tartrazine/classifier.go
2017-05-30 09:07:58 +02:00

101 lines
2.6 KiB
Go

package slinguist
import (
"math"
"gopkg.in/src-d/simple-linguist.v1/internal/tokenizer"
)
// GetLanguageByClassifier takes in a content and a list of candidates, and apply the classifier's Classify method to
// get the most probably language. If classifier is null then DefaultClassfier will be used.
func GetLanguageByClassifier(content []byte, candidates []string, classifier Classifier) string {
if classifier == nil {
classifier = DefaultClassifier
}
scores := classifier.Classify(content, candidates)
if len(scores) == 0 {
return OtherLanguage
}
return getLangugeHigherScore(scores)
}
func getLangugeHigherScore(scores map[string]float64) string {
var language string
higher := -math.MaxFloat64
for lang, score := range scores {
if higher < score {
language = lang
higher = score
}
}
return language
}
// Classifier is the interface that contains the method Classify which is in charge to assign scores to the possibles candidates.
// The scores must order the candidates so as the highest score be the most probably language of the content.
type Classifier interface {
Classify(content []byte, candidates []string) map[string]float64
}
type classifier struct {
languagesLogProbabilities map[string]float64
tokensLogProbabilities map[string]map[string]float64
tokensTotal float64
}
func (c *classifier) Classify(content []byte, candidates []string) map[string]float64 {
if len(content) == 0 {
return nil
}
var languages []string
if len(candidates) == 0 {
languages = c.knownLangs()
} else {
languages = make([]string, 0, len(candidates))
for _, candidate := range candidates {
if lang, ok := GetLanguageByAlias(candidate); ok {
languages = append(languages, lang)
}
}
}
tokens := tokenizer.Tokenize(content)
scores := make(map[string]float64, len(languages))
for _, language := range languages {
scores[language] = c.tokensLogProbability(tokens, language) + c.languagesLogProbabilities[language]
}
return scores
}
func (c *classifier) knownLangs() []string {
langs := make([]string, 0, len(c.languagesLogProbabilities))
for lang := range c.languagesLogProbabilities {
langs = append(langs, lang)
}
return langs
}
func (c *classifier) tokensLogProbability(tokens []string, language string) float64 {
var sum float64
for _, token := range tokens {
sum += c.tokenProbability(token, language)
}
return sum
}
func (c *classifier) tokenProbability(token, language string) float64 {
tokenProb, ok := c.tokensLogProbabilities[language][token]
if !ok {
tokenProb = math.Log(1.000000 / c.tokensTotal)
}
return tokenProb
}