mirror of https://github.com/carlostrub/sisyphus
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
101 lines
2.6 KiB
Go
101 lines
2.6 KiB
Go
/*
|
|
Part of this code is borrowed from github.com/jbrukh/bayesian published under a BSD3CLAUSE License
|
|
*/
|
|
|
|
package sisyphus
|
|
|
|
import (
|
|
"math"
|
|
"strconv"
|
|
|
|
"github.com/boltdb/bolt"
|
|
)
|
|
|
|
// classificationPriors returns the prior probabilities for good and junk
|
|
// classes.
|
|
func classificationPriors(db *bolt.DB) (g, j float64) {
|
|
|
|
db.View(func(tx *bolt.Tx) error {
|
|
b := tx.Bucket([]byte("Wordlists"))
|
|
good := b.Bucket([]byte("Good"))
|
|
gN := float64(good.Stats().KeyN)
|
|
junk := b.Bucket([]byte("Junk"))
|
|
jN := float64(junk.Stats().KeyN)
|
|
|
|
g = gN / (gN + jN)
|
|
j = jN / (gN + jN)
|
|
|
|
return nil
|
|
})
|
|
|
|
return
|
|
}
|
|
|
|
// classificationWordProb returns P(W|C_j) -- the probability of seeing
|
|
// a particular word W in a document of this class.
|
|
func classificationWordProb(db *bolt.DB, word string) (g, j float64) {
|
|
|
|
db.View(func(tx *bolt.Tx) error {
|
|
b := tx.Bucket([]byte("Wordlists"))
|
|
good := b.Bucket([]byte("Good"))
|
|
gNString := string(good.Get([]byte(word)))
|
|
gN, _ := strconv.ParseFloat(gNString, 64)
|
|
junk := b.Bucket([]byte("Junk"))
|
|
jNString := string(junk.Get([]byte(word)))
|
|
jN, _ := strconv.ParseFloat(jNString, 64)
|
|
|
|
p := tx.Bucket([]byte("Processed"))
|
|
counters := p.Bucket([]byte("Counters"))
|
|
jString := string(counters.Get([]byte("Junk")))
|
|
j, _ := strconv.ParseFloat(jString, 64)
|
|
mails := p.Bucket([]byte("Mails"))
|
|
pN := mails.Stats().KeyN
|
|
|
|
g = gN / (float64(pN) - j)
|
|
j = jN / j
|
|
|
|
return nil
|
|
})
|
|
|
|
return g, j
|
|
}
|
|
|
|
// LogScores produces "log-likelihood"-like scores that can
|
|
// be used to classify documents into classes.
|
|
//
|
|
// The value of the score is proportional to the likelihood,
|
|
// as determined by the classifier, that the given document
|
|
// belongs to the given class. This is true even when scores
|
|
// returned are negative, which they will be (since we are
|
|
// taking logs of probabilities).
|
|
//
|
|
// The index j of the score corresponds to the class given
|
|
// by c.Classes[j].
|
|
//
|
|
// Additionally returned are "inx" and "strict" values. The
|
|
// inx corresponds to the maximum score in the array. If more
|
|
// than one of the scores holds the maximum values, then
|
|
// strict is false.
|
|
//
|
|
// Unlike c.Probabilities(), this function is not prone to
|
|
// floating point underflow and is relatively safe to use.
|
|
func LogScores(db *bolt.DB, wordlist []string) (scoreG, scoreJ float64, junk bool) {
|
|
|
|
priorG, priorJ := classificationPriors(db)
|
|
|
|
// calculate the scores
|
|
scoreG = math.Log(priorG)
|
|
scoreJ = math.Log(priorJ)
|
|
for _, word := range wordlist {
|
|
gP, jP := classificationWordProb(db, word)
|
|
scoreG += math.Log(gP)
|
|
scoreJ += math.Log(jP)
|
|
}
|
|
|
|
if scoreJ == math.Max(scoreG, scoreJ) {
|
|
junk = true
|
|
}
|
|
|
|
return scoreG, scoreJ, junk
|
|
}
|