mblondel

"""Gaussian processes"""

# Author: Mathieu Blondel <[email protected]>
# 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

mblondel

"""Kernel K-means"""

# Author: Mathieu Blondel <[email protected]>
# 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

mblondel

"""Variable-length curve averaging"""

# Author: Mathieu Blondel <[email protected]>
# License: BSD 3 clause

import numpy as np
from scipy.interpolate import interp1d


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

mblondel

# (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.

mblondel

# (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

mblondel

"""Local regression"""

# Author: Mathieu Blondel <[email protected]>
# License: BSD 3 clause

import numpy as np

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

mblondel

"""
NMF by coordinate descent, designed for sparse data (without missing values)
"""

# Author: Mathieu Blondel <[email protected]>
# License: BSD 3 clause

import numpy as np
import scipy.sparse as sp
import numba

mblondel

def test():
    print(i)

i = 1

test()

mblondel

"""
License: BSD
Author: Mathieu Blondel

Implements three algorithms for projecting a vector onto the simplex: sort, pivot and bisection.

For details and references, see the following paper:

Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex
Mathieu Blondel, Akinori Fujino, and Naonori Ueda.

mblondel

"""
Multiclass SVMs (Crammer-Singer formulation).

A pure Python re-implementation of:

Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex.
Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
ICPR 2014.
http://www.mblondel.org/publications/mblondel-icpr2014.pdf
"""