108 lines
2.7 KiB
Go
Vendored
108 lines
2.7 KiB
Go
Vendored
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 }
|