def test_JFAMachine():
# Creates a UBM
weights = numpy.array([0.4, 0.6], 'float64')
means = numpy.array([[1, 6, 2], [4, 3, 2]], 'float64')
variances = numpy.array([[1, 2, 1], [2, 1, 2]], 'float64')
ubm = GMMMachine(2,3)
ubm.weights = weights
ubm.means = means
ubm.variances = variances
# Creates a JFABase
U = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], 'float64')
V = numpy.array([[6, 5], [4, 3], [2, 1], [1, 2], [3, 4], [5, 6]], 'float64')
d = numpy.array([0, 1, 0, 1, 0, 1], 'float64')
base = JFABase(ubm,2,2)
base.u = U
base.v = V
base.d = d
# Creates a JFAMachine
y = numpy.array([1,2], 'float64')
z = numpy.array([3,4,1,2,0,1], 'float64')
m = JFAMachine(base)
m.y = y
m.z = z
n_gaussians,dim,ru,rv = m.shape
supervector_length = m.supervector_length
assert n_gaussians == 2
assert dim == 3
assert supervector_length == 6
assert ru == 2
assert rv == 2
assert (m.y == y).all()
assert (m.z == z).all()
# Saves and loads
filename = str(tempfile.mkstemp(".hdf5")[1])
m.save(bob.io.base.HDF5File(filename, 'w'))
m_loaded = JFAMachine(bob.io.base.HDF5File(filename))
m_loaded.jfa_base = base
assert m == m_loaded
assert (m != m_loaded) is False
assert m.is_similar_to(m_loaded)
# Copy constructor
mc = JFAMachine(m)
assert m == mc
# Variant
#mv = JFAMachine()
# Checks for correctness
#mv.jfa_base = base
#m.y = y
#m.z = z
#assert m.dim_c == 2
#assert m.dim_d == 3
#assert m.dim_cd == 6
#assert m.dim_ru == 2
#assert m.dim_rv == 2
#assert (m.y == y).all()
#assert (m.z == z).all()
# Defines GMMStats
gs = GMMStats(2,3)
log_likelihood = -3.
T = 1
n = numpy.array([0.4, 0.6], 'float64')
sumpx = numpy.array([[1., 2., 3.], [4., 5., 6.]], 'float64')
sumpxx = numpy.array([[10., 20., 30.], [40., 50., 60.]], 'float64')
gs.log_likelihood = log_likelihood
gs.t = T
gs.n = n
gs.sum_px = sumpx
gs.sum_pxx = sumpxx
# Forward GMMStats and check estimated value of the x speaker factor
eps = 1e-10
x_ref = numpy.array([0.291042849767692, 0.310273618998444], 'float64')
score_ref = -2.111577181208289
score = m.log_likelihood(gs)
assert numpy.allclose(m.x, x_ref, eps)
assert abs(score_ref-score) < eps
# x and Ux
x = numpy.ndarray((2,), numpy.float64)
m.estimate_x(gs, x)
n_gaussians, dim,_,_ = m.shape
x_py = estimate_x(n_gaussians, dim, ubm.mean_supervector, ubm.variance_supervector, U, n, sumpx)
assert numpy.allclose(x, x_py, eps)
ux = numpy.ndarray((6,), numpy.float64)
m.estimate_ux(gs, ux)
n_gaussians, dim,_,_ = m.shape
ux_py = estimate_ux(n_gaussians, dim, ubm.mean_supervector, ubm.variance_supervector, U, n, sumpx)
assert numpy.allclose(ux, ux_py, eps)
assert numpy.allclose(m.x, x, eps)
score = m.forward_ux(gs, ux)
assert abs(score_ref-score) < eps
# Clean-up
os.unlink(filename)
from train import GMM_Stats as GMMStats
from train import JFA_Base as JFABase
from train import JFA_Machine as JFAMachine
from train import JFA_Trainer as JFATrainer
F1 = numpy.array(
[0.3833, 0.4516, 0.6173, 0.2277, 0.5755, 0.8044, 0.5301, 0.9861, 0.2751, 0.0300, 0.2486, 0.5357]).reshape((6, 2))
F2 = numpy.array(
[0.0871, 0.6838, 0.8021, 0.7837, 0.9891, 0.5341, 0.0669, 0.8854, 0.9394, 0.8990, 0.0182, 0.6259]).reshape((6, 2))
F = [F1, F2]
N1 = numpy.array([0.1379, 0.1821, 0.2178, 0.0418]).reshape((2, 2))
N2 = numpy.array([0.1069, 0.9397, 0.6164, 0.3545]).reshape((2, 2))
N = [N1, N2]
gs11 = GMMStats(2, 3)
gs11.n = N1[:, 0]
gs11.sum_Px = F1[:, 0].reshape(2, 3)
gs12 = GMMStats(2, 3)
gs12.n = N1[:, 1]
gs12.sum_Px = F1[:, 1].reshape(2, 3)
gs21 = GMMStats(2, 3)
gs21.n = N2[:, 0]
gs21.sum_Px = F2[:, 0].reshape(2, 3)
gs22 = GMMStats(2, 3)
gs22.n = N2[:, 1]
gs22.sum_Px = F2[:, 1].reshape(2, 3)
TRAINING_STATS = [
[gs11, gs12], # person 1
