文件列表:

project.clj (294, 2010-11-29)
src (0, 2010-11-29)
src\bayesian_classifier (0, 2010-11-29)
src\bayesian_classifier\core.clj (8850, 2010-11-29)
test (0, 2010-11-29)
test\bayesian_classifier (0, 2010-11-29)
test\bayesian_classifier\test (0, 2010-11-29)
test\bayesian_classifier\test\core.clj (5725, 2010-11-29)

# bayesian-classifier A naive bayesian classifier supporting both traditional classification and Paul Graham's variant outlined in "A Plan for Spam" ## Usage make-classifier accepts N category names as keywords and returns a classifer: (def *healthy-sick-classifier* (make-classifier :healthy :sick)) If you wish to use agents or atoms for your concurrency mechanism then by all means, do this: (def *healthy-sick-classifier-atom* (atom (make-classifier :healthy :sick)) (def *healthy-sick-classifier-agent* (agent (make-classifier :healthy :sick)) Next, you'll want to give it some training data. learn! will throw an exception if you attempt to classify data into a concept/class that is currently not in the classifer. learn will just roll with this and create the new concept/class on the fly. (learn! *healthy-sick-classifier* "fred" :sick) (swap! learn! *healthy-sick-classifier* "fred" :sick) (send *healthy-sick-classifier-agent* learn! "fred" :first) Use one of the support methods to categorize new data. These classifications algo's utilize protocols to ensure type transparency. They will work seemless with vanilla classifier maps, atoms or agents: (p-of-class-given-token *healthy-sick-classifier* "betty") (p-of-class-given-token-graham *healthy-sick-classifier* "mary") Persisting trained classifiers is trivial: (save-classifier *healthy-sick-classifer* "chicken.txt") (load-classifier "chicken.txt") ## Installation Typical leiningen project rules apply. ## License Copyright (C) 2010 Do What Thou Wilt Shall Be The Whole Of The Law.

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：