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());
 
        switch (method) {
        case StatisticalMethod::MEAN:
            return compute_mean_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::VARIANCE:
            return compute_variance_statistic(data, num_windows, m_hop_size, m_window_size, m_sample_variance);
        case StatisticalMethod::STD_DEV:
            return compute_std_dev_statistic(data, num_windows, m_hop_size, m_window_size, m_sample_variance);
        case StatisticalMethod::SKEWNESS:
            return compute_skewness_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::KURTOSIS:
            return compute_kurtosis_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::MEDIAN:
            return compute_median_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::PERCENTILE:
            return compute_percentile_statistic(data, num_windows, m_hop_size, m_window_size, m_percentile_value);
        case StatisticalMethod::ENTROPY:
            return compute_entropy_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::MIN:
            return compute_min_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::MAX:
            return compute_max_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::RANGE:
            return compute_range_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::SUM:
            return compute_sum_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::COUNT:
            return compute_count_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::RMS:
            return compute_rms_energy(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::MAD:
            return compute_mad_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::CV:
            return compute_cv_statistic(data, num_windows, m_hop_size, m_window_size, m_sample_variance);
        case StatisticalMethod::MODE:
            return compute_mode_statistic(data, num_windows, m_hop_size, m_window_size);
        case StatisticalMethod::ZSCORE:
            return compute_zscore_statistic(data, num_windows, m_hop_size, m_window_size, m_sample_variance);
        default:
            return compute_mean_statistic(data, num_windows, m_hop_size, m_window_size);
        }
    }

References MayaFlux::Yantra::compute_count_statistic(), MayaFlux::Yantra::compute_cv_statistic(), MayaFlux::Yantra::compute_entropy_statistic(), MayaFlux::Yantra::compute_kurtosis_statistic(), MayaFlux::Yantra::compute_mad_statistic(), MayaFlux::Yantra::compute_max_statistic(), MayaFlux::Yantra::compute_mean_statistic(), MayaFlux::Yantra::compute_median_statistic(), MayaFlux::Yantra::compute_min_statistic(), MayaFlux::Yantra::compute_mode_statistic(), MayaFlux::Yantra::compute_percentile_statistic(), MayaFlux::Yantra::compute_range_statistic(), MayaFlux::Yantra::compute_rms_energy(), MayaFlux::Yantra::compute_skewness_statistic(), MayaFlux::Yantra::compute_std_dev_statistic(), MayaFlux::Yantra::compute_sum_statistic(), MayaFlux::Yantra::compute_variance_statistic(), and MayaFlux::Yantra::compute_zscore_statistic().

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