zaphar

-module(date_util).
-compile(export_all).

epoch() ->
    now_to_seconds(now())
.   
    
epoch_hires() ->
    now_to_seconds_hires(now())
.   

archaelus

-module(netmask).
-export([in_network/3]).

in_network(Net, CIDR, IP)
  when is_binary(Net), is_binary(IP), is_integer(CIDR) ->
    <<NetworkPrefix:CIDR/bits, _/bits>> = Net,
    case IP of
        <<NetworkPrefix:CIDR/bits, _Host/bits>> ->
            in_net;
        _ -> not_in_net

tompaton

#!/usr/bin/env python

# kd-tree index and nearest neighbour search
# includes doctests, run with: python -m doctest kdtree.py

class KDTree(object):
    """
    kd-tree spatial index and nearest neighbour search
    http://en.wikipedia.org/wiki/Kd-tree
    """

jdmaturen

import logging

from math import exp
from random import random
from time import sleep
from time import time
from uuid import uuid1

from redis.exceptions import WatchError

antirez

diff --git a/iPhoneTrackingAppDelegate.m b/iPhoneTrackingAppDelegate.m
index 1d22ecb..4af2bcf 100644
--- a/iPhoneTrackingAppDelegate.m
+++ b/iPhoneTrackingAppDelegate.m
@@ -146,7 +146,7 @@
     return NO;
   }

-  const float precision = 100;
+  const float precision = 10000;

dreid

#!/bin/sh

OTP_VERSION=$(erl -noinput \
                  -noshell \
                  -eval 'io:fwrite(erlang:system_info(otp_release)).' \
                  -s init stop)

PLT_DIR="$HOME/.dialyzer/${OTP_VERSION}"

CURRENT_APP=$(basename $(pwd));

jlong

var parser = document.createElement('a');
parser.href = "http://example.com:3000/pathname/?search=test#hash";

parser.protocol; // => "http:"
parser.hostname; // => "example.com"
parser.port;     // => "3000"
parser.pathname; // => "/pathname/"
parser.search;   // => "?search=test"
parser.hash;     // => "#hash"
parser.host;     // => "example.com:3000"

robertoaloi

Start an instance of the Erlang run-time system

$ erl

Show information about processes

> i().

Spawn a new process which sleeps for 10 seconds

maxlapshin

-module(crc17).

-export([calc/1]).

calc(List) ->  calc(List,16#FFFF).

calc(<<>>,CRC) ->
   CRC;
calc(<<Value:8,Rest/binary>>,CRC) ->
  Index = (CRC bxor Value) band 255,

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