fabianp

# .. Memory benchmarks for SciPy's Singular Value Decomposition ..
# .. Author: Fabian Pedregosa <fabian@fseoane.net>

import numpy as np
from scipy.sparse import linalg as splinalg
from scipy import sparse, linalg
import pylab as pl
from memory_profiler import memory_usage

dims = np.arange(500, 1500, 20)

fabianp

fabianp

import numpy as np

# Author : Fabian Pedregosa <fabian@fseoane.net>
#
# License : BSD


def isotonic_regression_new(w, y, x_min=None, x_max=None):
    """
    Solve the isotonic regression with complete ordering model:

fabianp

# -*- coding: utf-8 -*-
"""
Strong rules for coordinate descent

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

import numpy as np
from scipy import linalg

fabianp

# -*- coding: utf-8 -*-
"""
Minimalistic implementation of l1 minimization via coordinate descent.

Reference: www.jstatsoft.org/v33/i01/paper

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

import numpy as np

fabianp

import numpy as np

# Author : Alexandre Gramfort
# license : BSD


def pav(y):
    """
    PAV uses the pair adjacent violators method to produce a monotonic
    smoothing of y

fabianp

import numpy as np

def isotonic_regression(w, y, x_min=None, x_max=None):
    """
    Solve the isotonic regression model:

        min Sum w_i (y_i - x_i) ** 2

        subject to x_min = x_1 <= x_2 ... <= x_n = x_max

fabianp

import sys, os, termios, tty
pid, fd = os.forkpty()

term_info = termios.tcgetattr(sys.stdin.fileno())

if pid < 0:
    raise ValueError("Early exit: could not fork process")

elif pid == 0:
    # .. child ..

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.

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