diff --git a/bob/learn/em/__init__.py b/bob/learn/em/__init__.py
index 32e88b6260e4c319f79f9eba847dc5686a7ffdd7..511980f2a5df956d95a64ad25b5b2907a3a9b8ef 100644
--- a/bob/learn/em/__init__.py
+++ b/bob/learn/em/__init__.py
@@ -1,11 +1,11 @@
import bob.extension
+from .factor_analysis import ISVMachine, JFAMachine
from .gmm import GMMMachine, GMMStats
from .k_means import KMeansMachine
from .linear_scoring import linear_scoring # noqa: F401
from .wccn import WCCN
from .whitening import Whitening
-from .factor_analysis import ISVMachine, JFAMachine
def get_config():
diff --git a/bob/learn/em/factor_analysis.py b/bob/learn/em/factor_analysis.py
index 1d0b81edff2f27fdfa0e74ec2d7c05913b6c2896..dc62f4fcac009db2d5aedfd424b71934abe66221 100644
--- a/bob/learn/em/factor_analysis.py
+++ b/bob/learn/em/factor_analysis.py
@@ -6,7 +6,9 @@ import logging
import numpy as np
+from h5py import File as HDF5File
from sklearn.base import BaseEstimator
+
from . import linear_scoring
logger = logging.getLogger(__name__)
diff --git a/bob/learn/em/test/test_jfa.py b/bob/learn/em/test/test_jfa.py
index 23086de322ac17fc7deedc63343bdc934138a3a9..c96f59e1916f3b10b4b41b3a8d61be2d2fdaca73 100644
--- a/bob/learn/em/test/test_jfa.py
+++ b/bob/learn/em/test/test_jfa.py
@@ -7,8 +7,8 @@
# Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
import numpy as np
+
from bob.learn.em import GMMMachine, GMMStats, ISVMachine, JFAMachine
-import copy
def test_JFAMachine():
@@ -97,4 +97,3 @@ def test_ISVMachine():
score_ref = -3.280498193082100
assert abs(score_ref - score) < eps
- pass
diff --git a/bob/learn/em/test/test_jfa_trainer.py b/bob/learn/em/test/test_jfa_trainer.py
index f9e51e4473da960988edc2c1cfc1237b7e4d2b5c..11e97dafa2fe8516ae03186781b3bdcbe30e8b8a 100644
--- a/bob/learn/em/test/test_jfa_trainer.py
+++ b/bob/learn/em/test/test_jfa_trainer.py
@@ -6,9 +6,11 @@
#
# Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
+import copy
+
import numpy as np
+
from bob.learn.em import GMMMachine, GMMStats, ISVMachine, JFAMachine
-import copy
# Define Training set and initial values for tests
F1 = np.array(
@@ -120,12 +122,11 @@ def test_JFATrainAndEnrol():
ubm.means = UBM_MEAN.reshape((2, 3))
ubm.variances = UBM_VAR.reshape((2, 3))
it = JFAMachine(ubm, 2, 2, em_iterations=10)
- # n_acc, f_acc = it.initialize(TRAINING_STATS_X, TRAINING_STATS_y)
+
it.U = copy.deepcopy(M_u)
it.V = copy.deepcopy(M_v)
it.D = copy.deepcopy(M_d)
it.fit(TRAINING_STATS_X, TRAINING_STATS_y)
- # bob.learn.em.train_jfa(t, mb, TRAINING_STATS, initialize=False)
v_ref = np.array(
[
@@ -211,28 +212,6 @@ def test_JFATrainAndEnrol():
assert np.allclose(latent_y, y_ref, eps)
assert np.allclose(latent_z, z_ref, eps)
- # Testing exceptions
- """
- nose.tools.assert_raises(RuntimeError, t.initialize, mb, [1, 2, 2])
- nose.tools.assert_raises(RuntimeError, t.initialize, mb, [[1, 2, 2]])
- nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [1, 2, 2])
- nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [[1, 2, 2]])
- nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [1, 2, 2])
- nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [[1, 2, 2]])
-
- nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [1, 2, 2])
- nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [[1, 2, 2]])
- nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [1, 2, 2])
- nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [[1, 2, 2]])
-
- nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [1, 2, 2])
- nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [[1, 2, 2]])
- nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [1, 2, 2])
- nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [[1, 2, 2]])
-
- nose.tools.assert_raises(RuntimeError, t.enroll, m, [[1, 2, 2]], 5)
- """
-
def test_ISVTrainAndEnrol():
# Train and enroll an 'ISVMachine'
@@ -325,13 +304,6 @@ def test_ISVTrainAndEnrol():
latent_z = it.enroll(gse, 5)
assert np.allclose(latent_z, z_ref, eps)
- # Testing exceptions
- # nose.tools.assert_raises(RuntimeError, t.initialize, mb, [1, 2, 2])
- # nose.tools.assert_raises(RuntimeError, t.initialize, mb, [[1, 2, 2]])
- # nose.tools.assert_raises(RuntimeError, t.e_step, mb, [1, 2, 2])
- # nose.tools.assert_raises(RuntimeError, t.e_step, mb, [[1, 2, 2]])
- # nose.tools.assert_raises(RuntimeError, t.enroll, m, [[1, 2, 2]], 5)
-
def test_JFATrainInitialize():
# Check that the initialization is consistent and using the rng (cf. issue #118)
diff --git a/doc/plot/plot_ISV.py b/doc/plot/plot_ISV.py
index 85e88c0b0acd07fc42b9eeef83413139d6e98268..0a9dec10ec494b802d07d592116b509d05becce4 100644
--- a/doc/plot/plot_ISV.py
+++ b/doc/plot/plot_ISV.py
@@ -1,8 +1,9 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
from sklearn.datasets import load_iris
import bob.learn.em
-import matplotlib.pyplot as plt
-import numpy as np
np.random.seed(2) # FIXING A SEED
diff --git a/doc/plot/plot_JFA.py b/doc/plot/plot_JFA.py
index c67572b095549ecac6e26d3f3eeab78bfc9aa42e..2585d0c6fee64ba3654c4bd61a20c28c0e5201d4 100644
--- a/doc/plot/plot_JFA.py
+++ b/doc/plot/plot_JFA.py
@@ -1,8 +1,9 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
from sklearn.datasets import load_iris
import bob.learn.em
-import matplotlib.pyplot as plt
-import numpy as np
np.random.seed(2) # FIXING A SEED