def test_JFATrainer_updateYandV():
# test the JFATrainer for updating Y and V
v_ref = numpy.array([
0.7228, 0.7892, 0.6475, 0.6080, 0.8631, 0.8416, 1.6512, 1.6068, 0.0500,
0.0101, 0.4325, 0.6719
]).reshape((6, 2))
y1 = numpy.array([0., 0.])
y2 = numpy.array([0., 0.])
y3 = numpy.array([0.9630, 1.3868])
y4 = numpy.array([0.0426, -0.3721])
y = [y1, y2]
# call the updateY function
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm, 2, 2)
t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
m.d = M_d
t.__X__ = M_x
t.__Y__ = y
t.__Z__ = M_z
t.e_step_v(m, TRAINING_STATS)
t.m_step_v(m, TRAINING_STATS)
# Expected results(JFA cookbook, matlab)
assert equals(t.__Y__[0], y3, 2e-4)
assert equals(t.__Y__[1], y4, 2e-4)
assert equals(m.v, v_ref, 2e-4)
def test_JFATrainInitialize():
# Check that the initialization is consistent and using the rng (cf. issue #118)
eps = 1e-10
# UBM GMM
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
## JFA
jb = JFABase(ubm, 2, 2)
# first round
rng = bob.core.random.mt19937(0)
jt = JFATrainer()
#jt.rng = rng
jt.initialize(jb, TRAINING_STATS, rng)
u1 = jb.u
v1 = jb.v
d1 = jb.d
# second round
rng = bob.core.random.mt19937(0)
jt.initialize(jb, TRAINING_STATS, rng)
u2 = jb.u
v2 = jb.v
d2 = jb.d
assert numpy.allclose(u1, u2, eps)
assert numpy.allclose(v1, v2, eps)
assert numpy.allclose(d1, d2, eps)
def test_ISVTrainInitialize():
# Check that the initialization is consistent and using the rng (cf. issue #118)
eps = 1e-10
# UBM GMM
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
## ISV
ib = ISVBase(ubm, 2)
# first round
rng = bob.core.random.mt19937(0)
it = ISVTrainer(10)
#it.rng = rng
it.initialize(ib, TRAINING_STATS, rng)
u1 = ib.u
d1 = ib.d
# second round
rng = bob.core.random.mt19937(0)
#it.rng = rng
it.initialize(ib, TRAINING_STATS, rng)
u2 = ib.u
d2 = ib.d
assert numpy.allclose(u1, u2, eps)
assert numpy.allclose(d1, d2, eps)
def test_JFATrainer_updateXandU():
# test the JFATrainer for updating X and U
u_ref = numpy.array([
0.6729, 0.3408, 0.0544, 1.0653, 0.5399, 1.3035, 2.4995, 0.4385, 0.1292,
-0.0576, 1.1962, 0.0117
]).reshape((6, 2))
x1 = numpy.array([0., 0., 0., 0.]).reshape((2, 2))
x2 = numpy.array([0., 0., 0., 0.]).reshape((2, 2))
x3 = numpy.array([0.2143, 1.8275, 3.1979, 0.1227]).reshape((2, 2))
x4 = numpy.array([-1.3861, 0.2359, 5.3326, -0.7914]).reshape((2, 2))
x = [x1, x2]
# call the updateX function
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm, 2, 2)
t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
m.d = M_d
t.__X__ = x
t.__Y__ = M_y
t.__Z__ = M_z
t.e_step_u(m, TRAINING_STATS)
t.m_step_u(m, TRAINING_STATS)
# Expected results(JFA cookbook, matlab)
assert equals(t.__X__[0], x3, 2e-4)
assert equals(t.__X__[1], x4, 2e-4)
assert equals(m.u, u_ref, 2e-4)
def test_JFATrainer_updateZandD():
# test the JFATrainer for updating Z and D
d_ref = numpy.array([0.3110, 1.0138, 0.8297, 1.0382, 0.0095, 0.6320])
z1 = numpy.array([0., 0., 0., 0., 0., 0.])
z2 = numpy.array([0., 0., 0., 0., 0., 0.])
z3_ref = numpy.array([0.3256, 1.8633, 0.6480, 0.8085, -0.0432, 0.2885])
z4_ref = numpy.array([-0.3324, -0.1474, -0.4404, -0.4529, 0.0484, -0.5848])
z = [z1, z2]
# call the updateZ function
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm, 2, 2)
t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
m.d = M_d
t.__X__ = M_x
t.__Y__ = M_y
t.__Z__ = z
t.e_step_d(m, TRAINING_STATS)
t.m_step_d(m, TRAINING_STATS)
# Expected results(JFA cookbook, matlab)
assert equals(t.__Z__[0], z3_ref, 2e-4)
assert equals(t.__Z__[1], z4_ref, 2e-4)
assert equals(m.d, d_ref, 2e-4)
def test_ISVTrainInitialize(): # Check that the initialization is consistent and using the rng (cf. issue #118) eps = 1e-10 # UBM GMM ubm = GMMMachine(2,3) ubm.mean_supervector = UBM_MEAN ubm.variance_supervector = UBM_VAR ## ISV ib = ISVBase(ubm, 2) # first round rng = bob.core.random.mt19937(0) it = ISVTrainer(10) #it.rng = rng it.initialize(ib, TRAINING_STATS, rng) u1 = ib.u d1 = ib.d # second round rng = bob.core.random.mt19937(0) #it.rng = rng it.initialize(ib, TRAINING_STATS, rng) u2 = ib.u d2 = ib.d assert numpy.allclose(u1, u2, eps) assert numpy.allclose(d1, d2, eps)
def test_JFATrainer_updateYandV():
# test the JFATrainer for updating Y and V
v_ref = numpy.array( [0.7228, 0.7892, 0.6475, 0.6080, 0.8631, 0.8416,
1.6512, 1.6068, 0.0500, 0.0101, 0.4325, 0.6719]).reshape((6,2))
y1 = numpy.array([0., 0.])
y2 = numpy.array([0., 0.])
y3 = numpy.array([0.9630, 1.3868])
y4 = numpy.array([0.0426, -0.3721])
y=[y1, y2]
# call the updateY function
ubm = GMMMachine(2,3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm,2,2)
t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
m.d = M_d
t.__X__ = M_x
t.__Y__ = y
t.__Z__ = M_z
t.e_step_v(m, TRAINING_STATS)
t.m_step_v(m, TRAINING_STATS)
# Expected results(JFA cookbook, matlab)
assert equals(t.__Y__[0], y3, 2e-4)
assert equals(t.__Y__[1], y4, 2e-4)
assert equals(m.v, v_ref, 2e-4)
def test_JFATrainInitialize(): # Check that the initialization is consistent and using the rng (cf. issue #118) eps = 1e-10 # UBM GMM ubm = GMMMachine(2,3) ubm.mean_supervector = UBM_MEAN ubm.variance_supervector = UBM_VAR ## JFA jb = JFABase(ubm, 2, 2) # first round rng = bob.core.random.mt19937(0) jt = JFATrainer() #jt.rng = rng jt.initialize(jb, TRAINING_STATS, rng) u1 = jb.u v1 = jb.v d1 = jb.d # second round rng = bob.core.random.mt19937(0) jt.initialize(jb, TRAINING_STATS, rng) u2 = jb.u v2 = jb.v d2 = jb.d assert numpy.allclose(u1, u2, eps) assert numpy.allclose(v1, v2, eps) assert numpy.allclose(d1, d2, eps)
def test_JFATrainer_updateZandD(): # test the JFATrainer for updating Z and D d_ref = numpy.array([0.3110, 1.0138, 0.8297, 1.0382, 0.0095, 0.6320]) z1 = numpy.array([0., 0., 0., 0., 0., 0.]) z2 = numpy.array([0., 0., 0., 0., 0., 0.]) z3_ref = numpy.array([0.3256, 1.8633, 0.6480, 0.8085, -0.0432, 0.2885]) z4_ref = numpy.array([-0.3324, -0.1474, -0.4404, -0.4529, 0.0484, -0.5848]) z=[z1, z2] # call the updateZ function ubm = GMMMachine(2,3) ubm.mean_supervector = UBM_MEAN ubm.variance_supervector = UBM_VAR m = JFABase(ubm,2,2) t = JFATrainer() t.initialize(m, TRAINING_STATS) m.u = M_u m.v = M_v m.d = M_d t.__X__ = M_x t.__Y__ = M_y t.__Z__ = z t.e_step_d(m, TRAINING_STATS) t.m_step_d(m, TRAINING_STATS) # Expected results(JFA cookbook, matlab) assert equals(t.__Z__[0], z3_ref, 2e-4) assert equals(t.__Z__[1], z4_ref, 2e-4) assert equals(m.d, d_ref, 2e-4)
def test_JFATrainer_updateXandU():
# test the JFATrainer for updating X and U
u_ref = numpy.array( [0.6729, 0.3408, 0.0544, 1.0653, 0.5399, 1.3035,
2.4995, 0.4385, 0.1292, -0.0576, 1.1962, 0.0117]).reshape((6,2))
x1 = numpy.array([0., 0., 0., 0.]).reshape((2,2))
x2 = numpy.array([0., 0., 0., 0.]).reshape((2,2))
x3 = numpy.array([0.2143, 1.8275, 3.1979, 0.1227]).reshape((2,2))
x4 = numpy.array([-1.3861, 0.2359, 5.3326, -0.7914]).reshape((2,2))
x = [x1, x2]
# call the updateX function
ubm = GMMMachine(2,3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
m = JFABase(ubm,2,2)
t = JFATrainer()
t.initialize(m, TRAINING_STATS)
m.u = M_u
m.v = M_v
m.d = M_d
t.__X__ = x
t.__Y__ = M_y
t.__Z__ = M_z
t.e_step_u(m, TRAINING_STATS)
t.m_step_u(m, TRAINING_STATS)
# Expected results(JFA cookbook, matlab)
assert equals(t.__X__[0], x3, 2e-4)
assert equals(t.__X__[1], x4, 2e-4)
assert equals(m.u, u_ref, 2e-4)
def test_ISVTrainAndEnrol():
# Train and enroll an 'ISVMachine'
eps = 1e-10
d_ref = numpy.array([
0.39601136, 0.07348469, 0.47712682, 0.44738127, 0.43179856, 0.45086029
], 'float64')
u_ref = numpy.array([[0.855125642430777, 0.563104284748032],
[-0.325497865404680, 1.923598985291687],
[0.511575659503837, 1.964288663083095],
[9.330165761678115, 1.073623827995043],
[0.511099245664012, 0.278551249248978],
[5.065578541930268, 0.509565618051587]], 'float64')
z_ref = numpy.array([
-0.079315777443826, 0.092702428248543, -0.342488761656616,
-0.059922635809136, 0.133539981073604, 0.213118695516570
], 'float64')
# Calls the train function
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
mb = ISVBase(ubm, 2)
t = ISVTrainer(4.)
t.initialize(mb, TRAINING_STATS)
mb.u = M_u
for i in range(10):
t.e_step(mb, TRAINING_STATS)
t.m_step(mb)
assert numpy.allclose(mb.d, d_ref, eps)
assert numpy.allclose(mb.u, u_ref, eps)
# Calls the enroll function
m = ISVMachine(mb)
Ne = numpy.array([0.1579, 0.9245, 0.1323, 0.2458]).reshape((2, 2))
Fe = numpy.array([
0.1579, 0.1925, 0.3242, 0.1234, 0.2354, 0.2734, 0.2514, 0.5874, 0.3345,
0.2463, 0.4789, 0.5236
]).reshape((6, 2))
gse1 = GMMStats(2, 3)
gse1.n = Ne[:, 0]
gse1.sum_px = Fe[:, 0].reshape(2, 3)
gse2 = GMMStats(2, 3)
gse2.n = Ne[:, 1]
gse2.sum_px = Fe[:, 1].reshape(2, 3)
gse = [gse1, gse2]
t.enroll(m, gse, 5)
assert numpy.allclose(m.z, z_ref, eps)
#Testing exceptions
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.e_step, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.e_step, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.enroll, m, [[1, 2, 2]], 5)
def test_ISVTrainAndEnrol():
# Train and enroll an 'ISVMachine'
eps = 1e-10
d_ref = numpy.array([0.39601136, 0.07348469, 0.47712682, 0.44738127, 0.43179856, 0.45086029], 'float64')
u_ref = numpy.array([[0.855125642430777, 0.563104284748032], [-0.325497865404680, 1.923598985291687], [0.511575659503837, 1.964288663083095], [9.330165761678115, 1.073623827995043], [0.511099245664012, 0.278551249248978], [5.065578541930268, 0.509565618051587]], 'float64')
z_ref = numpy.array([-0.079315777443826, 0.092702428248543, -0.342488761656616, -0.059922635809136 , 0.133539981073604, 0.213118695516570], 'float64')
# Calls the train function
ubm = GMMMachine(2,3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
mb = ISVBase(ubm,2)
t = ISVTrainer(4.)
t.initialize(mb, TRAINING_STATS)
mb.u = M_u
for i in range(10):
t.e_step(mb, TRAINING_STATS)
t.m_step(mb)
assert numpy.allclose(mb.d, d_ref, eps)
assert numpy.allclose(mb.u, u_ref, eps)
# Calls the enroll function
m = ISVMachine(mb)
Ne = numpy.array([0.1579, 0.9245, 0.1323, 0.2458]).reshape((2,2))
Fe = numpy.array([0.1579, 0.1925, 0.3242, 0.1234, 0.2354, 0.2734, 0.2514, 0.5874, 0.3345, 0.2463, 0.4789, 0.5236]).reshape((6,2))
gse1 = GMMStats(2,3)
gse1.n = Ne[:,0]
gse1.sum_px = Fe[:,0].reshape(2,3)
gse2 = GMMStats(2,3)
gse2.n = Ne[:,1]
gse2.sum_px = Fe[:,1].reshape(2,3)
gse = [gse1, gse2]
t.enroll(m, gse, 5)
assert numpy.allclose(m.z, z_ref, eps)
#Testing exceptions
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.e_step, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.e_step, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.enroll, m, [[1,2,2]],5)
def test_JFATrainAndEnrol():
# Train and enroll a JFAMachine
# Calls the train function
ubm = GMMMachine(2, 3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
mb = JFABase(ubm, 2, 2)
t = JFATrainer()
t.initialize(mb, TRAINING_STATS)
mb.u = M_u
mb.v = M_v
mb.d = M_d
bob.learn.em.train_jfa(t, mb, TRAINING_STATS, initialize=False)
v_ref = numpy.array([[0.245364911936476, 0.978133261775424],
[0.769646805052223, 0.940070736856596],
[0.310779202800089, 1.456332053893072],
[0.184760934399551, 2.265139705602147],
[0.701987784039800, 0.081632150899400],
[0.074344030229297, 1.090248340917255]], 'float64')
u_ref = numpy.array([[0.049424652628448, 0.060480486336896],
[0.178104127464007, 1.884873813495153],
[1.204011484266777, 2.281351307871720],
[7.278512126426286, -0.390966087173334],
[-0.084424326581145, -0.081725474934414],
[4.042143689831097, -0.262576386580701]], 'float64')
d_ref = numpy.array([
9.648467e-18, 2.63720683155e-12, 2.11822157653706e-10, 9.1047243e-17,
1.41163442535567e-10, 3.30581e-19
], 'float64')
eps = 1e-10
assert numpy.allclose(mb.v, v_ref, eps)
assert numpy.allclose(mb.u, u_ref, eps)
assert numpy.allclose(mb.d, d_ref, eps)
# Calls the enroll function
m = JFAMachine(mb)
Ne = numpy.array([0.1579, 0.9245, 0.1323, 0.2458]).reshape((2, 2))
Fe = numpy.array([
0.1579, 0.1925, 0.3242, 0.1234, 0.2354, 0.2734, 0.2514, 0.5874, 0.3345,
0.2463, 0.4789, 0.5236
]).reshape((6, 2))
gse1 = GMMStats(2, 3)
gse1.n = Ne[:, 0]
gse1.sum_px = Fe[:, 0].reshape(2, 3)
gse2 = GMMStats(2, 3)
gse2.n = Ne[:, 1]
gse2.sum_px = Fe[:, 1].reshape(2, 3)
gse = [gse1, gse2]
t.enroll(m, gse, 5)
y_ref = numpy.array([0.555991469319657, 0.002773650670010], 'float64')
z_ref = numpy.array([
8.2228e-20, 3.15216909492e-13, -1.48616735364395e-10, 1.0625905e-17,
3.7150503117895e-11, 1.71104e-19
], 'float64')
assert numpy.allclose(m.y, y_ref, eps)
assert numpy.allclose(m.z, z_ref, eps)
#Testing exceptions
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [1, 2, 2])
nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [[1, 2, 2]])
nose.tools.assert_raises(RuntimeError, t.enroll, m, [[1, 2, 2]], 5)
def test_JFATrainAndEnrol():
# Train and enroll a JFAMachine
# Calls the train function
ubm = GMMMachine(2,3)
ubm.mean_supervector = UBM_MEAN
ubm.variance_supervector = UBM_VAR
mb = JFABase(ubm, 2, 2)
t = JFATrainer()
t.initialize(mb, TRAINING_STATS)
mb.u = M_u
mb.v = M_v
mb.d = M_d
bob.learn.em.train_jfa(t,mb, TRAINING_STATS, initialize=False)
v_ref = numpy.array([[0.245364911936476, 0.978133261775424], [0.769646805052223, 0.940070736856596], [0.310779202800089, 1.456332053893072],
[0.184760934399551, 2.265139705602147], [0.701987784039800, 0.081632150899400], [0.074344030229297, 1.090248340917255]], 'float64')
u_ref = numpy.array([[0.049424652628448, 0.060480486336896], [0.178104127464007, 1.884873813495153], [1.204011484266777, 2.281351307871720],
[7.278512126426286, -0.390966087173334], [-0.084424326581145, -0.081725474934414], [4.042143689831097, -0.262576386580701]], 'float64')
d_ref = numpy.array([9.648467e-18, 2.63720683155e-12, 2.11822157653706e-10, 9.1047243e-17, 1.41163442535567e-10, 3.30581e-19], 'float64')
eps = 1e-10
assert numpy.allclose(mb.v, v_ref, eps)
assert numpy.allclose(mb.u, u_ref, eps)
assert numpy.allclose(mb.d, d_ref, eps)
# Calls the enroll function
m = JFAMachine(mb)
Ne = numpy.array([0.1579, 0.9245, 0.1323, 0.2458]).reshape((2,2))
Fe = numpy.array([0.1579, 0.1925, 0.3242, 0.1234, 0.2354, 0.2734, 0.2514, 0.5874, 0.3345, 0.2463, 0.4789, 0.5236]).reshape((6,2))
gse1 = GMMStats(2,3)
gse1.n = Ne[:,0]
gse1.sum_px = Fe[:,0].reshape(2,3)
gse2 = GMMStats(2,3)
gse2.n = Ne[:,1]
gse2.sum_px = Fe[:,1].reshape(2,3)
gse = [gse1, gse2]
t.enroll(m, gse, 5)
y_ref = numpy.array([0.555991469319657, 0.002773650670010], 'float64')
z_ref = numpy.array([8.2228e-20, 3.15216909492e-13, -1.48616735364395e-10, 1.0625905e-17, 3.7150503117895e-11, 1.71104e-19], 'float64')
assert numpy.allclose(m.y, y_ref, eps)
assert numpy.allclose(m.z, z_ref, eps)
#Testing exceptions
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.initialize, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [1,2,2])
nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [[1,2,2]])
nose.tools.assert_raises(RuntimeError, t.enroll, m, [[1,2,2]],5)
