public static double[,] ToCorrelation(this CovarianceMatrix covariancematrix)
{
var normalized = new double[covariancematrix.RowCount, covariancematrix.ColumnCount];
for (int r = 0; r < covariancematrix.RowCount; r++)
{
for (int c = 0; c < covariancematrix.ColumnCount; c++)
{
normalized[r, c] = covariancematrix[r, c] / (System.Math.Sqrt(covariancematrix[r, r]) * System.Math.Sqrt(covariancematrix[c, c]));
}
}
return(normalized);
}
/// <summary>
/// Create a covariance matrix from a delimited file
/// </summary>
/// <param name="filename">The name of the file from which to read</param>
/// <param name="delimiter">The delimiter to use - default ','</param>
/// <returns></returns>
public static CovarianceMatrix Create(string filename, char delimiter = ',')
{
CsvFileDescription inputFileDescription = new CsvFileDescription
{
SeparatorChar = delimiter, // comma delimited
FirstLineHasColumnNames = false // column names in first record
};
CsvContext cc = new CsvContext();
var cov = cc.Read <CsvDataRow>(filename, inputFileDescription);
var heading = from line in cov
from item in line
where item.LineNbr == 1
select item.Value;
var tickers = from t in heading.Skip(1)
select t;
var covdict = new Dictionary <string, Dictionary <string, double> >();
foreach (var row in cov.Skip(1))
{
var ticker = row.First().Value;
var values = row.Skip(1);
int count = 0;
var tempdict = new Dictionary <string, double>();
foreach (var v in values)
{
tempdict.Add(tickers.ToArray()[count], Convert.ToDouble(v.Value));
count++;
}
covdict.Add(ticker, tempdict);
}
return(CovarianceMatrix.Create(covdict));
}