KNN.rar_KDD_The Process_k-nearest neighbor _kdd knn_knn kdd

Bayesian Logic (BLOG) inference engine version 0.3 Copyright (c) 2007, 2008, Massachusetts Institute of Technology Copyright (c) 2005, 2006, Regents of the University of California All rights reserved. This software is distributed under the license included in LICENSE.txt. Lead author: Brian Milch, bmilch@gmail.com Supervisors: Prof. Stuart Russell (Berkeley), Prof. Leslie Kaelbling (MIT) Contributors: Mart� Bolivar, Brendan Clark, Rodrigo de Salvo Braz, Michael Haimes, Keith Henderson, Kristian Kersting, Andrey Kolobov, Bhaskara Marthi, Daniel L. Ong, David Sontag, Luke S. Zettlemoyer Online Information ------------------ For documentation and the latest version of the BLOG inference engine, please see: http://people.csail.mit.edu/milch/blog License ------- The BLOG inference engine is distributed under the BSD license, as given in LICENSE.txt. This distribution also includes several third-party packages, which are distributed under their own licenses: * a modified version of the CUP v0.10k parser generator * a modified version of the JLex 1.2.6 lexical analyzer generator * the JAMA 1.0.1 matrix package * the JUnit 4.5 unit testing framework (source code available from junit.org) Installation ------------ The following installation instructions assume that you're using a machine with the Java SDK (version 1.5 or newer), the "make" utility, and the ability to run a shell script. Decompressing the ZIP archive that you downloaded will yield a directory called "blog-0.3". You can put this directory wherever you like. To compile the source code, go into this directory and type "make". If "make" finishes with no errors, the inference engine is ready to run. Please see CHANGELOG.txt for changes since the previous version. Getting Started --------------- To get you started with BLOG, we'll show you how to reproduce the experiment from our IJCAI-05 paper. The top-level BLOG directory contains a shell script called "runblog", which invokes java with the proper classpath. There is also a subdirectory called "examples" that contains several example BLOG models. To run inference on the urn-and-balls scenario from our paper, give the command: ./runblog examples/balls/poisson-prior-noisy.mblog examples/balls/all-same.eblog examples/balls/num-balls.qblog The program will do inference and print out the posterior distribution over the number of balls in the urn, given 10 draws that all appear to be the same color. By default, the program does 10000 samples of likelihood weighting. You can compare its output to the correct posterior distribution, which is given in a comment at the end of examples/balls/poisson-prior-noisy.mblog. To find out how to do more with the BLOG inference engine, please see the user manual at: http://people.csail.mit.edu/milch/blog/manual What Can It Do? --------------- The BLOG inference engine can parse any model written in the BLOG language that we introduced in our SRL-04 and IJCAI-05 papers. It includes a full set of built-in types (integers, strings, real numbers, vectors, matrices) and can use arbitrary conditional probability distributions (CPDs) in the form of Java classes that implement a certain interface. Models can include arbitrary first-order formulas. As noted in our papers, some BLOG models do not actually define unique probability distributions, because they contain cycles or infinite receding chains. The current version of the inference engine does not make any effort to detect whether a model is well-defined or not. On some ill-defined models, the inference algorithms will end up in infinite loops. This version of the inference engine includes three general-purpose inference algorithms: rejection sampling (as in our IJCAI-05 paper), likelihood weighting (as in our AISTATS-05 paper), and a Metropolis-Hastings algorithm where the proposal distribution just samples values for variables given their parents. These algorithms are very slow, but they can still yield interesting results on toy problems. The inference engine also allows modelers to plug in their own Metropolis-Hastings proposal distributions: the proposal distribution can propose arbitrary changes to the current world, and the engine will compute the acceptance probability. We include a hand-crafted split-merge proposal distribution for the urn-and-balls scenario as an example. The inference engine also includes two exact inference algorithms that work on BLOG models with known objects (that is, with no number statements). One of these is the variable elimination algorithm; the other is first-order variable elimination with counting formulas (C-FOVE) described at AAAI 2008. We plan to include parameter estimation capabilities -- specifically Monte Carlo EM -- in a future version of the engine. However, the current version has no learning code. How to Help ----------- The main reason we're releasing the BLOG inference engine code is so that other people can use it, evaluate the strengths and weaknesses of the BLOG language, and develop new inference and learning algorithms. It would also be great to have help improving the inference engine's interface and building utilities to work with it. If you have feedback, bug reports, ideas for improvement, or new code, please send email to Brian Milch at bmilch@gmail.com.