absolutelyNoWarranty

## broken_kde.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                absolutelyNoWarranty
                / broken_kde.ipynb
            
            
              Created
              February 5, 2019 09:09
            
              
                Broken kde plot in arviz
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## cookie-clicker.js
function clickCookie() {
  document.getElementById("bigCookie").onclick();
}

function recurClickCookie() {
  clickCookie();
  setTimeout(recurClickCookie, 8);
}
recurClickCookie();

## pdf-joiner.py
from pyPdf import PdfFileReader, PdfFileWriter
import sys, os

if len(sys.argv)>1:
    output_name = sys.argv[1]
else:
    output_name = "all.pdf"

pdfs = filter(lambda f: os.path.splitext(f)[1]=='.pdf', os.walk(".").next()[2])
pdfs.sort()

## gp-regression-demo.r
# Demo of Gaussian process regression with R
# James Keirstead
# 5 April 2012

# Chapter 2 of Rasmussen and Williams's book `Gaussian Processes
# for Machine Learning' provides a detailed explanation of the
# math for Gaussian process regression.  It doesn't provide
# much in the way of code though.  This Gist is a brief demo
# of the basic elements of Gaussian process regression, as
# described on pages 13 to 16.
	from pyPdf import PdfFileReader, PdfFileWriter
	import sys, os

	if len(sys.argv)>1:
	output_name = sys.argv[1]
	else:
	output_name = "all.pdf"

	pdfs = filter(lambda f: os.path.splitext(f)[1]=='.pdf', os.walk(".").next()[2])
	pdfs.sort()
	# Demo of Gaussian process regression with R
	# James Keirstead
	# 5 April 2012

	# Chapter 2 of Rasmussen and Williams's book `Gaussian Processes
	# for Machine Learning' provides a detailed explanation of the
	# math for Gaussian process regression. It doesn't provide
	# much in the way of code though. This Gist is a brief demo
	# of the basic elements of Gaussian process regression, as
	# described on pages 13 to 16.