Struct opendp::measures::ZeroConcentratedDivergence

pub struct ZeroConcentratedDivergence<Q>(PhantomData<fn() -> Q>);

$\rho$-zero concentrated differential privacy.

The greatest zero-concentrated divergence between any randomly selected subset of the support.

Proof Definition

d-closeness

For any two vectors $u, v \in \texttt{D}$ and any $d$ of generic type $\texttt{Q}$, define $P$ and $Q$ to be the distributions of $M(u)$ and $M(v)$. We say that $u, v$ are $d$-close under the alpha-Renyi divergence measure (abbreviated as $D_{\alpha}$) whenever

D_{\alpha}(P \| Q) = \frac{1}{1 - \alpha} \mathbb{E}_{x \sim Q} \Big[\ln \left( \dfrac{P(x)}{Q(x)} \right)^\alpha \Big] \leq d \alpha.

for all possible choices of $\alpha \in (1, \infty)$.

Tuple Fields

0: PhantomData<fn() -> Q>

Trait Implementations

impl<Q: InfAdd + Zero + Clone> BasicCompositionMeasure for ZeroConcentratedDivergence<Q>

fn concurrent(&self) -> Fallible<bool>

fn compose(&self, d_i: Vec<Self::Distance>) -> Fallible<Self::Distance>

impl<Q> Clone for ZeroConcentratedDivergence<Q>

fn clone(&self) -> Self

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<Q> Debug for ZeroConcentratedDivergence<Q>

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<Q> Default for ZeroConcentratedDivergence<Q>

fn default() -> Self

Returns the “default value” for a type.

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<i128>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<i16>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<i32>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<i64>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<i8>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<isize>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<u128>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<u16>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<u32>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<u64>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<u8>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for AtomDomain<usize>

where f32: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<i128>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<i16>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<i32>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<i64>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<i8>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<isize>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<u128>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<u16>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<u32>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<u64>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<u8>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f32>, QI> for VectorDomain<AtomDomain<usize>>

where f32: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl GaussianDomain<ZeroConcentratedDivergence<f32>, f32> for AtomDomain<f32>

type InputMetric = AbsoluteDistance<f32>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl GaussianDomain<ZeroConcentratedDivergence<f32>, f32> for VectorDomain<AtomDomain<f32>>

type InputMetric = LpDistance<2, f32>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f32, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f32>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<i128>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<i16>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<i32>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<i64>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<i8>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<isize>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<u128>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<u16>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<u32>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<u64>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<u8>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for AtomDomain<usize>

where f64: InfCast<QI>,

type InputMetric = AbsoluteDistance<QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<i128>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<i16>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<i32>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<i64>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<i8>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<isize>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<u128>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<u16>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<u32>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<u64>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<u8>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<QI: Number> GaussianDomain<ZeroConcentratedDivergence<f64>, QI> for VectorDomain<AtomDomain<usize>>

where f64: InfCast<QI>,

type InputMetric = LpDistance<2, QI>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl GaussianDomain<ZeroConcentratedDivergence<f64>, f64> for AtomDomain<f64>

type InputMetric = AbsoluteDistance<f64>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl GaussianDomain<ZeroConcentratedDivergence<f64>, f64> for VectorDomain<AtomDomain<f64>>

type InputMetric = LpDistance<2, f64>

fn make_gaussian( input_domain: Self, input_metric: Self::InputMetric, scale: f64, k: Option<i32> ) -> Fallible<Measurement<Self, Self::Carrier, Self::InputMetric, ZeroConcentratedDivergence<f64>>>

impl<MI, QO> GaussianMeasure<MI> for ZeroConcentratedDivergence<QO>

where MI: Metric, MI::Distance: Number, QO: Float + InfCast<MI::Distance>, RBig: TryFrom<QO>,

type Atom = QO

fn new_forward_map( scale: Self::Atom, relaxation: Self::Atom ) -> Fallible<PrivacyMap<MI, Self>>

impl<Q> Measure for ZeroConcentratedDivergence<Q>

type Distance = Q

Proof Definition

impl<Q> PartialEq for ZeroConcentratedDivergence<Q>

fn eq(&self, _other: &Self) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.