gre

/*
 * This work is free. 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 the COPYING file for more details.
 */
/*
 * Easing Functions - inspired from http://gizma.com/easing/
 * only considering the t value for the range [0, 1] => [0, 1]
 */
EasingFunctions = {

marktheunissen

This playbook has been removed as it is now very outdated.

martinseener

filter {
 grok {
  pattern => ['(?:%{SYSLOGTIMESTAMP:timestamp}|%{TIMESTAMP_ISO8601:timestamp8601}) (?:%{SYSLOGHOST:logsource}) (?:%{YEAR}): (?:%{MONTHNUM}):(?:%{MONTHDAY})-(?:%{HOUR}):(?:%{MINUTE}):(?:%{SECOND}) (?:%{SYSLOGHOST}) (?:%{SYSLOGPROG}): (?<messagebody>(?:id=\"%{INT:utm_id}\" severity=\"%{LOGLEVEL:utm_severity}\" sys=\"%{DATA:utm_sys}\" sub=\"%{DATA:utm_sub}\" name=\"%{DATA:utm_name}\" action=\"%{DATA:utm_action}\" fwrule=\"%{INT:utm_ulogd_fwrule}\" initf=\"%{DATA:utm_ulogd_initf}\" outitf=\"%{DATA:utm_ulogd_outif}\" (?:srcmac=\"%{GREEDYDATA:utm_ulogd_srcmac}\" dstmac=\"%{GREEDYDATA:utm_ulogd_dstmac}\"|srcmac=\"%{GREEDYDATA:utm_ulogd_srcmac}\") srcip=\"%{IP:utm_srcip}\" dstip=\"%{IP:utm_dstip}\" proto=\"%{INT:utm_protocol}\" length=\"%{INT:utm_ulogd_pkglength}\" tos=\"%{DATA:utm_ulogd_tos}\" prec=\"%{DATA:utm_ulogd_prec}\" ttl=\"%{INT:utm_ulogd_ttl}\" srcport=\"%{INT:utm_srcport}\" dstport=\"%{INT:utm_dstport}\" tcpflags=\"%{DATA:utm_ulogd_tcpflags}\"|id=\"%{INT:utm_id}\" severity=\"%{LOGLEVEL:utm

ftrain

;; This is at: https://gist.github.com/8655399

;; So we want a rhyming dictionary in Clojure. Jack Rusher put up
;; this code here:
;; 
;; https://gist.github.com/jackrusher/8640437
;;
;; I'm going to study this code and learn as I go.
;;
;; First I put it in a namespace.

syhw

"""
A deep neural network with or w/o dropout in one file.

License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
"""

import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams

markwalkom

input {
  elasticsearch {
    hosts => [ "HOSTNAME_HERE" ]
    port => "9200"
    index => "INDEXNAME_HERE"
    size => 1000
    scroll => "5m"
    docinfo => true
    scan => true
  }

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

maxim

#!/usr/bin/env bash
#
# gh-dl-release! It works!
# 
# This script downloads an asset from latest or specific Github release of a
# private repo. Feel free to extract more of the variables into command line
# parameters.
#
# PREREQUISITES
#

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward