This gist shows how to create a GIF screencast using only free OS X tools: QuickTime, ffmpeg, and gifsicle.
To capture the video (filesize: 19MB), using the free "QuickTime Player" application:
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 |
# 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) |
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; | |
} ); | |
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 |
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.
@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 |
# 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 |
#Mac OS X