public RealMatrix CovarianceToCorrelation(RealMatrix covarianceMatrix)
{
int nVars = covarianceMatrix.getColumnDimension();
RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
for (int i = 0; i < nVars; i++)
{
double sigma = Math.Sqrt(covarianceMatrix.getEntry(i, i));
outMatrix.setEntry(i, i, 1.0D);
for (int j = 0; j < i; j++)
{
double entry = covarianceMatrix.getEntry(i, j) / (sigma * Math.Sqrt(covarianceMatrix.getEntry(j, j)));
outMatrix.setEntry(i, j, entry);
outMatrix.setEntry(j, i, entry);
}
}
return(outMatrix);
}
public RealMatrix ComputeCorrelationMatrix(RealMatrix matrix)
{
CheckSufficientData(matrix);
int nVars = matrix.getColumnDimension();
RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
for (int i = 0; i < nVars; i++)
{
for (int j = 0; j < i; j++)
{
double corr = Correlation(matrix.getColumn(i), matrix.getColumn(j));
outMatrix.setEntry(i, j, corr);
outMatrix.setEntry(j, i, corr);
}
outMatrix.setEntry(i, i, 1.0D);
}
return(outMatrix);
}
protected RealMatrix ComputeCovarianceMatrix(RealMatrix matrix, bool biasCorrected) //throws MathIllegalArgumentException
{
int dimension = matrix.getColumnDimension();
Variance variance = new Variance(biasCorrected);
RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension);
for (int i = 0; i < dimension; i++)
{
for (int j = 0; j < i; j++)
{
double cov = GetCovarianve(matrix.getColumn(i), matrix.getColumn(j), biasCorrected);
outMatrix.setEntry(i, j, cov);
outMatrix.setEntry(j, i, cov);
}
outMatrix.setEntry(i, i, variance.Evaluate(matrix.getColumn(i)));
}
return(outMatrix);
}
