diff --git a/bob/bio/base/test/test_algorithms.py b/bob/bio/base/test/test_algorithms.py index 45df58e28735a086cd2e16bc3ceb9aefc66bbbdc..4b31dd111dd054de1449a84c297b57be59175a2d 100644 --- a/bob/bio/base/test/test_algorithms.py +++ b/bob/bio/base/test/test_algorithms.py @@ -180,7 +180,7 @@ def test_lda(): # enroll model from random features enroll = utils.random_training_set(5, 5, 0., 255., seed=21) model = lda1.enroll(enroll) - _compare(model, pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_model.hdf5'), lda1.write_model, lda1.read_model) + _compare(model, pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_model.hdf5'), lda1.write_model, lda1.read_model) # compare model with probe probe = lda1.read_feature(pkg_resources.resource_filename('bob.bio.base.test', 'data/lda_projected.hdf5')) reference_score = -233.30450012 @@ -335,8 +335,13 @@ def test_plda(): plda1.load_enroller(reference_file) plda3.load_enroller(temp_file) - assert plda1.pca_machine.is_similar_to(plda3.pca_machine) - assert plda1.plda_base.is_similar_to(plda3.plda_base) + numpy.testing.assert_array_almost_equal(plda1.pca_machine.weights, plda3.pca_machine.weights) + numpy.testing.assert_array_almost_equal(plda1.pca_machine.biases, plda3.pca_machine.biases) + numpy.testing.assert_array_almost_equal(abs(plda1.plda_base.f), abs(plda3.plda_base.f)) + numpy.testing.assert_array_almost_equal(plda1.plda_base.g, plda3.plda_base.g) + numpy.testing.assert_array_almost_equal(plda1.plda_base.mu, plda3.plda_base.mu) + numpy.testing.assert_array_almost_equal(plda1.plda_base.sigma, plda3.plda_base.sigma) + numpy.testing.assert_array_almost_equal(plda1.plda_base.variance_threshold, plda3.plda_base.variance_threshold) finally: if os.path.exists(temp_file): os.remove(temp_file)