chrisveness

/**
 * Encrypts plaintext using AES-GCM with supplied password, for decryption with aesGcmDecrypt().
 *                                                                      (c) Chris Veness MIT Licence
 *
 * @param   {String} plaintext - Plaintext to be encrypted.
 * @param   {String} password - Password to use to encrypt plaintext.
 * @returns {String} Encrypted ciphertext.
 *
 * @example
 *   const ciphertext = await aesGcmEncrypt('my secret text', 'pw');

sixtenbe

#!/usr/bin/python2


# Copyright (C) 2016 Sixten Bergman
# License WTFPL
#
# This program is free software. It comes without any warranty, to the extent
# permitted by applicable law. 
# You can redistribute it and/or modify it under the terms of the Do What The
# Fuck You Want To Public License, Version 2, as published by Sam Hocevar. See

CristinaSolana

1. Clone your fork:

git clone git@github.com:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

dcb9

package main

import (
	"fmt"

	"github.com/ethereum/go-ethereum/accounts"
	"github.com/ethereum/go-ethereum/common/hexutil"
	"github.com/ethereum/go-ethereum/crypto"
)

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

rochacbruno

#!/usr/bin/env python

# Haversine formula example in Python
# Author: Wayne Dyck

import math

def distance(origin, destination):
    lat1, lon1 = origin
    lat2, lon2 = destination

douglascayers

/**
 * The function you pass to `asyncPoll` should return a promise
 * that resolves with object that satisfies this interface.
 *
 * The `done` property indicates to the async poller whether to
 * continue polling or not.
 *
 * When done is `true` that means you've got what you need
 * and the poller will resolve with `data`.
 *

alexalemi

import math
class Welford(object):
    """ Implements Welford's algorithm for computing a running mean
    and standard deviation as described at: 
        http://www.johndcook.com/standard_deviation.html

    can take single values or iterables

    Properties:
        mean    - returns the mean

jonathanlurie

/*
    Author: Jonathan Lurie - http://me.jonathanlurie.fr
    License: MIT
    
    The point of this little gist is to fix the issue of losing
    typed arrays when calling the default JSON serilization.
    The default mode has for effect to convert typed arrays into
    object like that: {0: 0.1, 1: 0.2, 2: 0.3} what used to be
    Float32Array([0.1, 0.2, 0.3]) and once it takes the shape of an
    object, there is no way to get it back in an automated way!

seanlinsley

// spec: https://github.com/tc39/proposal-weakrefs
// the spec contains an [iterable WeakMap implementation](https://github.com/tc39/proposal-weakrefs#iterable-weakmaps)
// NOTE: this WeakSet implementation is incomplete, only does what I needed
// In Firefox Nightly, visit about:config and enable javascript.options.experimental.weakrefs

class IterableWeakSet extends Set {
  add(el) {
    super.add(new WeakRef(el))
  }
  forEach(fn) {