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Files
paper-manager/backend/global/question.go
2023-04-09 23:30:08 +08:00

105 lines
2.8 KiB
Go

package global
import (
"encoding/binary"
"encoding/hex"
"encoding/json"
"github.com/corona10/goimagehash"
"github.com/fumiama/paper-manager/backend/utils"
)
// QuestionJSON is the struct representation of File.Questions
type QuestionJSON struct {
Name string `json:"name"` // Name is name or Question ID
Points int `json:"points,omitempty"` // Points is sum of subs' points or self
Rate float64 `json:"rate,omitempty"` // Rate is the avg(non-leaf) or max(leaf) similarity
Sub []QuestionJSON `json:"sub,omitempty"`
}
type Question struct {
ID int64 // ID is the first 8 bytes of the Plain's md5
Path string // Path is the question's docx position
Plain string // Plain is the plain text of the question (like markdown format)
Images []byte // Images is json of the image dhash in XML, ex. ['rId1': '1234567890abcdef', ...]
Vector []byte // Vector is json of {word: freq, ...}
Dup []byte // Dup is json of {queid: rate, ...}
}
// GetDuplicateRate calc q & que's dup rate
func (q *Question) GetDuplicateRate(que *Question) (float64, error) {
v1, v2 := make(map[string]uint8, 64), make(map[string]uint8, 64)
m1, m2 := make(map[string]string, 64), make(map[string]string, 64)
if len(q.Images) > 2 {
err := json.Unmarshal(q.Images, &m1)
if err != nil {
return 0, err
}
}
if len(que.Images) > 2 {
err := json.Unmarshal(que.Images, &m2)
if err != nil {
return 0, err
}
}
if len(q.Vector) > 2 {
err := json.Unmarshal(q.Vector, &v1)
if err != nil {
return 0, err
}
}
if len(que.Vector) > 2 {
err := json.Unmarshal(que.Vector, &v2)
if err != nil {
return 0, err
}
}
imgdsts := uint64(0)
for _, dhstr2 := range m2 {
d, err := hex.DecodeString(dhstr2)
if err != nil {
return 0, err
}
dh2 := goimagehash.NewImageHash(binary.LittleEndian.Uint64(d), goimagehash.DHash)
r := 0
for _, dhstr1 := range m1 {
d, err := hex.DecodeString(dhstr1)
if err != nil {
return 0, err
}
dh1 := goimagehash.NewImageHash(binary.LittleEndian.Uint64(d), goimagehash.DHash)
dst, err := dh2.Distance(dh1)
if err != nil {
return 0, err
}
if dst > r {
r = dst
}
}
imgdsts += uint64(r)
}
imgdupr := 0.0
if len(m2) > 0 {
imgdupr = float64(imgdsts) / float64(len(m2)) / 64.0
}
v1space := make([]uint8, 0, len(v1)+len(v2))
v2space := make([]uint8, 0, len(v1)+len(v2))
for k, v := range v1 {
v1space = append(v1space, v)
if tv, ok := v2[k]; ok {
v2space = append(v2space, tv)
delete(v2, k)
} else {
v2space = append(v2space, 0)
}
}
for _, v := range v2 {
v1space = append(v1space, 0)
v2space = append(v2space, v)
}
if imgdupr > 0 {
return (8*utils.Similarity(v1space, v2space) + 2*imgdupr) / 10.0, nil
}
return utils.Similarity(v1space, v2space), nil
}