mblondel

"""Kernel K-means"""

# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause

import numpy as np

from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state

willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

mbostock

license: gpl-3.0
redirect: https://observablehq.com/@d3/d3-color-schemes

dsparks

doInstall <- TRUE
toInstall <- c("alphahull")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Generate some sample data:
myData <- data.frame(x = rnorm(500), y = rnorm(500))
# With a unit-circle hole in the center:
myData <- myData[sqrt(rowSums(myData^2)) > 1, ]
plot(myData)

cengel

library(shiny)
library(datasets)
library(ggplot2) # load ggplot

# Define server logic required to plot various variables against mpg
shinyServer(function(input, output) {
  
  # Compute the forumla text in a reactive function since it is 
  # shared by the output$caption and output$mpgPlot functions
  formulaText <- reactive(function() {

dsparks

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("ggplot2")
if(doInstall){install.packages(toInstall, repos = "http://cran.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Generate some randomly-distributed data
nObs <- 5000
myData <- data.frame(X = rnorm(nObs), Y = rnorm(nObs))
nClusters <- 7  # Cluster it
kMeans <- kmeans(myData, centers = nClusters)

bobthecat

require(inline)
require(RcppArmadillo)

## extract cosine similarity between columns
cosine <- function(x) {
    y <- t(x) %*% x
    res <- 1 - y / (sqrt(diag(y)) %*% t(sqrt(diag(y))))
    return(res)
}

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.

simecek

# the code uses 'facebook' function from the previous gist (https://gist.github.com/1634662) or 
# see the original http://romainfrancois.blog.free.fr/index.php?post/2012/01/15/Crawling-facebook-with-R

# scrape the list of friends
friends <- facebook( path="me/friends" , access_token=access_token)
# extract Facebook IDs
friends.id <- sapply(friends$data, function(x) x$id)
# extract names 
friends.name <- sapply(friends$data, function(x)  iconv(x$name,"UTF-8","ASCII//TRANSLIT"))
# short names to initials 

mbostock

license: gpl-3.0
redirect: https://observablehq.com/@d3/d3-horizon-chart