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https://github.com/jbrukh/bayesian

Naive Bayesian Classification for Golang.
https://github.com/jbrukh/bayesian

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Naive Bayesian Classification for Golang.

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# Naive Bayesian Classification



Perform naive Bayesian classification into an arbitrary number of classes on sets of strings. `bayesian` also supports term frequency-inverse document frequency calculations ([TF-IDF](https://www.wikiwand.com/en/Tf%E2%80%93idf)).



Copyright (c) 2011-2017. Jake Brukhman. ([email protected]).

All rights reserved.  See the LICENSE file for BSD-style license.



------------



## Background



This is meant to be an low-entry barrier Go library for basic Bayesian classification. See code comments for a refresher on naive Bayesian classifiers, and please take some time to understand underflow edge cases as this otherwise may result in innacurate classifications.



------------



## Installation



Using the go command:



```shell

go get github.com/jbrukh/bayesian

go install !$

```



------------



## Documentation



See the GoPkgDoc documentation [here](https://godoc.org/github.com/jbrukh/bayesian).



------------



## Features



- Conditional probability and "log-likelihood"-like scoring.

- Underflow detection.

- Simple persistence of classifiers.

- Statistics.

- TF-IDF support.



------------



## Example 1 (Simple Classification)



To use the classifier, first you must create some classes

and train it:



```go

import "github.com/jbrukh/bayesian"



const (

    Good bayesian.Class = "Good"

    Bad  bayesian.Class = "Bad"

)



classifier := bayesian.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:



```go

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:



```go

probs, likely, _ := classifier.ProbScores(

                        []string{"tall", "girl"},

                     )

```



## Example 2 (TF-IDF Support)



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 calling classification methods such as `LogScores`, `SafeProbScores`, and `ProbScores`)



```go

import "github.com/jbrukh/bayesian"



const (

    Good bayesian.Class = "Good"

    Bad bayesian.Class = "Bad"

)



// Create a classifier with TF-IDF support.

classifier := bayesian.NewClassifierTfIdf(Good, Bad)



goodStuff := []string{"tall", "rich", "handsome"}

badStuff  := []string{"poor", "smelly", "ugly"}



classifier.Learn(goodStuff, Good)

classifier.Learn(badStuff,  Bad)



// Required

classifier.ConvertTermsFreqToTfIdf()

```



Then you can ascertain the scores of each class and

the most likely class your data belongs to:



```go

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:



```go

probs, likely, _ := classifier.ProbScores(

    []string{"tall", "girl"},

)

```



Use wisely.