lrvick

from tasks import task1

def get_results(queries): 
    query_procs = task1.delay(queries).get()
    tasks = []
    for query_proc in query_procs:
        tasks.extend(query_proc.subtasks) 

    while tasks:
        current_task = tasks.pop(0)

lrvick

from geventwebsocket.handler import WebSocketHandler
from gevent.pywsgi import WSGIServer
from flask import Flask, request, render_template

app = Flask(__name__)

@app.route('/')
def index():
    return render_template('index.html')

lrvick

#The goal is to emulate the following bash line properly in python:
#wget -O - "https://github.com/downloads/Tawlk/synt/sample_data.bz2" | bzcat | sqlite3 sample_data.db

import bz2                                                                       
import sqlite3                                                                   
import time                                                                      
import urllib2                                                                   
import os                                                                        
from cStringIO import StringIO                                                   

lrvick

from nltk.probability import DictionaryProbDist
from nltk import NaiveBayesClassifier

train_samples = {
    'I hate you and you are a bad person': 'neg',
    'I love you and you are a good person': 'pos',
    'I fail at everything and I want to kill people' : 'neg',
    'I win at everything and I want to love people' : 'pos',
    'sad are things are heppening. fml' : 'neg',
    'good are things are heppening. gbu' : 'pos',

lrvick

# Author: Pieter Noordhuis
# Description: Simple demo to showcase Redis PubSub with EventMachine
#
# Update 7 Oct 2010:
# - This example does *not* appear to work with Chrome >=6.0. Apparently,
#   the WebSocket protocol implementation in the cramp gem does not work
#   well with Chrome's (newer) WebSocket implementation.
#
# Requirements:
# - rubygems: eventmachine, thin, cramp, sinatra, yajl-ruby

lrvick

from nltk.probability import DictionaryProbDist
from nltk import NaiveBayesClassifier
from nltk import FreqDist

train_samples = {
    'I hate you and you are a bad person': 'neg',
    'I love you and you are a good person': 'pos',
    'I fail at everything and I want to kill people' : 'neg',
    'I win at everything and I want to love people' : 'pos',
    'sad are things are heppening. fml' : 'neg',

lrvick

from nltk.probability import ELEProbDist, FreqDist
from nltk import NaiveBayesClassifier
from collections import defaultdict

train_samples = {
    'I hate you and you are a bad person': 'neg',
    'I love you and you are a good person': 'pos',
    'I fail at everything and I want to kill people' : 'neg',
    'I win at everything and I want to love people' : 'pos',
    'sad are things are heppening. fml' : 'neg',

lrvick

#include <Wire.h>
#include <LiquidCrystal.h>
#include <SPI.h>
#include <Ethernet.h>

byte mac[] = { 0xDE, 0xAD, 0xBE, 0xEF, 0xCA, 0xFE };

EthernetServer server(80);

LiquidCrystal lcd(0);

lrvick

var subset_sum = function(items, target) {
    var perms = [], layer = 0, depth = 4, attempts = 0, sum, perm,
    ss = function(items) {
        var item = items.shift();
        for (i = 0; i < items.length; i++) {
            attempts = attempts + 1;
            if (attempts <= items.length * items.length) {
                if (layer === 0) {
                    perm = [items[0], items[i]];
                } else {

lrvick

<?php
$mime_type = "image/png"   ; // mime type of file to pretend to be
$compression = "95"        ; // compression
$trimming = "yes"          ; // trim canvas after rendering, yes or no
$canvas_height = "800"     ; // height of entire canvas
$canvas_width = "800"      ; // width of entire canvas
$shadow_color = "black"    ; // color of drop shadow
$size[0] = "140"           ; // size in pixils for top image
$size[1] = "120"           ; // size in pixils for middle image
$size[2] = "100`"          ; // size in pixils for bottom image