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 .
Hi, 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, Anand > > 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. -- https://mail.python.org/mailman/listinfo/python-list
