mathurinm

from os.path import join as pjoin
import numpy as np
from numpy.linalg import norm
import time
import mne
from mne.datasets import sample
from mne.inverse_sparse.mxne_inverse import (_to_fixed_ori, _prepare_gain)
from mne.inverse_sparse.mxne_optim import mixed_norm_solver, norm_l2inf, norm_l21
# from mne.inverse_sparse.mxne_optim import _mixed_norm_solver_cd

mathurinm

import os
import mne
from mne.datasets import somato

data_path = somato.data_path()

raw_fname = data_path + '/MEG/somato/sef_raw_sss.fif'
trans = data_path + '/MEG/somato/sef_raw_sss-trans.fif'
src = data_path + '/subjects/somato/bem/somato-oct-6-src.fif'
bem = data_path + '/subjects/somato/bem/somato-5120-bem-sol.fif'

mathurinm

from pyface.qt import QtGui, QtCore
%matplotlib qt

mathurinm

# obtained with priceless help from Olivier Grisel
# rpy2 is available via pip: pip install rpy2
import numpy as np
from rpy2 import robjects
import rpy2.robjects.packages as rpackages
from rpy2.robjects import numpy2ri
from rpy2.robjects import pandas2ri

# we use function ltsReg of package robustbase
if __name__ == "__main__":

mathurinm

\begin{wrapfigure}{h!}{0.42\textwidth}
\centering
\includegraphics[width=0.42\textwidth]{prebuiltimages/KL_Ball.pdf}\\
\caption{\label{plot:q_set_example}Example of sets $\Qset_{\alpha}(\Prob_{Z})$ and $\Qset_{\alpha}(\Prob_{Z})$ with  $\balpha < \alpha$.}
\end{wrapfigure}

mathurinm

import matplotlib.pyplot as plt
from matplotlib import rc
import seaborn as sns

rc('font', **{'family': 'sans-serif',
              'sans-serif': ['Computer Modern Roman']})
params = {'axes.labelsize': 12,
          'font.size': 12,
          'legend.fontsize': 12,
          'xtick.labelsize': 10,

mathurinm

"""l-bfgs-b L1-Logistic Regression solver"""

# Author: Vlad Niculae <vlad@vene.ro>
# Suggested by Mathieu Blondel

from __future__ import division, print_function

import numpy as np
from scipy.optimize import fmin_l_bfgs_b

mathurinm

import numpy as np
from numba import njit
import matplotlib.pyplot as plt
from scipy.linalg import toeplitz
from numpy.linalg import norm
from sklearn.utils import check_random_state
from sklearn.linear_model import LinearRegression


def data(n_samples, n_features, rho=0.5, seed=24):

mathurinm

import numpy as np
from numba import njit
from numpy.linalg import norm
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

from libsvmdata import fetch_libsvm
# from celer.datasets import make_correlated_data

mathurinm

from celer import Lasso
import matplotlib.pyplot as plt
from scipy.optimize import fmin_bfgs
from numpy.linalg import norm
import numpy as np

from celer.datasets import make_correlated_data
import seaborn as sns

c_list = sns.color_palette("colorblind")