markus-beuckelmann

#!/usr/bin/env python3
# -*- coding: UTF-8 -*-

# Gaussian Process regression (one–dimensional)
# Can easily be adjusted to deal with noisy data, too.

import numpy as np

class GaussianProcess(object):
    ''' A simple Gaussian Process model. '''

markus-beuckelmann

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markus-beuckelmann

#!/usr/bin/env python
# -*- coding: UTF-8 -*-

# Roughly based on: http://stackoverflow.com/questions/11443302/compiling-numpy-with-openblas-integration

from __future__ import print_function

import numpy as np
from time import time

markus-beuckelmann

def wikipedia_articles_in_category(baseurl, subcategories = False, prefix = ''):
	''' Returns a tuple of (title, url, category). '''

	from lxml import html
	from requests import get

	page = get(baseurl)
	tree = html.fromstring(page.text)
	articles, category = [], prefix + ''.join(tree.xpath('//h1[@id="firstHeading"]/span/text()')).split(':')[-1]
	seperator = '::'