mathurinm

## lbfgs_l1logistic.py
"""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

## fail_reweighted_right_audi.py
"""
================================================================
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
================================================================

Runs an (ir)MxNE (L1/L2 [1]_ or L0.5/L2 [2]_ mixed norm) inverse solver.
L0.5/L2 is done with irMxNE which allows for sparser
source estimates with less amplitude bias due to the non-convexity
of the L0.5/L2 mixed norm penalty.

## mtl_bench.py
"""
Benchmark of MultiTaskLasso
"""
import gc
from itertools import product
from time import time
import numpy as np
import pandas as pd

from sklearn.datasets import make_regression
	"""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
	"""
	================================================================
	Compute sparse inverse solution with mixed norm: MxNE and irMxNE
	================================================================

	Runs an (ir)MxNE (L1/L2 [1]_ or L0.5/L2 [2]_ mixed norm) inverse solver.
	L0.5/L2 is done with irMxNE which allows for sparser
	source estimates with less amplitude bias due to the non-convexity
	of the L0.5/L2 mixed norm penalty.
	"""
	Benchmark of MultiTaskLasso
	"""
	import gc
	from itertools import product
	from time import time
	import numpy as np
	import pandas as pd

	from sklearn.datasets import make_regression