Account Options

  1. Sign in
The old Google Groups will be going away soon, but your browser is incompatible with the new version.
Google Groups Home
« Groups Home
comp.lang.python
+ new post
About this group
Subscribe to this group
This is a Usenet group - learn more
Message from discussion Localized Type Inference of Atomic Types in Python
The group you are posting to is a Usenet group. Messages posted to this group will make your email address visible to anyone on the Internet.
Your reply message has not been sent.
Your post was successful
 
From:
To:
Cc:
Followup To:
Add Cc | Add Followup-to | Edit Subject
Subject:
Validation:
For verification purposes please type the characters you see in the picture below or the numbers you hear by clicking the accessibility icon. Listen and type the numbers you hear
 
Brett C.  
View profile  
 More options May 24 2005, 7:11 pm
Newsgroups: comp.lang.python
From: "Brett C." <b...@OCF.Berkeley.EDU>
Date: Tue, 24 May 2005 16:11:34 -0700
Local: Tues, May 24 2005 7:11 pm
Subject: Localized Type Inference of Atomic Types in Python
Print | View thread | Show original | Report this message | Find messages by this author
My thesis, "Localized Type Inference of Atomic Types in Python", was
successfully defended today for my MS in Computer Science at the California
Polytechnic State University, San Luis Obispo.  With that stamp of approval I
am releasing it to the world.  You can grab a copy at
http://www.drifty.org/thesis.pdf .

For those of you who attended my talk at PyCon 2005 this is the thesis that
stemmed from the presented data.

As of this exact moment I am not planning to release the source code mainly
because it's a mess, I am not in the mood to pull the patches together, and the
last thing I want happening is people finding mistakes in the code.  =)  But if
enough people request the source I will take the time to generate a tar.bz2
file of patches against the 2.3.4 source release and put them up somewhere.

Below is the abstract culled directly from the thesis itself.

-Brett C.

---------------------------------
ABSTRACT

Types serve multiple purposes in programming.  One such purpose is in providing
information to allow for improved performance.  Unfortunately, specifying the
types of all variables in a program does not always fit within the design of a
programming language.

Python is a language where specifying types does not fit within the language
design.  An open source, dynamic programming language, Python does not support
type specifications of variables.  This limits the opportunities in Python for
performance optimizations based on type information  compared to languages that
do allow or require the specification of types.

Type inference is a way to derive the needed type information for optimizations
based on types without requiring type specifications in the source code of a
program.  By inferring the types of variables based on flow control and other
hints in a program, the type information can be derived and used in a
constructive manner.

This thesis is an exploration of implementing a type inference algorithm for
Python without changing the semantics of the language.  It also explores the
benefit of adding type annotations to method calls in order to garner more type
information.


 
You must Sign in before you can post messages.
To post a message you must first join this group.
Please update your nickname on the subscription settings page before posting.
You do not have the permission required to post.
Create a group - Google Groups - Google Home - Terms of Service - Privacy Policy
©2013 Google