public static double GetManhattanDistance(this KDReading _reading, KDReading reading)
{
double sum = 0;
for (int i = 0; i < 26; i++)
{
sum += Math.Abs(_reading.LetterMeasurements[i] - reading.LetterMeasurements[i]);
}
return(sum);
}
public static double GetEuclideanDistance(this KDReading _reading, KDReading reading)
{
double sum = 0;
for (int i = 0; i < 26; i++)
{
sum += (_reading.LetterMeasurements[i] - reading.LetterMeasurements[i]) * (_reading.LetterMeasurements[i] - reading.LetterMeasurements[i]);
}
return(Math.Sqrt(sum));
}
public static double GetMahalanobisDistance(this KDReading _reading, KDReading[] userReadings)
{
int vectorSize = _reading.LetterMeasurements.Length,
setSize = userReadings.Length;
double[] v1 = Array.ConvertAll(_reading.LetterMeasurements, item => (double)item);
var vectors = userReadings.Select(reading => reading.LetterMeasurements);
var meanVector = vectors.Select(vector => Array.ConvertAll(vector, val => (double)val)).Mean();
var vectorOfDistancesFromMean = v1.Zip(meanVector, (a, b) => a - b);
double[,] matrixOfDistancesFromMean = new double[1, vectorSize],
matrixOfDistancesFromMeanTransposed = new double[vectorSize, 1];
for (int i = 0; i < vectorSize; i++)
{
matrixOfDistancesFromMean[0, i] = matrixOfDistancesFromMeanTransposed[i, 0] = vectorOfDistancesFromMean.ElementAt(i);
}
var covarianceMatrix = new double[vectorSize, vectorSize];
covarianceMatrix.Initialize();
for (int i = 0; i < setSize; i++)
{
var sample = Array.ConvertAll(userReadings[i].LetterMeasurements, val => (double)val);
var sampleDistanceFromMean = sample.Zip(meanVector, (a, b) => a - b);
double[,] matrixSampleDistanceFromMean = new double[1, vectorSize],
matrixSampleDistanceFromMeanTransposed = new double[vectorSize, 1];
for (int j = 0; j < vectorSize; j++)
{
matrixSampleDistanceFromMean[0, j] = matrixSampleDistanceFromMeanTransposed[j, 0] = sampleDistanceFromMean.ElementAt(j);
}
var product = matrixSampleDistanceFromMeanTransposed.Dot(matrixSampleDistanceFromMean);
covarianceMatrix = covarianceMatrix.Add(product);
}
covarianceMatrix = covarianceMatrix.Divide(setSize);
try
{
covarianceMatrix = covarianceMatrix.Inverse();
}
catch (Exception)
{
covarianceMatrix = covarianceMatrix.PseudoInverse();
}
var outputMatrix = matrixOfDistancesFromMean.Dot(covarianceMatrix).Dot(matrixOfDistancesFromMeanTransposed);
return(Math.Sqrt(outputMatrix[0, 0]));
}
public static double GetChebyshevDistance(this KDReading _reading, KDReading reading)
{
double max = 0;
for (int i = 0; i < 26; i++)
{
int difference = Math.Abs(_reading.LetterMeasurements[i] - reading.LetterMeasurements[i]);
if (difference > max)
{
max = difference;
}
}
return(max);
}
public static string EvaluateUserName(this KDReading _reading, KDReading[] userReadings, KDDistanceMethod distanceMethod, int?k)
{
IEnumerable <KDReading> orderedReadings;
switch (distanceMethod)
{
case KDDistanceMethod.Euclid:
orderedReadings = userReadings.OrderBy(reading => reading.GetEuclideanDistance(_reading));
break;
case KDDistanceMethod.Manhattan:
orderedReadings = userReadings.OrderBy(reading => reading.GetManhattanDistance(_reading));
break;
case KDDistanceMethod.Chebyshev:
orderedReadings = userReadings.OrderBy(reading => reading.GetChebyshevDistance(_reading));
break;
case KDDistanceMethod.Mahalanobis:
var groupedReadings = userReadings.GroupBy(reading => reading.Name);
var orderedReadingsGroupes = groupedReadings.OrderBy(group => _reading.GetMahalanobisDistance(group.ToArray()));
var user = orderedReadingsGroupes.First().First().Name;
//var output = new System.Text.StringBuilder();
//foreach (var group in orderedReadingsGroupes)
// output.Append(" " + group.First().Name + " " + _reading.GetMahalanobisDistance(group.ToArray()).ToString() + "\n");
//System.Windows.MessageBox.Show(output.ToString());
return(user);
default:
throw new ArgumentNullException("No distance calculation method selected");
}
var selectedReadings = orderedReadings.Take(k.Value).GroupBy(reading => reading.Name);
var maxCount = selectedReadings.Max(readings => readings.Count());
var recognizedUser = selectedReadings.Where(readings => readings.Count() == maxCount).First().First().Name;
return(recognizedUser);
}
