diff --git a/bob/learn/em/test/test_jfa_trainer.py b/bob/learn/em/test/test_factor_analysis.py
similarity index 82%
rename from bob/learn/em/test/test_jfa_trainer.py
rename to bob/learn/em/test/test_factor_analysis.py
index e38beeccf939bc52923cd5f7853c7126028d072d..21d3741e46a1f6de8b46a7526bebbc18507ff6ca 100644
--- a/bob/learn/em/test/test_jfa_trainer.py
+++ b/bob/learn/em/test/test_factor_analysis.py
@@ -1,10 +1,7 @@
#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
# Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
# Tiago Freitas Pereira <tiago.pereira@idiap.ch>
-# Tue Jul 19 12:16:17 2011 +0200
-#
-# Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
+# Amir Mohammadi <amir.mohammadi@idiap.ch>
import copy
@@ -507,3 +504,86 @@ def test_ISVTrainInitialize():
np.testing.assert_allclose(u1, u2, rtol=eps, atol=1e-8)
np.testing.assert_allclose(d1, d2, rtol=eps, atol=1e-8)
+
+
+def test_JFAMachine():
+
+ eps = 1e-10
+
+ # Creates a UBM
+ ubm = GMMMachine(2, 3)
+ ubm.weights = np.array([0.4, 0.6], "float64")
+ ubm.means = np.array([[1, 6, 2], [4, 3, 2]], "float64")
+ ubm.variances = np.array([[1, 2, 1], [2, 1, 2]], "float64")
+
+ # Defines GMMStats
+ gs = GMMStats(2, 3)
+ gs.log_likelihood = -3.0
+ gs.t = 1
+ gs.n = np.array([0.4, 0.6], "float64")
+ gs.sum_px = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "float64")
+ gs.sum_pxx = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
+
+ # Creates a JFAMachine
+ m = JFAMachine(ubm, 2, 2, em_iterations=10)
+ m.U = np.array(
+ [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], "float64"
+ )
+ m.V = np.array([[6, 5], [4, 3], [2, 1], [1, 2], [3, 4], [5, 6]], "float64")
+ m.D = np.array([0, 1, 0, 1, 0, 1], "float64")
+
+ # Preparing the model
+ y = np.array([1, 2], "float64")
+ z = np.array([3, 4, 1, 2, 0, 1], "float64")
+ model = [y, z]
+
+ score_ref = -2.111577181208289
+ 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_with_array(model, X)
+ np.testing.assert_allclose(score, score_ref, atol=eps)
+
+
+def test_ISVMachine():
+
+ eps = 1e-10
+
+ # Creates a UBM
+ ubm = GMMMachine(2, 3)
+ ubm.weights = np.array([0.4, 0.6], "float64")
+ ubm.means = np.array([[1, 6, 2], [4, 3, 2]], "float64")
+ ubm.variances = np.array([[1, 2, 1], [2, 1, 2]], "float64")
+
+ # Creates a ISVMachine
+ isv_machine = ISVMachine(ubm=ubm, r_U=2, em_iterations=10)
+ isv_machine.U = np.array(
+ [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], "float64"
+ )
+ # base.v = numpy.array([[0], [0], [0], [0], [0], [0]], 'float64')
+ isv_machine.D = np.array([0, 1, 0, 1, 0, 1], "float64")
+
+ # Defines GMMStats
+ gs = GMMStats(2, 3)
+ gs.log_likelihood = -3.0
+ gs.t = 1
+ gs.n = np.array([0.4, 0.6], "float64")
+ gs.sum_px = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "float64")
+ gs.sum_pxx = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
+
+ # Enrolled model
+ latent_z = np.array([3, 4, 1, 2, 0, 1], "float64")
+ score = isv_machine.score(latent_z, gs)
+ score_ref = -3.280498193082100
+ 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 = -1.2343813195374242
+ score = isv_machine.score_with_array(latent_z, X)
+ np.testing.assert_allclose(score, score_ref, atol=eps)
diff --git a/bob/learn/em/test/test_jfa.py b/bob/learn/em/test/test_jfa.py
deleted file mode 100644
index dc15b71f02dd9553e582f38481611e56e4467810..0000000000000000000000000000000000000000
--- a/bob/learn/em/test/test_jfa.py
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
-# Tiago Freitas Pereira <tiago.pereira@idiap.ch>
-# Tue Jul 19 12:16:17 2011 +0200
-#
-# Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
-
-import numpy as np
-
-from bob.learn.em import GMMMachine, GMMStats, ISVMachine, JFAMachine
-
-
-def test_JFAMachine():
-
- eps = 1e-10
-
- # Creates a UBM
- ubm = GMMMachine(2, 3)
- ubm.weights = np.array([0.4, 0.6], "float64")
- ubm.means = np.array([[1, 6, 2], [4, 3, 2]], "float64")
- ubm.variances = np.array([[1, 2, 1], [2, 1, 2]], "float64")
-
- # Defines GMMStats
- gs = GMMStats(2, 3)
- gs.log_likelihood = -3.0
- gs.t = 1
- gs.n = np.array([0.4, 0.6], "float64")
- gs.sum_px = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "float64")
- gs.sum_pxx = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
-
- # Creates a JFAMachine
- m = JFAMachine(ubm, 2, 2, em_iterations=10)
- m.U = np.array(
- [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], "float64"
- )
- m.V = np.array([[6, 5], [4, 3], [2, 1], [1, 2], [3, 4], [5, 6]], "float64")
- m.D = np.array([0, 1, 0, 1, 0, 1], "float64")
-
- # Preparing the model
- y = np.array([1, 2], "float64")
- z = np.array([3, 4, 1, 2, 0, 1], "float64")
- model = [y, z]
-
- score_ref = -2.111577181208289
- 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_with_array(model, X)
- np.testing.assert_allclose(score, score_ref, atol=eps)
-
-
-def test_ISVMachine():
-
- eps = 1e-10
-
- # Creates a UBM
- ubm = GMMMachine(2, 3)
- ubm.weights = np.array([0.4, 0.6], "float64")
- ubm.means = np.array([[1, 6, 2], [4, 3, 2]], "float64")
- ubm.variances = np.array([[1, 2, 1], [2, 1, 2]], "float64")
-
- # Creates a ISVMachine
- isv_machine = ISVMachine(ubm=ubm, r_U=2, em_iterations=10)
- isv_machine.U = np.array(
- [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], "float64"
- )
- # base.v = numpy.array([[0], [0], [0], [0], [0], [0]], 'float64')
- isv_machine.D = np.array([0, 1, 0, 1, 0, 1], "float64")
-
- # Defines GMMStats
- gs = GMMStats(2, 3)
- gs.log_likelihood = -3.0
- gs.t = 1
- gs.n = np.array([0.4, 0.6], "float64")
- gs.sum_px = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "float64")
- gs.sum_pxx = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
-
- # Enrolled model
- latent_z = np.array([3, 4, 1, 2, 0, 1], "float64")
- score = isv_machine.score(latent_z, gs)
- score_ref = -3.280498193082100
- 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 = -1.2343813195374242
- score = isv_machine.score_with_array(latent_z, X)
- np.testing.assert_allclose(score, score_ref, atol=eps)