diff options
Diffstat (limited to 'test/test_parameters.py')
| -rwxr-xr-x | test/test_parameters.py | 46 |
1 files changed, 44 insertions, 2 deletions
diff --git a/test/test_parameters.py b/test/test_parameters.py index 57ab166..5d7ec84 100755 --- a/test/test_parameters.py +++ b/test/test_parameters.py @@ -3,6 +3,7 @@ from dfatool import dfatool as dt from dfatool import parameters from dfatool.utils import by_name_to_by_param +from dfatool.functions import analytic import unittest import numpy as np @@ -24,9 +25,16 @@ class TestModels(unittest.TestCase): ) def test_parameter_detection_linear(self): - rng = np.random.default_rng() + rng = np.random.default_rng(seed=1312) X = np.arange(200) % 50 Y = X + rng.normal(size=X.size) + parameter_names = ["p_mod5", "p_linear"] + + # Test input data: + # * param[0] ("p_mod5") == X % 5 (bogus data to test detection of non-influence) + # * param[1] ("p_linear") == X + # * TX power == X ± gaussian noise + # -> TX power depends linearly on "p_linear" by_name = { "TX": { "param": [(x % 5, x) for x in X], @@ -35,17 +43,51 @@ class TestModels(unittest.TestCase): } } by_param = by_name_to_by_param(by_name) - stats = parameters.ParamStats(by_name, by_param, ["p_mod5", "p_linear"], dict()) + stats = parameters.ParamStats(by_name, by_param, parameter_names, dict()) self.assertEqual(stats.depends_on_param("TX", "power", "p_mod5"), False) self.assertEqual(stats.depends_on_param("TX", "power", "p_linear"), True) + # Fit individual functions for each parameter (only "p_linear" in this case) + paramfit = dt.ParallelParamFit(by_param) paramfit.enqueue("TX", "power", 1, "p_linear") paramfit.fit() fit_result = paramfit.get_result("TX", "power") self.assertEqual(fit_result["p_linear"]["best"], "linear") + self.assertEqual("p_mod5" not in fit_result, True) + + # Fit a single function for all parameters (still only "p_linear" in this case) + + combined_fit = analytic.function_powerset(fit_result, parameter_names, 0) + + self.assertEqual( + combined_fit._model_str, + "0 + regression_arg(0) + regression_arg(1) * parameter(p_linear)", + ) + self.assertEqual( + combined_fit._function_str, + "0 + reg_param[0] + reg_param[1] * model_param[1]", + ) + + combined_fit.fit(by_param, "TX", "power") + + self.assertEqual(combined_fit.fit_success, True) + + self.assertEqual(combined_fit.is_predictable([None, None]), False) + self.assertEqual(combined_fit.is_predictable([None, 0]), True) + self.assertEqual(combined_fit.is_predictable([None, 50]), True) + self.assertEqual(combined_fit.is_predictable([0, None]), False) + self.assertEqual(combined_fit.is_predictable([50, None]), False) + self.assertEqual(combined_fit.is_predictable([0, 0]), True) + self.assertEqual(combined_fit.is_predictable([0, 50]), True) + self.assertEqual(combined_fit.is_predictable([50, 0]), True) + self.assertEqual(combined_fit.is_predictable([50, 50]), True) + + # The function should be linear without offset or skew + for i in range(100): + self.assertAlmostEqual(combined_fit.eval([None, i]), i, places=0) if __name__ == "__main__": |