xqbumu/bayesian

Naive Bayesian Classification for Golang.

Naive Bayesian Classification with TF-IDF support

Perform naive Bayesian classification into an arbitrary number of classes on sets of strings.

Copyright (c) 2011. Jake Brukhman. ([email protected]). All rights reserved. See the LICENSE file for BSD-style license.

Forked from github.com/jbrukh/bayesian

Added TF-IDF (term frequency–inverse document frequency) capability. Gain quite a bit of accurancy !

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Background

See code comments for a refresher on naive Bayesian classifiers.

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Installation

Using the go command:

go get github.com/jbrukh/bayesian
go install !$

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Documentation

See the GoPkgDoc documentation here.

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Features

• Conditional probability and "log-likelihood"-like scoring.
• Underflow detection.
• Simple persistence of classifiers.
• Statistics.

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Example 1 (plain no tf-idf)

To use the classifier, first you must create some classes and train it:

import . "bayesian"

const (
    Good Class = "Good"
    Bad Class = "Bad"
)

classifier := NewClassifier(Good, Bad)
goodStuff := []string{"tall", "rich", "handsome"}
badStuff  := []string{"poor", "smelly", "ugly"}
classifier.Learn(goodStuff, Good)
classifier.Learn(badStuff,  Bad)

Then you can ascertain the scores of each class and the most likely class your data belongs to:

scores, likely, _ := classifier.LogScores(
                        []string{"tall", "girl"}
                     )

Magnitude of the score indicates likelihood. Alternatively (but with some risk of float underflow), you can obtain actual probabilities:

probs, likely, _ := classifier.ProbScores(
                        []string{"tall", "girl"}
                     )

Example 2 (TF-IDF)

To use the TF-IDF classifier, first you must create some classes and train it AND you need to call ConvertTermsFreqToTfIdf() AFTER training and before Classifying methods(LogScore,ProbSafeScore,ProbScore)

import . "bayesian"

const (
    Good Class = "Good"
    Bad Class = "Bad"
)

classifier := NewClassiferTfIdf(Good, Bad) // Extra constructor
goodStuff := []string{"tall", "rich", "handsome"}
badStuff  := []string{"poor", "smelly", "ugly"}
classifier.Learn(goodStuff, Good)
classifier.Learn(badStuff,  Bad)

classifier.ConvertTermsFreqToTfIdf() // IMPORTANT !!

Then you can ascertain the scores of each class and the most likely class your data belongs to:

scores, likely, _ := classifier.LogScores(
                        []string{"tall", "girl"}
                     )

Magnitude of the score indicates likelihood. Alternatively (but with some risk of float underflow), you can obtain actual probabilities:

probs, likely, _ := classifier.ProbScores(
                        []string{"tall", "girl"}
                     )

Use wisely.