[factor_analysis] merge tests into one file

bob/learn/em/test/test_jfa_trainer.py → bob/learn/em/test/test_factor_analysis.py

 #!/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)

bob/learn/em/test/test_jfa.py

-#!/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)