127 lines
3.1 KiB
Markdown
127 lines
3.1 KiB
Markdown
# Naive Bayesian Classification
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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)).
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Copyright (c) 2011-2017. Jake Brukhman. (jbrukh@gmail.com).
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All rights reserved. See the LICENSE file for BSD-style license.
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------------
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## Background
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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.
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------------
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## Installation
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Using the go command:
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```shell
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go get github.com/jbrukh/bayesian
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go install !$
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```
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------------
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## Documentation
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See the GoPkgDoc documentation [here](https://godoc.org/github.com/jbrukh/bayesian).
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------------
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## Features
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- Conditional probability and "log-likelihood"-like scoring.
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- Underflow detection.
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- Simple persistence of classifiers.
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- Statistics.
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- TF-IDF support.
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------------
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## Example 1 (Simple Classification)
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To use the classifier, first you must create some classes
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and train it:
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```go
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import "github.com/jbrukh/bayesian"
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const (
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Good bayesian.Class = "Good"
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Bad bayesian.Class = "Bad"
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)
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classifier := bayesian.NewClassifier(Good, Bad)
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goodStuff := []string{"tall", "rich", "handsome"}
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badStuff := []string{"poor", "smelly", "ugly"}
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classifier.Learn(goodStuff, Good)
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classifier.Learn(badStuff, Bad)
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```
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Then you can ascertain the scores of each class and
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the most likely class your data belongs to:
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```go
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scores, likely, _ := classifier.LogScores(
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[]string{"tall", "girl"},
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)
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```
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Magnitude of the score indicates likelihood. Alternatively (but
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with some risk of float underflow), you can obtain actual probabilities:
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```go
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probs, likely, _ := classifier.ProbScores(
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[]string{"tall", "girl"},
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)
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```
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## Example 2 (TF-IDF Support)
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To use the TF-IDF classifier, first you must create some classes
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and train it and you need to call ConvertTermsFreqToTfIdf() AFTER training
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and before calling classification methods such as `LogScores`, `SafeProbScores`, and `ProbScores`)
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```go
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import "github.com/jbrukh/bayesian"
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const (
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Good bayesian.Class = "Good"
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Bad bayesian.Class = "Bad"
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)
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// Create a classifier with TF-IDF support.
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classifier := bayesian.NewClassifierTfIdf(Good, Bad)
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goodStuff := []string{"tall", "rich", "handsome"}
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badStuff := []string{"poor", "smelly", "ugly"}
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classifier.Learn(goodStuff, Good)
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classifier.Learn(badStuff, Bad)
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// Required
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classifier.ConvertTermsFreqToTfIdf()
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```
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Then you can ascertain the scores of each class and
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the most likely class your data belongs to:
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```go
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scores, likely, _ := classifier.LogScores(
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[]string{"tall", "girl"},
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)
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```
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Magnitude of the score indicates likelihood. Alternatively (but
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with some risk of float underflow), you can obtain actual probabilities:
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```go
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probs, likely, _ := classifier.ProbScores(
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[]string{"tall", "girl"},
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)
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```
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Use wisely.
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