Unit test for generate_multi_modal_data function.
This test checks whether the function returns an array of correct size and also if the data
follows the expected distribution by checking whether the mean and standard deviation are
close to the expected values.
Raises:
| Type |
Description |
AssertionError
|
If the returned array is not of the correct size. |
AssertionError
|
If the mean or standard deviation of the generated data is too far from the expected value. |
Source code in uncertaintyplayground/tests/test_generate_data.py
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32 | def test_generate_multi_modal_data():
"""
Unit test for `generate_multi_modal_data` function.
This test checks whether the function returns an array of correct size and also if the data
follows the expected distribution by checking whether the mean and standard deviation are
close to the expected values.
Raises:
AssertionError: If the returned array is not of the correct size.
AssertionError: If the mean or standard deviation of the generated data is too far from the expected value.
"""
num_samples = 10000
modes = [
{'mean': -3.0, 'std_dev': 0.5, 'weight': 0.3},
{'mean': 0.0, 'std_dev': 1.0, 'weight': 0.4},
{'mean': 3.0, 'std_dev': 0.7, 'weight': 0.3}
]
y = generate_multi_modal_data(num_samples, modes)
assert len(y) == num_samples, "The generated data does not have the correct number of samples."
# Check if the mean and std deviation are close to expected values
for mode in modes:
mode_samples = [sample for sample in y if abs(sample-mode['mean']) < 3*mode['std_dev']]
assert abs(np.mean(mode_samples)-mode['mean']) < 0.1, f"For mode with mean {mode['mean']}, generated data does not have correct mean."
assert abs(np.std(mode_samples)-mode['std_dev']) < 0.1, f"For mode with mean {mode['mean']}, generated data does not have correct std deviation."
|