| |
| |
| """scipy cdist(cosine) and linear_sum_assignment vs diar_exact::assign, bitwise, on random and edge inputs.""" |
| import numpy as np |
| from hypothesis import given, settings, strategies as st |
| from hypothesis.extra import numpy as hnp |
| from scipy.optimize import linear_sum_assignment |
| from scipy.spatial.distance import cdist |
|
|
| from conftest import bits_equal |
|
|
| fin = st.floats(-1e3, 1e3, allow_nan=False, width=64) |
|
|
|
|
| @settings(max_examples=500) |
| @given(st.integers(1, 9), st.integers(1, 6), st.integers(1, 300), st.integers(0, 2**31 - 1)) |
| def test_cdist_cosine(core, ra, rb, cols, seed): |
| rng = np.random.default_rng(seed) |
| xa, xb = rng.standard_normal((ra, cols)), rng.standard_normal((rb, cols)) |
| assert bits_equal(core.cdist_cosine(xa, xb), cdist(xa, xb, "cosine")) |
|
|
|
|
| @given(hnp.arrays(np.float64, st.tuples(st.integers(1, 6), st.integers(1, 9)), elements=fin), st.booleans()) |
| def test_lsap(core, cost, maximize): |
| a, b = linear_sum_assignment(cost, maximize=maximize) |
| assert core.lsap(np.ascontiguousarray(cost), maximize) == (list(a), list(b)) |
|
|
|
|
| def test_lsap_ties(core): |
| for cost in (np.zeros((4, 4)), np.ones((4, 2)), np.ones((2, 5)), np.array([[1.0, 1.0], [1.0, 1.0], [0.5, 0.5]])): |
| for mx in (False, True): |
| a, b = linear_sum_assignment(cost, maximize=mx) |
| assert core.lsap(np.ascontiguousarray(cost), mx) == (list(a), list(b)) |
|
|
