fabianp

import numpy as np
from scipy import linalg

def ridge(A, b, alphas):
    """Return coefficients for regularized least squares 
               ||A x - b|| + alpha ||x||
    """ 
    U, s, V = linalg.svd(X, full_matrices=False)
    d = np.dot(U.T, y) / (s + alphas[:, np.newaxis] / s)
    return np.dot(d, V)

fabianp

"""
Ridge coefficients as a function of the regularization parameter
----------------------------------------------------------------

And highlight in dashed lines the optimal value by cross-validation.

Author: Fabian Pedregosa -- <fabian@fseoane.net>
"""
print __doc__

fabianp

cimport numpy as np
import numpy as np
np.import_array()

def chkfinite_double(np.ndarray X):

    cdef int i, length
    cdef np.PyArrayObject val
    cdef np.flatiter iter

fabianp

"""
Plot memory usage of a numeric computation using numpy and scipy
"""

"""Get process information"""

import time, sys, os
import linecache

if sys.platform.startswith('linux'):

fabianp

"""
usage : simple_client.py torrent_file
"""
#!/usr/bin/env python
# Copyright Arvid Norberg 2008. Use, modification and distribution is
# subject to the Boost Software License, Version 1.0. (See accompanying
# file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)

# with modifications by Fabian Pedregosa <fabian@fseoane.net>
# October 2011

fabianp

def _solve(A, b, solver, tol):
    # helper method for ridge_regression, A is symmetric positive

    if solver == 'auto':
        if hasattr(A, 'todense'):
            solver = 'sparse_cg'
        else:
            solver = 'dense_cholesky'

    if solver == 'sparse_cg':

fabianp

"""
Low rank approximation for the lena image
"""
import numpy as np
import scipy as sp
from scipy import linalg
import pylab as pl

X = sp.lena().astype(np.float)
pl.gray()

fabianp

import numpy as np
from scipy import linalg, optimize

MAX_ITER = 100


def group_lasso(X, y, alpha, groups, max_iter=MAX_ITER, rtol=1e-6,
             verbose=False):
    """
    Linear least-squares with l2/l1 regularization solver.

fabianp

/*
 * pidgin
 *
 * Pidgin is the legal property of its developers, whose names are too numerous
 * to list here.  Please refer to the COPYRIGHT file distributed with this
 * source distribution.
 *
 * 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

fabianp

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: 

    "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer 1999

    "Learning to rank from medical imaging data." Pedregosa, Fabian, et al., 
    Machine Learning in Medical Imaging 2012.