Francois Vanderseypen Orbifold

## node-d3.js
var fs = require('fs');
var path = require('path');
var d3 = require('d3');
const jsdom = require("jsdom");
const JSDOM = jsdom.JSDOM;

var chartWidth = 500, chartHeight = 500;

var arc = d3.svg.arc()
    .outerRadius(chartWidth / 2 - 10)

## train_ner.py
# http://nlpforhackers.io/training-ner-large-dataset/
# http://gmb.let.rug.nl/data.php
import os
from nltk import conlltags2tree


def to_conll_iob(annotated_sentence):
    """
    `annotated_sentence` = list of triplets [(w1, t1, iob1), ...]
    Transform a pseudo-IOB notation: O, PERSON, PERSON, O, O, LOCATION, O

## Seq2seq.py
'''Sequence to sequence example in Keras (character-level).

This script demonstrates how to implement a basic character-level
sequence-to-sequence model. We apply it to translating
short English sentences into short French sentences,
character-by-character. Note that it is fairly unusual to
do character-level machine translation, as word-level
models are more common in this domain.

# Summary of the algorithm

## Intends.ipynb

      
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                Orbifold
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              Created
              January 27, 2018 06:43
            
              
                Mapping language to intends
              
          
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## epochs.py
import argparse

import matplotlib.pyplot as plt
import numpy as np
from keras.layers.core import Dense
from keras.models import Sequential
from numpy import array
from scipy import signal


## LSTM_translation.py
# The [Anki repository](http://www.manythings.org/anki/) has a lot of sentence-pairs to learn a language and they are ideal to train a translation network.
# To judge the quality of a translation it helps to understand a bit both languages so in my case
#  the [Dutch-English](http://www.manythings.org/anki/nld-eng.zip),
# [French-English](http://www.manythings.org/anki/fra-eng.zip)
# and [German-English](http://www.manythings.org/anki/deu-eng.zip) were perfect.

import string
import re
from pickle import dump
from unicodedata import normalize

## SemanticsWithPython.ipynb

      
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              March 13, 2018 08:23
            
              
                An intro to using RDFLib and triples in Python
              
          
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## smote.md

      
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    Intro

Imbalanced data typically refers to a problem with classification problems where the classes are not represented equally.For example, you may have a 2-class (binary) classification problem with 100 instances (rows). A total of 80 instances are labeled with Class-1 and the remaining 20 instances are labeled with Class-2. This is an imbalanced dataset and the ratio of Class-1 to Class-2 instances is 80:20 or more concisely 4:1. You can have a class imbalance problem on two-class classification problems as well as multi-class classification problems. Most techniques can be used on either.
Most classification data sets do not have exactly equal number of instances in each class, but a small difference often does not matter.
There are problems where a class imbalance is not just common, it is expected. For example, in datasets like those that characterize fraudulent transactions are imbalanced. The vast majority of the transactions will be in the “Not-Fraud” class and a very small minority will be

  
## tfjs_cosine.html
<!DOCTYPE html>
<html lang="en" xmlns="http://www.w3.org/1999/xhtml">

<head>
  <meta charset="utf-8" />
  <title></title>
  <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script>
  <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.4/lodash.min.js"></script>
  <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/Faker/3.1.0/faker.min.js"></script>
  <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.0.0-beta/js/bootstrap.min.js"></script>

## entailment_train.py
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import urllib
import sys
import os
import zipfile

glove_vectors_file = "glove.6B.50d.txt"
	var fs = require('fs');
	var path = require('path');
	var d3 = require('d3');
	const jsdom = require("jsdom");
	const JSDOM = jsdom.JSDOM;

	var chartWidth = 500, chartHeight = 500;

	var arc = d3.svg.arc()
	.outerRadius(chartWidth / 2 - 10)
	# http://nlpforhackers.io/training-ner-large-dataset/
	# http://gmb.let.rug.nl/data.php
	import os
	from nltk import conlltags2tree


	def to_conll_iob(annotated_sentence):
	"""
	`annotated_sentence` = list of triplets [(w1, t1, iob1), ...]
	Transform a pseudo-IOB notation: O, PERSON, PERSON, O, O, LOCATION, O
	'''Sequence to sequence example in Keras (character-level).

	This script demonstrates how to implement a basic character-level
	sequence-to-sequence model. We apply it to translating
	short English sentences into short French sentences,
	character-by-character. Note that it is fairly unusual to
	do character-level machine translation, as word-level
	models are more common in this domain.

	# Summary of the algorithm
	import argparse

	import matplotlib.pyplot as plt
	import numpy as np
	from keras.layers.core import Dense
	from keras.models import Sequential
	from numpy import array
	from scipy import signal
	# The [Anki repository](http://www.manythings.org/anki/) has a lot of sentence-pairs to learn a language and they are ideal to train a translation network.
	# To judge the quality of a translation it helps to understand a bit both languages so in my case
	# the [Dutch-English](http://www.manythings.org/anki/nld-eng.zip),
	# [French-English](http://www.manythings.org/anki/fra-eng.zip)
	# and [German-English](http://www.manythings.org/anki/deu-eng.zip) were perfect.

	import string
	import re
	from pickle import dump
	from unicodedata import normalize
	<!DOCTYPE html>
	<html lang="en" xmlns="http://www.w3.org/1999/xhtml">

	<head>
	<meta charset="utf-8" />
	<title></title>
	<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script>
	<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.4/lodash.min.js"></script>
	<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/Faker/3.1.0/faker.min.js"></script>
	<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.0.0-beta/js/bootstrap.min.js"></script>
	import tensorflow as tf
	import numpy as np
	import matplotlib.pyplot as plt
	import matplotlib.ticker as ticker
	import urllib
	import sys
	import os
	import zipfile

	glove_vectors_file = "glove.6B.50d.txt"