lunaluxie

def numerical_derivative(func, func_input, respect_to_index=0, h=0.0001):
  """Compute the numerical derivative of a function

  Args:
    func (function): A function
    func_input (list): A list of inputs given to the function
    respect_to_index (int): The index of the value the derivative is calculated with respect to
    h (float): the amount to tweak the function with to compute the gradient

  Returns:

lunaluxie

# Import a bunch of models
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.cross_decomposition import PLSRegression
from sklearn.ensemble import AdaBoostRegressor

lunaluxie

"""
Small utility that lets you compute and compare cryptographically secure password hashes
"""

def hash_password(password, salt=None, iterations=100000):
	"""
	Compute hash for a string using SHA1

	input:
		String: password

lunaluxie

def send_email(user, pwd, recipient, subject, body):
    import smtplib

    gmail_user = user
    gmail_pwd = pwd
    FROM = user
    TO = recipient if type(recipient) is list else [recipient]
    SUBJECT = subject
    TEXT = body

lunaluxie

import base64


def encode(key, string):
    encoded_chars = []
    for i in xrange(len(string)):
        key_c = key[i % len(key)]
        encoded_c = chr(abs(ord(string[i]) + ord(key_c) % 256))
        encoded_chars.append(encoded_c)
    encoded_string = "".join(encoded_chars)

lunaluxie

from PIL import Image
import numpy as np
img = Image.open('img.jpg', "r")

pixels = np.asarray(img)

add = [0,0,0]
index = 0

for row in pixels:

lunaluxie

import random
import numpy

dict_weighted = {"word1" : 0.1, "word2" : 0.9} # keys = words, values = weights. Values must sum to 1

def create_d_sentence(dict_weighted):
	length = random.randint(5,15)
	keys = []
	values = []