TomDLT

import time
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.datasets import fetch_mldata


if __name__ == "__main__":

    mnist = fetch_mldata('MNIST original')

    X = mnist.data[::10]

TomDLT

# Author: Tom Dupre la Tour
# License: BSD 3 clause

Greedy Coordinate Descent for Non-Negative Matrix Factorization
in scikit-learn.

To change the Coordinate Descent into a Greedy Coordinate Descent, 
change the call to 
_update_cdnmf_fast(W, HHt, XHt, shuffle, seed) (in nmf.py)
into

TomDLT

import gc
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_rcv1
from sklearn.preprocessing import LabelBinarizer
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model.logistic import _multinomial_loss
from sklearn.externals.joblib import Memory

TomDLT

# Author: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
# License: BSD 3 clause

from __future__ import print_function
from time import time

import numpy as np
import scipy.sparse as sp
import matplotlib.pyplot as plt
import pandas

TomDLT

import numpy as np
import matplotlib.pyplot as plt


def monte_carlo(n_trajects, n_clients, n_iter, capacity=None, plot=False):
	# maximum capacity by car is here unlimited
	if capacity is None:
		capacity = n_clients

	# draw within an uniform distribution

TomDLT

import warnings
import neo.io

def smr2sig(filename, smr_nums=[0]):
    """
    read signal from .smr file (Spike2)

    parameters
    ----------
    filename    : name of file

TomDLT

from sklearn.datasets import fetch_olivetti_faces
from sklearn.decomposition.nmf import NMF
from sklearn.externals.joblib import Parallel, delayed


def fit(X):
    nmf.fit(X)


def nmf_test(n_jobs=-1, backend='multiprocessing'):

TomDLT

from __future__ import print_function
import warnings

import numpy as np
import neo.io
from mne.io.array import RawArray
from mne import create_info


def _smr(filename):

TomDLT

import re
import os

# find existing links
matches = []
with open('whats_new.rst', 'r') as f:
    for line in f.readlines():
        m = re.search('`[A-Za-z üéö@.ä-]*`_', line)
        if m is not None:
            matches.append(m.group(0))

TomDLT

from __future__ import print_function
import sys
import time


# copy from MNE-Python
class ProgressBar():
    """Class for generating a command-line progressbar
    Parameters
    ----------