GaelVaroquaux

### Keybase proof

I hereby claim:

  * I am GaelVaroquaux on github.
  * I am gaelvaroquaux (https://keybase.io/gaelvaroquaux) on keybase.
  * I have a public key whose fingerprint is 44B8 B843 6321 47EB 59A9  8992 6C52 6A43 ABE0 36FC

To claim this, I am signing this object:

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)

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

#!/usr/bin/env python
# gvaroquaux (adapted from tdh.net gmail com)
# 31.August.2011
# try clustering module in scikits.learn
import numpy as np
from scipy import linalg

from sklearn.metrics.pairwise import euclidean_distances
from sklearn.cluster import SpectralClustering

GaelVaroquaux

Bootstrapping a SciPy-related community project

This document is a lightning talk: it only gives pointers, you need to Google and read references

Goal

GaelVaroquaux

"""
Benching I/O with joblib and other libraries. Comment and
un-comment what you are interested in.

Warning: this is slow, and the benchs are easily offset by other disk
activity.
"""
import os
import time
import shutil

GaelVaroquaux

# Licence : BSD
# Author: Gael Varoquaux

from time import time

import numpy as np
import pylab as pl
from scipy import linalg, ndimage

from sklearn import linear_model

GaelVaroquaux

import numpy as np
import sklearn.linear_model as lm

X = np.array([[ -2.18252949e-01,  -8.21949578e-02,  -4.64055457e-02,
         -1.78405908e-01,  -1.93863740e-01,   5.30667625e-02,
          1.83851107e-01,   1.23426449e-01,   1.97396315e-01,
         -2.12615837e-01,   7.06452283e-02,  -1.94509405e-01,
         -9.77929516e-02,   2.07135018e-01,  -3.40368338e-02,
          2.02970673e-01,  -2.28669466e-01,   4.17398420e-02,
          1.80163132e-01,   3.24254938e-02,  -2.41198452e-03,

GaelVaroquaux

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)

GaelVaroquaux

import numpy as np
import matplotlib.pyplot as plt

from sklearn.linear_model import Ridge, Lasso
from sklearn.cross_validation import ShuffleSplit
from sklearn.grid_search import GridSearchCV
from sklearn.utils import check_random_state
from sklearn import datasets