Mathieu Blondel mblondel

## letor_metrics.py
# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause

import numpy as np


def ranking_precision_score(y_true, y_score, k=10):
    """Precision at rank k

    Parameters

## matrix_sketch.py
# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause

import numpy as np
from scipy.linalg import svd


def frequent_directions(A, ell, verbose=False):
    """
    Return the sketch of matrix A.

## curve_averaging.py
"""Variable-length curve averaging"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np
from scipy.interpolate import interp1d


def curves_mean_std(X, Y, kind="linear"):

## kernel_kmeans.py
"""Kernel K-means"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

## gaussian_process.py
"""Gaussian processes"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np
from scipy.linalg import cholesky, solve_triangular

from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.metrics.pairwise import pairwise_kernels

## sparse_multiclass_numba.py
"""
(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.

## imputer.py
# (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:

## xref.txt

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

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

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


## lbfgs_nnls.py
# (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


## kernel_sgd.py
# 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