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https://github.com/jbrukh/bayesian
Naive Bayesian Classification for Golang.
https://github.com/jbrukh/bayesian
Last synced: 3 months ago
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Naive Bayesian Classification for Golang.
- Host: GitHub
- URL: https://github.com/jbrukh/bayesian
- Owner: jbrukh
- License: other
- Created: 2011-11-23T04:17:00.000Z (about 13 years ago)
- Default Branch: master
- Last Pushed: 2023-11-17T14:32:45.000Z (about 1 year ago)
- Last Synced: 2024-07-31T20:52:14.882Z (5 months ago)
- Language: Go
- Homepage:
- Size: 71.3 KB
- Stars: 796
- Watchers: 34
- Forks: 128
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.