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');

erynofwales

import Foundation

public protocol AlmostEquatable {
    @warn_unused_result
    func ==~(lhs: Self, rhs: Self) -> Bool
}

public protocol EquatableWithinEpsilon: Strideable {
    static var Epsilon: Self.Stride { get }
}

yusugomori

# -*- coding: utf-8 -*-

import sys
import numpy

numpy.seterr(all='ignore')


'''

kirsteins

var initalArray = [1, 2, 3]
let pointer: UnsafeMutablePointer<Int> = UnsafeMutablePointer(initalArray)
let arrary = Array(UnsafeBufferPointer(start: pointer, count: initalArray.count))

natecook1000

// Open-ended range operators
// 
// 100... is equivalent to 100...Int.max
// ...-100 is equivalent to Int.min...-100
// ..<3 is equivalent to Int.min..<3

import Swift

/// Conforming types provide static `max` and `min` constants.
protocol MinMaxType {

brentp

# -*- coding: utf-8 -*-

u"""
Beta regression for modeling rates and proportions.

References
----------
Grün, Bettina, Ioannis Kosmidis, and Achim Zeileis. Extended beta regression
in R: Shaken, stirred, mixed, and partitioned. No. 2011-22. Working Papers in
Economics and Statistics, 2011.

chris-rock

// Part of https://github.com/chris-rock/node-crypto-examples

// Nodejs encryption of buffers
var crypto = require('crypto'),
    algorithm = 'aes-256-ctr',
    password = 'd6F3Efeq';

var fs = require('fs');
var zlib = require('zlib');

rjz

// Depends on `through`
//
//     $ npm install through
//
// Usage:
//
//     $ echo 'hello' | node stdin-and-fs-stream.js 
//     $ echo 'hello' > tmp && node stdin-and-fs-stream.js tmp
//
var fs = require('fs'),

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

repustate

import requests

API_KEY = 'YOUR_API_KEY'
BASE_URL = 'http://social.repustate.com/%(api_key)s/%(call)s.json'

# Create a new data source.
kwargs = {'api_key':API_KEY, 'call':'add-datasource'}
response = requests.post(BASE_URL % kwargs, {'name':'Rob Ford', 'language':'en', 'niche':'general'})
datasource_id = response.json()['datasource_id']