public void CalculateEigenVector()
{
var evd = JudgementMatrix.Evd();
var eigenVectors = evd.EigenVectors;
var eigenValues = evd.EigenValues;
DenseVector weight = new DenseVector(eigenValues.Count);
for (int i = 0; i < eigenValues.Count;i++ )
{
weight[i] = eigenValues[i].Real;
}
weight = weight / (weight.Sum());
WeightVector = weight;
maxEigenValue = WeightVector.AbsoluteMaximum();
if(WeightVector==null)
{
MessageBox.Show("判断矩阵错误，无法计算权重！");
}
}
public static double Covariance(DenseVector x, DenseVector y)
{
var cov = new DenseVector(x.Count);
double xMean = x.Mean();
double yMean = y.Mean();
if (xMean.Equals(double.NaN) || yMean.Equals(double.NaN))
return double.NaN;
for (int i = 0; i < x.Count; i++)
{
cov[i] = (x[i] - xMean)*(y[i] - yMean);
}
return ((double) 1/(x.Count - 1))*cov.Sum();
}
public static double Kurtosis(this double[] data)
{
var d = new DenseVector(data);
var num = new DenseVector(d.Count);
var denom = new DenseVector(d.Count);
d.CopyTo(num);
d.CopyTo(denom);
for (int i = 0; i < num.Count; i++)
{
num[i] = Math.Pow(d[i] - d.Mean(), 4);
denom[i] = Math.Pow(d[i] - d.Mean(), 2);
}
return (num.Sum()*num.Count)/(Math.Pow(denom.Sum(), 2));
}
