Skip to content
Snippets Groups Projects
Commit de459cd2 authored by Baptiste Bauvin's avatar Baptiste Bauvin
Browse files

Cleaning

parent fb8b2fe4
Branches
Tags
No related merge requests found
Show whitespace changes
Inline Side-by-side
multiview_platform/mono_multi_view_classifiers/utils/dataset.py
+ 37
− 2
View file @ de459cd2
......@@ -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]))
......
multiview_platform/mono_multi_view_classifiers/utils/parameters.py deleted 100644 → 0
+ 0
− 145
View file @ fb8b2fe4
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
multiview_platform/mono_multi_view_classifiers/utils/transformations.py
+ 1
− 0
View file @ de459cd2
......@@ -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:
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment