public IActionResult SearchRecipes([FromQuery] string recipe_name = "")
{
if (string.IsNullOrEmpty(recipe_name))
{
return(BadRequest());
}
var requiredRecipe = recipe_name.Split(' ');
var recipes = new
{
Recipes = db.Recipes
.ToList()
.Where(recipe => requiredRecipe
.All(requiredRecipeName => recipe.Name.Replace(" ", new Regex(@"\W")).Split(' ')
.Where(e => !string.IsNullOrEmpty(e) && e.Length >= 3)
.Any(recipeName => DamerauLevenshtein.DamerauLevenshteinDistance(requiredRecipeName, recipeName) <= 3 &&
DiceCoefficient.FindDiceCoefficient(requiredRecipeName, recipeName) > 0.4)))
.Select(r => new
{
r.Name,
id_recipe = r.Id,
id_image = r.ImageId,
r.Rating,
total_time = r.TimeCook
})
};
return(Ok(recipes));
}
private static void Main(string[] args)
{
/****************************************************/
/* BagOfTokensSimilarity */
/****************************************************/
// the recommend method for complex similarity on more words
var bagOfTokens = new BagOfWordsSimilarity();
const string pattern = "John Smith";
const string targetText = "Mr. John Smith, Jr.";
// using normalized string and tokenizer returns score 1.0
var resultingSim = bagOfTokens.GetSimilarity(new Tokenizer(new NormalizedString(pattern)),
new Tokenizer(new NormalizedString(targetText)));
/****************************************************/
/* Levenshtein */
/****************************************************/
const string nameCorrect = "martha";
const string nameError = "marhta";
// Levenshtein distance (implements interface IDistance)
// & similarity (implements interface ISimilarity)
var lev = new Levenshtein();
// returns edit distance 2
var distLev = lev.GetDistance(nameCorrect, nameError);
// normalized string removes special symbols, diacritics and make case insensitivity
// returns score 0.67
var simLev = lev.GetSimilarity(new NormalizedString(nameCorrect), new NormalizedString(nameError));
/****************************************************/
/* Damerau-Levenshtein */
/****************************************************/
// DamerauLevenshtein implements IDistance and ISimilarity
var damLev = new DamerauLevenshtein();
// returns edit distance 1
var distDamLev = damLev.GetDistance(nameCorrect, nameError);
// returns score 0.83
var simDamLev = damLev.GetSimilarity(nameCorrect, nameError);
/****************************************************/
/* Jaro, Jaro-Winler */
/****************************************************/
// Jaro, Jaro-Winkler implements only ISimilarity
var nameFirst = new Token("dwayne");
var nameSecond = new Token("duane");
var jaro = new Jaro();
var jaroWinkler = new JaroWinkler();
jaroWinkler.GetSimilarity(nameFirst, nameSecond);
/****************************************************/
/* Q-grams coefficient */
/****************************************************/
// q-grams similarity coefficient - Dice, Jaccard, Overlap
// with different q-grams type
var diceUnigrams = new DiceCoefficient <Bigram>();
var jaccardBigrams = new JaccardCoefficient <Bigram>();
var overlapTrigrams = new OverlapCoefficient <Bigram>();
// returns score 0.5
var jaccardSim = jaccardBigrams.GetSimilarity(pattern, targetText);
// returns score 0.67
var diceSim = diceUnigrams.GetSimilarity(pattern, targetText);
// returns score 1.0
var overlapSim = overlapTrigrams.GetSimilarity(pattern, targetText);
Console.ReadKey();
}
