alexott

;;; minimial-cedet-config.el --- Working configuration for CEDET from bzr

;; Copyright (C) Alex Ott
;;
;; Author: Alex Ott <alexott@gmail.com>
;; Keywords: cedet, C++, Java
;; Requirements: CEDET from bzr (http://cedet.sourceforge.net/bzr-repo.shtml)

;; Do checkout of fresh CEDET, and use this config (don't forget to change path below)

laanwj

This has moved to https://github.com/bitcoin/bitcoin/blob/master/doc/reduce-memory.md

alexpearce

import sqlite3
import random

# Create the database
con = sqlite3.connect('/path/to/database.db')
con.execute('CREATE TABLE runs (run INTEGER PRIMARY KEY)')

# Insert many runs...
runs = range(int(1e3))
random.shuffle(runs)

alexpearce

import timeit
import os

DB_PATH = 'database.db'
# Number of runs to generate
NRUNS = int(1e6)

insert_setup = """import sqlite3
con = sqlite3.connect('{0}')
con.execute('CREATE TABLE runs (run INTEGER PRIMARY KEY)')

yangchenyun

/* 
 * @fileoverview Program to free the content in kindle books as plain HTML.
 *     
 * This is largely based on reverse engineering kindle cloud app
 * (https://read.amazon.com) to read book data from webSQL.
 * 
 * Access to kindle library is required to download this book.
 */

// The Kindle Compression Module copied from http://read.amazon.com application

mrocklin

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

kayousterhout

Generating Flame Graphs for Apache Spark

Flame graphs are a nifty debugging tool to determine where CPU time is being spent. Using the Java Flight recorder, you can do this for Java processes without adding significant runtime overhead.

When are flame graphs useful?

Shivaram Venkataraman and I have found these flame recordings to be useful for diagnosing coarse-grained performance problems. We started using them at the suggestion of Josh Rosen, who quickly made one for the Spark scheduler when we were talking to him about why the scheduler caps out at a throughput of a few thousand tasks per second. Josh generated a graph similar to the one below, which illustrates that a significant amount of time is spent in serialization (if you click in the top right hand corner and search for "serialize", you can see that 78.6% of the sampled CPU time was spent in serialization). We used this insight to spee

apehex

/* 
 * @fileoverview Program to free the content in kindle books as plain HTML.
 *     
 * This is largely based on reverse engineering kindle cloud app
 * (https://read.amazon.com) to read book data from webSQL.
 * 
 * Access to kindle library is required to download this book.
 */

// The Kindle Compression Module copied from http://read.amazon.com application

vingkan

Algorithmic Audit: QII

A big moving company gets so many applications that it has started using an automated algorithm to decide who to hire. You have been called in as an independent consultant to determine if the hiring algorithm is biased against women. The algorithm is proprietary so you cannot access its source code. Instead, you will learn how to perform an algorithmic audit to measure potential biases.

In this activity, you will edit the influence.py module.

Applicant Data

Each applicant's data is stored as a list with five elements. Each element is a string representing a different attribute: