Go-Bayesian is a Go package for doing classification using Naive-Bayes algorithm. There are two Naive-Bayes models that implemented in this package, which are Multinomial TF and Multinomial Boolean.
For basic classifying, you can do it like this:
import (
"fmt"
"github.com/RadhiFadlillah/go-bayesian"
)
// Declare class
const (
Good bayesian.Class = "good"
Bad bayesian.Class = "bad"
)
func main() {
// New Multinomial TF classifier
classifier := bayesian.NewClassifier(bayesian.MultinomialTf)
// Do learning using two documents
classifier.Learn(
NewDocument(Good, "tall", "handsome", "rich"),
NewDocument(Bad, "bald", "poor", "ugly"),
)
// Classify tokens from a document
allScores, class, certain := classifier.Classify("the", "tall", "man")
fmt.Println(allScores, class, certain)
}You also can save the classifier to a file for later use. Useful to avoid repeating learning process :
func main() {
// New Multinomial TF classifier
classifier := bayesian.NewClassifier(bayesian.MultinomialTf)
classifier.Learn(
NewDocument(Good, "tall", "handsome", "rich"),
NewDocument(Bad, "bald", "poor", "ugly"),
)
// Save classifier to file
err := classifier.SaveClassifierToFile("./my-classifier")
if err != nil {
panic(err)
}
}Later, you can create a new Classifier from that file :
func main() {
// New classifier from file
classifier, err := bayesian.NewClassifierFromFile("./my-classifier")
if err != nil {
panic(err)
}
}- Raschka, S. 2014. Naive Bayes and Text Classification I - Introduction and Theory. (PDF and Website)
- Metsis, V., Androutsopoulos, I., and Paliouras, G. 2006. Spam Filtering with Naive Bayes – Which Naive Bayes ?. Proceeding of CEAS 2006 - Third Conference on Email and Anti-Spam. California, USA, July 27-28, 2006. (PDF)
- Lecture slides from the Stanford Coursera course by Dan Jurafsky and Christopher Manning.
Go-Bayesian is distributed using MIT license.