rougier

;; Material colors from https://material.io/design/color/
(defconst levels
  (list "L50"  "L100" "L200" "L300" "L400"
        "L500" "L600" "L700" "L800" "L900"
        "A100" "A200" "A400" "A700"))

(defconst red
  (list "#FFEBEE" "#FFCDD2" "#EF9A9A" "#E57373" "#EF5350"
        "#F44336" "#E53935" "#D32F2F" "#C62828" "#B71C1C"
        "#FF8A80" "#FF5252" "#FF1744" "#D50000"))

fonnesbeck

#!/bin/sh

hash brew &> /dev/null
if [ $? -eq 1 ]; then
    echo 'Installing Homebrew ...'
    ruby -e "$(curl -fsSL https://raw.github.com/Homebrew/homebrew/go/install)"
fi

# Ensure Homebrew formulae are updated
brew update

mike-lawrence

library(MASS)
library(lme4)

generate_data = function(
	n # number of units
	, k # number of trials within each condition within each unit
	, noise # measurement noise variance
	, I # population intercept
	, vI # across-units variance of intercepts
	, A # population A effect

mike-lawrence

library(plyr)
library(MASS)
library(lme4a)

generate_data = function(
	n # number of units
	, k # number of trials within each condition within each unit
	, noise # measurement noise variance
	, I # population intercept
	, vI # across-units variance of intercepts

carlohamalainen

; http://carlo-hamalainen.net/blog/2011/08/04/ggplot2-from-clojure

; To dump the plot to a file:
(use '(com.evocomputing rincanter)) ; https://github.com/jolby/rincanter
(r-eval "library(ggplot2)")
(r-eval-raw "qplot(rating, data=movies, geom=\"histogram\")") ; see http://had.co.nz/ggplot2/geom_histogram.html
(r-eval "ggsave('histogram-example.png')")

; To display on your screen (Unix example; see rincanter docs for alternatives to x11() call)
(use '(com.evocomputing rincanter))

brendano

How much text versus metadata is in a tweet?
Brendan O'Connor (brenocon.com), 2011-06-13
http://twitter.com/brendan642/status/80473880111742976

What's it mean to compare the amount of text versus metadata?
Let's start with raw size of the data that comes over the wire from Twitter.

## Get tweets out of a sample stream archive.
## (e.g. curl http://stream.twitter.com/1/statuses/sample.json)
% cat tweets.2011-05-19 | grep -P '"text":' | head -100000 > 100k_tweets

HarlanH

# Demo of techniques to visualize the predictions made by a categorization model.

library(ROCR)
library(ggplot2)

load(url('http://dl.dropbox.com/u/7644953/classifier-visualization.Rdata'))

pred.df$actual.bin <- ifelse(pred.df$actual == 'yes', 1, 0)
pred.df <- pred.df[order(pred.df$predicted, decreasing=TRUE), ]

mike-lawrence

#define two useful helper functions
colMean = function(x){
	dimx = dim(x)
	.Internal(colMeans(x,dimx[1],dimx[2],na.rm=TRUE))
}
colVar = function(x){
	dimx = dim(x)
	x.mean = .Internal(colMeans(x,dimx[1],dimx[2],na.rm=TRUE))
	err = t(t(x)-x.mean)
	err.sq = err*err

drewconway

require(RCurl)
require(XML)

google.counts<-function(s){
    search.url<-paste("http://www.google.com/search?q=",gsub(" ","+",s),sep="")
    search.html<-getURL(search.url)
    parse.search<-htmlTreeParse(search.html,useInternalNodes = TRUE)
    search.nodes<-getNodeSet(parse.search,"//div[@id='resultStats']")
    search.value<-strsplit(xmlValue(search.nodes[[1]])," ",fixed=TRUE)[[1]][2]
    return(as.numeric(gsub(",","",search.value,fixed=TRUE)))

mblondel

# Mathieu Blondel, October 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg

def linear_kernel(x1, x2):
    return np.dot(x1, x2)

def polynomial_kernel(x, y, p=3):