private void SetAverageCoordinates(Centroid centroid)
{
var samplePoints = SamplePoints.Where(point => point.NearsetPointId == centroid.Id).ToArray();
if (samplePoints.Length != 0)
{
List <double> averages = new List <double>();
for (var i = 0; i < samplePoints.First().Coordinates.Count; i++)
{
averages.Add(0.0);
}
foreach (var samplePoint in samplePoints)
{
for (var i = 0; i < averages.Count; i++)
{
averages[i] += samplePoint.Coordinates[i];
}
}
for (var i = 0; i < averages.Count; i++)
{
averages[i] /= samplePoints.Length;
}
MaxCentroidShift = Euclides.CalculateDistance(new SamplePoint(averages),
centroid);
centroid.Coordinates = averages;
}
}
private double MeanDistanceToKNearestCentroids(Centroid centroid)
{
List <double> distances = new List <double>();
foreach (var examinedCentorid in Centroids.Where(e => centroid.Id != e.Id))
{
distances.Add(Euclides.CalculateDistance(examinedCentorid, centroid));
}
distances.Sort();
return(distances.Where(e => e <= distances[NeighbourNumber]).Sum() / NeighbourNumber);
}
public double CalculateNetworkOutput(SamplePoint samplePoint)
{
List <double> hiddenLayerOutput = new List <double>();
for (int i = 0; i < HiddenNeuronsNumber; i++)
{
hiddenLayerOutput.Add(BasisFunction(Euclides.CalculateDistance(samplePoint, Centroids[i]),
_betas[i]));
}
_neuron.Inputs = hiddenLayerOutput;
_neuron.CalculateOutput();
return(_neuron.Output);
}
private void CalculateHiddenLayerOutputs()
{
_hiddenLayerOutputs.Clear();
for (var j = 0; j < SamplePoints.Count; j++)
{
SamplePoint samplePoint = SamplePoints[j];
_hiddenLayerOutputs.Add(new List <double>());
for (int i = 0; i < HiddenNeuronsNumber; i++)
{
_hiddenLayerOutputs[j]
.Add(BasisFunction(Euclides.CalculateDistance(samplePoint, Centroids[i]), _betas[i]));
}
}
}
private int GetTheNearestCentroidsId(SamplePoint samplePoint)
{
int nearestCentroidId = Centroids.First().Id;
double nearestDistance = Euclides.CalculateDistance(samplePoint, Centroids.First());
foreach (var centroid in Centroids)
{
double distance = Euclides.CalculateDistance(samplePoint, centroid);
if (distance < nearestDistance)
{
nearestCentroidId = centroid.Id;
nearestDistance = distance;
}
}
return(nearestCentroidId);
}
private void generarLlavesPrivadas()
{
bool bandera;
//Se obtiene el modulo
n = ToBigInteger(operaciones.obtenerModulo(1));
// Se genera na que es un no invertible
do
{
nb = RandomBigInteger(2, n);
} while (Euclides.moduloInverso(n, nb));
// Se genera ka1 que es un invertible
do
{
kb1 = RandomBigInteger(2, n);
bandera = Euclides.moduloInverso(n, kb1);
kb1_inv = Euclides.inv;
} while (!bandera);
// Se genera ka2 que es un invertible
do
{
kb2 = RandomBigInteger(2, n);
bandera = Euclides.moduloInverso(n, kb2);
kb2_inv = Euclides.inv;
if (bandera)
{
if (kb2 == kb1)
{
bandera = false;
}
}
} while (!bandera);
// Se genera ka3 que es un invertible
do
{
kb3 = RandomBigInteger(2, n);
bandera = Euclides.moduloInverso(n, kb3);
kb3_inv = Euclides.inv;
if (bandera)
{
if (kb2 == kb3 || kb3 == kb1)
{
bandera = false;
}
}
} while (!bandera);
}
