glouppe

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CSC1:N:C(C2:C:C:C:C:C:2):N:C(N2CCOCC2):[S+]:1.[IH2+],Chem_7
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CN(C)C(C1=C(O)C(C2:C:C:C:C:C:2)N(C2:C:C:C:C:C:2)C1=O)N1CCOCC1,Chem_9

glouppe

import multiprocessing as mp
import queue
import threading
 
def buffered_gen_mp(source_gen, buffer_size=2):
    """
    Generator that runs a slow source generator in a separate process.
    buffer_size: the maximal number of items to pre-generate (length of the buffer)
    """
    if buffer_size < 2:

glouppe

import numpy as np

from sklearn.cross_validation import train_test_split
from sklearn.datasets import fetch_mldata
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import GradientBoostingClassifier
from rep.estimators import TMVAClassifier

from functools import partial

glouppe

import sys
sys.path.append("/usr/lib/python2.7/dist-packages/")
sys.path.append("/usr/local/lib/python2.7/dist-packages/")

import string
import re

from joblib import Parallel, delayed
from invenio.dbquery import run_sql
from invenio.bibauthorid_dbinterface import get_title_of_paper

glouppe

import numpy as np
import argparse
import cPickle
import scipy.cluster.hierarchy as hac

from itertools import groupby
from itertools import product
from scipy.sparse import lil_matrix
from scipy.sparse import issparse
from scipy.spatial.distance import squareform

glouppe

import numpy as np
import os

from collections import defaultdict
from git import Repo
from scipy.sparse import csc_matrix

path = "/home/gilles/Sources/scikit-learn/sklearn/"
extensions = ["py", "pyx", "pxd"]

glouppe

import numpy
import random
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
# Define training and testing sets
inds = numpy.arange(len(mnist.data))
test_i = random.sample(xrange(len(inds)), int(0.1*len(inds)))
train_i = numpy.delete(inds, test_i)
X_train = mnist.data[train_i].astype(numpy.double)
y_train = mnist.target[train_i].astype(numpy.double)