public string GetBestMatchWord(string OriginalWord)
{
EnsureIndexed();
var existing = indexReader.DocFreq(new Term("word", OriginalWord));
if (existing > 0)
{
return(OriginalWord);
}
var suggestions = _luceneChecker.SuggestSimilar(OriginalWord, 10, null, "word", true);
var jaro = new JaroWinklerDistance();
var leven = new LevenshteinDistance();
var ngram = new NGramDistance();
var metrics = suggestions.Select(s => new
{
word = s,
freq = indexReader.DocFreq(new Term("word", s)),
jaro = jaro.GetDistance(OriginalWord, s),
leven = leven.GetDistance(OriginalWord, s),
ngram = ngram.GetDistance(OriginalWord, s)
})
.OrderByDescending(metric =>
(
(metric.freq / 100f) +
metric.jaro +
metric.leven +
metric.ngram
)
/ 4f
)
.ToList();
return(metrics.Select(m => m.word).FirstOrDefault());
}
public IActionResult Get(int id)
{
try
{
var poemsString = Manipulations.GetDataByUrl(Settings.Poems);
var poems = JsonSerializer.Deserialize <List <Poem> >(poemsString);
var etalonPoem = Settings.EtalonPoem;
var poem = Manipulations.GetRandomPoem(poems);
var distance = JaroWinklerDistance.distance(poem, etalonPoem);
var person = new Person()
{
Poem = poem,
Distance = Math.Round(distance, 2)
};
using (var client = new HttpClient())
{
client.BaseAddress = new Uri(Settings.Localhost);
var res = client.PostAsJsonAsync($"getinfo/{id}", person).Result;
}
return(StatusCode(StatusCodes.Status200OK));
}
catch (Exception ex)
{
return(StatusCode(StatusCodes.Status500InternalServerError));
}
}
public static double findSimilarDictionaryWord(string word, double maxSimilarity, int index, List<string> equalMinDistanceDictWordList)
{
index = index - _minWordLength;
word = word.ToLower();
double NewSimilarity = 0;
int WordLength = word.Length;
if ((WordLength + index) < 0)
return maxSimilarity;
if ((WordLength + index) >= _IndexDictionary.Length)
return maxSimilarity;
if (_IndexDictionary[WordLength + index] == null)
return maxSimilarity;
for (int j = 0; j < _IndexDictionary[WordLength + index].Count; j++)
{
JaroWinklerDistance JaroDist = new JaroWinklerDistance();
NGramDistance ng = new NGramDistance();
JaccardDistance jd = new JaccardDistance();
NewSimilarity = jd.GetDistance(word, _IndexDictionary[WordLength + index][j]);//(double)JaroDist.GetDistance(word, _IndexDictionary[WordLenght - 1 + index][j]);
if (NewSimilarity > maxSimilarity)
{
equalMinDistanceDictWordList.Clear();
equalMinDistanceDictWordList.Add(_IndexDictionary[WordLength + index][j]);
maxSimilarity = NewSimilarity;
}
else if (NewSimilarity == maxSimilarity)
equalMinDistanceDictWordList.Add(_IndexDictionary[WordLength + index][j]);
}
return maxSimilarity;
}
public static double findSimilarDictionaryWord(string word, double maxSimilarity, int index, Dictionary<string, double> equalMinDistanceDictWordList)
{
try
{
double distancethreshold = 0.3;
index = index - _minWordLength;
double NewDistance = 0;
int WordLenght = word.Length;
if ((WordLenght + index) < 0)
return maxSimilarity;
if ((WordLenght + index) >= _IndexDictionary.Length)
return maxSimilarity;
if (_IndexDictionary[WordLenght - 1 + index] == null)
return 0;
for (int j = 0; j < _IndexDictionary[WordLenght - 1 + index].Count; j++)
{
JaroWinklerDistance JaroDist = new JaroWinklerDistance();
NGramDistance ng = new NGramDistance();
JaccardDistance jd = new JaccardDistance();
string temp = _IndexDictionary[WordLenght - 1 + index][j];
NewDistance = jd.GetDistance(word, temp);
double NewDistance2 = -1;
if (NewDistance < NewDistance2)
NewDistance = NewDistance2;
if (NewDistance > maxSimilarity)
{
foreach (var item in equalMinDistanceDictWordList.ToList())
{
if (item.Value <= NewDistance - distancethreshold)
equalMinDistanceDictWordList.Remove(item.Key);
}
tempReplacement = temp;
if (!equalMinDistanceDictWordList.ContainsKey(temp))
equalMinDistanceDictWordList.Add(temp, NewDistance);
//else
// equalMinDistanceDictWordList[tempReplacement] = NewDistance;
maxSimilarity = NewDistance;
}
else if (NewDistance <= maxSimilarity + distancethreshold && NewDistance >= maxSimilarity - distancethreshold && NewDistance > 0)
if (!equalMinDistanceDictWordList.ContainsKey(temp))
equalMinDistanceDictWordList.Add(temp, NewDistance);
}
}
catch (Exception e)
{
throw e;
}
return maxSimilarity;
}
private IEnumerable <User> sort(IEnumerable <User> users)
{
int uId = Int32.Parse(Session["UserId"].ToString());
User currentUser = db.Users.Find(uId);
List <Int32> scores = new List <Int32>();
int score;
foreach (User user in users)
{
score = 0;
if (currentUser.Gender != null)
{
if (user.Gender == null)
{
score += 20;
}
else if ((currentUser.Gender != "Other" && user.Gender != currentUser.Gender) ||
(currentUser.Gender == "Other" && user.Gender == "Other"))
{
score += 50;
}
}
if (currentUser.Occupation != null && user.Occupation != null &&
JaroWinklerDistance.proximity(currentUser.Occupation, user.Occupation) >= 0.8)
{
score += 10;
}
if (currentUser.City != null && user.City != null &&
JaroWinklerDistance.proximity(currentUser.City, user.City) >= 0.8)
{
score += 10;
}
if (currentUser.Likes != null && user.Likes != null)
{
score += matchWordsScore(currentUser.Likes, user.Likes);
}
if (currentUser.Dislikes != null && user.Dislikes != null)
{
score += matchWordsScore(currentUser.Dislikes, user.Dislikes);
}
if (currentUser.Hobbies != null && user.Hobbies != null)
{
score += matchWordsScore(currentUser.Hobbies, user.Hobbies);
}
if (currentUser.Bio != null && user.Bio != null)
{
score += matchWordsScore(currentUser.Bio, user.Bio);
}
scores.Add(score);
}
var orderedZip = scores.Zip(users, (x, y) => new { x, y })
.OrderByDescending(pair => pair.x)
.ToList();
users = orderedZip.Select(pair => pair.y).ToList();
return(users);
}
public AlternateWordList GetAlternateWordList(string OriginalWord, int NumberToReturn)
{
var wordList = new AlternateWordList();
wordList.OriginalWord = OriginalWord;
EnsureIndexed();
var existing = indexReader.DocFreq(new Term("word", OriginalWord));
wordList.OriginalWordFrequency = existing;
var suggestions = _luceneChecker.SuggestSimilar(OriginalWord, NumberToReturn, null, "word", true);
var jaro = new JaroWinklerDistance();
var leven = new LevenshteinDistance();
var ngram = new NGramDistance();
var metrics = suggestions.Select(s => new
{
word = s,
freq = indexReader.DocFreq(new Term("word", s)),
jaro = jaro.GetDistance(OriginalWord, s),
leven = leven.GetDistance(OriginalWord, s),
ngram = ngram.GetDistance(OriginalWord, s)
})
.OrderByDescending(metric =>
(
(metric.freq / 100f) +
metric.jaro +
metric.leven +
metric.ngram
)
/ 4f
)
.ToList();
var list = new List <AlternateWord>();
var sortOrder = 1;
foreach (var item in metrics)
{
var altWord = new AlternateWord();
altWord.Word = item.word;
altWord.Frequency = item.freq;
altWord.JaroWinkler = item.jaro;
altWord.Levenshtein = item.leven;
altWord.NGram = item.ngram;
altWord.BestMatchScore = ((item.freq / 100f) + item.jaro + item.leven + item.ngram) / 4f;
altWord.BestMatchSortOrder = sortOrder;
list.Add(altWord);
sortOrder++;
}
wordList.Words = list;
return(wordList);
}
protected void Page_Load(object sender, EventArgs e)
{
string s1 = "Holmes";
string s2 = "Holmes2136";
Response.Write(JaroWinklerDistance.GetDistance(s1, s2));
Response.Write("<BR>");
Response.Write(new LevenshteinDistance().GetDistance(s1, s2));
Response.Write("<BR>");
}
public static void Main(String[] args)
{
var jw = new JaroWinklerDistance();
// Obtém o texto informado depois de converter para maíusculo e remover os acentos
var texto1 = jw.RemoveDiacritics(args[0].ToUpper());
var texto2 = jw.RemoveDiacritics(args[1].ToUpper());
// Calcula a distância de Jaro-Winkler
double retorno = jw.Proximity(texto1, texto2);
Console.WriteLine("Proximidade: " + retorno);
}
public List <string> GetTopSuggestions(string value, int numberOfItems)
{
EnsureIndexed();
var suggestionCollection = new List <string>();
var existing = _indexReader.DocFreq(new Term(SpellCheckerConstants.SpellCheckerKey, value));
if (existing > 0)// the fist one will be correct of exist
{
suggestionCollection.Add(value);
}
var suggestions = _checker.SuggestSimilar(value, numberOfItems, null, SpellCheckerConstants.SpellCheckerKey, true);
var jaro = new JaroWinklerDistance();
var leven = new LevenshteinDistance();
var ngram = new NGramDistance();
var metrics = suggestions.Select(s => new
{
word = s,
freq = _indexReader.DocFreq(new Term(SpellCheckerConstants.SpellCheckerKey, s)),
jaro = jaro.GetDistance(value, s),
leven = leven.GetDistance(value, s),
ngram = ngram.GetDistance(value, s)
})
.OrderByDescending(metric => metric.jaro)
.ThenByDescending(m => m.ngram)
.ThenByDescending(metric =>
(
metric.freq / 100f +
metric.leven
)
/ 2f
)
.ToList();
var wordsOnly = metrics.Select(m => m.word).ToList();
suggestionCollection.AddRange(wordsOnly);
return(suggestionCollection);
}
public double GetDistance(string text)
{
string[] sentences = text.Split('\n').Where(x => !string.IsNullOrWhiteSpace(x)).ToArray();
double maxCost = 0, curCost = 0;
if (sentences.Length > 1)
{
for (int i = 0; i < sentences.Length - 1; i++)
{
curCost = JaroWinklerDistance.Distance(sentences[i], sentences[i + 1]);
if (maxCost < curCost)
{
maxCost = curCost;
}
}
}
else if (sentences.Length == 1)
{
maxCost = JaroWinklerDistance.Distance(sentences[0], "");
}
return(maxCost);
}
public static double findSimilarDictionaryWord(string word, double maxSimilarity, int index, List<string> equalMinDistanceDictWordList, bool exact)
{
index = index - _minWordLength;
int WordLength = word.Length;
int index2 = index;
if (index < 0 || (WordLength>=2 && char.IsUpper(word[0])&&!char.IsUpper(word[1])) )
index2 = 0;
word = word.ToLower();
bool noSpace = false;
if (word.CompareTo(word.Trim()) == 0)
noSpace = true;
else
word = word.Trim();
double NewSimilarity = 0;
if ((WordLength + index) < 0)
return maxSimilarity;
if ((WordLength + index) >= _IndexDictionary.Length)
return maxSimilarity;
if (_IndexDictionary[WordLength + index] == null)
return maxSimilarity;
for (int j = 0; j < _IndexDictionary[WordLength + index].Count; j++)
{
JaroWinklerDistance JaroDist = new JaroWinklerDistance();
NGramDistance ng = new NGramDistance();
JaccardDistance jd = new JaccardDistance();
string temp = _IndexDictionary[WordLength + index][j];
if(noSpace&&temp.CompareTo(word)==0)
{
equalMinDistanceDictWordList.Clear();
equalMinDistanceDictWordList.Add(temp);
return 10;
}
else if (temp.Contains(word))
{
equalMinDistanceDictWordList.Add(/*item);*/temp);
maxSimilarity = 1;
}
else if(index <= 2)
{
for (int i = 0; i <= index2; i++)
{
string s = temp.Substring(i);
string s2 = temp.Substring(0, temp.Length - index2);
//Console.WriteLine(item);
if (!exact)
NewSimilarity = Math.Max(jd.GetDistance(word, s), jd.GetDistance(word, s2));
else
{
NewSimilarity = jd.GetDistance(word, temp);
if (NewSimilarity == 1)
{
equalMinDistanceDictWordList.Clear();
equalMinDistanceDictWordList.Add(s);
maxSimilarity = NewSimilarity;
}
return maxSimilarity;
}
if (NewSimilarity > .33)
{
//equalMinDistanceDictWordList.Clear();
equalMinDistanceDictWordList.Add(/*item);*/temp);
maxSimilarity = NewSimilarity;
break;
}
}
}
}
return maxSimilarity;
}
public void ShouldReturnJaroWinklerDistanceOf1()
{
var result = JaroWinklerDistance.CalculateProximity(InputPassword, InputPassword);
Assert.IsTrue(Math.Abs(result - 1.0) < 0.0000001);
}
public void ShouldReturnJaroWinklerDistanceOfPoint5()
{
var result = JaroWinklerDistance.CalculateProximity(InputPassword, InputUsername);
Assert.IsTrue(Math.Abs(result - 0.500) < 0.0000001);
}
public void ShouldSetJaroWinklerDistanceWeightPrefixLength3()
{
Algorithm = new JaroWinklerDistance(InputPassword, InputPasswrod, prefixLength: PrefixLength);
Assert.AreEqual(PrefixLength, Algorithm.PrefixLength);
}
public void ShouldSetJaroWinklerDistanceWeightThresholdPoint8()
{
Algorithm = new JaroWinklerDistance(InputPassword, InputPasswrod, WeightThresholdPoint8);
Assert.AreEqual(WeightThresholdPoint8, Algorithm.WeightThreshold);
}
public void ShouldSetJaroWinklerDistanceInput2()
{
Algorithm = new JaroWinklerDistance(InputPassword, InputPasswrod);
Assert.AreEqual(InputPasswrod, Algorithm.Input2);
}
public void ShouldReturnJaroWinklerDistanceInstance()
{
Algorithm = new JaroWinklerDistance(InputPassword, InputPasswrod);
Assert.IsNotNull(Algorithm);
}
public void ShouldReturnJaroWinklerDistanceOfPoint025()
{
var result = JaroWinklerDistance.CalculateDistance(InputPassword, InputPasswrod);
Assert.IsTrue(Math.Abs(result - 0.025) < 0.0000001);
}
