Skip to content

Instantly share code, notes, and snippets.


pjstein pjstein

View GitHub Profile
gre / easing.js
Last active Jun 24, 2022
Simple Easing Functions in Javascript - see
View easing.js
* Easing Functions - inspired from
* only considering the t value for the range [0, 1] => [0, 1]
EasingFunctions = {
// no easing, no acceleration
linear: t => t,
// accelerating from zero velocity
easeInQuad: t => t*t,
// decelerating to zero velocity
marktheunissen / pedantically_commented_playbook.yml
Last active Jun 6, 2022 — forked from phred/pedantically_commented_playbook.yml
Insanely complete Ansible playbook, showing off all the options
View pedantically_commented_playbook.yml
This playbook has been removed as it is now very outdated.
martinseener / gist:5247292
Last active Jul 1, 2019
Grok Sophos UTM 9.x Pattern (for logstash) (Simple)
View gist:5247292
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 / rhymes.clj
Last active Jan 4, 2021
Annotated rhyming dictionary
View rhymes.clj
;; This is at:
;; So we want a rhyming dictionary in Clojure. Jack Rusher put up
;; this code here:
;; I'm going to study this code and learn as I go.
;; First I put it in a namespace.
syhw /
Last active Dec 22, 2021
A simple deep neural network with or w/o dropout in one file.
A deep neural network with or w/o dropout in one file.
License: Do What The Fuck You Want to Public License
import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams
markwalkom / logstash.conf
Last active Apr 29, 2022
Reindexing Elasticsearch with Logstash 2.0
View logstash.conf
input {
elasticsearch {
hosts => [ "HOSTNAME_HERE" ]
port => "9200"
size => 1000
scroll => "5m"
docinfo => true
scan => true
karpathy /
Last active Jun 30, 2022
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
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 / gh-dl-release
Last active Jul 2, 2022
Download assets from private Github releases
View gh-dl-release
#!/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.
yrevar / imagenet1000_clsidx_to_labels.txt
Last active Jul 1, 2022
text: imagenet 1000 class idx to human readable labels (Fox, E., & Guestrin, C. (n.d.). Coursera Machine Learning Specialization.)
View imagenet1000_clsidx_to_labels.txt
{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 /
Created May 30, 2016
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
""" 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