pmbaumgartner

#!/bin/sh

xrdb $HOME/.Xresources
xsetroot -solid grey -cursor_name left_ptr
autocutsel -fork
#x-terminal-emulator -geometry 80x24+10+10 -ls -title "$VNCDESKTOP Desktop" &
#x-window-manager &
# Fix to make GNOME work
export XKL_XMODMAP_DISABLE=1
/etc/X11/Xsession

pmbaumgartner

<?xml version="1.0"?>
<!-- Add more negative to training. -->
<opencv_storage>
<cars3 type_id="opencv-haar-classifier">
  <size>
    20 20</size>
  <stages>
    <_>
      <!-- stage 0 -->
      <trees>

pmbaumgartner

card_values = {
    'a' : 11,
    'k' : 10,
    'q' : 10,
    'j' : 10,
    '10' : 10,
    '9' : 9,
    '8' : 8,
    '7' : 7,
    '6' : 6,

pmbaumgartner

import random
from string import ascii_uppercase

def get_difficulty():

    difficulty_length = {'easy': (4, 6),
                         'medium': (6, 8),
                         'hard': (8, 20)
                         }

pmbaumgartner

pmbaumgartner

pmbaumgartner

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Github Commits - Forecast Components\n",
    "\n",
    "Data from [BigQuery Github Dataset](https://cloud.google.com/bigquery/public-data/github) using the following query:\n",
    "\n",

pmbaumgartner

redis: redis-server
celery_worker: celery -A tasks worker -l info

pmbaumgartner

import gensim.downloader as gensim_api
import umap
import requests
import pandas as pd
from numpy import log10

import plotly
import plotly.graph_objs as go

w2v_model = gensim_api.load('word2vec-google-news-300')

pmbaumgartner