donkey-hotei

from time import time 
from sklearn.datasets import load_files
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans 
from sklearn import metrics 

def bench_k_means(estimator, name, data, labels):
    t0 = time()
    sample_size = len(data)

donkey-hotei

#!/usr/bin/python 
"""
Simple Sentiment Analyzer over the Cornell Moview Review Dataset
"""
import string 
import numpy as np 
import matplotlib.pyplot as plt 
from random import randint
# nltk has some tools that might be useful... 
from nltk.corpus import stopwords 

donkey-hotei

#!/usr/bin/python
import numpy as np
from sklearn.dataset import load_files

def split_neg_pos_docs(data):
	"""
	Splits the dataset into negative and negative sets.
	"""
	negative_documents = b' '.join([ data.data[i] for i in range(len(data.data)) if data.target[i] == 0 ])
	positive_documents = b' '.join([ data.data[i] for i in range(len(data.data)) if data.target[i] == 1 ])

donkey-hotei

#!/usr/bin/python3
from core.vector import Vector
from core.GF2 import one, zero

from util.factoring_util import dumb_factor, intsqrt, primes, prod


def int2GF2(integer):
    """ Convert integer to a binary one or zero.
        0 if int is even else 1

donkey-hotei

"""
Analyzing voting data using SLAL
"""
# from Vector import Vector
from Vector import list2vector

US_file = open('US_Senate_voting_data_109.txt')
UN_file = open('UN_voting_data.txt')
US_voting_data = []
UN_voting_data = []

donkey-hotei

import sys
sys.path.append('../../'    )
from core.vector import Vector
from util.matrixutil import rowdict2matrix 

def read_training_data(fname, D=None):
    """Given a file in appropriate format, and given a set D of features,
    returns the pair (A, b) consisting of 
    a P-by-D matrix A and a P-Vector b,
    where P is a set of patient identification integers (IDs).

donkey-hotei

(defclass Noeud [object]
  ;; Node class for representing outcomes
  [[--init-- (fn [self]
               (setv self.acheter None)  ; edge to buy node
               (setv self.rester None) ; edge to stay node
               (setv self.solde 0)  ; balance at current node
               (setv self.machine None) ; each node has a machine associated, except first node
               (setv self.jour None) ; init node object attrs
               None)]
   [--str-- (fn [self] 

donkey-hotei

#!/usr/bin/python
from pwn import *

if __name__ == '__main__':
    # context.binary = "precision"  # name of binary
    host = "54.210.15.77"  # host ip
    port = 1259
    connection = remote(host, port)  # expose connection to service
    context.bits = 32  # since we're working with a 32-bit binary
    response = connection.recvline()

donkey-hotei

#!/usr/bin/python
# -*- coding: latin-1 -*-
# a python stealth port scanner

from logging import getLogger, ERROR
getLogger("scapy.runtime").setLevel(ERROR)
from scapy.all import *
from datetime import datetime
from time import stdftime

donkey-hotei

#!/usr/bin/python

# CAM Table Overflow is all about flooding a
# switches CAM table with a lot of fake MAC
# addresses to drive the switch into HUB mode.

from scapy.all import *
from random import randint
import subprocess
# import sys