Struct SensitivityAnalyzer 

pub struct SensitivityAnalyzer { /* private fields */ }

Sensitivity analyzer

Implementations

impl SensitivityAnalyzer

pub fn new() -> Self

pub fn initialize(&mut self) -> Result<(), StatisticalError>

pub fn register_function(&mut self, name: &str, func: SensitivityFunction)

Register a named sensitivity function so it can be looked up by name from compute_sensitivity / get_sensitivity.

pub fn compute_sensitivity( &mut self, operation: &str, data: &[f64], ) -> Result<f64, StatisticalError>

Compute the L1 sensitivity of a statistical operation over data.

Sensitivity is the maximum change in the operation’s output when a single record is added or removed. The following closed-form approximations are used (each assumes a bounded domain where one record can shift a value by at most 1.0):

• mean: 1/n — one record moves the mean by 1/n.
• sum: 1.0 — one record changes the sum by at most 1.
• count: 1.0 — one record changes the count by 1.
• median: range / n — adjacent-element approximation.
• variance: (max-min)^2 / n — bounded shift approximation.
• histogram: 1.0 — one record changes a single bin by 1.

Results are cached keyed by operation so repeated DP queries reuse the computed sensitivity.

pub fn get_sensitivity( &mut self, operation: &str, data: &[f64], ) -> Result<f64, StatisticalError>

Get the sensitivity for an operation, returning the cached value when available and computing (and caching) it otherwise.