diff --git a/src/bob/learn/em/factor_analysis.py b/src/bob/learn/em/factor_analysis.py
index 64ae3640af7a38bfcfc505073e58b3e544683df4..d31ba5304f34511d768392f2eb6c4088d9fd9118 100644
--- a/src/bob/learn/em/factor_analysis.py
+++ b/src/bob/learn/em/factor_analysis.py
@@ -15,7 +15,7 @@ from sklearn.base import BaseEstimator
from sklearn.utils import check_consistent_length
from sklearn.utils.multiclass import unique_labels
-from .gmm import GMMMachine
+from .gmm import GMMMachine, GMMStats
from .linear_scoring import linear_scoring
from .utils import check_and_persist_dask_input
@@ -764,8 +764,8 @@ class FactorAnalysisBase(BaseEstimator):
fn_z_i = np.array(fn_z_i, like=X_i[0].n)
# Looping over the sessions
- for session_id in range(len(X_i)):
- n_i = X_i[session_id].n
+ for session_id, x_i_s in enumerate(X_i):
+ n_i = x_i_s.n
tmp_CD = np.repeat(n_i, self.feature_dimension)
x_i_h = latent_x_i[:, session_id]
@@ -1170,7 +1170,7 @@ class FactorAnalysisBase(BaseEstimator):
# Looping over the sessions of a ;ane;
for session_id, x_i_s in enumerate(X_i):
- n_i = x_i_s[session_id].n
+ n_i = x_i_s.n
U_dot_x = U @ latent_x_i[:, session_id]
tmp_CD = np.repeat(n_i, self.feature_dimension)
fn_y_i -= tmp_CD * U_dot_x
@@ -1214,7 +1214,9 @@ class FactorAnalysisBase(BaseEstimator):
sigma_c = self.ubm.variances[:, np.newaxis]
- n_i_c = np.expand_dims(X_i.n[:, np.newaxis], axis=2)
+ n_sum = sum(x_i_s.n for x_i_s in X_i)
+
+ n_i_c = np.expand_dims(n_sum[:, np.newaxis], axis=2)
id_plus_us_prod_inv = I + (((UcT / sigma_c) @ Uc) * n_i_c).sum(axis=0)
id_plus_us_prod_inv = np.linalg.inv(id_plus_us_prod_inv)
@@ -1233,8 +1235,11 @@ class FactorAnalysisBase(BaseEstimator):
"""
- n = X_i.n[:, np.newaxis]
- f = X_i.sum_px
+ n_sum = sum(x_i_s.n for x_i_s in X_i)
+ sum_px_sum = sum(x_i_s.sum_px for x_i_s in X_i)
+
+ n = n_sum[:, np.newaxis]
+ f = sum_px_sum
fn_x = f - self.ubm.means * n
@@ -1259,7 +1264,7 @@ class FactorAnalysisBase(BaseEstimator):
"""
- return self.score(model, self.ubm.acc_stats(data))
+ return self.score(model, [self.ubm.acc_stats(d) for d in data])
def fit_using_array(self, X, y):
"""Fits the model using a numpy array or a dask array as input
@@ -1600,13 +1605,7 @@ class ISVMachine(FactorAnalysisBase):
"""
- template_data = data[0]
-
- if len(data) > 1:
- for d in data[1:]:
- template_data += d
-
- x = self.estimate_x(template_data)
+ x = self.estimate_x(data)
Ux = self._U @ x
# TODO: I don't know why this is not the enrolled model
@@ -1614,13 +1613,26 @@ class ISVMachine(FactorAnalysisBase):
# m + Dz
z = self.D * latent_z + self.mean_supervector
+ # Support a probe template and a list of probe templates.
+ if isinstance(data[0], GMMStats) and len(data) > 1:
+ data_sum = sum(data[1:], start=data[0])
+ elif not isinstance(data[0], GMMStats):
+ data_sum = []
+ for d in data:
+ if len(d) > 1:
+ data_sum.append(sum(d[1:], start=d[0]))
+ else:
+ data_sum.append(d[0])
+ else:
+ data_sum = data
+
return linear_scoring(
z.reshape((self.ubm.n_gaussians, self.feature_dimension)),
self.ubm,
- template_data,
+ data_sum,
Ux.reshape((self.ubm.n_gaussians, self.feature_dimension)),
frame_length_normalization=True,
- )[0][0]
+ )[0]
class JFAMachine(FactorAnalysisBase):
@@ -2254,12 +2266,6 @@ class JFAMachine(FactorAnalysisBase):
latent_y = model[0]
latent_z = model[1]
- template_data = data[0]
-
- if len(data > 1):
- for d in data[1:]:
- template_data += d
-
x = self.estimate_x(data)
Ux = self._U @ x
@@ -2268,10 +2274,23 @@ class JFAMachine(FactorAnalysisBase):
# m + Vy + Dz
zy = self.V @ latent_y + self.D * latent_z + self.mean_supervector
+ # Support a probe template and a list of probe templates.
+ if isinstance(data[0], GMMStats) and len(data) > 1:
+ data_sum = sum(data[1:], start=data[0])
+ elif not isinstance(data[0], GMMStats):
+ data_sum = []
+ for d in data:
+ if len(d) > 1:
+ data_sum.append(sum(d[1:], start=d[0]))
+ else:
+ data_sum.append(d[0])
+ else:
+ data_sum = data
+
return linear_scoring(
zy.reshape((self.ubm.n_gaussians, self.feature_dimension)),
self.ubm,
- data,
+ data_sum,
Ux.reshape((self.ubm.n_gaussians, self.feature_dimension)),
frame_length_normalization=True,
)[0]
diff --git a/tests/test_factor_analysis.py b/tests/test_factor_analysis.py
index 8c9f8a343a1d1e4c71cc2a46b36fbeabc2fe280e..25d09626a4df2635a22e06d0b5c505bd4d93a04c 100644
--- a/tests/test_factor_analysis.py
+++ b/tests/test_factor_analysis.py
@@ -114,7 +114,7 @@ M_y = [y1, y2]
M_x = [x1, x2]
-def test_JFATrainAndEnrol():
+def test_JFA_train_and_enroll():
# Train and enroll a JFAMachine
# Calls the train function
@@ -213,7 +213,7 @@ def test_JFATrainAndEnrol():
np.testing.assert_allclose(latent_z, z_ref, rtol=eps, atol=1e-8)
-def test_ISVTrainAndEnrol():
+def test_ISV_train_and_enroll():
# Train and enroll an 'ISVMachine'
eps = 1e-10
@@ -253,9 +253,8 @@ def test_ISVTrainAndEnrol():
"float64",
)
- """
- Calls the train function
- """
+ # Calls the train function
+
ubm = GMMMachine(2, 3)
ubm.means = UBM_MEAN.reshape((2, 3))
ubm.variances = UBM_VAR.reshape((2, 3))
@@ -274,9 +273,7 @@ def test_ISVTrainAndEnrol():
np.testing.assert_allclose(it.D, d_ref, rtol=eps, atol=1e-8)
np.testing.assert_allclose(it.U, u_ref, rtol=eps, atol=1e-8)
- """
- Calls the enroll function
- """
+ # Calls the enroll function
Ne = np.array([0.1579, 0.9245, 0.1323, 0.2458]).reshape((2, 2))
Fe = np.array(
@@ -307,7 +304,7 @@ def test_ISVTrainAndEnrol():
np.testing.assert_allclose(latent_z, z_ref, rtol=eps, atol=1e-8)
-def test_JFATrainInitialize():
+def test_JFA_train_initialize():
# Check that the initialization is consistent and using the rng (cf. issue #118)
eps = 1e-10
@@ -338,7 +335,7 @@ def test_JFATrainInitialize():
np.testing.assert_allclose(d1, d2, rtol=eps, atol=1e-8)
-def test_ISVTrainInitialize():
+def test_ISV_train_initialize():
# Check that the initialization is consistent and using the rng (cf. issue #118)
eps = 1e-10
@@ -398,14 +395,14 @@ def test_JFAMachine():
model = [y, z]
score_ref = -2.111577181208289
- score = m.score(model, gs)
+ score = m.score(model, [gs])
np.testing.assert_allclose(score, score_ref, atol=eps)
# Scoring with numpy array
np.random.seed(0)
X = np.random.normal(loc=0.0, scale=1.0, size=(50, 3))
score_ref = 2.028009315286946
- score = m.score_using_array(model, X)
+ score = m.score_using_array(model, [X])
np.testing.assert_allclose(score, score_ref, atol=eps)
@@ -437,7 +434,7 @@ def test_ISVMachine():
# Enrolled model
latent_z = np.array([3, 4, 1, 2, 0, 1], "float64")
- score = isv_machine.score(latent_z, gs)
+ score = isv_machine.score(latent_z, [gs])
score_ref = -3.280498193082100
np.testing.assert_allclose(score, score_ref, atol=eps)
@@ -445,7 +442,7 @@ def test_ISVMachine():
np.random.seed(0)
X = np.random.normal(loc=0.0, scale=1.0, size=(50, 3))
score_ref = -1.2343813195374242
- score = isv_machine.score_using_array(latent_z, X)
+ score = isv_machine.score_using_array(latent_z, [X])
np.testing.assert_allclose(score, score_ref, atol=eps)
@@ -547,7 +544,7 @@ def test_ISV_JFA_fit():
if prior is None:
ubm = None
# we still provide an initial UBM because KMeans training is not
- # determenistic depending on inputting numpy or dask arrays
+ # deterministic depending on inputting numpy or dask arrays
ubm_kwargs = dict(n_gaussians=2, ubm=_create_ubm_prior(means))
else:
ubm = _create_ubm_prior(means)