def test_sample(self, uniform_mock):
"""Sample use the inverse-transform method to generate new samples."""
# Setup
instance = Bivariate(CopulaTypes.FRANK)
instance.tau = 0.5
instance.theta = instance.compute_theta()
uniform_mock.return_value = np.array([0.1, 0.2, 0.4, 0.6, 0.8])
expected_result = np.array([[6.080069565509917e-06, 0.1],
[6.080069565509917e-06, 0.2],
[6.080069565509917e-06, 0.4],
[6.080069565509917e-06, 0.6],
[4.500185268624483e-06, 0.8]])
expected_uniform_call_args_list = [((0, 1, 5), {}), ((0, 1, 5), {})]
# Run
result = instance.sample(5)
# Check
assert isinstance(result, np.ndarray)
assert result.shape == (5, 2)
compare_nested_iterables(result, expected_result)
assert uniform_mock.call_args_list == expected_uniform_call_args_list
def test_cdf_value_if_all_other_arg_are_one(self):
"""Test of the analytical properties of copulas on a range of values of theta."""
# Setup
instance = Bivariate(CopulaTypes.FRANK)
tau_values = np.linspace(-1.0, 1.0, 20)[1:-1]
# Run/Check
for tau in tau_values:
instance.tau = tau
instance.theta = instance.compute_theta()
copula_single_arg_not_one(instance, tolerance=1E-03)
def test_cdf_zero_if_single_arg_is_zero(self):
"""Test of the analytical properties of copulas on a range of values of theta."""
# Setup
instance = Bivariate(CopulaTypes.FRANK)
tau_values = np.linspace(-1.0, 1.0, 20)[1:-1]
# Run/Check
for tau in tau_values:
instance.tau = tau
instance.theta = instance.compute_theta()
copula_zero_if_arg_zero(instance)
def test_sample_random_state(self):
"""If random_state is set, the samples are the same."""
# Setup
instance = Bivariate(CopulaTypes.FRANK, random_seed=0)
instance.tau = 0.5
instance.theta = instance.compute_theta()
expected_result = np.array([[3.66330927e-06, 5.48813504e-01],
[6.08006957e-06, 7.15189366e-01],
[5.27582646e-06, 6.02763376e-01],
[5.58315848e-06, 5.44883183e-01],
[6.08006957e-06, 4.23654799e-01]])
# Run
result = instance.sample(5)
# Check
compare_nested_iterables(result, expected_result)
def test_sample_random_state(self):
"""If random_state is set, the samples are the same."""
# Setup
instance = Bivariate(CopulaTypes.CLAYTON, random_seed=0)
instance.tau = 0.5
instance.theta = instance.compute_theta()
expected_result = np.array([[0.68627770, 0.54881350],
[0.64059280, 0.71518937],
[0.90594782, 0.60276338],
[0.96040856, 0.54488318],
[0.40876969, 0.42365480]])
# Run
result = instance.sample(5)
# Check
compare_nested_iterables(result, expected_result)
def test_sample(self, uniform_mock):
"""Sample use the inverse-transform method to generate new samples."""
# Setup
instance = Bivariate(CopulaTypes.CLAYTON)
instance.tau = 0.5
instance.theta = instance.compute_theta()
uniform_mock.return_value = np.array([0.1, 0.2, 0.4, 0.6, 0.8])
expected_result = np.array([[0.05233100, 0.1], [0.14271095, 0.2],
[0.39959746, 0.4], [0.68567125, 0.6],
[0.89420523, 0.8]])
expected_uniform_call_args_list = [((0, 1, 5), {}), ((0, 1, 5), {})]
# Run
result = instance.sample(5)
# Check
assert isinstance(result, np.ndarray)
assert result.shape == (5, 2)
compare_nested_iterables(result, expected_result)
assert uniform_mock.call_args_list == expected_uniform_call_args_list
