83), gamma=(1.17, 0.56), theta=(0.932, 0.832), d=euclidean, dn=euclidean, nl=1, F=1.2, CR=0.25, Combination=elitism, **ukwargs): r"""Set the parameters for the algorithm. Args: n (int): Number of individuals in population. alpha (Optional[List[float]]): Factor for fickleness index function :math:`\in [0, 1]`. gamma (Optional[List[float]]): Factor for external irregularity index function :math:`\in [0, \infty)`. theta (Optional[List[float]]): Factor for internal irregularity index function :math:`\in [0, \infty)`. d (Optional[Callable[[float, float], float]]): function that takes two arguments that are function values and calcs the distance between them. dn (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]]): function that takes two arguments that are points in function landscape and calcs the distance between them. nl (Optional[float]): Normalized range for neighborhood search :math:`\in (0, 1]`. F (Optional[float]): Mutation parameter. CR (Optional[float]): Crossover parameter :math:`\in [0, 1]`. Combination (Optional[Callable[[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, float, float, float, float, float, float, Task, mtrand.RandomState], Tuple[numpy.ndarray, float]]]): Function for combining individuals to get new position/individual. See Also: * :func:`WeOptPy.algorithms.Algorithm.setParameters` * Combination methods: * :func:`WeOptPy.algorithms.other.Elitism` * :func:`WeOptPy.algorithms.other.Crossover` * :func:`WeOptPy.algorithms.other.Sequential` """ Algorithm.set_parameters(self, n=n, **ukwargs) self.alpha, self.gamma, self.theta, self.d, self.dn, self.nl, self.F, self.CR, self.Combination = alpha, gamma, theta, d, dn, nl, F, CR, Combination
class AlgorithmBaseTestCase(TestCase):
r"""Test case for testing Algorithm class.
Date:
April 2019
Author:
Klemen Berkovič
Attributes:
seed (int): Starting seed of random generator.
rnd (mtrand.RandomState): Random generator.
a (Algorithm): Algorithm to use for testing.
See Also:
* :class:`NiaPy.algorithms.Individual`
"""
def setUp(self):
self.seed = 1
self.rnd = rnd.RandomState(self.seed)
self.a = Algorithm(seed=self.seed)
def test_algorithm_info_fine(self):
r"""Check if method works fine."""
i = Algorithm.algorithm_info()
self.assertIsNotNone(i)
def test_algorithm_getParameters_fine(self):
r"""Check if method works fine."""
algo = Algorithm()
params = algo.get_parameters()
self.assertIsNotNone(params)
def test_type_parameters_fine(self):
d = Algorithm.type_parameters()
self.assertIsNotNone(d)
def test_init_population_numpy_fine(self):
r"""Test if custome generation initialization works ok."""
a = Algorithm(n=10, init_pop_func=init_pop_numpy)
t = Task(d=20, benchmark=Sphere())
self.assertTrue(
np.array_equal(np.full((10, t.D), 0.0),
a.init_population(t)[0]))
def test_init_population_individual_fine(self):
r"""Test if custom generation initialization works ok."""
a = Algorithm(n=10,
init_pop_func=init_pop_individual,
itype=Individual)
t = Task(d=20, benchmark=Sphere())
i = Individual(x=np.full(t.D, 0.0), task=t)
pop, fpop, args, kwargs = a.init_population(t)
for e in pop:
self.assertEqual(i, e)
def test_set_parameters(self):
self.a.set_parameters(t=None, a=20)
self.assertRaises(AttributeError,
lambda: self.assertEqual(self.a.a, None))
def test_randint_fine(self):
o = self.a.randint(maximum=20, minimum=10, d=[10, 10])
self.assertEqual(o.shape, (10, 10))
self.assertTrue(np.array_equal(self.rnd.randint(10, 20, (10, 10)), o))
o = self.a.randint(maximum=20, minimum=10, d=(10, 5))
self.assertEqual(o.shape, (10, 5))
self.assertTrue(np.array_equal(self.rnd.randint(10, 20, (10, 5)), o))
o = self.a.randint(maximum=20, minimum=10, d=10)
self.assertEqual(o.shape, (10, ))
self.assertTrue(np.array_equal(self.rnd.randint(10, 20, 10), o))
def test_randn_fine(self):
a = self.a.randn([1, 2])
self.assertEqual(a.shape, (1, 2))
self.assertTrue(np.array_equal(self.rnd.randn(1, 2), a))
a = self.a.randn(1)
self.assertEqual(len(a), 1)
self.assertTrue(np.array_equal(self.rnd.randn(1), a))
a = self.a.randn(2)
self.assertEqual(len(a), 2)
self.assertTrue(np.array_equal(self.rnd.randn(2), a))
a = self.a.randn()
self.assertIsInstance(a, float)
self.assertTrue(np.array_equal(self.rnd.randn(), a))
def test_uniform_fine(self):
a = self.a.uniform(-10, 10, [10, 10])
self.assertEqual(a.shape, (10, 10))
self.assertTrue(np.array_equal(self.rnd.uniform(-10, 10, (10, 10)), a))
a = self.a.uniform(4, 10, (4, 10))
self.assertEqual(len(a), 4)
self.assertEqual(len(a[0]), 10)
self.assertTrue(np.array_equal(self.rnd.uniform(4, 10, (4, 10)), a))
a = self.a.uniform(1, 4, 2)
self.assertEqual(len(a), 2)
self.assertTrue(np.array_equal(self.rnd.uniform(1, 4, 2), a))
a = self.a.uniform(10, 100)
self.assertIsInstance(a, float)
self.assertEqual(self.rnd.uniform(10, 100), a)
def test_normal_fine(self):
a = self.a.normal(-10, 10, [10, 10])
self.assertEqual(a.shape, (10, 10))
self.assertTrue(np.array_equal(self.rnd.normal(-10, 10, (10, 10)), a))
a = self.a.normal(4, 10, (4, 10))
self.assertEqual(len(a), 4)
self.assertEqual(len(a[0]), 10)
self.assertTrue(np.array_equal(self.rnd.normal(4, 10, (4, 10)), a))
a = self.a.normal(1, 4, 2)
self.assertEqual(len(a), 2)
self.assertTrue(np.array_equal(self.rnd.normal(1, 4, 2), a))
a = self.a.normal(10, 100)
self.assertIsInstance(a, float)
self.assertEqual(self.rnd.normal(10, 100), a)
