EpiphanyMachine

#!/usr/bin/env node

console.log("Hello");

EpiphanyMachine

Keybase proof

I hereby claim:

• I am EpiphanyMachine on github.
• I am ghilkert (https://keybase.io/ghilkert) on keybase.
• I have a public key whose fingerprint is 73FB 620A D527 AB3A AEE5 C58E 2F61 C35A 4201 2521

To claim this, I am signing this object:

EpiphanyMachine

#!/bin/bash

# PIA Iptables Manager
# Version 0.5
# Modified by: [email protected]
# Originally Created by ShadowSpectre <[email protected]>
# https://www.privateinternetaccess.com/forum/discussion/1151/pia-iptables-manager-new

clear
echo ".: PIA Iptables Manager v0.4 :."

EpiphanyMachine

function toRoman(number) {
    'use strict';
    var v = [1000,  900, 500,  400, 100,   90,  50,   40,  10,    9,   5,    4,   1],  // set values
        r = [ 'M', 'CM', 'D', 'CD', 'C', 'XC', 'L', 'XL', 'X', 'IX', 'V', 'IV', 'I'],  // set numerals
        n = Math.round(number), // clean number, round to nearest integer
        limit = 3999, // set max limit to check integers against
        s = '', // create empty string for numerals
        val; // create temp val variable

    // check if number is greater than the limit

EpiphanyMachine

// thanks to:
// Adam - https://github.com/acreeger
// Juan - https://github.com/nilnullzip

// Note that I decided the search order heuristic should be based solely on the person being visited
// and the goal person. The position within the graph should not be considered, so the graph edges
// are not annotated. Rather a heuristic function is used that takes as input only two people. I believe
// this makes more sense if the heuristic is based on things like number of friends or similarity in
// things like: school, city, job, etc.

EpiphanyMachine

var dict = require('dictionary');

var solveJumble = function (string) {
    'use strict';
    var originalBag = string.split('');
    var anagrams = [];
    var findAnagrams = function (bag, word) {
        var tempBag, tempWord;
        for (var i = 0; i < bag.length; i++) {
            tempBag = bag.slice();

EpiphanyMachine

#
# write a function that takes a string of text and returns true if
# the parentheses, brackets, and braces are balanced and false otherwise.
# 
# Example:
#  balanceParens('[](){}'); // true
#  balanceParens('[({})]');   // true
#  balanceParens('[(]{)}'); // false
#
# Step 3:

EpiphanyMachine

var paths = function (n) {
    var time = new Date();
    var board = [];
    for (var i = 0; i < n; i++) {
        board.push([]);
        for (var j = 0; j < n; j++) {
            board[i].push(0);
        }
    }
    board[0][0] = 1;

EpiphanyMachine

features = [] # make new empty list
counter = 0
for row in train:
# for row in range(len(train)-1): # for every row in the data
    # print row
    # data we need to create features
    # items needed to test questions
# PANDAS USAGE
    question_tokens = nltk.word_tokenize(row[0]) # all tokens (words, punc, etc)
    question_tags = nltk.pos_tag(question_tokens) # all tokens and parts of speech in lists