set_analysis_parameter()

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

void MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::set_analysis_parameter ( const std::string &  name, std::any  value  )

inlineoverrideprotectedvirtual

Handle analysis-specific parameters.

Reimplemented from MayaFlux::Yantra::UniversalAnalyzer< InputType, OutputType >.

Definition at line 396 of file StatisticalAnalyzer.hpp.

    {
        try {
            if (name == "method") {
                try {
                    auto method_str = safe_any_cast_or_throw<std::string>(value);
                    m_method = string_to_method(method_str);
                } catch (const std::runtime_error&) {
                    auto method_enum = safe_any_cast_or_throw<StatisticalMethod>(value);
                    m_method = method_enum;
                }
            } else if (name == "window_size") {
                auto size = safe_any_cast_or_throw<uint32_t>(value);
                m_window_size = size;
                validate_window_parameters();
            } else if (name == "hop_size") {
                auto size = safe_any_cast_or_throw<uint32_t>(value);
                m_hop_size = size;
                validate_window_parameters();
            } else if (name == "classification_enabled") {
                auto enabled = safe_any_cast_or_throw<bool>(value);
                m_classification_enabled = enabled;
            } else if (name == "percentile") {
                auto percentile = safe_any_cast_or_throw<double>(value);
                if (percentile < 0.0 || percentile > 100.0) {
                    error<std::invalid_argument>(Journal::Component::Yantra, Journal::Context::ComputeMatrix, std::source_location::current(), "Percentile must be between 0.0 and 100.0, got: {}", percentile);
                }
                m_percentile_value = percentile;
            } else if (name == "sample_variance") {
                auto sample = safe_any_cast_or_throw<bool>(value);
                m_sample_variance = sample;
            } else {
                base_type::set_analysis_parameter(name, std::move(value));
            }
        } catch (const std::runtime_error& e) {
            error_rethrow(Journal::Component::Yantra, Journal::Context::ComputeMatrix, std::source_location::current(), "Failed to set parameter '{}': {}", name, e.what());
        }
    }

References MayaFlux::Journal::ComputeMatrix, MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::m_classification_enabled, MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::m_hop_size, MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::m_method, MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::m_percentile_value, MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::m_sample_variance, MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::m_window_size, MayaFlux::Yantra::UniversalAnalyzer< InputType, OutputType >::set_analysis_parameter(), MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::string_to_method(), MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::validate_window_parameters(), and MayaFlux::Journal::Yantra.

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