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