infinex

# force a reload of the config file
unbind r
bind r source-file ~/.tmux.conf

set -g history-limit 5000

set -g default-terminal "screen-256color"
set -g status-left-length 24

# remap prefix from 'C-b' to 'C-a'

infinex

CREATE TEMP FUNCTION
cleanup(text
  STRING
  )
  RETURNS
  STRUCT <
  key string,
  value string>
  LANGUAGE js AS """
  return JSON.parse(text);

infinex

# Install ucu4c via Brew
brew install icu4c

# Create relative symlinks for icu4c
brew link --force icu4c

# Install pyicu via pip
# Make sure ICU_VERSION matches the one you just installed
sudo ICU_VERSION=60.2 pip install pyicu

infinex

WITH
  mydate AS(
  SELECT
    day
  FROM
    UNNEST( GENERATE_DATE_ARRAY( DATE_TRUNC(DATE_SUB(CURRENT_DATE,INTERVAL 1 YEAR),MONTH), CURRENT_DATE(), INTERVAL 1 DAY) ) AS day ),
  randomdata AS(
  SELECT
    t1.*,
    ROUND(t1.revenue*rand()) AS user

infinex

#standard sql

SELECT "Current Month" as type,cast(TIMESTAMP_TRUNC(CURRENT_TIMESTAMP(), MONTH) as date)
UNION ALL
SELECT "Week ending",DATE_ADD(cast(TIMESTAMP_TRUNC(CURRENT_TIMESTAMP(), WEEK) as date),interval 7 DAY)
UNION ALL
SELECT "Current Month",DATE_TRUNC(CURRENT_DATE,MONTH)
UNION ALL
SELECT "First day of last Month",DATE_TRUNC(DATE_SUB(CURRENT_DATE,INTERVAL 1 MONTH),MONTH)
UNION ALL

infinex

#standard sql

#https://stackoverflow.com/questions/38929121/duplicating-records-to-fill-gap-between-dates-in-google-bigquery

SELECT day
FROM UNNEST(
    GENERATE_DATE_ARRAY(
    DATE_TRUNC(DATE_SUB(CURRENT_DATE,INTERVAL 1 YEAR),MONTH)
    , CURRENT_DATE(), INTERVAL 1 DAY)
) AS day

infinex

#BiDirectional LSTM
#Training Data
# Embedding1,Embedding2,Labels
# 1 Jurong,Jurng,1
# 2 Jurong,Jrung,1
# 3 Jurong,Bishan,0
# Purpose: To  create measure to determine how similar sequences of inputs are to each other. 

import os
import random

infinex

import numpy as np
import tensorflow as tf
import tensorflow.contrib.eager as tfe
tfe.enable_eager_execution()

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

# Training Parameters
learning_rate = 0.001

infinex

LOAD CSV FROM ‘https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat' AS line
CREATE (:Airport { myid:toInt(line[0]),name: line[1], lat: line[6],lon: line[7]})
CREATE INDEX ON :Airport(myid)
LOAD CSV FROM ‘https://raw.githubusercontent.com/jpatokal/openflights/master/data/routes.dat' AS line
MATCH (from:Airport {myid: toInt(line[3])})
MATCH (to:Airport {myid: toInt(line[5])})
MERGE (from)-[r:route]->(to)
ON MATCH SET r.count = r.count + 1 ON CREATE SET r.count = 1

infinex

library(ggmap)
airports <- read.csv(“https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat", header = FALSE)
names(airports) <- c(“id”, “name”, “city”, “country”, “code”, 
 “icao”, “lat”, “lon”, “altitude”, “timezone”, 
 “dst”,”tz”)
location <- c( 115,0 )
location
map <- get_map(location, zoom=3,maptype=’toner-lite’)
p <- ggmap(map) 
p