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Signature Description Parameters 
template<typename T>
Matrix<T, matrix_orient::column_major>
covariance_matrix(std::vector<const char *> &&col_names,
                  normalization_type norm_type =
                      normalization_type::none) const;
This calculates and returns the variance/covariance matrix of the specified columns, optionally normalizing the columns first.
If you normalize the data with z-score method first, you will get a correlation matrix.
 T: Type of the named columns
col_names: Vector of column names
norm_type: The method to normalize the columns first before calculations. Default is not normalizing
 
static void test_covariance_matrix()  {

    std::cout << "\nTesting covariance_matrix( ) ..." << std::endl;

    StrDataFrame    df;

    try  {
        df.read("IBM.csv", io_format::csv2);
    }
    catch (const DataFrameError &ex)  {
        std::cout << ex.what() << std::endl;
    }

    const auto  cov_mat = df.covariance_matrix<double>({ "IBM_Close", "IBM_Open", "IBM_High", "IBM_Low" });

    assert(cov_mat.rows() == 4);
    assert(cov_mat.cols() == 4);
    assert(std::fabs(cov_mat(0, 0) - 1467.58) < 0.01);
    assert(std::fabs(cov_mat(0, 2) - 1469.69) < 0.01);
    assert(std::fabs(cov_mat(2, 1) - 1469.48) < 0.01);
    assert(std::fabs(cov_mat(2, 2) - 1472.86) < 0.01);
    assert(std::fabs(cov_mat(3, 2) - 1466.15) < 0.01);
    assert(std::fabs(cov_mat(3, 3) - 1461.0) < 0.01);

    const auto  cov_mat2 = df.covariance_matrix<double>({ "IBM_Close", "IBM_Open", "IBM_High", "IBM_Low" },
                                                        normalization_type::z_score);

    assert(cov_mat2.rows() == 4);
    assert(cov_mat2.cols() == 4);
    assert(std::fabs(cov_mat2(0, 0) - 1.0) < 0.01);
    assert(std::fabs(cov_mat2(0, 2) - 0.99964) < 0.00001);
    assert(std::fabs(cov_mat2(2, 1) - 0.99963) < 0.00001);
    assert(std::fabs(cov_mat2(2, 2) - 1.0) < 0.01);
    assert(std::fabs(cov_mat2(3, 2) - 0.99948) < 0.00001);
    assert(std::fabs(cov_mat2(3, 3) - 1.0) < 0.01);
}

C++ DataFrame