/// <summary>
/// 相関係数の行列を得る
/// </summary>
/// <param name="designMatrix"></param>
/// <returns></returns>
public static double[,] Corelation(double[,] designMatrix)
{
double[] sum = new double[designMatrix.GetLength(1)];
for (int i = 0; i < designMatrix.GetLength(0); i++)
{
for (int j = 0; j < designMatrix.GetLength(1); j++)
{
sum[j] += designMatrix[i, j];
}
}
double[,] sumMultiply = new double[designMatrix.GetLength(1), designMatrix.GetLength(1)];
for (int i = 0; i < designMatrix.GetLength(0); i++)
{
for (int j = 0; j < designMatrix.GetLength(1); j++)
{
for (int j2 = j; j2 < designMatrix.GetLength(1); j2++)
{
sumMultiply[j, j2] += designMatrix[i, j] * designMatrix[i, j2];
}
}
}
//共分散行列
double[,] varianceCovarianceMatrix = new double[designMatrix.GetLength(1), designMatrix.GetLength(1)];
for (int j = 0; j < designMatrix.GetLength(1); j++)
{
for (int j2 = j; j2 < designMatrix.GetLength(1); j2++)
{
//varianceMatrix[j, j2] = sumMultiply[j, j2] / designMatrix.GetLength(0) - sum[j] * sum[j2] / designMatrix.GetLength(0) / designMatrix.GetLength(0);
//丸め込み誤差対策
//varianceMatrix[j, j2] = (sumMultiply[j, j2] - sum[j] * sum[j2] / designMatrix.GetLength(0)) / designMatrix.GetLength(0);
//相関係数を計算する場合、行ベクトル数nの割り算を省略できる
varianceCovarianceMatrix[j, j2] = sumMultiply[j, j2] - sum[j] * sum[j2] / designMatrix.GetLength(0);
}
}
double[,] average = DesignMatrix.Average(designMatrix);
double[,] corelationMatrix = new double[designMatrix.GetLength(1), designMatrix.GetLength(1)];
for (int j = 0; j < designMatrix.GetLength(1); j++)
{
for (int j2 = j; j2 < designMatrix.GetLength(1); j2++)
{
corelationMatrix[j, j2] = varianceCovarianceMatrix[j, j2] / Math.Sqrt(varianceCovarianceMatrix[j, j] * varianceCovarianceMatrix[j2, j2]);
corelationMatrix[j2, j] = corelationMatrix[j, j2];
}
}
return(corelationMatrix);
}
/// <summary>
/// 共分散行列の逆行列
/// Calculate Inverse of the covariance matrix.
/// </summary>
/// <param name="designMatrix"></param>
/// <returns></returns>
public static double[,] InverseVarianceCovarianceMatrix(double[,] designMatrix)
{
return(Matrix.Inverse(DesignMatrix.VarianceCovariance(designMatrix)));
}
