Localized Type Inference of Atomic Types in Python
anand at chatimity.com
anand at chatimity.com
Sat Feb 15 15:13:51 CET 2014
On Wednesday, May 25, 2005 4:41:34 AM UTC+5:30, Brett C. wrote:
> 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 .
This link seems to be down. Can you point us to some current link? Am trying to contribute to https://code.google.com/p/py2c/ and reading up on type inference for python.
Thanks and Regards,
> 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.
> 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
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