| import numpy as np |
| import pytest |
|
|
| from starVLA.scoring.residual_cache import ( |
| interpolate_grid_scores, |
| percentile_intensity_masses, |
| quantile_anchor_positions, |
| ) |
|
|
|
|
| def test_quantile_grid_is_unique_and_covers_endpoints(): |
| positions = quantile_anchor_positions(101, 8) |
| assert len(positions) == 8 |
| assert positions[0] == 0 |
| assert positions[-1] == 100 |
| assert np.all(np.diff(positions) > 0) |
|
|
|
|
| def test_quantile_grid_uses_every_short_episode_anchor(): |
| np.testing.assert_array_equal(quantile_anchor_positions(3, 8), [0, 1, 2]) |
|
|
|
|
| def test_interpolation_preserves_grid_values(): |
| positions = np.asarray([0, 2, 4]) |
| scores = np.asarray([0.0, 2.0, 0.0]) |
| interpolated = interpolate_grid_scores(5, positions, scores) |
| np.testing.assert_allclose(interpolated, [0.0, 1.0, 2.0, 1.0, 0.0]) |
|
|
|
|
| def test_interpolation_requires_endpoint_coverage(): |
| with pytest.raises(ValueError, match="endpoints"): |
| interpolate_grid_scores(5, np.asarray([1, 4]), np.asarray([0.0, 1.0])) |
|
|
|
|
| def test_percentile_masses_have_floor_and_preserve_order(): |
| masses = percentile_intensity_masses( |
| np.asarray([5.0, 1.0, 3.0]), |
| epsilon=0.1, |
| ) |
| np.testing.assert_allclose(masses, [1.1, 0.1, 0.6]) |
|
|
|
|
