Francois Vanderseypen Orbifold

## TF_linear_estimator.py
#!/usr/bin/env python3

# This demonstrates the usage of input_fn with numpy data
# and estimators.

import tensorflow as tf
tf.enable_eager_execution()
assert tf.executing_eagerly()
import tensorflow.contrib.eager as tfe
# too much info otherwise

## H2OForecasting.r
install.packages("timetk")
install.packages("tidyquant")
library(h2o)        # Awesome ML Library
library(timetk)     # Toolkit for working with time series in R
library(tidyquant)  # Loads tidyverse, financial pkgs, used to get data

beer_sales_tbl <- tq_get("S4248SM144NCEN", get = "economic.data", from = "2010-01-01", to = "2017-10-27")
beer_sales_tbl %>%
  ggplot(aes(date, price)) +
  # Train Region

## bb84.py

from numpy import matrix
from math import pow, sqrt
from random import randint
import sys, argparse

class qubit():
	def __init__(self,initial_state):
		if initial_state:
			self.__state = matrix([[0],[1]])

## rsa.py

#========================================
# create public and private keys
#========================================

from Crypto.PublicKey import RSA

key = RSA.generate(2048)
private_key = key.exportKey()
with open("./private.pem", "wb") as f:

## tda.py
import numpy as np
from collections import defaultdict
import json
import itertools
from sklearn import cluster, preprocessing, manifold
from datetime import datetime

class KeplerMapper(object):
	def __init__(self, cluster_algorithm=cluster.DBSCAN(eps=0.5,min_samples=3), nr_cubes=10,
				 overlap_perc=0.1, scaler=preprocessing.MinMaxScaler(), reducer=None, color_function="distance_origin",

## FeatureValuation.ipynb

      
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                Orbifold
                / FeatureValuation.ipynb
            
            
              Last active
              July 14, 2018 18:35
            
              
                Using a random forest to valuate features
              
          
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## 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"

## 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>

## smote.md

      
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                Orbifold
                / smote.md
            
            
              Created
              May 10, 2018 17:01
            
          
    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

  
## SemanticsWithPython.ipynb

      
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                Orbifold
                / SemanticsWithPython.ipynb
            
            
              Created
              March 13, 2018 08:23
            
              
                An intro to using RDFLib and triples in Python
              
          
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	#!/usr/bin/env python3

	# This demonstrates the usage of input_fn with numpy data
	# and estimators.

	import tensorflow as tf
	tf.enable_eager_execution()
	assert tf.executing_eagerly()
	import tensorflow.contrib.eager as tfe
	# too much info otherwise
	install.packages("timetk")
	install.packages("tidyquant")
	library(h2o) # Awesome ML Library
	library(timetk) # Toolkit for working with time series in R
	library(tidyquant) # Loads tidyverse, financial pkgs, used to get data

	beer_sales_tbl <- tq_get("S4248SM144NCEN", get = "economic.data", from = "2010-01-01", to = "2017-10-27")
	beer_sales_tbl %>%
	ggplot(aes(date, price)) +
	# Train Region

	from numpy import matrix
	from math import pow, sqrt
	from random import randint
	import sys, argparse

	class qubit():
	def __init__(self,initial_state):
	if initial_state:
	self.__state = matrix([[0],[1]])

	#========================================
	# create public and private keys
	#========================================

	from Crypto.PublicKey import RSA

	key = RSA.generate(2048)
	private_key = key.exportKey()
	with open("./private.pem", "wb") as f:
	import numpy as np
	from collections import defaultdict
	import json
	import itertools
	from sklearn import cluster, preprocessing, manifold
	from datetime import datetime

	class KeplerMapper(object):
	def __init__(self, cluster_algorithm=cluster.DBSCAN(eps=0.5,min_samples=3), nr_cubes=10,
	overlap_perc=0.1, scaler=preprocessing.MinMaxScaler(), reducer=None, color_function="distance_origin",
	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"
	<!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>