public void ngramset_edit_distance(List <string> mnemonics1, List <string> mnemonics2, int n)
{
Ngram ngram1 = new Ngram(mnemonics1, n);
Ngram ngram2 = new Ngram(mnemonics2, n);
int set_size = ngram1.ngramSet.Count;
//int reduced_set_size = set_size - Math.Max(mnemonics1.FindIndex(delegate (string data) { return data == ""; }), mnemonics2.FindIndex(delegate (string data) { return data == ""; }));
int ngram_size = n;
int[,] matrix = ngram_matrix(ngram1.ngramSet, ngram2.ngramSet);
var hungarian = new HungarianAlgorithm(matrix);
int[] hungarian_indexes = hungarian.Run();
double ngram_edit_distance = getTotal(matrix, hungarian_indexes) / set_size / ngram_size;
double edit_distance = calc_edit_distance(mnemonics1, mnemonics2);
double slope1_ratio = calculate_slope1_ratio(hungarian_indexes);
double index_similarity2 = calculate_continuous_equal_slope(hungarian_indexes, 2);
double index_similarity3 = calculate_continuous_equal_slope(hungarian_indexes, 3);
this.similarity = new ngram_similarity(ngram_edit_distance, edit_distance, slope1_ratio, index_similarity2, index_similarity3);
}
private int[,] ngram_matrix(List <List <string> > set1, List <List <string> > set2)
{
int standard = set1.Count() / 2;
int[,] matrix = new int[set1.Count(), set2.Count()];
Ngram ngram = new Ngram();
double count = set1[0].Count();
for (int i = 0; i < set1.Count(); i++)
{
for (int j = 0; j < set2.Count(); j++)
{
int lcs = ngram.lcs(set1[i], set2[j]);
if (lcs < (count * 0.7))
{
lcs = 0;
}
matrix[i, j] = (2 * standard) - lcs;
}
}
return(matrix);
}
public SimilarityAnalyzer(string output_file_name)
{
ngram = new Ngram();
timer = new Stopwatch();
}
public PackerIdentificator()
{
ngram = new Ngram();
}