Removed spaces

Removed dels

Removed extra chars

Removed extra chars

bob/bio/gmm/algorithm/GMM.py

@@ -114,7 +114,6 @@ class GMM (Algorithm):
     logger.info("  -> Training K-Means")
 
     # Reseting the pseudo random number generator so we can have the same initialization for serial and parallel execution. 
-    del self.rng
     self.rng = bob.core.random.mt19937(self.init_seed)
     bob.learn.em.train(self.kmeans_trainer, kmeans, array, self.kmeans_training_iterations, self.training_threshold, rng=self.rng)
 
@@ -130,7 +129,6 @@ class GMM (Algorithm):
     # Trains the GMM
     logger.info("  -> Training GMM")
     # Reseting the pseudo random number generator so we can have the same initialization for serial and parallel execution. 
-    del self.rng
     self.rng = bob.core.random.mt19937(self.init_seed)
     bob.learn.em.train(self.ubm_trainer, self.ubm, array, self.gmm_training_iterations, self.training_threshold, rng=self.rng)
 
@@ -205,7 +203,7 @@ class GMM (Algorithm):
     logger.debug(" .... Enrolling with %d feature vectors", array.shape[0])
 
     gmm = bob.learn.em.GMMMachine(self.ubm)
-    gmm.set_variance_thresholds(self.variance_threshold)    
+    gmm.set_variance_thresholds(self.variance_threshold)
     bob.learn.em.train(self.enroll_trainer, gmm, array, self.gmm_enroll_iterations, self.training_threshold, rng=self.rng)
     return gmm
 

bob/bio/gmm/algorithm/ISV.py

@@ -65,7 +65,6 @@ class ISV (GMM):
     self.isvbase = bob.learn.em.ISVBase(self.ubm, self.subspace_dimension_of_u)
     # train ISV model
     # Reseting the pseudo random number generator so we can have the same initialization for serial and parallel execution. 
-    del self.rng
     self.rng = bob.core.random.mt19937(self.init_seed)
     bob.learn.em.train(self.isv_trainer, self.isvbase, data, self.isv_training_iterations, rng=self.rng)
 

bob/bio/gmm/algorithm/IVector.py

@@ -95,7 +95,6 @@ class IVector (GMM):
     self.tv = bob.learn.em.IVectorMachine(self.ubm, self.subspace_dimension_of_t, self.variance_threshold)
 
     # Reseting the pseudo random number generator so we can have the same initialization for serial and parallel execution. 
-    del self.rng
     self.rng = bob.core.random.mt19937(self.init_seed)
 
     # train IVector model
@@ -131,7 +130,6 @@ class IVector (GMM):
     input_dim = training_features[0].shape[1]
 
     # Reseting the pseudo random number generator so we can have the same initialization for serial and parallel execution. 
-    del self.rng
     self.rng = bob.core.random.mt19937(self.init_seed)
     
     self.plda_base = bob.learn.em.PLDABase(input_dim, self.plda_dim_F, self.plda_dim_G, variance_flooring)

bob/bio/gmm/test/test_algorithms.py

@@ -238,7 +238,7 @@ def test_isv():
 
   # compare model with probe
   probe = isv1.read_feature(pkg_resources.resource_filename('bob.bio.gmm.test', 'data/isv_projected.hdf5'))
-  reference_score = 0.02136783885580
+  reference_score = 0.02136784
   assert abs(isv1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (isv1.score(model, probe), reference_score)
 #  assert abs(isv1.score_for_multiple_probes(model, [probe]*4) - reference_score) < 1e-5, isv1.score_for_multiple_probes(model, [probe, probe])
   # TODO: Why is the score not identical for multiple copies of the same probe?
@@ -387,7 +387,7 @@ def test_ivector_cosine():
 
   # compare model with probe
   probe = ivec1.read_feature(pkg_resources.resource_filename('bob.bio.gmm.test', 'data/ivector_projected.hdf5'))
-  reference_score = -0.00187151477
+  reference_score = -0.00187151
   assert abs(ivec1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (ivec1.score(model, probe), reference_score)
   # TODO: implement that
   assert abs(ivec1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-5
@@ -425,6 +425,7 @@ def test_ivector_plda():
     assert os.path.exists(temp_file)
 
     if regenerate_refs: shutil.copy(temp_file, reference_file)
+
     # check projection matrix
     ivec1.load_projector(reference_file)
     ivec2.load_projector(temp_file)
@@ -444,13 +445,13 @@ def test_ivector_plda():
   random_features = utils.random_training_set((20,45), count=5, minimum=-5., maximum=5.)
   enroll_features = [ivec1.project(feature) for feature in random_features]
 
-  model = ivec1.enroll(enroll_features)  
+  model = ivec1.enroll(enroll_features)
   _compare(model, pkg_resources.resource_filename('bob.bio.gmm.test', 'data/ivector2_model.hdf5'), ivec1.write_model, ivec1.read_model)
 
   # compare model with probe
   probe = ivec1.read_feature(pkg_resources.resource_filename('bob.bio.gmm.test', 'data/ivector2_projected.hdf5'))
   logger.info("%f" %ivec1.score(model, probe))
-  reference_score = 1.2187982243
+  reference_score = 1.21879822
   assert abs(ivec1.score(model, probe) - reference_score) < 1e-5, "The scores differ: %3.8f, %3.8f" % (ivec1.score(model, probe), reference_score)
   assert abs(ivec1.score_for_multiple_probes(model, [probe, probe]) - reference_score) < 1e-5
 
@@ -509,7 +510,7 @@ def test_ivector_lda_wccn_plda():
   # enroll model from random features
   random_features = utils.random_training_set((20,45), count=5, minimum=-5., maximum=5.)
   enroll_features = [ivec1.project(feature) for feature in random_features]
-  model = ivec1.enroll(enroll_features)  
+  model = ivec1.enroll(enroll_features)
   _compare(model, pkg_resources.resource_filename('bob.bio.gmm.test', 'data/ivector3_model.hdf5'), ivec1.write_model, ivec1.read_model)
 
   # compare model with probe

bob/bio/gmm/test/test_scripts.py

@@ -28,9 +28,6 @@ def _verify(parameters, test_dir, sub_dir, ref_modifier="", score_modifier=('sco
 
     # assert that the score file exists
     score_files = [os.path.join(test_dir, sub_dir, 'Default', norm, '%s-dev%s'%score_modifier) for norm in ('nonorm',  'ztnorm')]
-
-    #import ipdb; ipdb.set_trace()
-
     assert os.path.exists(score_files[0]), "Score file %s does not exist" % score_files[0]
     assert os.path.exists(score_files[1]), "Score file %s does not exist" % score_files[1]
 

bob/bio/gmm/tools/gmm.py

@@ -33,7 +33,6 @@ def kmeans_initialize(algorithm, extractor, limit_data = None, force = False, al
     kmeans_machine = bob.learn.em.KMeansMachine(algorithm.gaussians, data.shape[1])
     # Creates the KMeansTrainer and call the initialization procedure
     # Reseting the pseudo random number generator so we can have the same initialization for serial and parallel execution. 
-    del algorithm.rng
     algorithm.rng = bob.core.random.mt19937(algorithm.init_seed)
     
     algorithm.kmeans_trainer.initialize(kmeans_machine, data, algorithm.rng)