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require 'pp'
def ngram(n = 1, text)
  data = []

  0.upto(text.size-n) do |i|
    data << text[i..i+n-1]
  end
  data
end

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import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.List;
import java.util.Arrays;
import java.util.HashSet;
import java.util.Set;
import com.google.common.primitives.Ints;

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import fileinput
import networkx as nx

g = nx.Graph() # 無向グラフ

file = "sample.txt"
# file = "facebook_combined.txt"
for line in fileinput.input(file):
  edge = list(map(int, line.split()))
  g.add_edge(edge[0], edge[1])

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# coding: utf-8

import numpy as np
import sys
from sklearn.feature_extraction.text import CountVectorizer


def logistic_function(mat):
    return 1/(1+np.exp(-mat))

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# coding: utf-8

import numpy as np
import sys
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.cross_validation import train_test_split

def logistic_function(mat):
    return 1/(1+np.exp(-mat))

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# coding: utf-8

import sys
import math
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import preprocessing
import scipy.special

fname = sys.argv[1]

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*.aux
*.bbl
*.dvi
*.blg
*.fdb_latexmk
*.fls
*.log
*.synctex.gz
*.toc
*.lot

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