cov.hpp

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#pragma once
 
#include <type_traits>
 
#include "NumCpp/Core/Enums.hpp"
#include "NumCpp/Core/Internal/StaticAsserts.hpp"
#include "NumCpp/Functions/mean.hpp"
#include "NumCpp/NdArray.hpp"
 
namespace nc
{
    //============================================================================
    // Method Description:
    template<typename dtype>
    NdArray<double> cov(const NdArray<dtype>& x, Bias bias = Bias::NO)
    {
        STATIC_ASSERT_ARITHMETIC(dtype);
 
        const auto varMeans = mean(x, Axis::COL);
        const auto numVars  = x.numRows();
        const auto numObs   = x.numCols();
        const auto normilizationFactor =
            bias == Bias::YES ? static_cast<double>(numObs) : static_cast<double>(numObs - 1);
        using IndexType = typename std::remove_const<decltype(numVars)>::type;
 
        // upper triangle
        auto covariance = NdArray<double>(numVars);
        for (IndexType i = 0; i < numVars; ++i)
        {
            const auto var1Mean = varMeans[i];
 
            for (IndexType j = i; j < numVars; ++j)
            {
                const auto var2Mean = varMeans[j];
 
                double sum = 0.;
                for (IndexType iObs = 0; iObs < numObs; ++iObs)
                {
                    sum += (x(i, iObs) - var1Mean) * (x(j, iObs) - var2Mean);
                }
 
                covariance(i, j) = sum / normilizationFactor;
            }
        }
 
        // fill in the rest of the symmetric matrix
        for (IndexType j = 0; j < numVars; ++j)
        {
            for (IndexType i = j + 1; i < numVars; ++i)
            {
                covariance(i, j) = covariance(j, i);
            }
        }
 
        return covariance;
    }
    NdArray<double> cov(const NdArray<dtype>& x, Bias bias = Bias::NO) {…}
} // namespace nc