1
1
mirror of https://github.com/go-gitea/gitea synced 2024-12-25 10:04:27 +00:00
gitea/vendor/github.com/go-enry/go-enry/v2/classifier.go
2020-04-15 20:40:39 +03:00

108 lines
2.7 KiB
Go

package enry
import (
"math"
"sort"
"github.com/go-enry/go-enry/v2/internal/tokenizer"
)
// classifier is the interface in charge to detect the possible languages of the given content based on a set of
// candidates. Candidates is a map which can be used to assign weights to languages dynamically.
type classifier interface {
classify(content []byte, candidates map[string]float64) (languages []string)
}
type naiveBayes struct {
languagesLogProbabilities map[string]float64
tokensLogProbabilities map[string]map[string]float64
tokensTotal float64
}
type scoredLanguage struct {
language string
score float64
}
// classify returns a sorted slice of possible languages sorted by decreasing language's probability
func (c *naiveBayes) classify(content []byte, candidates map[string]float64) []string {
var languages map[string]float64
if len(candidates) == 0 {
languages = c.knownLangs()
} else {
languages = make(map[string]float64, len(candidates))
for candidate, weight := range candidates {
if lang, ok := GetLanguageByAlias(candidate); ok {
candidate = lang
}
languages[candidate] = weight
}
}
empty := len(content) == 0
scoredLangs := make([]*scoredLanguage, 0, len(languages))
var tokens []string
if !empty {
tokens = tokenizer.Tokenize(content)
}
for language := range languages {
score := c.languagesLogProbabilities[language]
if !empty {
score += c.tokensLogProbability(tokens, language)
}
scoredLangs = append(scoredLangs, &scoredLanguage{
language: language,
score: score,
})
}
return sortLanguagesByScore(scoredLangs)
}
func sortLanguagesByScore(scoredLangs []*scoredLanguage) []string {
sort.Stable(byScore(scoredLangs))
sortedLanguages := make([]string, 0, len(scoredLangs))
for _, scoredLang := range scoredLangs {
sortedLanguages = append(sortedLanguages, scoredLang.language)
}
return sortedLanguages
}
func (c *naiveBayes) knownLangs() map[string]float64 {
langs := make(map[string]float64, len(c.languagesLogProbabilities))
for lang := range c.languagesLogProbabilities {
langs[lang]++
}
return langs
}
func (c *naiveBayes) tokensLogProbability(tokens []string, language string) float64 {
var sum float64
for _, token := range tokens {
sum += c.tokenProbability(token, language)
}
return sum
}
func (c *naiveBayes) tokenProbability(token, language string) float64 {
tokenProb, ok := c.tokensLogProbabilities[language][token]
if !ok {
tokenProb = math.Log(1.000000 / c.tokensTotal)
}
return tokenProb
}
type byScore []*scoredLanguage
func (b byScore) Len() int { return len(b) }
func (b byScore) Swap(i, j int) { b[i], b[j] = b[j], b[i] }
func (b byScore) Less(i, j int) bool { return b[j].score < b[i].score }