compute_statistical_values()

template<ComputeData InputType = std::vector<Kakshya::DataVariant>, ComputeData OutputType = Eigen::VectorXd>

std::vector< double > MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::compute_statistical_values ( std::span< const double >  data, StatisticalMethod  method  ) const

inlineprivate

Compute statistical values using span (zero-copy processing)

Definition at line 486 of file StatisticalAnalyzer.hpp.

    {
        const size_t num_windows = calculate_num_windows(data.size());
 
        namespace D = MayaFlux::Kinesis::Discrete;
 
        switch (method) {
        case StatisticalMethod::MEAN:
            return D::mean(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::VARIANCE:
            return D::variance(data, num_windows, m_hop_size, m_window_size, m_sample_variance);
        case StatisticalMethod::STD_DEV:
            return D::std_dev(data, num_windows, m_hop_size, m_window_size, m_sample_variance);
        case StatisticalMethod::SKEWNESS:
            return D::skewness(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::KURTOSIS:
            return D::kurtosis(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::MEDIAN:
            return D::median(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::PERCENTILE:
            return D::percentile(data, num_windows, m_hop_size, m_window_size, m_percentile_value);
        case StatisticalMethod::ENTROPY:
            return D::entropy(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::MIN:
            return D::min(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::MAX:
            return D::max(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::RANGE:
            return D::range(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::SUM:
            return D::sum(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::COUNT:
            return D::count(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::RMS:
            return D::rms(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::MAD:
            return D::mad(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::CV:
            return D::coefficient_of_variation(data, num_windows, m_hop_size, m_window_size, m_sample_variance);
        case StatisticalMethod::MODE:
            return D::mode(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::ZSCORE:
            return D::mean_zscore(data, num_windows, m_hop_size, m_window_size, m_sample_variance);
        default:
            return D::mean(data, num_windows, m_hop_size, m_window_size);
        }
    }