diff --git a/multiview_platform/mono_multi_view_classifiers/utils/dataset.py b/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
index 2caae5411c20639f8ba428bfbaadbe07ec164d8e..b00059035d1a63ee51f766cb451e663e23509ad1 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/dataset.py
@@ -157,12 +157,31 @@ class Dataset():
return self.dataset["Metadata"].attrs["datasetLength"]
def get_view_dict(self):
+ """
+ Returns the dictionary with view indices as keys and the corresponding
+ names as values
+ """
view_dict = {}
for view_index in range(self.nb_view):
view_dict[self.dataset["View" + str(view_index)].attrs["name"]] = view_index
return view_dict
def get_label_names(self, decode=True, example_indices=None):
+ """
+ Used to get the list of the label names for the give set of examples
+
+ Parameters
+ ----------
+ decode : bool
+ If True, will decode the label names before lsiting them
+
+ example_indices : numpy.ndarray
+ The array containig the indices of the needed examples
+
+ Returns
+ -------
+
+ """
example_indices = self.init_example_indces(example_indices)
selected_labels = self.get_labels(example_indices)
if decode:
@@ -175,12 +194,26 @@ class Dataset():
if label in selected_labels]
def init_example_indces(self, example_indices=None):
+ """If no example indices are provided, selects all the examples."""
if example_indices is None:
return range(self.get_nb_examples())
else:
return example_indices
def get_v(self, view_index, example_indices=None):
+ """
+ Selects the view to extract
+ Parameters
+ ----------
+ view_index : int
+ The index of the view to extract
+ example_indices : numpy.ndarray
+ The array containing the indices of the examples to extract.
+
+ Returns
+ -------
+ A numpy.ndarray containing the view data for the needed examples
+ """
example_indices = self.init_example_indces(example_indices)
if type(example_indices) is int:
return self.dataset["View" + str(view_index)][example_indices, :]
@@ -203,10 +236,12 @@ class Dataset():
return sparse_mat
- def get_shape(self, example_indices=None):
- return self.get_v(0,example_indices=example_indices).shape
+ def get_shape(self, view_index=0, example_indices=None):
+ """Gets the shape of the needed view"""
+ return self.get_v(view_index,example_indices=example_indices).shape
def get_nb_class(self, example_indices=None):
+ """Gets the number of class of the dataset"""
example_indices = self.init_example_indces(example_indices)
return len(np.unique(self.dataset["Labels"][()][example_indices]))
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/parameters.py b/multiview_platform/mono_multi_view_classifiers/utils/parameters.py
deleted file mode 100644
index 2b61691f20124cb20fd7872aa8c44f5757397f02..0000000000000000000000000000000000000000
--- a/multiview_platform/mono_multi_view_classifiers/utils/parameters.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import numpy as np
-
-
-class Parameter_pdata(object):
- class __Parameter_pdata:
- nbr_i = 0
- # option de renormalisation des donnees
- # la séparation se faisant à une permutation pret et à un facteur de
- # renormalisation pret, on peut choisir de normaliser les données au debut
- # de l'algo et/ou à chaque iteration de l'algo et/ou à la fin de l'algo
- # on normalise A ou S
- _data_norm = {'FlagInit': True, 'FlagIter': False, 'FlagEnd': False}
- # % on normalise suivant les colonnes (1) 'dim' (norme des colonnes à 1) ou les
- # 'dim'% lignes (2) (norme des lignes à 1)
- _Norm = {'p': 1, 'dim': 1, 'x': 'A'}
- _list_mode = ['real', 'simul']
- _list_x = ['A', 'S']
-
- def __init__(self):
- self._Norm['p'] = 1
- self._Norm['dim'] = 1
- self._Norm['x'] = self._list_x[0]
- self.mode = self._list_mode[1]
- self.sigma = 20000
- self.dim = 1
- if self.nbr_i > 0:
- raise ValueError("Instance of class Parameter_pdata can be only one")
- self.nbr_i += 1
-
- def __str__(self):
- return repr(self)
-
- instance = None
-
- # def __init__(self, arg):
- # if not Parameter_pdata.instance:
- # Parameter_pdata.instance = Parameter_pdata.__Parameter_pdata(arg)
- # else:
- # Parameter_pdata.instance.val = arg
-
- def __new__(cls): # _new_ est toujours une méthode de classe
- if not Parameter_pdata.instance:
- Parameter_pdata.instance = Parameter_pdata.__Parameter_pdata()
- return Parameter_pdata.instance
-
- def __getattr__(self, attr):
- return getattr(self.instance, attr)
-
- # def __setattr__(self, attr, val):
- # return setattr(self.instance, attr, val)
-
- def __setattr__(self, name):
- return setattr(self.instance, name)
-
-
-class Parameter_palgo(object):
- class __Parameter_palgo:
-
- nbr_i = 0
- _list_algo = ['BCVMFB', 'PALS', 'STALS', 'LSfro', 'LSkl']
- _stop = {'DifA': False, 'DifS': False,
- 'ObjFct': True, 'threshold': np.finfo(float).eps}
- _pfwt = {'w': 'db6', 'family_pfwt': 'db',
- 'level': 10, 'K': 4,
- 'Ls': 3000, 'L1': 3000, 'L2': 3000}
- # _wavelette_type = ['db', 'db6']
- # 'LS' pour Lee et Seung
- # 'Lips' pour la constante de Lipschitz
- # 'PALM' pas de preconditionnement
- _list_precond = ['LS', 'Lips', 'PALM']
-
- def __init__(self):
- self.flagWave = False
- self.val = None
- algo_value = self._list_algo[1]
- self._algo = algo_value
- self.gamma = 0.99
- self.inf = np.inf
- self.eps = np.finfo(float).eps
- self.niter = 1000
- self.eta_inf = 'eps'
- self.eta_sup = 'inf'
- self.alpha_A = 0.0
- self.p_A = 1
- self.p_S = 1
- self.alpha_S = 0.0
- # self.level = 10
- self.alpha_S_eval = False
- self.stopThreshold = 10e-5,
- self.precond = 'LS' # 'LS' pour Lee et Seung
- self.F = None
- self.Fstar = None
- self.verbose = False
-
- if self.nbr_i > 0:
- raise ValueError("Instance of class Parameter_pdata can be only one")
- self.nbr_i += 1
-
- def __str__(self):
- return repr(self) + repr(self.val)
-
- @property
- def algo(self):
- return self._algo
-
- @algo.setter
- def algo(self, algo_value):
- if algo_value not in self._list_algo:
- raise NameError("parameter algo must be in %s" % self._list_algo)
- else:
- self._algo = algo_value
-
- instance = None
-
- # def __init__(self, arg):
- # if not Parameter_pdata.instance:
- # Parameter_pdata.instance = Parameter_pdata.__Parameter_pdata(arg)
- # else:
- # Parameter_pdata.instance.val = arg
-
- def __new__(cls): # _new_ est toujours une méthode de classe
- if not Parameter_palgo.instance:
- Parameter_palgo.instance = Parameter_palgo.__Parameter_palgo()
- return Parameter_palgo.instance
-
- def __getattr__(self, attr):
- return getattr(self.instance, attr)
-
- # def __setattr__(self, attr, val):
- # return setattr(self.instance, attr, val)
-
- def __setattr__(self, name):
- return setattr(self.instance, name)
-
-
-if __name__ == '__main__':
- a = Parameter_pdata()
- a = Parameter_pdata()
- b = Parameter_pdata()
- b.val = 6
- b.x = 8
- a.x = 10
- param = Parameter_palgo()
- algo = param._list_algo[3]
- param.algo = algo
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/transformations.py b/multiview_platform/mono_multi_view_classifiers/utils/transformations.py
index 9d26ddde8bd02fea2ef1176385ac251260027e40..9d78ee1e94d04bc84b4ed575e01bdac521b76425 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/transformations.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/transformations.py
@@ -2,6 +2,7 @@ import numpy as np
def sign_labels(labels):
+ """Used to transform 0/1 labels to -1/1 labels"""
if set(labels) == (0, 1):
return np.array([label if label != 0 else -1 for label in labels])
else: