<?xml version="1.0" encoding="UTF-8"?>
<!--Generated by Squarespace Site Server v5.11.81 (http://www.squarespace.com/) on Fri, 24 Feb 2012 15:21:31 GMT--><rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:wfw="http://wellformedweb.org/CommentAPI/" xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd" xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0"><channel><title>Updates</title><link>http://markwatson.us/updates/</link><description></description><lastBuildDate>Sat, 07 Jan 2012 22:00:14 +0000</lastBuildDate><copyright></copyright><language>en-US</language><generator>Squarespace Site Server v5.11.81 (http://www.squarespace.com/)</generator><item><title>Functional Lagrange Interpolation</title><dc:creator>Mark Watson</dc:creator><pubDate>Fri, 06 May 2011 20:52:00 +0000</pubDate><link>http://markwatson.us/updates/functional-lagrange-interpolation.html</link><guid isPermaLink="false">765880:8971761:14482320</guid><description><![CDATA[<p>I recently implemented Lagrange’s interpolation method in python, and I thought I would share it here. I’m posting it because I think it’s the most nested functions I ever used in python. I would have like to use partial functions, but python doesn’t support those (well not without using functools), so I made do with nested functions...</p>

<p>Probably don’t use the code in a production environment or for anything serious because it’s probably very slow. I’m 90% sure numpy has a better implementation.</p>

<pre class="brush: python">
# Tested on Windows 7 and Python 2.7

# the code
def lagrangian_interpolate(samples):
    """
    Takes some samples as a list of tuples and returns a function that's
    a lagrangian interpolation of all the samples.
    """
    X = 0 # the tuple index of the X variable in the samples
    Y = 1 # the tuple index of the Y variable in the samples
    n = len(samples)
    # define the L function as a function generator that generates L functions
    # for a given i
    def L(i):
        "This function generates an L function for a given x_i"
        def L_gen(x):
            ret = []
            for j in xrange(n):
                if j != i:
                    ret.append((x - samples[j][X])/(samples[i][X] - samples[j][X]))
            return reduce(lambda a,b: a*b, ret)
        return L_gen

    return lambda x: sum(L(i)(x) * samples[i][Y] for i in xrange(n))

# main
prob_1 = lagrangian_interpolate([(2,1.4142),(2.5,1.5811),(3.0,1.7321)])
print prob_1(2.2)

prob_1_b = lagrangian_interpolate([(2,1.4142),(2.5,1.5811),(2.7,1.6432)])
print prob_1_b(2.2)

prob_2 = lagrangian_interpolate([(2.0,1.4142),(2.5,1.5811),(3.0,1.7321),(3.5,1.8708)])
print prob_2(2.8)
</pre>]]></description><wfw:commentRss>http://markwatson.us/updates/rss-comments-entry-14482320.xml</wfw:commentRss></item><item><title>Python super_split()</title><dc:creator>Mark Watson</dc:creator><pubDate>Mon, 02 May 2011 20:55:00 +0000</pubDate><link>http://markwatson.us/updates/python-super_split.html</link><guid isPermaLink="false">765880:8971761:14482351</guid><description><![CDATA[<p>I recently needed some code that splits python strings based on multiple delimitors, so I wrote a quick function to do so. I figured it&rsquo;s generally useful so I&rsquo;m posting it here.</p>
<pre class="brush: python">def super_split(string, delim):
    segment = ''
    for c in string:
        if c in delim:
            yield segment
            segment = ''
        else:
            segment += c
    yield segment

</pre>]]></description><wfw:commentRss>http://markwatson.us/updates/rss-comments-entry-14482351.xml</wfw:commentRss></item><item><title>DMOZ Scraper</title><dc:creator>Mark Watson</dc:creator><pubDate>Sat, 30 Apr 2011 19:57:00 +0000</pubDate><link>http://markwatson.us/updates/dmoz-scraper-1.html</link><guid isPermaLink="false">765880:8971761:14481761</guid><description><![CDATA[<p>I’m really only posting this code so I can check out the syntax highlighter in jekyll.</p>

<p>It’s a class that scrapes URLs from Dmoz given a search term. Not fancy, I’m just using it for a machine learning project. It does what I need for now.</p>

<pre class="brush: python">
import mechanize
from BeautifulSoup import BeautifulSoup

class Dmoz(object):
    def __init__(self):
        self.br = mechanize.Browser()

    def get_page_urls(self, term):
        result = self.br.open("http://www.dmoz.org/search?q="+term)
        result_html = result.read()
        soup = BeautifulSoup(result_html)
        sites_obj = soup.find('ol', {"class": "site"})
        if sites_obj:
            sites = sites_obj('li')
            urls = [x('a', recursive=False)[0]['href'] for x in sites]
            return urls
        else:
            return []

def main():
    # eg:
    dm = Dmoz()
    print dm.get_page_urls("Computer Science")

if __name__ == "__main__":
    main()
</pre>]]></description><wfw:commentRss>http://markwatson.us/updates/rss-comments-entry-14481761.xml</wfw:commentRss></item></channel></rss>
