Toni Cebrián tonicebrian

I hereby claim:

To claim this, I am signing this object:

	❯ stack install shakespeare 2&> /tmp/aux.txt
	sydtest.cabal is up-to-date
	sydtest-aeson.cabal is up-to-date
	sydtest-amqp.cabal is up-to-date
	sydtest-autodocodec.cabal is up-to-date

	for var in MY_ENV_VAR_1 MY_ENV_VAR_2; do
	if [[ -z "${!var:-}" ]];
	then
	echo "You need to provide a value for env variable $var"
	exit 1
	fi
	done

	CREATE INDEX ON :Block(height);
	CREATE INDEX ON :Transaction(hash);
	CREATE INDEX ON :Output(tx_hash, output_index);
	CREATE INDEX ON :Address(address_string);

	LOAD CSV WITH HEADERS FROM "http://storage.googleapis.com/bitcoin-neo4j-etl/blocks.csv" AS row
	MERGE (b:Block {height: toInt(row.number)})
	ON CREATE SET
	b.hash = row.hash,
	b.size = toInt(row.size),

	# Create a SSH connection
	import paramiko
	import os

	ssh = paramiko.SSHClient()
	ssh._policy = paramiko.WarningPolicy()
	ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())

	ssh_config = paramiko.SSHConfig()
	user_config_file = os.path.expanduser("~/.ssh/config")

	# =============
	# 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

	#!/bin/bash

	for branch in `git branch -r \| grep -v HEAD`;do echo -e `git show --format="%ci\t%cr\t%cN" $branch \| head -n 1` \\t$branch; done \| sort -r