Projects tagged ‘algorithms’ and ‘python’

NLTK — the Natural Language Toolkit — is a suite of open source Python modules, linguistic data and documentation for research and development in natural language processing, supporting dozens of ... [More] NLP tasks, with distributions for Windows, Mac OSX and Linux. [Less]

NetworkX (NX) is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Features: * Includes standard graph-theoretic and ... [More] statistical physics functions * Easy exchange of network algorithms between applications, disciplines, and platforms * Includes many classic graphs and synthetic networks * Nodes and edges can be "anything" (e.g. time-series, text, images, XML records) * Exploits existing code from high-quality legacy software in C, C++, Fortran, etc. * Open source (encourages community input) * Unit-tested Additional benefits due to Python: * Allows fast prototyping of new algorithms * Easy to teach * Multi-platform * Allows easy access to almost any database [Less]

This is another library for creating and manipulating graphs. There is simply no other graph library out there which can be handle graphs of the size the author is confronted with efficiently. ... [More] Whenever possible igraph tries to be also user friendly and portable. igraph started as an additional package to the GNU R statistical environment, and still some functions which are hard to implement in C are available only in R (like interactive graphics). Most functions are however now written in C and they can be compiled without R as a separate library. [Less]

The Python File Format Interface, briefly PyFFI, is an open source Python library for processing block structured binary files: * Simple: Reading, writing, and manipulating complex binary files in ... [More] a Python environment is easy! Currently, PyFFI supports the NetImmerse/Gamebryo NIF and KFM formats, CryTek's CGF format, the DDS format, and the TGA format. * Batteries included: Many tools for files used by 3D games, such as a stripifier, tangent space calculator, 2d/3d hull algorithms, inertia calculator, as well as a general purpose file editor QSkope (using PyQt4), are included. * Modular: Its highly modular design makes it easy to add support for new formats, and also to extend existing functionality. [Less]

Pyevolve was developed to be a complete genetic algorithm framework written in pure python.

PyGression GP is a symbolic regression system written in Python using genetic programming techniques. It's inspired by TinyGP, a highly optimised GP system that was originally developed to meet the ... [More] specifications set out in the TinyGP competition of the Genetic and Evolutionary Computation Conference (GECCO) 2004. Start here to know what is all about: PyGression GPRationale: Why are we doing this?What is Symbolic Regression?What are Evolutionary Algorithms?What does Symbolic Regression have to do with Evolutionary Algorithms?Design Overview [Less]

GPath is an evolutionary graph search algorithm that tries to solve the single-source shortest path problem for a graph, in which each node has a coordinate attached. It's based on the idea of ... [More] chemotaxis, the method by which many single and multi-cellular organisms find their food. [Less]

python-graph is a library for working with graphs in Python. This software provides a suitable data structure for representing graphs and a whole set of important algorithms. The code is ... [More] appropriately documented and API reference is generated automatically by epydoc. Comments, bug reports and suggestions are welcome. Current release: 1.6.2 - Sep 30, 2009 The 1.6.x series is our refactoring series. Along the next releases, we'll change the API so we can better prepare the codebase to new features. If you want a softer, directed transition, upgrade your code to every release in the 1.6.x series. On the other hand, if you'd rather fix everything at once, you can wait for 1.7.0. Please check our Changelog for detailed information. Installing If you have easy_install on your system, you can simply run: # easy_install python-graph-core And, optionally, for Dot-Language support (requires pydot): # easy_install python-graph-dot Otherwise, you can download a package from the Downloads page. Provided features and algorithms: Support for directed, undirected, weighted and non-weighted graphs Support for hypergraphs Canonical operations XML import and export DOT-Language output (for usage with Graphviz) Random graph generation Accessibility (transitive closure) Breadth-first search Critical path algorithm Cut-vertex and cut-edge identification Cycle detection Depth-first search Heuristic search (A* algorithm) Identification of connected components Minimum spanning tree (Prim's algorithm) Mutual-accessibility (strongly connected components) Shortest path search (Dijkstra's algorithm) Topological sorting Transitive edge identification [Less]

PyevolveDescriptionThis project focus on development of a complete cross-platform (Windows, Linux) framework for Genetic Algorithms in pure python. The project is in active development, and I'm ... [More] doing great efforts to release a good version and documentation soon. Main features: Many selector algorithms like: Rank Selector Uniform Selector Roulette Wheel Selector Tournament Selector Pure python, you can use in any platform which runs python; Easy to use, easy for end-user; Simple to customize Create new representations, genetic operators; You can view the Tutorial - Christian S. Perone News 05/08/2008 Added documentation to the code; Created API Docs. 04/08/2008 I've created a Tutorial. 31/07/2008 Released Pyevolve 0.3 (changes in ChangeLogVersion03)! 29/06/2008 Good news, Pyevolve was figured in the "10 Must-Have Python Packages for Social Scientists" ! 25/01/2008 Released Pyevolve 0.2 (changes in ChangeLogVersion02)! Some code example (SimpleCodeExample)from pyevolve import G1DList from pyevolve import GSimpleGA from pyevolve import Selectors def eval_func(ind): score = 0.0 for x in range(0,len(ind)): if ind[x]==0: score += 0.1 return score # Genome instance genome = G1DList.G1DList(7) genome.setInitParams(rangemin=0, rangemax=6) # The evaluator function (objective function) genome.evaluator.set(eval_func) # Genetic Algorithm Instance ga = GSimpleGA.GSimpleGA(genome) ga.selector.set(Selectors.GRouletteWheel) # Do the evolution ga.evolve() # Best individual print ga.bestIndividual() And the result: - GenomeBase Evaluated: True Score: 0.70 Fitness: 0.70 Slot name: Evaluator (Count: 1) Name: eval_func Doc: No documentation ! Slot name: Initializator (Count: 1) Name: G1DListInitializatorInteger Doc: This is the init function !! Slot name: Mutator (Count: 1) Name: G1DListMutatorSwap Doc: No documentation ! Slot name: Crossover (Count: 1) Name: G1DListCrossoverSinglePoint Doc: No documentation ! - G1DList List size: 7 List: [0, 0, 0, 0, 0, 0, 0] [Less]

still in construction... check the Notes and Contents.