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import csv, sqlite3, sys

file_to_read = sys.argv[1]
db_name = sys.argv[2]
table_name = sys.argv[3]
manifest = sys.argv[4] # lists header names and data types
con = sqlite3.connect("./"+db_name)
cur = con.cursor()
# Read Manifest
# Make create table query using manifest

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# The first step is to load the pre-trained vectors into python. The example below uses glove data.
import os
GLOVE_DIR = "/path/to/pretrained/embeddings/glove.6B/"
embeddings_index = {}
f = open(os.path.join(GLOVE_DIR, 'glove.6B.100d.txt'), "r")
for line in f:
  values = line.split()
  word = values[0]
  coefs = np.asarray(values[1:], dtype='float32')
  embeddings_index[word] = coefs

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# Toy Example of LSTM

## Relevant Links
* https://www.kaggle.com/amirrezaeian/time-series-data-analysis-using-lstm-tutorial
* https://stackoverflow.com/questions/13703720/converting-between-datetime-timestamp-and-datetime64
* https://visualstudiomagazine.com/articles/2014/01/01/how-to-standardize-data-for-neural-networks.aspx

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# -*- 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. 

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{
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	"text": "RT @TEN000HOURS: The MIGOS of AAU Basketball... https:\/\/t.co\/h5lGeWHqpu",
	"source": "\u003ca href=\"http:\/\/twitter.com\/download\/iphone\" rel=\"nofollow\"\u003eTwitter for iPhone\u003c\/a\u003e",
	"truncated": false,
	"in_reply_to_status_id": null,
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####
# Eigens
#####

# How to calculate covariance matrix
# Great video: https://www.youtube.com/watch?v=9B5vEVjH2Pk
dat <- as.matrix(
  cbind(c(90, 90, 60, 30, 30),
        c(80, 60, 50, 40, 20),
        c(40, 80, 70, 70, 70))

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