Gavin Hackeling gavinmh

## example.ipynb

      
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              August 14, 2020 16:31
            
              
                example.ipynb
              
          
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## viterbi.py
# -*- coding: utf-8 -*-
"""
This is an example of a basic optical character recognition system.
Some components, such as the featurizer, are missing, and have been replaced
with data that I made up.

This system recognizes words produced from an alphabet of 2 letters: 'l' and 'o'.
Words that can be recognized include, 'lol', 'lolol', 'and loooooll'.
We'll assume that this system is used to digitize hand-written notes by Redditors,
or something.

## rcv1-topics.txt
CCAT: CORPORATE/INDUSTRIAL
	C11: STRATEGY/PLANS
	C12: LEGAL/JUDICIAL
	C13: REGULATION/POLICY
	C14: SHARE LISTINGS
	C15: PERFORMANCE
		C151: ACCOUNTS/EARNINGS
		C1511   child-description: ANNUAL RESULTS
		C152: COMMENT/FORECASTS
	C16: INSOLVENCY/LIQUIDITY

## cassandra-notes.md

      
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              July 23, 2020 20:52
            
              
                Cassandra Notes
              
          
    Cassandra Notes

Introduction

Apache Cassandra is an open source, distributed database management system. Cassandra is designed to handle large amounts of data across many commodity servers. Cassandra uses a query language named CQL.
Cassandra's data model is a partitioned row store; Cassandra combines elements of key-value stores and tabular/columnar databases. Like a relational database, Cassandra stores data in tables, called column families, that have defined columns and associated data types. Each row in a column family is uniquely identified by a key. Each row has multiple columns, each of which has a timestamp, name, and value. Unlike a relational database, each row in a column family does not need to have the same set of columns. At any time, a column may be added to one or more rows. If this explanation is unclear, you might think of column families instead as sets of key-value pairs, in which the values are nested sets of key-value pairs.
The following depicts two rows of a column-family fro

  
## interests-visualization.html
<html><head><meta charset="utf-8" /></head><body><script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type="text/javascript">/**
* plotly.js v1.43.1
* Copyright 2012-2018, Plotly, Inc.
* All rights reserved.
* Licensed under the MIT license
*/
!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Plotly=t()}}(function(){return function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l="function"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error("Cannot find module '"+o+"'");throw c.code="MODULE_NOT_FOUND",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,function(t){return i(e[o][1][t]||t)},u,u.exports,t,e,r,n)}return r[o].exports}for(var a="function"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}}()({

## ner.py
# -*- coding: utf-8 -*-
'''

'''
from nltk import sent_tokenize, word_tokenize, pos_tag, ne_chunk


def extract_entities(text):
	entities = []
	for sentence in sent_tokenize(text):

## socks_proxy.sh
ssh -D 1337 -f -C -q -N user@remote -p 22

## gist:6834934
# Bayes' Theorem

Let's pretend that you wish to find the probability that two events, A and B, occur.
If A and B are independent events, then probability that A and B both occur is

    P(A)P(B).

However, A and B might be related events. If they are not independent, the probability that A and B both occur is

    P(A)P(B|A)

## psql.md

      
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                gavinmh
                / psql.md
            
            
              Created
              September 5, 2017 02:47
                — forked from cimmanon/psql.md
            
              
                PostgreSQL cheat sheet for MySQL users
              
          
    I use PostgreSQL via the psql client.  If you use a different client (eg. pgAdmin, etc.), I don't know how much will translate over.
One nice difference between psql and mysql (cli) is that if you press CTRL+C, it won't exit the client.
User administration

Login as superuser (via shell)

psql -U postgres

  
## pil_to_numpy.py
import numpy
import PIL

# Convert Image to array
img = PIL.Image.open("foo.jpg").convert("L")
arr = numpy.array(img)

# Convert array to Image
img = PIL.Image.fromarray(arr)
	# -- coding: utf-8 --
	"""
	This is an example of a basic optical character recognition system.
	Some components, such as the featurizer, are missing, and have been replaced
	with data that I made up.

	This system recognizes words produced from an alphabet of 2 letters: 'l' and 'o'.
	Words that can be recognized include, 'lol', 'lolol', 'and loooooll'.
	We'll assume that this system is used to digitize hand-written notes by Redditors,
	or something.
	CCAT: CORPORATE/INDUSTRIAL
	C11: STRATEGY/PLANS
	C12: LEGAL/JUDICIAL
	C13: REGULATION/POLICY
	C14: SHARE LISTINGS
	C15: PERFORMANCE
	C151: ACCOUNTS/EARNINGS
	C1511 child-description: ANNUAL RESULTS
	C152: COMMENT/FORECASTS
	C16: INSOLVENCY/LIQUIDITY
	<html><head><meta charset="utf-8" /></head><body><script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type="text/javascript">/**
	* plotly.js v1.43.1
	* Copyright 2012-2018, Plotly, Inc.
	* All rights reserved.
	* Licensed under the MIT license
	*/
	!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Plotly=t()}}(function(){return function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l="function"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error("Cannot find module '"+o+"'");throw c.code="MODULE_NOT_FOUND",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,function(t){return i(e[o][1][t]\|\|t)},u,u.exports,t,e,r,n)}return r[o].exports}for(var a="function"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}}()({
	# -- coding: utf-8 --
	'''

	'''
	from nltk import sent_tokenize, word_tokenize, pos_tag, ne_chunk


	def extract_entities(text):
	entities = []
	for sentence in sent_tokenize(text):
	# Bayes' Theorem

	Let's pretend that you wish to find the probability that two events, A and B, occur.
	If A and B are independent events, then probability that A and B both occur is

	P(A)P(B).

	However, A and B might be related events. If they are not independent, the probability that A and B both occur is

	P(A)P(B\|A)
	import numpy
	import PIL

	# Convert Image to array
	img = PIL.Image.open("foo.jpg").convert("L")
	arr = numpy.array(img)

	# Convert array to Image
	img = PIL.Image.fromarray(arr)