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)

ruphy

// SPDX-License-Identifier: MIT
//
// This code will add a GPT3() function in your Google Sheets
// This code is originally inspired from https://twitter.com/fabianstelzer/status/1572926883179778050
// To use it, insert your API key below, open Google Sheets -> Extensions -> Apps Script -> Copy & Paste this -> Save
//
// Usage: =GPT3(prompt, max_tokens (default=15), model (default=davinci))
// Example usage: =GPT3("Once upon a time,", 1000, "davinci")

var API_KEY = "your-API-key";

zpea

#!/bin/bash

#
# video conversion script for publishing as HTML 5 video, via videojs (with hd button extension)
# 2011 by zpea
# feel free to use as public domain / Creative Commons CC0 1.0 (http://creativecommons.org/publicdomain/zero/1.0/)
#

FFMPEG=/usr/bin/ffmpeg
HD_SUFFIX='_hd'

benmarwick

# get recent changes from wikipedia
library(rvest)
n_changes <- 5000
recent_changes_url <- paste0("https://en.wikipedia.org/w/index.php?title=Special:RecentChanges&limit=", n_changes , "&days=1")

# connect to website
html <- read_html(recent_changes_url)

ihercowitz

#!/usr/bin/env python

import Image
import os, sys

def resizeImage(infile, dir, output_dir="", size=(1024,768)):
     outfile = os.path.splitext(infile)[0]+"_resized"
     extension = os.path.splitext(infile)[1]

     if extension.lower()!= ".jpg":

cdiener

# This line imports the modules we will need. The first is the sys module used
# to read the command line arguments. Second the Python Imaging Library to read
# the image and third numpy, a linear algebra/vector/matrix module.
import sys; from PIL import Image; import numpy as np

# This is a list of characters from low to high "blackness" in order to map the
# intensities of the image to ascii characters 
chars = np.asarray(list(' .,:;irsXA253hMHGS#9B&@'))
 
# Check whether all necessary command line arguments were given, if not exit and show a

benmarwick

# Set working directory
dir <- "C:\\" # adjust to suit
setwd(dir)

# configure variables and filenames for MALLET
## here using MALLET's built-in example data and
## variables from http://programminghistorian.org/lessons/topic-modeling-and-mallet

# folder containing txt files for MALLET to work on
importdir <- "C:\\mallet-2.0.7\\sample-data\\web\\en"

rccordell

# This script builds on Aleszu Bajak's excellent 
# [tutorial on building a course website using R Markdown and Github pages](http://www.storybench.org/convert-google-doc-rmarkdown-publish-github-pages/). 
# I was excited about the concept but wanted to automate a few of the production steps: namely generating the HTML files 
# for the site from the RMD pages (which Aleszu describes doing one-by-one) and generating the site navigation menu, 
# which Aleszu handcodes in the `_site.yml` file. This script should automate both processes, though it may have some quirks 
# unique to my setup that you'd want to tweak to fit your own. It's likely more loquacious than necessary as well, so feel free 
# to condense as you can. Ideally, each time you make updates to your RMD files you can run this script to generate updated HTML 
# pages and a new `_site.yml`. Then commit changes to Github and you're up and running!

# Once you've got everything configured for your own site below, you should be able to run `source('rend

portableant

#!/usr/bin/python
## Split audio files into chunks
## Daniel Pett 1/5/2020
__author__ = 'portableant'
## Tested on Python 2.7.16 - yes I know I need to upgrade.

import argparse
import os
import speech_recognition as sr

rccordell

---
title: "Programming Literary Bots"
author: "Ryan Cordell"
date: "3/12/2017"
output: html_document
---

## Acknowledgements

This version of my twitterbot assignment was adapted from [an original written in Python](https://www.dropbox.com/s/r1py3zazde2turk/Trendingmore.py?dl=0), which itself adapted code written by Mark Sample. That orginal bot tweeted (I've since stopped it) at [Quoth the Ravbot](https://twitter.com/Quoth__the). The current version owes much to advice and code borrowed from two colleagues at Northeastern University: Jonathan Fitzgerald and Benjamin Schmidt.