CamDavidsonPilon

{
  "lines.linewidth": 2.0,
  "examples.download": true,
  "patch.linewidth": 0.5,
  "legend.fancybox": true,
  "axes.color_cycle": [
    "#30a2da",
    "#fc4f30",
    "#e5ae38",
    "#6d904f",

stucchio

from matplotlib import use

from pylab import *
from scipy.stats import beta, norm, uniform
from random import random
from numpy import *
import numpy as np
import os

# Input data

jdmaturen

"""
Implementation of the beta-geometric/NBD (BG/NBD) model from '"Counting Your Customers" the Easy Way: An Alternative to
the Pareto/NBD Model' (Fader, Hardie and Lee 2005) http://brucehardie.com/papers/018/fader_et_al_mksc_05.pdf and
accompanying technical note http://www.brucehardie.com/notes/004/

Apache 2 License
"""
from math import log, exp

import numpy as np

tkb

{
 "metadata": {
  "name": "unpivoting-csv-pandas"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

planetoftheweb

<?php
require 'tmhOAuth.php'; // Get it from: https://github.com/themattharris/tmhOAuth

// Use the data from http://dev.twitter.com/apps to fill out this info
// notice the slight name difference in the last two items)

$connection = new tmhOAuth(array(
  'consumer_key' => '',
	'consumer_secret' => '',
	'user_token' => '', //access token

tonicebrian

# =============
# Introduction
# =============
# I've been doing some data mining lately and specially looking into `Gradient
# Boosting Trees <http://en.wikipedia.org/wiki/Gradient_boosting>`_ since it is
# claimed that this is one of the techniques with best performance out of the
# box.  In order to have a better understanding of the technique I've reproduced
# the example of section *10.14.1 California Housing* in the book `The Elements of Statistical Learning <http://www-stat.stanford.edu/~tibs/ElemStatLearn/>`_.
# Each point of this dataset represents the house value of a property with some
# attributes of that house. You can get the data and the description of those

johntyree

#!/usr/bin/env sh

# Download lists, unpack and filter, write to stdout
curl -s https://www.iblocklist.com/lists.php \
  | sed -n "s/.*value='\(http:.*=bt_.*\)'.*/\1/p" \
  | xargs wget -O - \
  | gunzip \
  | egrep -v '^#'

huyng

### MATPLOTLIBRC FORMAT

# This is a sample matplotlib configuration file - you can find a copy
# of it on your system in
# site-packages/matplotlib/mpl-data/matplotlibrc.  If you edit it
# there, please note that it will be overridden in your next install.
# If you want to keep a permanent local copy that will not be
# over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux
# like systems) and C:\Documents and Settings\yourname\.matplotlib
# (win32 systems).