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)