as matrix

bpdl/data_utils.py

@@ -1135,7 +1135,7 @@ def dataset_load_weights(path_base, name_csv=CSV_NAME_WEIGHTS, img_names=None):
  encoding = np.array([[int(x) for x in c.split(';')] for c in coding])
  # the new encoding with pattern names
  else:
- encoding = df.as_matrix()
+ encoding = df.values
  return np.array(encoding)
 
 

experiments/experiment_general.py

@@ -240,7 +240,7 @@ def load_list_img_names(path_csv, path_in=''):
  assert os.path.exists(path_csv), '%s' % path_csv
  df = pd.read_csv(path_csv, index_col=False, header=None)
  assert len(df.columns) == 1, 'assume just single column'
- list_names = df.as_matrix()[:, 0].tolist()
+ list_names = df.values[:, 0].tolist()
  # if the input path was set and the list are just names, no complete paths
  if os.path.exists(path_in) and not all(os.path.exists(p) for p in list_names):
  # to each image name add the input path

experiments/run_experiments.py

@@ -35,12 +35,12 @@
 
 sys.path += [os.path.abspath('.'), os.path.abspath('..')] # Add path to root
 from bpdl.utilities import is_list_like, is_iterable
-from bpdl.data_utils import (DIR_NAME_DICTIONARY, DEFAULT_NAME_DATASET,
-   dataset_compose_atlas, find_images, dataset_load_images)
+from bpdl.data_utils import (
+ DIR_NAME_DICTIONARY, DEFAULT_NAME_DATASET, dataset_compose_atlas, find_images, dataset_load_images)
 from bpdl.dictionary_learning import bpdl_pipeline
 from bpdl.pattern_atlas import init_atlas_mosaic
-from experiments.experiment_general import (SYNTH_PARAMS, REAL_PARAMS,
-   SYNTH_PATH_APD, parse_params)
+from experiments.experiment_general import (
+ SYNTH_PARAMS, REAL_PARAMS, SYNTH_PATH_APD, parse_params)
 from experiments.experiment_methods import (
  ExperimentSparsePCA, ExperimentFastICA, ExperimentDictLearn, ExperimentNMF,
  ExperimentSpectClust, ExperimentCanICA, ExperimentMSDL, ExperimentBPDL, DICT_ATLAS_INIT)
@@ -64,7 +64,6 @@
  'max_iter': 25, # 250, 150
 })
 
-REAL_PARAMS = REAL_PARAMS
 REAL_PARAMS.update({
  'method': LIST_METHODS,
  'max_iter': 25, # 250, 150