aliayoubi

<?php

/**
 * API Request Caching
 *
 *	Use server-side caching to store API request's as JSON at a set 
 *	interval, rather than each pageload.
 * 
 * @arg Argument description and usage info
 */

aliayoubi

Other people's projects:

• https://github.com/laracasts/URL-Shortener URL Shortener by Jeffrey Way
• https://github.com/basco-johnkevin/laravelsnippets Source code of http://laravelsnippets.com website
• https://github.com/jesseterry/FusionInvoice FusionInvoice is built for freelancers and small businesses who need a simple, yet powerful self-hosted web based invoicing system.
• https://github.com/ezynda3/burnmsg is a self-destructing encrypted message app. A demo is also running on https://burnmsg.com
• https://github.com/martindilling/auto-generating-gallery the demo is at http://gallerydemo.martindilling.com/

My projects (tutorials are on my blog at http://maxoffsky.com):

• https://github.com/CodepadME/laravel-tricks - Source of Laravel-tricks.com

aliayoubi

documents = [ dict(
            email=open("conference/%d.txt" % n).read().strip(), 
            category='conference') for n in range(1,372) ]
documents.extend([ dict( 
            email=open("job/%d.txt" % n).read().strip(),
            category='job') for n in range(1,275)])
documents.extend([ dict( 
            email=open("spam/%d.txt" % n).read().strip(), 
            category='spam') for n in range(1,799) ])

aliayoubi

from __future__ import division
import string
import math

tokenize = lambda doc: doc.lower().split(" ")

document_0 = "China has a strong economy that is growing at a rapid pace. However politically it differs greatly from the US Economy."
document_1 = "At last, China seems serious about confronting an endemic problem: domestic violence and corruption."
document_2 = "Japan's prime minister, Shinzo Abe, is working towards healing the economic turmoil in his own country for his view on the future of his people."
document_3 = "Vladimir Putin is working hard to fix the economy in Russia as the Ruble has tumbled."

aliayoubi

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aliayoubi

    def accuracy(self, questions, restrict_vocab=30000, most_similar=most_similar, lowercase=True):
        """
        Compute accuracy of the model. `questions` is a filename where lines are
        4-tuples of words, split into sections by ": SECTION NAME" lines.
        See https://code.google.com/p/word2vec/source/browse/trunk/questions-words.txt for an example.

        The accuracy is reported (=printed to log and returned as a list) for each
        section separately, plus there's one aggregate summary at the end.

        Use `restrict_vocab` to ignore all questions containing a word whose frequency