private static double EstimateCharacters(String s, IWeightEstimator e)
{
double sum = 0.0;
for (int ix = 0; ix < s.Length; ix++)
{
sum += Math.Min(e.Insert(s[ix]), e.Delete(s[ix]));
}
return(sum);
}
public static double Distance(String s1, String s2, IWeightEstimator weight)
{
int s1Len = s1.Length;
if (s1Len == 0)
{
return(EstimateCharacters(s2, weight));
}
if (s2.Length == 0)
{
return(EstimateCharacters(s1, weight));
}
// we use a flat array for better performance. we address it by
// s1ix + s1len * s2ix. this modification improves performance
// by about 30%, which is definitely worth the extra complexity.
var matrix = new double[(s1Len + 1) * (s2.Length + 1)];
for (int col = 0; col <= s2.Length; col++)
{
matrix[col * s1Len] = col;
}
for (int row = 0; row <= s1Len; row++)
{
matrix[row] = row;
}
for (int ix1 = 0; ix1 < s1Len; ix1++)
{
char ch1 = s1[ix1];
for (int ix2 = 0; ix2 < s2.Length; ix2++)
{
double cost;
char ch2 = s2[ix2];
cost = ch1 == ch2 ? 0 : weight.Substitute(ch1, s2[ix2]);
double left = matrix[ix1 + ((ix2 + 1) * s1Len)] +
weight.Delete(ch1);
double above = matrix[ix1 + 1 + (ix2 * s1Len)] +
weight.Insert(ch2);
double aboveleft = matrix[ix1 + (ix2 * s1Len)] + cost;
matrix[ix1 + 1 + ((ix2 + 1) * s1Len)] =
Math.Min(left, Math.Min(above, aboveleft));
}
}
// for (int ix1 = 0; ix1 <= s1len; ix1++) {
// for (int ix2 = 0; ix2 <= s2.length(); ix2++) {
// System.out.print(matrix[ix1 + (ix2 * s1len)] + " ");
// }
// System.out.println();
// }
return(matrix[s1Len + (s2.Length * s1Len)]);
}
public static double Distance(String s1, String s2, IWeightEstimator weight)
{
int s1Len = s1.Length;
if (s1Len == 0)
return EstimateCharacters(s2, weight);
if (s2.Length == 0)
return EstimateCharacters(s1, weight);
// we use a flat array for better performance. we address it by
// s1ix + s1len * s2ix. this modification improves performance
// by about 30%, which is definitely worth the extra complexity.
var matrix = new double[(s1Len + 1)*(s2.Length + 1)];
for (int col = 0; col <= s2.Length; col++)
matrix[col*s1Len] = col;
for (int row = 0; row <= s1Len; row++)
matrix[row] = row;
for (int ix1 = 0; ix1 < s1Len; ix1++)
{
char ch1 = s1[ix1];
for (int ix2 = 0; ix2 < s2.Length; ix2++)
{
double cost;
char ch2 = s2[ix2];
cost = ch1 == ch2 ? 0 : weight.Substitute(ch1, s2[ix2]);
double left = matrix[ix1 + ((ix2 + 1)*s1Len)] +
weight.Delete(ch1);
double above = matrix[ix1 + 1 + (ix2*s1Len)] +
weight.Insert(ch2);
double aboveleft = matrix[ix1 + (ix2*s1Len)] + cost;
matrix[ix1 + 1 + ((ix2 + 1)*s1Len)] =
Math.Min(left, Math.Min(above, aboveleft));
}
}
// for (int ix1 = 0; ix1 <= s1len; ix1++) {
// for (int ix2 = 0; ix2 <= s2.length(); ix2++) {
// System.out.print(matrix[ix1 + (ix2 * s1len)] + " ");
// }
// System.out.println();
// }
return matrix[s1Len + (s2.Length*s1Len)];
}
private static double EstimateCharacters(String s, IWeightEstimator e)
{
double sum = 0.0;
for (int ix = 0; ix < s.Length; ix++)
sum += Math.Min(e.Insert(s[ix]), e.Delete(s[ix]));
return sum;
}
public void SetEstimator(IWeightEstimator estimator)
{
_estimator = estimator;
}
public WeightedLevenshtein()
{
_estimator = new DefaultWeightEstimator();
}
public void SetEstimator(IWeightEstimator estimator)
{
_estimator = estimator;
}
public WeightedLevenshtein()
{
_estimator = new DefaultWeightEstimator();
}
