Go implementation of markov chains for textual data.
You can find out more about markov chains here and here
import (
"github.com/mb-14/gomarkov"
"fmt"
"strings"
"io/ioutil"
)
func main() {
//Create a chain of order 2
chain := gomarkov.NewChain(2)
//Feed in training data
chain.Add(strings.Split("I want a cheese burger", " "))
chain.Add(strings.Split("I want a chilled sprite", " "))
chain.Add(strings.Split("I want to go to the movies", " "))
//Get transition probability of a sequence
prob, _ := chain.TransitionProbability("a", []string{"I", "want"})
fmt.Println(prob)
//Output: 0.6666666666666666
//You can even generate new text based on an initial seed
chain.Add(strings.Split("Mother should I build the wall?", " "))
chain.Add(strings.Split("Mother should I run for President?", " "))
chain.Add(strings.Split("Mother should I trust the government?", " "))
next, _ := chain.Generate([]string{"should", "I"})
fmt.Println(next)
//The chain is JSON serializable
jsonObj, _ := json.Marshal(chain)
err := ioutil.WriteFile("model.json", jsonObj, 0644)
if err != nil {
fmt.Println(err)
}
}