def test__sample_sample(self):
"""_constant_sample returns a constant array of num_samples length."""
# Setup
instance = Univariate()
instance.constant_value = 15
expected_result = np.array([15, 15, 15, 15, 15])
# Run
result = instance._constant_sample(5)
# Check
compare_nested_iterables(result, expected_result)
def test__constant_percent_point(self):
"""constant_percent_point only is self.constant_value in non-zero probabilities."""
# Setup
instance = Univariate()
instance.constant_value = 3
X = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5])
expected_result = np.array([3, 3, 3, 3, 3, 3])
# Run
result = instance._constant_percent_point(X)
# Check
compare_nested_iterables(result, expected_result)
def test__constant_probability_density(self):
"""constant_probability_density only is 1 in self.constant_value."""
# Setup
instance = Univariate()
instance.constant_value = 3
X = np.array([1, 2, 3, 4, 5])
expected_result = np.array([0, 0, 1, 0, 0])
# Run
result = instance._constant_probability_density(X)
# Check
compare_nested_iterables(result, expected_result)
def test__constant_cumulative_distribution(self):
"""constant_cumulative_distribution returns only 0 and 1."""
# Setup
instance = Univariate()
instance.constant_value = 3
X = np.array([1, 2, 3, 4, 5])
expected_result = np.array([0, 0, 1, 1, 1])
# Run
result = instance._constant_cumulative_distribution(X)
# Check
compare_nested_iterables(result, expected_result)
