aweinstein

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import Lasso, lars_path

np.random.seed(42)

def gen_data(n, m, k):
    X = np.random.randn(n, m)
    w = np.zeros((m, 1))
    i = np.arange(0, m)

paolo-losi

import numpy as np
import FuncDesigner as fd
from openopt import NLP

from sklearn.metrics.pairwise import rbf_kernel, polynomial_kernel


def theta(x):
    return 0.5 * (fd.sign(x) + 1.)

daien

"""
Multinomial Logistic Regression (MLR)
=====================================

Multiclass-classification with the MLR classifier


Authors: Adrien Gaidon & Jakob Verbeek

"""

mshivers

import scipy as sp
from scipy import optimize as opt
def nnlr(X, y, C):
    """
    Non-negative Logistic Regression with L2 regularizer
    """
    def lr_cost(X, y, theta, C):
        m = len(y)
        return (1./m) * (sp.dot(-y, sp.log(sigmoid(sp.dot(X, theta)))) \

satra

#DOES NOT WORK: GIFTI READ/WRITE is broken
import nibabel as nb
import nibabel.gifti as gifti

# load combined file
# created with: mris_convert --combinesurfs lh.pial rh.pial ./pial.gii
surf = gifti.read('pial.gii')

#load orig affine transform
ao = nb.load('orig.nii').get_affine()

mblondel

def online_mean_variance(iterable):
    mN = 0
    mM = 0.0
    mS = 0.0

    for x in iterable:
        mN += 1
        nextM = mM + (x - mM) / mN
        mS += (x - mM) * (x - nextM)

GaelVaroquaux

import numpy as np

def _compute_bins(x, n_bins=10):
    """ Find optimal bins from a univariate distribution

        Parameters
        ===========
        x: 1D array-like
            Samples
        n_bins: integer

GaelVaroquaux

"""A helper node to concatenate images in nipype"""

import os

from nipype.interfaces.base import TraitedSpec, File, CommandLineInputSpec, CommandLine
from nipype.utils.misc import isdefined

class ConcatImgInputSpec(CommandLineInputSpec):
    in_file1 = File(exists=True, argstr="-append %s", 
                                position=1, mandatory=True)