Corrected test file lookup.

xbob/boosting/tests/test_trainer_lut.py

@@ -39,7 +39,7 @@ class TestLutTrainer(unittest.TestCase):
         range_feature = max_feature
         trainer = xbob.boosting.trainer.LUTTrainer(range_feature,dimension_feature)
 
-        features = bob.io.load('xbob/boosting/tests/datafile.hdf5')
+        features = bob.io.load(bob.test.utils.datafile('datafile.hdf5', 'xbob.boosting', 'tests'))
 
         x_train1 = numpy.copy(features)
         x_train1[x_train1[:,selected_index] >=10, selected_index] = 9
@@ -68,7 +68,7 @@ class TestLutTrainer(unittest.TestCase):
         selected_index = 5
         range_feature = max_feature + delta
         trainer = xbob.boosting.trainer.LUTTrainer(range_feature, dimension_feature)
-        features = bob.io.load('xbob/boosting/tests/datafile.hdf5').astype(numpy.uint16)
+        features = bob.io.load(bob.test.utils.datafile('datafile.hdf5', 'xbob.boosting', 'tests')).astype(numpy.uint16)
 
         x_train = numpy.vstack((features, features))
         x_train[0:num_samples,selected_index] = x_train[0:num_samples,selected_index] + delta
@@ -91,9 +91,8 @@ class TestLutTrainer(unittest.TestCase):
         selected_index = 5
         range_feature = max_feature + delta
         trainer = xbob.boosting.trainer.LUTTrainer(range_feature, dimension_feature)
-        data_file = bob.io.File('xbob/boosting/tests/datafile.hdf5', 'r')
 
-        features = data_file.read().astype(numpy.uint16)
+        features = bob.io.load(bob.test.utils.datafile('datafile.hdf5', 'xbob.boosting', 'tests')).astype(numpy.uint16)
 
         x_train = numpy.vstack((features, features))
         x_train[0:num_samples,selected_index] = x_train[0:num_samples,selected_index] + delta