even4void

mha <- 1  # mean under the alternative
es <- 2   # observed effect (deviation from mean under H0)
x <- seq(-6, 6, length=1000)
dh0 <- dnorm(x, 0, 1)

show.it <- function(es, mha, verbose=FALSE) {
  dh1 <- dnorm(x, mha, 1)

  plot.new()
  plot.window(xlim=range(x), ylim=c(0,.6)) 

marcelcaraciolo

from numpy import loadtxt, zeros, ones, array, linspace, logspace, mean, std, arange
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from pylab import plot, show, xlabel, ylabel

#Evaluate the linear regression

def feature_normalize(X):
    '''
    Returns a normalized version of X where

timelyportfolio

# Function to implement a version of the improved moving average functionality
# introduced and tested in:
#
# Papailias, Fotis and Thomakos, Dimitrios D.,
# "An Improved Moving Average Technical Trading Rule,
# (September 11, 2011). Available at SSRN: http://ssrn.com/abstract=1926376
#
# Original code written by Kent Russell @ timelyportfolio.com
#
# Cross-checked by Dimitrios Thomakos on 11/29/2011, @ quantf.com

bradmontgomery

Web Scraping Workshop

Using Requests and Beautiful Soup, with the most recent Beautiful Soup 4 docs.

Getting Started

Install our tools (preferably in a new virtualenv):

pip install beautifulsoup4

mikesmullin

sudo apt-get install unzip;
wget -O /tmp/chromedriver.zip http://chromedriver.googlecode.com/files/chromedriver_linux64_19.0.1068.0.zip && sudo unzip /tmp/chromedriver.zip chromedriver -d /usr/local/bin/;

jeromyanglim

This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown) 
using [knitr](http://yihui.name/knitr/) in Rstudio 0.96.
This combination of tools provides an exciting improvement in usability for 
[reproducible analysis](http://stats.stackexchange.com/a/15006/183).
Specifically, this post 
(1) discusses getting started with R Markdown and `knitr` in  Rstudio 0.96;
(2) provides a basic example of producing console output and plots using R Markdown;
(3) highlights several code chunk options such as caching and controlling how input and output is displayed;
(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and 
(5) discusses the implications of R Markdown.

dreikanter

text = """Sample #line of #text to demonstrate #hashtags
extraction. Here is some duplicate ones: #text #hashtags #tags."""


def get_hashtags(text, order=False):
    tags = set([item.strip("#.,-\"\'&*^!") for item in text.split() if (item.startswith("#") and len(item) < 256)])
    return sorted(tags) if order else tags


print "\n".join(get_hashtags(text, True))

fototo

require(quantmod)
require(PerformanceAnalytics)
require(xtsExtra)
require(RColorBrewer)

#unfortunately don't feel like fighting IP lawyers so I cannot share this index data
portfolio <- read.csv("C:\\Users\\Kent.TLEAVELL_NT\\Documents\\old\\R\\lbustruu with tbill.csv",stringsAsFactors=FALSE)
portfolio <- portfolio[2:NROW(portfolio),2:NCOL(portfolio)]
portfolio <- portfolio[,c(1,3,5)]

mbostock

license: gpl-3.0

ddd1600

getCIK = function(ticker) {
  stopifnot(is.character(ticker))
  uri = "http://www.sec.gov/cgi-bin/browse-edgar"

  response = getForm(uri,CIK=ticker,action="getcompany")
  html = htmlParse(response)
  CIKNode = getNodeSet(html, "//acronym[@title=\"Central Index Key\"][text() = \"CIK\"]")
  CIKNodeText = sapply(CIKNode, function(x) xmlValue(getSibling(getSibling(x))))
  CIK = sub(" .*","",CIKNodeText)
  CIK = sub("^0*","",CIK)