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@hamajyotan
hamajyotan / extconf.rb
Created December 6, 2011 10:56
Data_Wrap_Struct example
require "mkmf"
$libs += " -lstdc++ "
create_makefile("human")
@seratch
seratch / build.sbt
Created December 1, 2011 06:07
Scala School - Testing with specs2 examples
organization := "net.seratch"
name := "sandbox"
version := "0.1"
scalaVersion := "2.9.1"
libraryDependencies ++= Seq(
"junit" % "junit" % "4.9" withSources(),
@sebthemonster
sebthemonster / pre-commit.rspec.rb
Created October 25, 2011 18:47 — forked from david-hodgetts/pre-commit
pre-commit git hook, runs rspec if branch is master
#!/usr/bin/env ruby
# pre-commit git hook
# will run rspec if and only if current branch is master
# script adapted from http://book.git-scm.com/5_git_hooks.html
# source : https://gist.github.com/1141992
NO_COMMIT_MESSAGE = "\aDID NOT COMMIT YOUR FILES!"
FORMAT = :html
@erichurst
erichurst / database.yml.example mysql2
Created May 9, 2011 02:58
Rails 3 database.yml examples
# MySQL. Versions 4.1 and 5.0 are recommended.
#
# Install the MySQL driver:
# gem install mysql2
#
# And be sure to use new-style password hashing:
# http://dev.mysql.com/doc/refman/5.0/en/old-client.html
development:
adapter: mysql2
encoding: utf8
@ofan
ofan / lisp.cpp
Last active January 29, 2025 11:21
Lisp interpreter in 90 lines of C++
Lisp interpreter in 90 lines of C++
I've enjoyed reading Peter Norvig's recent articles on Lisp. He implements a Scheme interpreter in 90 lines of Python in the first, and develops it further in the second.
Just for fun I wondered if I could write one in C++. My goals would be
1. A Lisp interpreter that would complete Peter's Lis.py test cases correctly...
2. ...in no more than 90 lines of C++.
Although I've been thinking about this for a few weeks, as I write this I have not written a line of the code. I'm pretty sure I will achieve 1, and 2 will be... a piece of cake!
@onyxfish
onyxfish / example1.py
Created March 5, 2010 16:51
Basic example of using NLTK for name entity extraction.
import nltk
with open('sample.txt', 'r') as f:
sample = f.read()
sentences = nltk.sent_tokenize(sample)
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True)