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121 lines
2.2 KiB
Go
121 lines
2.2 KiB
Go
package main
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import (
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"bufio"
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"flag"
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"fmt"
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"log"
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"os"
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"strings"
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"github.com/jbrukh/bayesian"
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"github.com/luksen/maildir"
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)
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const (
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// good is the class of good mails that are not supposed to be Spam
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good bayesian.Class = "Good"
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// junk is the class of Spam mails
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junk bayesian.Class = "Junk"
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)
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var (
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// Processed is a map of e-mail IDs and the value set to true if Junk
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Processed map[string]bool
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)
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// Mail includes the key of a mail in Maildir
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type Mail struct {
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Key string
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Subject, Body *string
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Junk bool
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}
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// Classifiers contains the classifiers for mail subjects and bodies
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type Classifiers struct {
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Subject, Body *bayesian.Classifier
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}
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// Index loads all mail keys from the Maildir directory for processing.
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func Index(d string) (m []Mail, err error) {
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g, err := maildir.Dir(d).Keys()
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if err != nil {
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return m, err
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}
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for _, val := range g {
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var new Mail
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new.Key = val
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m = append(m, new)
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}
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j, err := maildir.Dir(d + "/.Junk").Keys()
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if err != nil {
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return m, err
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}
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for _, val := range j {
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var new Mail
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new.Key = val
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new.Junk = true
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m = append(m, new)
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}
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return m, nil
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}
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// Learn initially classifies all mails and returns the respective classifiers.
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func (m Mail) Learn() (c Classifiers, err error) {
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return
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}
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// Clean prepares the mail's subject and body for training
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func (m Mail) Clean() error {
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return nil
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}
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// Load reads a mail's subject and body
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func (m Mail) Load(d string) error {
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message, err := maildir.Dir(d).Message(m.Key)
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if err != nil {
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return err
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}
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// get Subject
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subject := message.Header.Get("Subject")
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m.Subject = &subject
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// get Body
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var b []string
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bScanner := bufio.NewScanner(message.Body)
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for bScanner.Scan() {
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b = append(b, bScanner.Text())
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}
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body := strings.Join(b, " ")
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m.Body = &body
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return nil
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}
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func main() {
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// Get the Maildir to be handled
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wd, err := os.Getwd()
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if err != nil {
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log.Fatal(err)
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}
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maildir := flag.String("d", wd+"/Maildir", "Path of the Maildir to be handled")
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flag.Parse()
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// Load the Maildir content
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mails, err := Index(*maildir)
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if err != nil {
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log.Fatal(err)
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}
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fmt.Println(mails)
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// Create a classifier
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//classifier := bayesian.NewClassifier(Good, Junk)
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}
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