stephenLee

creates static libraries.

lists the shared libraries on which the object binary is dependent.

lists the symbols defined in the symbol table of an object file.

source /usr/local/bin/virtualenvwrapper.sh

	// avoid resource leaks when exceptions are thrown.
	// if an exception occurs after successful memory allocation but
	// before the delete statement executes, a memory leak could occur.
	// void memory_leak()
	//{
	// ClassA * ptr = new ClassA;
	// try {
	// ...
	// }
	// catch(...) {

	// A functor is any object that can be used with () in the manner of a function.
	// includes pointers to functions, and class objects for which the () operator (function call operator) is overloaded


	#include <iostream>
	#include <vector>
	#include <algorithm>

	using namespace std;

	<!DOCTYPE html>
	<meta charset="utf-8">
	<script src="http://d3js.org/d3.v2.min.js?2.9.3"></script>
	<style>

	.link {
	stroke: #ccc;
	}

	.node text {

	\DeclareMathOperator*{\argmax}{arg\,max} % in your preamble
	\DeclareMathOperator*{\argmin}{arg\,min} % in your preamble

	\argmax_{...} % in your formula
	\argmin_{...} % in your formula

	# Dirichlet process Gaussian mixture model

	import numpy as np
	from scipy.special import gammaln
	from scipy.linalg import cholesky
	from sliceSample import sliceSample

	def multinomialDraw(dist):
	"""Returns a single draw from the given multinomial distribution."""
	return np.random.multinomial(1, dist).argmax()

	import numpy as np
	from sklearn.datasets import fetch_20newsgroups
	from sklearn.feature_extraction.text import CountVectorizer

	def get_vectors(vocab_size=5000):
	newsgroups_train = fetch_20newsgroups(subset='train')
	vectorizer = CountVectorizer(max_df=.9, max_features=vocab_size)
	vecs = vectorizer.fit_transform(newsgroups_train.data)
	vocabulary = vectorizer.vocabulary
	terms = np.array(vocabulary.keys())

	import spark.SparkContext
	import SparkContext._

	/**
	* A port of [[http://blog.echen.me/2012/02/09/movie-recommendations-and-more-via-mapreduce-and-scalding/]]
	* to Spark.
	* Uses movie ratings data from MovieLens 100k dataset found at [[http://www.grouplens.org/node/73]]
	*/
	object MovieSimilarities {

	; map
	(map clojure.string/lower-case ["Java" "Imperative" "Weeping" "Clojure"])
	(map * [1 2 3 4] [5 6 7 8])

	; reduce
	(reduce max [0 -3 10 48])
	(reduce + 50 [1 2 3 4])

	; partial
	(def only-strings (partial filter string?))