Struct smartnoise_runtime::proto::Covariance

pub struct Covariance {
    pub finite_sample_correction: bool,
}

Covariance Component

Calculate covariance.

If data argument is provided as a 2D array, calculate covariance matrix. Otherwise, left and right 1D arrays are used to calculate a cross-covariance matrix between elements of the two arrays.

This struct represents an abstract computation. Arguments are provided via the graph. Additional options are set via the fields on this struct. The return is the result of the covariance on the arguments.

Arguments

• data - Array - 2D data array used to construct covariance matrix.
• left - Array - Left data array used to calculate cross-covariance matrix. Used only if data not provided.
• right - Array - Right data array used to calculate cross-covariance matrix. Used only if data not provided.

Returns

• Value - Array - Flattened covariance or cross-covariance matrix.

Fields

finite_sample_correction: bool

Whether or not to use the finite sample correction (Bessel’s correction).

Trait Implementations

impl Clone for Covariance

pub fn clone(&self) -> Covariance

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Component for Covariance

pub fn propagate_property(
    &self,
    _privacy_definition: &Option<PrivacyDefinition>,
    _public_arguments: IndexMap<IndexKey, &Value, RandomState>,
    properties: IndexMap<IndexKey, ValueProperties, RandomState>,
    node_id: u32
) -> Result<Warnable<ValueProperties>, Error>

Given properties known about private arguments, and public arguments, derive properties about the resulting data.

impl Debug for Covariance

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

Formats the value using the given formatter.

impl Default for Covariance

pub fn default() -> Covariance

Returns the “default value” for a type.

impl Evaluable for Covariance

fn evaluate(
    &self,
    _privacy_definition: &Option<PrivacyDefinition>,
    arguments: NodeArguments
) -> Result<ReleaseNode>

The concrete implementation of the abstract computation that the struct represents.

impl Message for Covariance

pub fn encode_raw<B>(&self, buf: &mut B) where
    B: BufMut, 

pub fn merge_field<B>(
    &mut self,
    tag: u32,
    wire_type: WireType,
    buf: &mut B,
    ctx: DecodeContext
) -> Result<(), DecodeError> where
    B: Buf, 

pub fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

pub fn clear(&mut self)

Clears the message, resetting all fields to their default.

pub fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
    B: BufMut, 

Encodes the message to a buffer.

pub fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
    B: BufMut, 

Encodes the message with a length-delimiter to a buffer.

pub fn decode<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

Decodes an instance of the message from a buffer.

pub fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

Decodes a length-delimited instance of the message from the buffer.

pub fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError> where
    B: Buf, 

Decodes an instance of the message from a buffer, and merges it into self.

pub fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError> where
    B: Buf, 

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq<Covariance> for Covariance

pub fn eq(&self, other: &Covariance) -> bool

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

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

This method tests for !=.

impl Sensitivity for Covariance

pub fn compute_sensitivity(
    &self,
    privacy_definition: &PrivacyDefinition,
    properties: &IndexMap<IndexKey, ValueProperties, RandomState>,
    sensitivity_type: &SensitivitySpace
) -> Result<Value, Error>

Covariance sensitivities are backed by the the proofs here.

impl StructuralPartialEq for Covariance