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An inference engine for extensional lambda-calculus

Project description

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# Pomagma

Pomagma is an inference engine for
[extensional untyped λ-calculus](/doc/philosophy.md).
Pomagma is useful for:

- simplifying code fragments expressed in pure λ-join calculus
- validating entire codebases of λ-terms and inequalities
- testing and validating systems of inequalities
- solving systems of inequalities

Pomagma follows a client-server database architecture
with a Python client library backed by a C++ database server.
The correctness of Pomagma's theory is being verified in the
[Hstar project](https://github.com/fritzo/hstar).

- [Installing](#installing)
- [Quick Start](#quick-start)
- [Get An Atlas](#get-an-atlas)
- [Using The Client Library](/doc/client.md)
- [Developing](/doc/README.md)
- [Dataflow Architecture](/doc/README.md#dataflow-architecture)
- [File Organization](/doc/README.md#file-organization)
- [Configuring](/doc/README.md#configuring)
- [Testing](/doc/README.md#testing)
- [Benchmarking](/doc/README.md#benchmarking)
- [Vetting changes](/doc/README.md#vetting-changes)
- [Philosophy](/doc/philosophy.md)

## Installing

The server targets Ubuntu 14.04 and 12.04, and installs in a python virtualenv.

git clone https://github.com/fritzo/pomagma
cd pomagma
. install.sh
make small-test # takes ~5 CPU minutes
make test # takes ~1 CPU hour

The client library supports Python 2.7.

pip install pomagma

## Quick Start

Start a local analysis server with the tiny default atlas

pomagma analyze # starts server, Ctrl-C to quit

Then in another terminal, start an interactive python client session

$ pomagma connect # starts a client session, Ctrl-D to quit
>>> simplify(['APP I I'])
[I]
>>> validate(['I'])
[{'is_bot': False, 'is_top': False}]
>>> solve('x', 'EQUAL x APP x x', max_solutions=4)
['I', 'BOT', 'TOP', 'V'],

Alternatively, connect using the Python client library

python
from pomagma import analyst
with analyst.connect() as db:
print db.simplify(["APP I I"])
print db.validate(["I"])
print db.solve('x', 'EQUAL x APP x x', max_solutions=4)

## Get an Atlas

Pomagma reasons about large programs by approximately locating code fragments
in an **atlas** of 10<sup>3</sup>-10<sup>5</sup> basic programs.
The more basic programs in an atlas,
the more accurate pomagma's analysis will be.
Pomagma ships with a tiny default atlas of ~2000 basic programs.

To get a large prebuilt atlas, put your AWS credentials in the environment and

pomagma pull # downloads latest atlas from S3 bucket

To start building a custom atlas from scratch

pomagma make max_size=10000 # kill and restart at any time

Pomagma is parallelized and needs lots of memory to build a large atlas.

| Atlas Size | Compute Time | Memory Space | Storage Space |
|---------------|--------------|--------------|---------------|
| 1 000 atoms | ~1 CPU hour | ~10MB | ~1MB |
| 10 000 atoms | ~1 CPU week | ~1GB | ~100MB |
| 100 000 atoms | ~1 CPU year | ~100GB | ~10GB |

## License

Copyright 2005-2015 Fritz Obermeyer.<br/>
All code is licensed under the [Apache 2.0 License](/LICENSE).

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