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Merge pull request #57 from TomFanella4/development
Sprint 3
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| package matching | ||
|
|
||
| import ( | ||
| "fmt" | ||
| "io/ioutil" | ||
| "math" | ||
| "meetover/backend/user" | ||
| "strings" | ||
|
|
||
| "gonum.org/v1/gonum/mat" | ||
| ) | ||
|
|
||
| // distanceThresh - minimum meaningful distance | ||
| var distanceThresh = 100.0 | ||
|
|
||
| // getStopWords - gets the stop words from file | ||
| func getStopWords(fileName string) map[string]bool { | ||
| b, err := ioutil.ReadFile(fileName) | ||
| if err != nil { | ||
| fmt.Print(err) | ||
| } | ||
| str := string(b) | ||
| swarr := strings.Split(str, "\n") | ||
| swm := make(map[string]bool) | ||
| for _, sw := range swarr { | ||
| swm[sw] = true | ||
| } | ||
| return swm | ||
| } | ||
|
|
||
| // StripStopWords - | ||
| func StripStopWords(str []string) []string { | ||
| stopFile := DataDir + "stopwords.txt" | ||
| stopWords := getStopWords(stopFile) | ||
| for i, w := range str { | ||
| if _, exists := stopWords[strings.ToLower(w)]; exists { | ||
| // fmt.Println("found :" + w) | ||
| str = append(str[:i], str[i+1:]...) | ||
| } | ||
| } | ||
| return str | ||
| } | ||
|
|
||
| // nestedDistance - distance metric between par vectors | ||
| func nestedDistance(src []*mat.VecDense, dst []*mat.VecDense) float64 { | ||
| d := 0.0 | ||
| for _, si := range src { | ||
| for _, di := range dst { | ||
| temp := mat.NewVecDense(WordModelDimension, nil) | ||
| temp.SubVec(si, di) | ||
| d += flattenVector(WordModelDimension, temp) | ||
| } | ||
| } | ||
| // fmt.Printf("distance: %f\n", d) | ||
| if d > distanceThresh { | ||
| return d | ||
| } | ||
| return math.MaxFloat64 | ||
| } | ||
| func flattenVector(rows int, vec mat.Matrix) float64 { | ||
| res := 0.0 | ||
| for i := 0; i < rows; i++ { | ||
| res += math.Abs(vec.At(i, 0)) | ||
| } | ||
| return res | ||
| } | ||
|
|
||
| // parToVector converts a string representation of user to numeric vector using | ||
| // the given word embeddings model | ||
| func parToVector(userStr string, model map[string][]float64) []*mat.VecDense { | ||
| res := []*mat.VecDense{} | ||
| par := strings.Split(userStr, " ") | ||
| par = StripStopWords(par) | ||
| i := 0 | ||
| for _, w := range par { | ||
| w = strings.TrimSpace(strings.ToLower(w)) | ||
| if len(w) <= 1 { | ||
| continue | ||
| } | ||
| if val, found := model[w]; found { | ||
| if len(val) == WordModelDimension { | ||
| vec := mat.NewVecDense(WordModelDimension, val) | ||
| res = append(res, vec) | ||
| i++ | ||
| } | ||
| } | ||
| if i > WordModelRandomParam { | ||
| break | ||
| } | ||
| } | ||
| for i < WordModelRandomParam { | ||
| w := par[random(0, len(par))] | ||
| w = strings.TrimSpace(strings.ToLower(w)) | ||
| if len(w) <= 1 { | ||
| continue | ||
| } | ||
| if val, found := model[w]; found { | ||
| if len(val) == WordModelDimension { | ||
| vec := mat.NewVecDense(WordModelDimension, val) | ||
| res = append(res, vec) | ||
| i++ | ||
| } | ||
| } | ||
| } | ||
| return res | ||
| } | ||
|
|
||
| // userToString converts the user object to a paragraph for vector translation | ||
| func userToString(u user.User) string { | ||
| var res string | ||
| res = u.MatchStatus.Greeting + " " + u.Profile.Headline + " " + u.Profile.Summary + " " + u.Profile.Industry | ||
| for _, pos := range u.Profile.Positions.Values { | ||
| res += pos.Company.Industry + " " | ||
| res += pos.Company.Name + " " | ||
| res += pos.Summary + " " | ||
| res += pos.Title | ||
| } | ||
| return res | ||
| } |
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