npinto

import numpy as np
from scipy import weave

def openmpSum(in_array):
    """
    Performs fast sum of an array using openmm
    """
    a = np.asarray(in_array)
    b = np.array([1.])
    N = int(np.prod(a.shape))

npinto

package trainableSegmentation.metrics;

/**
 *
 * License: GPL
 *
 * This program is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License 2
 * as published by the Free Software Foundation.
 *

npinto

import numpy as np
from scipy import misc

from skimage.transform import swirl

import connectomics_data as cd


def random_swirls(X, Y, n_swirls=1, order=0, rseed=None):

npinto

from scipy import ndimage
from scipy import misc
import numpy as np

import connectomics_data as cd


def get_mask(shape, p=0.999, sigma=10, th=0.2):
    r = np.random.binomial(1, p, size=shape).astype('f')
    r2 = ndimage.gaussian_filter(r, sigma)

npinto

import numpy as np
from scipy.optimize import brent

def steepest_descent(func, X0, grad, maxiter= 100, verbose = False,
                     project=None, alpha_0=1.0, gtol = 1e-5,  **args):

    def func_one(X,dir_):
        def inner(alpha):
            if project is None:
                return func(X + alpha*dir_)

npinto

npinto

# Copyright 1999-2011 Gentoo Foundation
# Distributed under the terms of the GNU General Public License v2
# $Header: /var/cvsroot/gentoo-x86/dev-lang/perl/perl-5.12.3.ebuild,v 1.1 2011/01/22 09:41:54 tove Exp $

EAPI=3

inherit eutils alternatives flag-o-matic toolchain-funcs multilib

PATCH_VER=1

npinto

import numpy as np
from scipy import ndimage
#from pylab import quiver

from scipy import misc
l = misc.lena()

yy, xx = np.indices(l.shape)

npinto

import numpy as np
from skimage.util.shape import view_as_windows, view_as_blocks
from sthor.util import filter_pad2d


def f_g(x):

    xr = view_as_windows(x, (2, 2)).copy()
    #print xr
    loss = 0.5 * (xr**2.).sum()

npinto

import numpy as np

def test_mcc_bug():
    np.random.seed(42)

    # 64 classes with different "scale variation"
    # 100 samples per class, 1000 dimensions
    C = np.random.randn(100, 64, 1000) + np.arange(64)[np.newaxis, :, np.newaxis]
    # single test point with 1000 dimensions
    x = np.random.randn(1000)