tobym

function pwdx {
  lsof -a -p $1 -d cwd -n | tail -1 | awk '{print $NF}'
}

strongh

##' Portland R User Group
##'
##' Debugging in R:
##' A Worst-Case Scenario Survival guide
##' Homer Strong, Qmedtrix

##' > Debugging is twice as hard as
##' > writing the code in the first place.
##' > Therefore, if you write the
##' > code as cleverly as possible,

dzhou

#!/usr/bin/env python
import urllib
import pprint
import amazonproduct
from BeautifulSoup import BeautifulSoup
from review import db

AWS_KEY = 'YOUR_AWS_KEY'
SECRET_KEY = 'YOUR_AWS_SECRET_KEY'
API_PAGE_LIMIT = 10

ohpauleez

(ns sample
  (:require [clojure-leap.core :as leap]
            [clojure-leap.hand :as l-hand]
            [clojure-leap.pointable :as l-pointable :refer [tip-position]]))

(defn process-frame [frame]
  (let [_ (println "Frame id:" (.id frame) "timestamp:" (.timestamp frame)
                   "hands:" (leap/hands frame) "fingers:" (leap/fingers frame) "tools:" (leap/tools frame))]
    (when-let [hand (and (leap/hands? frame) (leap/hand frame 0))]
      (let [fingers (leap/fingers hand)

quintona

package storm.cookbook.tfidf.functions;

import java.util.Map;

import storm.trident.operation.MultiReducer;
import storm.trident.operation.TridentCollector;
import storm.trident.operation.TridentMultiReducerContext;
import storm.trident.tuple.TridentTuple;
import backtype.storm.tuple.Values;

mrflip

Tuning Storm+Trident

Tuning a dataflow system is easy:

The First Rule of Dataflow Tuning:
* Ensure each stage is always ready to accept records, and
* Deliver each processed record promptly to its destination

jackrusher

(ns blue-ball
  (:use [seesaw core font graphics])
  (:require [clojure-leap.core :as leap]
            [clojure-leap.screen :as l-screen]))

;; these atoms contain the current x/y state from the Leap
(def x (atom 10))
(def y (atom 10))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Build the window/canvas

jkeirstead

// Predict from Gaussian Process
// All data parameters must be passed as a list to the Stan call
// Based on original file from https://code.google.com/p/stan/source/browse/src/models/misc/gaussian-process/

data {
int<lower=1> N1;
vector[N1] x1;
vector[N1] y1;
int<lower=1> N2;
vector[N2] x2;

hadley

I think the two most important messages that people can get from a short course are:

a) the material is important and worthwhile to learn (even if it's challenging), and b) it's possible to learn it!

For those reasons, I usually start by diving as quickly as possible into visualisation. I think it's a bad idea to start by explicitly teaching programming concepts (like data structures), because the pay off isn't obvious. If you start with visualisation, the pay off is really obvious and people are more motivated to push past any initial teething problems. In stat405, I used to start with some very basic templates that got people up and running with scatterplots and histograms - they wouldn't necessary understand the code, but they'd know which bits could be varied for different effects.

Apart from visualisation, I think the two most important topics to cover are tidy data (i.e. http://www.jstatsoft.org/v59/i10/ + tidyr) and data manipulation (dplyr). These are both important for when people go off and apply

chrishwiggins

---------- Forwarded message ----------
From: chris wiggins <chris.wiggins@[YYY].edu>
Date: Wed, Aug 1, 2012 at 7:26 PM
Subject: stats history
To: hadley@[XXX].edu
Cc: chris wiggins <chris.wiggins@[YYY].edu>


Dear Hadley: