matiskay

# To install the Python client library:
# pip install -U selenium

# Import the Selenium 2 namespace (aka "webdriver")
from selenium import webdriver

# iPhone
driver = webdriver.Remote(browser_name="iphone", command_executor='http://172.24.101.36:3001/hub')

# Android

matiskay

@import compass

$icons: sprite-map("icons/*.png")
$icons-hd: sprite-map("icons-hd/*.png")

i
  background: $icons
  display: inline-block
  @media (-webkit-min-device-pixel-ratio: 1.5), (min-resolution: 144dpi)
    background: $icons-hd

matiskay

Text Classification

To demonstrate text classification with Scikit Learn, we'll build a simple spam filter. While the filters in production for services like Gmail will obviously be vastly more sophisticated, the model we'll have by the end of this chapter is effective and surprisingly accurate.

Spam filtering is the "hello world" of document classification, but something to be aware of is that we aren't limited to two classes. The classifier we will be using supports multi-class classification, which opens up vast opportunities like author identification, support email routing, etc… However, in this example we'll just stick to two classes: SPAM and HAM.

For this exercise, we'll be using a combination of the Enron-Spam data sets and the SpamAssassin public corpus. Both are publicly available for download and are retreived from the internet during the setup phase of the example code that goes with this chapter.

Loading Examples

matiskay

require 'mechanize'

@username      = 'USERNAME'
@password      = 'PASSWORD'
@download_path = File.expand_path '~/videos'
@wget_cookie   = File.expand_path(File.dirname(__FILE__)) + '/wget-cookies.txt'

unless File.directory? @download_path
  puts "@{download_path} doesn't exist!"
  exit

matiskay

OS X Screencast to animated GIF

This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.

Instructions

To capture the video (filesize: 19MB), using the free "QuickTime Player" application:

matiskay

javascript:( function() {
    console.group( 'Performance Information for all entries of ' + window.location.href );
    console.log( '\n-> Duration is displayed in ms\n ' )
    
    var entries = window.performance.getEntries();
    
    entries = entries.sort( function( a, b ) {
        return b.duration - a.duration;
    } );
    

matiskay

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

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

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

matiskay

import numpy as np
import pylab as pl
import pandas as pd

from sklearn import svm
from sklearn import linear_model
from sklearn import tree

from sklearn.metrics import confusion_matrix

matiskay

import json
import geojson
from shapely.geometry import shape

o = {
   "coordinates": [[[23.314208, 37.768469], [24.039306, 37.768469], [24.039306, 38.214372], [23.314208, 38.214372], [23.314208, 37.768469]]], 
   "type": "Polygon"
}

s = json.dumps(o)

matiskay

Plotting Node Density

1. download the source data

    wget http://fred.dev.openstreetmap.org/density/tiles.13
    wget http://fred.dev.openstreetmap.org/density/tiles.16
   

2. convert to simple, gnuplot-readable text format

sed 's/([0-9]) z=([0-9]) x=([0-9]) y=([0-9])/\3 \4 \1/' < tiles.13 > tiles.13.txt