fingerprint.cpp

#include <iostream>
#include <string>

using namespace std;

#include "book.h"
#include "fingerprint.h"

Fingerprint getFingerprint(string text)
{
    Fingerprint f;
    for (int i = 0; i < N_GREEK_LETTERS; ++i)
        for (int j = 0; j < N_GREEK_LETTERS; ++j) {
            f.m_data[i][j] = 0;
        }
    for (int i = 0; i < text.length() - 1; ++i) {
        char first = text.at(i) - 'a';
        char second = text.at(i + 1) - 'a';
        (f.m_data[first][second])++;
    }
    /*
  int u = 0;
  printf("Unused: ");
  for (int i=0; i<N; ++i)
    for (int j=0; j<N; ++j) {
      if (f.data[i][j] == 0) {
        printf("%d-%d ",i,j);
        u++;
      }
    }
  printf("(%d instances)\n", u);
  */

    return f;
}

Fingerprint getFingerprint(Book b, int start, int length)
{
    string text = b.getText().substr(start, length);
    return getFingerprint(text);
}

int dist(Fingerprint f1, Fingerprint f2)
{
    int d = 0;
    for (int i = 0; i < N_GREEK_LETTERS; ++i)
        for (int j = 0; j < N_GREEK_LETTERS; ++j) {
            d += abs(f1.m_data[i][j] - f2.m_data[i][j]);
        }
    return d;
}

void printDist(Fingerprint f1, Fingerprint f2)
{
    cout << " ";
    for (int j = 0; j < N_GREEK_LETTERS; ++j)
        printf("%c", j + 'a');
    cout << endl;
    for (int i = 0; i < N_GREEK_LETTERS; ++i) {
        printf("%c", i + 'a');
        for (int j = 0; j < N_GREEK_LETTERS; ++j) {
            if (f1.m_data[i][j] == 0 && f2.m_data[i][j] == 0)
                cout << " ";
            else
                printf("%1d", abs(f1.m_data[i][j] - f2.m_data[i][j]));
        }
        cout << endl;
    }
}

int dist(const string &text1, const string &text2)
{
    return dist(getFingerprint(text1), getFingerprint(text2));
}

double jaccard_dist(const string &text1, const string &text2)
{
    int d1 = 0;
    int d2 = 0;
    int d = 0;
    Fingerprint f1 = getFingerprint(text1);
    Fingerprint f2 = getFingerprint(text2);
    for (int i = 0; i < N_GREEK_LETTERS; ++i)
        for (int j = 0; j < N_GREEK_LETTERS; ++j) {
            d1 += f1.m_data[i][j];
            d2 += f2.m_data[i][j];
            d += min(f1.m_data[i][j], f2.m_data[i][j]);
        }
    /* We compute the Jaccard similarity for bags
     * (see Leskovec, Rajamaran, Ullman: Mining of massive datasets,
     * Cambridge Univ. Press, 2014, p. 77, footnote 2)
     * with the redefinition of the union of bags.
     * Then we compute the Jaccard distance.
     */
    return 1 - ((double) d) / max(d1, d2);
}