bee-san

a = "a9993e364706816aba3e25717850c26c9cd0d89d"
b = "cb4cc28df0fdbe0ecf9d9662e294b118092a5735"

a = list(a)
b = list(b)

percent = 0

for counter, value in enumerate(a):
    if value == b[counter]:

bee-san

function prettify(document){
    // Turns an array of words into lowercase and removes stopwords
    const stopwords = ["a", "share", "linkthese", "about", "above", "after", "again", "against", "all", "am", "an", "and", "any","", "are","aren't","as","at","be","because","been","before","being","below","between","both","but","by","can't","cannot","could","couldn't","did","didn't","do","does","doesn't","doing","don't","down","during","each","few","for","from","further","had","hadn't","has","hasn't","have","haven't","having","he","he'd","he'll","he's","her","here","here's","hers","herself","him","himself","his","how","how's","i","i'd","i'll","i'm","i've","if","in","into","is","isn't","it","it's","its","itself","let's","me","more","most","mustn't","my","myself","no","nor","not","of","off","on","once","only","or","other","ought","our","ours","ourselves","out","over","own","same","shan't","she","she'd","she'll","she's","should","shouldn't","so","some","such","than","that","that's","the","their","theirs","them","them

bee-san

function prettify(document){
    // Turns an array of words into lowercase and removes stopwords
    const stopwords = stopwords_array
    // turn document into lowercase words, remove all stopwords
    var document = document.replace(/[.,]/g, '');
    let document_in_lowercase = document.split(" ").map(function(x){ return x.toLowerCase() });
    return document_in_lowercase.filter( x => !stopwords.includes(x) );
}

bee-san

For item in dictionary:
  if multiSet(WORD) == multiSet(item):
    print("elvis is an anagram of " + item)

bee-san

dictionary = ["dogs", "lives", "live", "train"]
WORD = "elvis"

bee-san

data = pd.read_csv("creditcardcsvpresent.csv
data.describe()

bee-san

function inverseDocumentFrequency(document){
    // calculates the inverse document frequency of every sentence
    const words_without_stopwords = prettify(document);
    const unique_words_set = uniqueWords(words_without_stopwords);

    const sentences = document.split(".").map(item => item.trim());
    sentences[0] = sentences[0].substring(146);

    const lengthOfDocuments = sentences.length;
    // prettifys each sentence so it doesn't have stopwords

bee-san

{
    "manifest_version": 2,
    "name": "TL;DR - Summary of BBC news articles",
    "version": "1.0",
  
    "description": "This extension creates a summary of BBC news articles using TF*IDF", 
  
    "icons": {
    "48": "icons/border-48.png"

bee-san

{
    "manifest_version": 2,
    "name": "TL;DR - Summary of BBC news articles",
    "version": "1.0",
  
    "description": "This extension creates a summary of BBC news articles using TF*IDF", 
    "content_scripts": [
      {
        "matches": ["*://www.bbc.co.uk/news/*"],
        "js": ["jquery.js", "tldr.js"]

bee-san

// get all text from .story-body within p tags on a BBC news web article
let $article = $('.story-body').find('p').text();
// insert text into body of document
let insert = $('.story-body').prepend(TFIDF($article));