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import os
import re
import faiss
from langchain import FAISS
import obsidiantools.api as otools
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.llms import OpenAI

os.environ["OPENAI_API_KEY"] = 'sk-********'

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from sklearn.metrics.pairwise import cosine_similarity

try:
  import spacy
except:
  !pip install spacy
  import spacy

try:
  from sentence_transformers import SentenceTransformer, util

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#!/usr/bin/env python

import os
import pkg_resources

def calc_container(path):
    total_size = 0
    for dirpath, dirnames, filenames in os.walk(path):
        for f in filenames:
            fp = os.path.join(dirpath, f)

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Hacking With cURL

A list of examples and references of hacking with Bash and the Curl command.

What the heck is cURL?

cURL is short for "Client URL" and is a computer software project providing a library (libcurl) and command-line tool (curl) first released in 1997. It is a free client-side URL transfer library that supports the following protocols: Cookies, DICT, FTP, FTPS, Gopher, HTTP/1, HTTP/2, HTTP POST, HTTP PUT, HTTP proxy tunneling, HTTPS, IMAP, Kerberos, LDAP, POP3, RTSP, SCP, and SMTP Although attack proxies like BurpSuitePro are very handy tools, cURL allows you to get a bit closer to the protocol level, leverage bash scripting and provides a bit more flexibility when you are working on a complex vulnerability.

cURL GET parameters

HTTP GET variables can be set by adding them to the URL.

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import sys
from time import sleep
from selenium import webdriver
from selenium.common.exceptions import TimeoutException
from selenium.common.exceptions import WebDriverException
from eliot import start_action, to_file, log_call
# from selenium.webdriver.common.keys import Keys
# from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
# from selenium.webdriver.common.by import By
# from selenium.webdriver.support import expected_conditions as EC

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GLPK Installation process on Windows 7 SP1

Prerequists:

• Visual C++ (for GLPK v4.65 you'll need VC v2010 Express)

• Download the latest GLPK zip package: https://sourceforge.net/projects/winglpk/files/winglpk/ then extract its content to the C:\glpk.

• Create the environment variable %GLPK_HOME% pointing to the C:\glpk path you've created, and add to the system path the following value: SET PATH=%PATH%;%GLPK_HOME%\w64

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// reference http://www.geocities.jp/tomtomf/JScience/JScience.htm
//
@GrabResolver (name='jcurl-repository', root='http://jcurl.berlios.de/m2/repo/')
@Grab(group = 'org.jscience', module='jscience', version='4.3.1')

import org.jscience.mathematics.number.Complex
import org.jscience.mathematics.vector.ComplexMatrix

//計算結果の出力様関数
def show(cm) {

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def text = "Going to convert this to Base64 encoding!"

// Encode
def encoded = text.bytes.encodeBase64().toString()
println encoded


// Decode
byte[] decoded = encoded.decodeBase64()
println new String(decoded)

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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd 
import math

class UpperConfidenceBound:
  def __init__(self, dataframe,  N, m):
    self.__dataset = dataframe
    self.__N = N
    self.__m = m

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"""
Update: (07.29.2022): Fix: train_test_split import
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================

For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten digit recognition, the
Bernoulli Restricted Boltzmann machine model (:class:`BernoulliRBM
<sklearn.neural_network.BernoulliRBM>`) can perform effective non-linear