def __init__(self, **kwargs):
Algorithm.__init__(self, name='FireflyAlgorithm', sName='FA', **kwargs)
def setUp(self): self.a = Algorithm()
def __init__(self, **kwargs):
if kwargs.get('name', None) == None:
Algorithm.__init__(self, name='KrillHerd', sName='KH', **kwargs)
else:
Algorithm.__init__(self, **kwargs)
def getParameters(self):
d = Algorithm.getParameters(self)
d.update({'delta': self.delta, 'Neighborhood': self.Neighborhood})
return d
def __init__(self, **kwargs):
Algorithm.__init__(self,
name='SineCosineAlgorithm',
sName='SCA',
**kwargs)
def initPopulation(self, task):
pop, fpop, d = Algorithm.initPopulation(self, task)
d.update({'S': zeros((self.NP, task.D))})
return pop, fpop, d
def test_algorithm_getParameters_fine(self):
r"""Check if method works fine."""
algo = Algorithm()
params = algo.getParameters()
self.assertIsNotNone(params)
def test_type_parameters_fine(self):
d = Algorithm.typeParameters()
self.assertIsNotNone(d)
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.algorithmInfo()
self.assertIsNotNone(i)
class AlgorithBaseTestCase(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.algorithmInfo()
self.assertIsNotNone(i)
def test_algorithm_getParameters_fine(self):
r"""Check if method works fine."""
algo = Algorithm()
params = algo.getParameters()
self.assertIsNotNone(params)
def test_type_parameters_fine(self):
d = Algorithm.typeParameters()
self.assertIsNotNone(d)
def test_init_population_numpy_fine(self):
r"""Test if custome generation initialization works ok."""
a = Algorithm(NP=10, InitPopFunc=init_pop_numpy)
t = Task(D=20, benchmark=MyBenchmark())
self.assertTrue(
array_equal(full((10, t.D), 0.0),
a.initPopulation(t)[0]))
def test_init_population_individual_fine(self):
r"""Test if custome generation initialization works ok."""
a = Algorithm(NP=10, InitPopFunc=init_pop_individual, itype=Individual)
t = Task(D=20, benchmark=MyBenchmark())
i = Individual(x=full(t.D, 0.0), task=t)
pop, fpop, d = a.initPopulation(t)
for e in pop:
self.assertEqual(i, e)
def test_setParameters(self):
self.a.setParameters(t=None, a=20)
self.assertRaises(AttributeError,
lambda: self.assertEqual(self.a.a, None))
def test_randint_fine(self):
o = self.a.randint(Nmax=20, Nmin=10, D=[10, 10])
self.assertEqual(o.shape, (10, 10))
self.assertTrue(array_equal(self.rnd.randint(10, 20, (10, 10)), o))
o = self.a.randint(Nmax=20, Nmin=10, D=(10, 5))
self.assertEqual(o.shape, (10, 5))
self.assertTrue(array_equal(self.rnd.randint(10, 20, (10, 5)), o))
o = self.a.randint(Nmax=20, Nmin=10, D=10)
self.assertEqual(o.shape, (10, ))
self.assertTrue(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(array_equal(self.rnd.randn(1, 2), a))
a = self.a.randn(1)
self.assertEqual(len(a), 1)
self.assertTrue(array_equal(self.rnd.randn(1), a))
a = self.a.randn(2)
self.assertEqual(len(a), 2)
self.assertTrue(array_equal(self.rnd.randn(2), a))
a = self.a.randn()
self.assertIsInstance(a, float)
self.assertTrue(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(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(array_equal(self.rnd.uniform(4, 10, (4, 10)), a))
a = self.a.uniform(1, 4, 2)
self.assertEqual(len(a), 2)
self.assertTrue(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(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(array_equal(self.rnd.normal(4, 10, (4, 10)), a))
a = self.a.normal(1, 4, 2)
self.assertEqual(len(a), 2)
self.assertTrue(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)
def getParameters(self):
d = Algorithm.getParameters(self)
d.pop('NP', None)
d.update({'N': self.NP, 'pa': self.pa, 'alpha': self.alpha})
return d
def __init__(self, **kwargs):
Algorithm.__init__(self, **kwargs)
self.LSs = [MTS_LS1, MTS_LS2, MTS_LS3]
def setParameters(self, **ukwargs):
r"""Set the algorithm parameters/arguments."""
Algorithm.setParameters(self, **ukwargs)
def test_init_population_numpy_fine(self): r"""Test if custome generation initialization works ok.""" a = Algorithm(NP=10, InitPopFunc=init_pop_numpy) t = Task(D=20, benchmark=MyBenchmark()) self.assertTrue(array_equal(full((10, t.D), 0.0), a.initPopulation(t)[0]))
