Logic programming in python
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Logic programming in Python allows for productive, readable programs using logic rules rather than loops or conditionals. The pyDatalog library adds logic programming capabilities to Python. Programs are defined with facts and clauses like Prolog, and can query SQL databases. PyDatalog memoizes results to avoid recomputation and supports features like aggregate functions. It allows applications like spreadsheet programming, expert systems, and cross-database queries. The presenter outlines roadmaps for adding an equation solver, transactions, and improving performance.
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• f[X]=2*X is equivalent to (f[X]==Y) <= (Y==2*x)
• Functions return only 1 value per key (aka dict())
• Functions can appear in expressions
• Resolution starts with the latest clause, and stops on first answer
• Support for aggregate functions : sum, min, max, …](https://image.slidesharecdn.com/logicprogramminginpython-140627095657-phpapp02/85/Logic-programming-in-python-12-320.jpg)
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Boolean Relational Operators in Python .............................................................................................. 5
Boolean Logical Operations .................................................................................................................. 7
Boolean Logical Operators in Python .................................................................................................. 10
Boolean Expressions Using Boolean Variables .................................................................................... 10
CheckPoint 3.1 ..................................................................................................................................... 11
The Nature of Algorithms................................................................................................ 12
Turing Machines and the Church-Turing Thesis ................................................................................. 12
Elements of Logical Structure in Algorithms ....................................................................................... 16
Tools for Describing Algorithms .......................................................................................................... 19
Linear Sequences ................................................................................................................................ 23
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Conditional Execution (Branching ) ................................................................................. 24
Binary Branching – Bypass vs. Choice ................................................................................................. 24
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- 1. Logic Programming in Python Pierre Carbonnelle FOSDEM 2014
- 2. No if’s, no loops, just the plain mathematical definition !
- 3. Logic programming (Prolog, SQL) Functional (Lisp, ..) Imperative / OO (C, Smalltalk)
- 4. Logic programming (Prolog, SQL) Functional (Lisp, ..) Imperative / OO (C, Smalltalk) Python Python + pyDatalog
- 5. Why Logic Programming in Python ? Be productive Be Readable Use AI Easy to learn Batteries included Query 11 SQL databases
- 6. The anatomy of a prolog program Fact Clause Query
- 7. The anatomy of a pyDatalog program
- 8. The anatomy of a pyDatalog program
- 9. Sample 1 Could be written in SQL too !
- 10. Sample 1 Loop Calculation Test Input/output polymorphism ! Shorter programs !
- 11. Sample 1 Batteries included Would need an equation solver
- 12. Sample 2 • f[X]=2*X is equivalent to (f[X]==Y) <= (Y==2*x) • Functions return only 1 value per key (aka dict()) • Functions can appear in expressions • Resolution starts with the latest clause, and stops on first answer • Support for aggregate functions : sum, min, max, …
- 13. Sample 3 Familiar tuple notation Memoized Faster Avoid infinite loop
- 14. Sample 4 Prefixed function to query Python classes or SQL databases
- 15. Other pyDatalog features • Runs on PyPy, Python 2, Python 3 • Accepts predicate definition written in Python • For I/O, performance, … • Thread-safe • For use in multi-threaded web framework • Multi-model : Switch from one Logic model (= set of clauses) to another
- 16. Applications Core technology Benefit Applications The SLG resolution algorithm determines the sequence of steps to resolve a query. Spreadsheet-programming : faster iterations, fewer bugs, more readable. Rule-based expert systems for pricing, tax calculation, access right, robot control, intelligent agent. Input/output polymorphism. Maximise code reuse : shorter program, simpler database query language. Cross-database queries, data integration. Intermediate results are memoized. Avoid re-calculation. Avoid infinite loop. Recursive algorithm on tree or graph structures, natural language processing, code analysis
- 17. Roadmap : Equation solver
- 18. Roadmap : Transactions • Assert facts within transactions, with possible roll-back • Use logic clauses to validate data operations • Use logic clauses to define triggers (condition => action) • Memoize results across queries within the same transaction • Translate Datalog queries into SQL for server-side processing Feedback, Help welcome !!
- 19. Roadmap : Performance • Rewrite critical sections in C • Exploit multi-threading in resolution engine Feedback, Help welcome !!
- 20. Why Logic Programming in Python ? Easy to learn Batteries included Query 11 SQL databases Be productive Be Readable Use AI