mblondel

"""
(C) August 2013, Mathieu Blondel
# License: BSD 3 clause

This is a Numba-based reimplementation of the block coordinate descent solver
(without line search) described in the paper:

    Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass
    Classification.  Mathieu Blondel, Kazuhiro Seki, and Kuniaki Uehara.
    Machine Learning, May 2013.

mblondel

# (C) Mathieu Blondel
# License: BSD 3 clause

import numpy as np
from numpy import ma
import scipy.sparse as sp


def _get_mask(X, missing_values, sparse=False):
    if sparse:

mblondel

paper.tex: main manuscript
supp.tex: supplementary material

Cross-referencing
-----------------

We want to cross-reference equations in paper.tex from supp.tex.

mblondel

# (C) Mathieu Blondel 2012
# License: BSD 3 clause

import numpy as np
from scipy.optimize import fmin_l_bfgs_b

from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.utils.extmath import safe_sparse_dot

mblondel

# Mathieu Blondel, May 2012
# License: BSD 3 clause

import numpy as np

def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False):
    XX = np.sum(X * X, axis=1)[:, np.newaxis]
    YY = np.sum(Y ** 2, axis=1)[np.newaxis, :]
    distances = np.dot(X, Y.T)
    distances *= -2

mblondel

# Mathieu Blondel, February 2012
# License: BSD 3 clause

# Port to Python of examples in chapter 5 of
# "Introductory Statistics with R" by Peter Dalgaard

import numpy as np
from scipy.stats import ttest_1samp, wilcoxon, ttest_ind, mannwhitneyu

# daily intake of energy in kJ for 11 women

mblondel

# Copyright Mathieu Blondel December 2011
# License: BSD 3 clause

import numpy as np
import pylab as pl

from sklearn.base import BaseEstimator
from sklearn.utils import check_random_state
from sklearn.cluster import MiniBatchKMeans
from sklearn.cluster import KMeans as KMeansGood

mblondel

def online_mean_variance(iterable):
    mN = 0
    mM = 0.0
    mS = 0.0

    for x in iterable:
        mN += 1
        nextM = mM + (x - mM) / mN
        mS += (x - mM) * (x - nextM)

mblondel

# (C) 2011 Mathieu Blondel
# License: BSD 3 clause

import numpy as np
import numpy.linalg as linalg
import pylab as pl
from cvxopt import matrix, solvers

np.random.seed(0)

mblondel

# Mathieu Blondel, October 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg

def linear_kernel(x1, x2):
    return np.dot(x1, x2)

def polynomial_kernel(x, y, p=3):