def test_score():
gmm1 = GMM(number_of_gaussians=2)
gmm1.load_model(
pkg_resources.resource_filename("bob.bio.gmm.test",
"data/gmm_ubm.hdf5"))
biometric_reference = GMMMachine.from_hdf5(
pkg_resources.resource_filename("bob.bio.gmm.test",
"data/gmm_enrolled.hdf5"),
ubm=gmm1.ubm,
)
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=seed_value)
reference_score = 0.6509
numpy.testing.assert_almost_equal(gmm1.score(biometric_reference, probe),
reference_score,
decimal=5)
multi_refs = gmm1.score_multiple_biometric_references(
[biometric_reference, biometric_reference, biometric_reference], probe)
assert multi_refs.shape == (3, 1), multi_refs.shape
numpy.testing.assert_almost_equal(multi_refs, reference_score, decimal=5)
# With not projected data
numpy.testing.assert_almost_equal(gmm1.score(biometric_reference,
probe_data),
reference_score,
decimal=5)
def test_projector():
"""Tests the projector."""
# Load the UBM
gmm1 = GMM(number_of_gaussians=2)
gmm1.ubm = GMMMachine.from_hdf5(
pkg_resources.resource_filename("bob.bio.gmm.test",
"data/gmm_ubm.hdf5"))
# Generate and project random feature
feature = utils.random_array((20, 45), -5.0, 5.0, seed=seed_value)
projected = gmm1.project(feature)
assert isinstance(projected, GMMStats)
reference_file = pkg_resources.resource_filename(
"bob.bio.gmm.test", "data/gmm_projected.hdf5")
if regenerate_refs:
projected.save(reference_file)
reference = GMMStats.from_hdf5(reference_file)
assert projected.is_similar_to(reference)
def test_GMMStats():
# Test a GMMStats
# Initializes a GMMStats
n_gaussians = 2
n_features = 3
gs = GMMStats(n_gaussians, n_features)
log_likelihood = -3.0
T = 57
n = np.array([4.37, 5.31], "float64")
sumpx = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "float64")
sumpxx = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
gs.log_likelihood = log_likelihood
gs.t = T
gs.n = n
gs.sum_px = sumpx
gs.sum_pxx = sumpxx
np.testing.assert_equal(gs.log_likelihood, log_likelihood)
np.testing.assert_equal(gs.t, T)
np.testing.assert_equal(gs.n, n)
np.testing.assert_equal(gs.sum_px, sumpx)
np.testing.assert_equal(gs.sum_pxx, sumpxx)
np.testing.assert_equal(gs.shape, (n_gaussians, n_features))
# Saves and reads from file using `from_hdf5`
filename = str(tempfile.mkstemp(".hdf5")[1])
gs.save(HDF5File(filename, "w"))
gs_loaded = GMMStats.from_hdf5(HDF5File(filename, "r"))
assert gs == gs_loaded
assert (gs != gs_loaded) is False
assert gs.is_similar_to(gs_loaded)
assert type(gs_loaded.n_gaussians) is np.int64
assert type(gs_loaded.n_features) is np.int64
assert type(gs_loaded.log_likelihood) is np.float64
# Saves and load from file using `load`
filename = str(tempfile.mkstemp(".hdf5")[1])
gs.save(hdf5=HDF5File(filename, "w"))
gs_loaded = GMMStats(n_gaussians, n_features)
gs_loaded.load(HDF5File(filename, "r"))
assert gs == gs_loaded
assert (gs != gs_loaded) is False
assert gs.is_similar_to(gs_loaded)
# Makes them different
gs_loaded.t = 58
assert (gs == gs_loaded) is False
assert gs != gs_loaded
assert not (gs.is_similar_to(gs_loaded))
# Accumulates from another GMMStats
gs2 = GMMStats(n_gaussians, n_features)
gs2.log_likelihood = log_likelihood
gs2.t = T
gs2.n = n.copy()
gs2.sum_px = sumpx.copy()
gs2.sum_pxx = sumpxx.copy()
gs2 += gs
np.testing.assert_equal(gs2.log_likelihood, 2 * log_likelihood)
np.testing.assert_equal(gs2.t, 2 * T)
np.testing.assert_almost_equal(gs2.n, 2 * n, decimal=8)
np.testing.assert_almost_equal(gs2.sum_px, 2 * sumpx, decimal=8)
np.testing.assert_almost_equal(gs2.sum_pxx, 2 * sumpxx, decimal=8)
# Re-init and checks for zeros
gs_loaded.init_fields()
np.testing.assert_equal(gs_loaded.log_likelihood, 0)
np.testing.assert_equal(gs_loaded.t, 0)
np.testing.assert_equal(gs_loaded.n, np.zeros((n_gaussians, )))
np.testing.assert_equal(gs_loaded.sum_px,
np.zeros((n_gaussians, n_features)))
np.testing.assert_equal(gs_loaded.sum_pxx,
np.zeros((n_gaussians, n_features)))
# Resize and checks size
assert gs_loaded.shape == (n_gaussians, n_features)
gs_loaded.resize(4, 5)
assert gs_loaded.shape == (4, 5)
assert gs_loaded.sum_px.shape[0] == 4
assert gs_loaded.sum_px.shape[1] == 5
# Clean-up
os.unlink(filename)
