mblondel

#!/usr/bin/env python
"""
Find the solution for the second order differential equation

u'' = -u

with u(0) = 10 and u'(0) = -5

using the Euler and the Runge-Kutta methods.

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

"""
(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

# Author: Mathieu Blondel
# License: BSD 3 clause

import numpy as np
from sklearn.isotonic import isotonic_regression


def projection_simplex(x, z=1):
    """
    Compute argmin_{p : p >= 0 and \sum_i p_i = z} ||p - x||

mblondel

# Author: Mathieu Blondel
# License: BSD 3 clause

import numpy as np


def projection_simplex(V, z=1, axis=None):
    """
    Projection of x onto the simplex, scaled by z:
        P(x; z) = argmin_{y >= 0, sum(y) = z} ||y - x||^2

mblondel

# -*- coding: utf-8 -*-

# Copyright (C) 2010 Mathieu Blondel
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,

mblondel

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

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

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

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

"""
Efficient implementation of FISTA.
"""

# Author: Mathieu Blondel
# License: BSD 3 clause

import numpy as np

mblondel

"""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