Uirá Caiado ucaiado

$ cd path/to/run/jupyter
$ nohup jupyter notebook --ip=0.0.0.0 --no-browser &
$ cat nohup.out

Setting up github and bitbucket on the same computer

Github will be the main account and bitbucket the secondary.

ssh-keygen -t rsa -C "github email"

Enter passphrase when prompted. If you see an option to save the passphrase in your keychain, do it for an easier life.

#ACADEMIC STUFF

####Some important codes

pandoc -S -o sample-paper.pdf --template=template1 --filter pandoc-citeproc sample-paper.md

	# source: https://facebook.github.io/prophet/docs/quick_start.html
	from fbprophet import Prophet

	COUNTRY = 'Italy'

	# filter data
	df_plot = df[df.countriesAndTerritories.isin([COUNTRY])]
	df_plot = (df_plot.set_index('dateRep'))['cases'].sort_index()
	m = Prophet(yearly_seasonality=False, daily_seasonality=True)
	m.add_seasonality(name='monthly', period=30.5, fourier_order=3)

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	#!/usr/bin/python
	# -- coding: utf-8 --
	"""
	Use data already downloaded to recover the bid ask prices of differet
	instruments

	@author: ucaiado

	Created on 05/02/2018
	"""

	# -- coding: utf-8 --
	"""
	Created on Mon Sep 23 23:16:44 2017

	@author: Marios Michailidis

	This is an example that performs stacking to improve mean squared error
	This examples uses 2 bases learners (a linear regression and a random forest)
	and linear regression (again) as a meta learner to achieve the best score.
	The initial train data are split in 2 halves to commence the stacking.

	import time
	import socket
	import math
	import json

	addr = ("127.0.0.1", 12000)

	clientSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
	clientSocket.settimeout(1)

	<style>
	@font-face {
	font-family: "Computer Modern";
	src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunss.otf');
	}
	@font-face {
	font-family: "Computer Modern";
	font-weight: bold;
	src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsx.otf');
	}