diff --git a/bob/bio/gmm/test/data/gmm_enrolled.hdf5 b/bob/bio/gmm/test/data/gmm_enrolled.hdf5 index 6466ed0a3fc524a6ab698f09540aa908f5108c22..4774b226e8122be1a1eae3549a5124e4f1906ada 100644 Binary files a/bob/bio/gmm/test/data/gmm_enrolled.hdf5 and b/bob/bio/gmm/test/data/gmm_enrolled.hdf5 differ diff --git a/bob/bio/gmm/test/data/gmm_projected.hdf5 b/bob/bio/gmm/test/data/gmm_projected.hdf5 index fc5e0a7c0b8f41d3b7d9a03d07c8e277f8bb5386..1d030594936b7b1212db6e12baa4956eec4d5dba 100644 Binary files a/bob/bio/gmm/test/data/gmm_projected.hdf5 and b/bob/bio/gmm/test/data/gmm_projected.hdf5 differ diff --git a/bob/bio/gmm/test/data/gmm_ubm.hdf5 b/bob/bio/gmm/test/data/gmm_ubm.hdf5 index 6a8abe718f958351135e5992621a733016127ccc..0349d0612863cbf2c3d1dcefa62c29997edb22d8 100644 Binary files a/bob/bio/gmm/test/data/gmm_ubm.hdf5 and b/bob/bio/gmm/test/data/gmm_ubm.hdf5 differ diff --git a/bob/bio/gmm/test/test_gmm.py b/bob/bio/gmm/test/test_gmm.py index e2434f0893f006453f65046a8999fa7d95ebabba..4277ee225b1a15fa28d12aacbde4ccec23ac9fcf 100644 --- a/bob/bio/gmm/test/test_gmm.py +++ b/bob/bio/gmm/test/test_gmm.py @@ -95,7 +95,7 @@ def test_projector(): ) # Generate and project random feature - feature = utils.random_array((20, 45), -5.0, 5.0, seed=84) + feature = utils.random_array((20, 45), -5.0, 5.0, seed=seed_value) projected = gmm1.project(feature) assert isinstance(projected, bob.learn.em.mixture.GMMStats) @@ -120,7 +120,7 @@ def test_enroll(): ) gmm1.ubm = ubm # Enroll the biometric reference from random features - enroll = utils.random_training_set((20, 45), 5, -5.0, 5.0, seed=21) + enroll = utils.random_training_set((20, 45), 5, -5.0, 5.0, seed=seed_value) biometric_reference = gmm1.enroll(enroll) assert not biometric_reference.is_similar_to(biometric_reference.ubm) assert isinstance(biometric_reference, GMMMachine) @@ -153,9 +153,9 @@ def test_score(): probe = GMMStats.from_hdf5( pkg_resources.resource_filename("bob.bio.gmm.test", "data/gmm_projected.hdf5") ) - probe_data = utils.random_array((20, 45), -5.0, 5.0, seed=84) + probe_data = utils.random_array((20, 45), -5.0, 5.0, seed=seed_value) - reference_score = -0.098980 + reference_score = 0.593373 numpy.testing.assert_almost_equal( gmm1.score(biometric_reference, probe), reference_score, decimal=5