From d41ca91adba99bb5a7b57ef9cd4fe5f54f94a783 Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Mon, 11 Apr 2022 14:38:26 +0200
Subject: [PATCH] remove duplicate tests
---
bob/learn/em/test/test_factor_analysis.py | 146 ----------------------
1 file changed, 146 deletions(-)
diff --git a/bob/learn/em/test/test_factor_analysis.py b/bob/learn/em/test/test_factor_analysis.py
index 21d3741..fc2ed15 100644
--- a/bob/learn/em/test/test_factor_analysis.py
+++ b/bob/learn/em/test/test_factor_analysis.py
@@ -210,82 +210,6 @@ def test_JFATrainAndEnrol():
np.testing.assert_allclose(latent_z, z_ref, rtol=eps, atol=1e-8)
-def test_JFATrainAndEnrolWithNumpy():
- # Train and enroll a JFAMachine
-
- # Calls the train function
- ubm = GMMMachine(2, 3)
- ubm.means = UBM_MEAN.reshape((2, 3))
- ubm.variances = UBM_VAR.reshape((2, 3))
- it = JFAMachine(ubm, 2, 2, em_iterations=10)
-
- it.U = copy.deepcopy(M_u)
- it.V = copy.deepcopy(M_v)
- it.D = copy.deepcopy(M_d)
- it.fit_using_stats(TRAINING_STATS_X, TRAINING_STATS_y)
-
- v_ref = np.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 = np.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 = np.array(
- [
- 9.648467e-18,
- 2.63720683155e-12,
- 2.11822157653706e-10,
- 9.1047243e-17,
- 1.41163442535567e-10,
- 3.30581e-19,
- ],
- "float64",
- )
-
- eps = 1e-10
- np.testing.assert_allclose(it.V, v_ref, rtol=eps, atol=1e-8)
- np.testing.assert_allclose(it.U, u_ref, rtol=eps, atol=1e-8)
- np.testing.assert_allclose(it.D, d_ref, rtol=eps, atol=1e-8)
-
- """
- Calls the enroll function with arrays as input
- """
-
- np.random.seed(0)
- X = np.random.normal(ubm.means[0], scale=0.5, size=(50, 3))
- latent_y_ref = np.array([-0.13922039, 0.10686916])
- latent_z_ref = np.array(
- [
- -1.37073043e-17,
- 1.15641870e-12,
- -8.29922598e-10,
- -4.17108194e-16,
- -2.27107305e-09,
- 2.94293314e-18,
- ]
- )
-
- latent_y, latent_z = it.enroll_with_array(X)
- np.testing.assert_allclose(latent_z, latent_z_ref, rtol=eps, atol=1e-8)
- np.testing.assert_allclose(latent_y, latent_y_ref, rtol=eps, atol=1e-8)
-
-
def test_ISVTrainAndEnrol():
# Train and enroll an 'ISVMachine'
@@ -379,76 +303,6 @@ def test_ISVTrainAndEnrol():
np.testing.assert_allclose(latent_z, z_ref, rtol=eps, atol=1e-8)
-def test_ISVTrainAndEnrolWithNumpy():
- # Train and enroll an 'ISVMachine'
-
- eps = 1e-10
- d_ref = np.array(
- [
- 0.39601136,
- 0.07348469,
- 0.47712682,
- 0.44738127,
- 0.43179856,
- 0.45086029,
- ],
- "float64",
- )
- u_ref = np.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",
- )
-
- """
- Calls the train function
- """
- ubm = GMMMachine(n_gaussians=2)
- ubm.means = UBM_MEAN.reshape((2, 3))
- ubm.variances = UBM_VAR.reshape((2, 3))
-
- it = ISVMachine(
- ubm=ubm,
- r_U=2,
- relevance_factor=4.0,
- em_iterations=10,
- )
-
- it.U = copy.deepcopy(M_u)
- it = it.fit_using_stats(TRAINING_STATS_X, TRAINING_STATS_y)
-
- 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 with arrays as input
- """
-
- np.random.seed(0)
- X = np.random.normal(ubm.means[0], scale=0.5, size=(50, 3))
- latent_z_ref = np.array(
- [
- [
- 0.01084525,
- 0.06039035,
- -0.16920933,
- -0.17321376,
- -0.9648409,
- 0.44581105,
- ]
- ]
- )
-
- latent_z = it.enroll_with_array(X)
- np.testing.assert_allclose(latent_z, latent_z_ref, rtol=eps, atol=1e-8)
-
-
def test_JFATrainInitialize():
# Check that the initialization is consistent and using the rng (cf. issue #118)
--
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