ghego

brew update
brew versions FORMULA
cd `brew --prefix`
git checkout HASH Library/Formula/FORMULA.rb  # use output of "brew versions"
brew install FORMULA
brew switch FORMULA VERSION
git checkout -- Library/Formula/FORMULA.rb    # reset formula

## Example: Using Subversion 1.6.17
#

ghego

[user]
	name = Francesco Mosconi
	email = [email protected]
[core]
	editor = subl
	whitespace = fix,-indent-with-non-tab,trailing-space,cr-at-eol
	excludesfile = ~/.gitignore
[web]
	browser = google-chrome
[instaweb]

ghego

# http://stackoverflow.com/questions/3497123/run-git-pull-over-all-subdirectories
find . -type d -depth 1 -exec git --git-dir={}/.git --work-tree=$PWD/{} pull origin master \;

ghego

#based on http://stackoverflow.com/questions/2720014/upgrading-all-packages-with-pip
conda list | grep '<pip>' | cut -d ' ' -f 1 | xargs -n1 pip install -U

ghego

cat in.json | sed 's/\(},\)/\1\'$'\n/g' | sed 's/\(\[\)/\1\'$'\n/g' | sed 's/\(\}\)\]/\1\'$'\n]/g' > out.json

ghego

## Some global parameters that you can change
VERSION=2.3.0
SPARK=spark-$VERSION-bin-hadoop2.7
INSTALL_PATH=$HOME/spark
URL=http://mirrors.ocf.berkeley.edu/apache/spark/spark-$VERSION/$SPARK.tgz

BASH_PROFILE=$HOME/.bash_profile

SPARK_PATH=$INSTALL_PATH/$SPARK
BIN_PATH=$SPARK_PATH/bin

ghego

"""
Usage: python remove_output.py notebook.ipynb [-o]
"""
import sys
import io
import os
from nbformat import read, write, current_nbformat
from argparse import ArgumentParser

def remove_outputs(nb):

ghego

#! $HOME/anaconda/bin/python
# swap $HOME with your absolute path if this doesn't work

import sys
import io
import os
from nbformat import read, write, current_nbformat
from argparse import ArgumentParser
from glob import glob
from shutil import rmtree

ghego

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
np.random.seed(1)

# train comes from the titantic dataset provided by 
# kaggle (https://www.kaggle.com/c/titanic/data)
data = pd.read_csv('./train.csv')

# Preprocess data

ghego

def nn(X, y, hidden_dim=10, learning_rate=0.01, epochs=100, debug=False):
    input_dim = X.shape[1]
    output_dim = y.shape[1]

    # Make our model
    model = dict(
        w0 = np.random.randn(input_dim, hidden_dim),
        w1 = np.random.randn(hidden_dim, output_dim)
    )