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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) |
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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"] |
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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 |
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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 |
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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 |
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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: |
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SMILES,CHEM_ID | |
COC1:C:C(C2C3=C(CCCC3=O)N(C3:C:C:C(C):C:C:3)C3=C2C(=O)CCC3):C([N+](=O)[O-]):C:C:1OC,Chem_1 | |
O=C1NC(N2CCCCC2)=NC1=CC1:C:C:C:S:1,Chem_2 | |
COC1:C:C(C=C2C(=O)N(C(=O)C3:C:C:C(Cl):C:C:3)N=C2C):C:C(OC):C:1OC,Chem_3 | |
CC#CC(O)(C(=O)OC1CCN(C)CC1)C1CCCCC1,Chem_4 | |
COC1:C:C:C(N=C(C)C(C)=NC2:C:C:C(OC):C:C:2):C:C:1,Chem_5 | |
CSC1:N:C(O):C(C#N):C(C2:C:C:C(C):C:C:2):N:1,Chem_6 | |
CSC1:N:C(C2:C:C:C:C:C:2):N:C(N2CCOCC2):[S+]:1.[IH2+],Chem_7 | |
CC1:C:C:C(C=C2N=C(NN=CC(O)C(O)C(O)CO)NC2=O):C:C:1,Chem_8 | |
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 |