zacstewart

import os
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import KFold
from sklearn.metrics import confusion_matrix, f1_score

NEWLINE = '\n'

dciccale

#!/usr/bin/env bash

# checks if branch has something pending
function parse_git_dirty() {
  git diff --quiet --ignore-submodules HEAD 2>/dev/null; [ $? -eq 1 ] && echo "*"
}

# gets the current git branch
function parse_git_branch() {
  git branch --no-color 2> /dev/null | sed -e '/^[^*]/d' -e "s/* \(.*\)/\1$(parse_git_dirty)/"

iamatypeofwalrus

What

Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.

What are we using? What do you need?

• An active AWS account. First time sign-ups are eligible for the free tier for a year
• One Micro Tier EC2 Instance
• With AWS we will use the stock Ubuntu Server AMI and customize it.
• Anaconda for Python.
• Coffee/Beer/Time

why-not

"""making a dataframe"""
df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))

"""quick way to create an interesting data frame to try things out""" 
df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd'])

"""convert a dictionary into a DataFrame"""
"""make the keys into columns"""
df = pd.DataFrame(dic, index=[0])

jsanz

package week6

import scala.io.Source

object collections {
  /* read a file of words */
  val in = Source.fromURL("http://lamp.epfl.ch/files/content/sites/lamp/files/teaching/progfun/linuxwords.txt")
  
  /* create a list and filter all words where *all* their characters are not letters (like dashes) */
  val words = in.getLines.toList filter (word => word forall (chr => chr.isLetter))

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname