def uniform_hypothesis(distribution: dist.Distribution, size: int):
sample = distribution.create_sample(size)
characteristics = Characteristics(sample)
hypothesis = Hypothesis()
hyp_distribution = dist.UniformDistribution(characteristics.min(),
characteristics.max())
hypothesis.check_hypothesis(sample, hyp_distribution)
def main():
size = 100
normal_distribution = dist.NormalDistribution(0, 1)
print('Normal for Normal')
normal_hypothesis(normal_distribution, size)
size = 20
laplace_distribution = dist.LaplaceDistribution(0, 1 / (2**0.5))
uniform_distribution = dist.UniformDistribution(-3**0.5, 3**0.5)
print('Laplace for Normal')
normal_hypothesis(laplace_distribution, size)
print('Uniform for Normal')
normal_hypothesis(uniform_distribution, size)
print('Normal for Laplace')
laplace_hypothesis(normal_distribution, size)
print('Normal for Uniform')
uniform_hypothesis(normal_distribution, size)
return 0
def test_uniform_incorrect_lowest(self):
test_class = dis.UniformDistribution()
self.assertRaises(Exception, test_class.set_variables, -1, 2, 3, 4, 5)
def test_uniform_five_to_ten(self):
test_class = dis.UniformDistribution()
test_class.set_variables(5, 10)
self.assertTrue(5.0 <= test_class.bootstrap() <= 10.0)
def test_uniform_eleven_constant(self):
test_class = dis.UniformDistribution()
test_class.set_variables(11, 11)
self.assertEqual(test_class.bootstrap(), 11.0)
def test_uniform_five_constant(self):
test_class = dis.UniformDistribution()
test_class.set_variables(5, 5, 3, 4, 5)
self.assertEqual(test_class.bootstrap(), 5.0)
def test_uniform_incorrect_multiplication_value(self):
test_class = dis.UniformDistribution()
self.assertRaises(Exception, test_class.set_variables, 1, 2, 3, -4, 5)