Struct GaussBN 

pub struct GaussBN { /* private fields */ }

A Gaussian Bayesian network.

Trait Implementations

impl AbsDiffEq for GaussBN

type Epsilon = f64

Used for specifying relative comparisons.

fn default_epsilon() -> Self::Epsilon

The default tolerance to use when testing values that are close together.

fn abs_diff_eq(&self, other: &Self, epsilon: Self::Epsilon) -> bool

A test for equality that uses the absolute difference to compute the approximate equality of two numbers.

fn abs_diff_ne(&self, other: &Rhs, epsilon: Self::Epsilon) -> bool

The inverse of AbsDiffEq::abs_diff_eq.

impl BN for GaussBN

type CPD = GaussCPD

The type of the CPD.

type Evidence = GaussEv

The type of the evidence.

type Sample = ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>

The type of the sample.

type Samples = GaussTable

The type of the samples.

fn new<I>(graph: DiGraph, cpds: I) -> Self

where I: IntoIterator<Item = Self::CPD>,

Constructs a new Bayesian network.

fn name(&self) -> Option<&str>

Returns the name of the model, if any.

fn description(&self) -> Option<&str>

Returns the description of the model, if any.

fn graph(&self) -> &DiGraph

Returns the underlying graph.

fn cpds(&self) -> &Map<String, Self::CPD>

Returns the a map labels-distributions.

fn parameters_size(&self) -> usize

Returns the parameters size.

fn topological_order(&self) -> &[usize]

Returns the topological order of the graph.

fn with_optionals<I>( name: Option<String>, description: Option<String>, graph: DiGraph, cpds: I, ) -> Self

where I: IntoIterator<Item = Self::CPD>,

Creates a new Bayesian network with optional fields.

impl<E> BNCausalInference<GaussBN> for CausalInference<'_, E>

where E: Modelled<GaussBN> + BNInference<GaussBN>,

fn cace_estimate( &self, x: &Set<usize>, y: &Set<usize>, z: &Set<usize>, ) -> Option<<GaussBN as BN>::CPD>

Estimate the conditional average causal effect of X on Y given Z as E(Y | do(X), Z).

fn ace_estimate(&self, x: &Set<usize>, y: &Set<usize>) -> Option<T::CPD>

Estimate the average causal effect of X on Y as E(Y | do(X)).

impl<R: Rng> BNInference<GaussBN> for ApproximateInference<'_, R, GaussBN, <GaussBN as BN>::Evidence>

fn estimate(&self, x: &Set<usize>, z: &Set<usize>) -> <GaussBN as BN>::CPD

Estimate the values of x conditioned on z using n samples.

impl<R: Rng> BNInference<GaussBN> for ApproximateInference<'_, R, GaussBN, ()>

fn estimate(&self, x: &Set<usize>, z: &Set<usize>) -> <GaussBN as BN>::CPD

Estimate the values of x conditioned on z using n samples.

impl<R: Rng> BNSampler<GaussBN> for ForwardSampler<'_, R, GaussBN>

type Sample = <GaussBN as BN>::Sample

The sample type.

type Samples = <GaussBN as BN>::Samples

The samples type.

fn sample(&self) -> Self::Sample

Sample a single instance from a Bayesian network.

fn sample_n(&self, n: usize) -> Self::Samples

Sample from a Bayesian network.

impl<R: Rng> BNSampler<GaussBN> for ImportanceSampler<'_, R, GaussBN, GaussEv>

type Sample = (ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>, f64)

The sample type.

type Samples = GaussWtdTable

The samples type.

fn sample(&self) -> Self::Sample

Sample a single instance from a Bayesian network.

fn sample_n(&self, n: usize) -> Self::Samples

Sample from a Bayesian network.

impl Clone for GaussBN

fn clone(&self) -> GaussBN

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GaussBN

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for GaussBN

fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>

where D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl JsonIO for GaussBN

fn from_json(json: &str) -> Self

Create an instance of the type from a JSON string.

fn to_json(&self) -> String

Convert the instance to a JSON string.

fn read_json(path: &str) -> Self

Create an instance of the type from a JSON file.

fn write_json(&self, path: &str)

Write the instance to a JSON file.

impl Labelled for GaussBN

fn labels(&self) -> &Labels

Returns the labels of the variables.

fn label_to_index(&self, x: &str) -> usize

Return the variable index for a given label.

fn index_to_label(&self, x: usize) -> &str

Return the label for a given variable index.

fn index_to(&self, x: usize, other: &Labels) -> usize

Maps an index from this model to another model with the same label.

fn indices_to(&self, x: &Set<usize>, other: &Labels) -> Set<usize>

Maps a set of indices from this model to another model with the same labels.

fn index_from(&self, x: usize, other: &Labels) -> usize

Maps an index from another model to this model with the same label.

fn indices_from(&self, x: &Set<usize>, other: &Labels) -> Set<usize>

Maps a set of indices from another model to this model with the same labels.

impl<E> ParBNCausalInference<GaussBN> for CausalInference<'_, E>

where E: Modelled<GaussBN> + ParBNInference<GaussBN>,

fn par_cace_estimate( &self, x: &Set<usize>, y: &Set<usize>, z: &Set<usize>, ) -> Option<<GaussBN as BN>::CPD>

Estimate the conditional average causal effect of X on Y given Z as E(Y | do(X), Z) in parallel.

fn par_ace_estimate(&self, x: &Set<usize>, y: &Set<usize>) -> Option<T::CPD>

Estimate the average causal effect of X on Y as E(Y | do(X)) in parallel.

impl<R: Rng + SeedableRng> ParBNInference<GaussBN> for ApproximateInference<'_, R, GaussBN, <GaussBN as BN>::Evidence>

fn par_estimate(&self, x: &Set<usize>, z: &Set<usize>) -> <GaussBN as BN>::CPD

Estimate the values of x conditioned on z using n samples, in parallel.

impl<R: Rng + SeedableRng> ParBNInference<GaussBN> for ApproximateInference<'_, R, GaussBN, ()>

fn par_estimate(&self, x: &Set<usize>, z: &Set<usize>) -> <GaussBN as BN>::CPD

Estimate the values of x conditioned on z using n samples, in parallel.

impl<R: Rng + SeedableRng> ParBNSampler<GaussBN> for ForwardSampler<'_, R, GaussBN>

type Samples = <GaussBN as BN>::Samples

The samples type.

fn par_sample_n(&self, n: usize) -> Self::Samples

Sample from a Bayesian network in parallel.

impl<R: Rng + SeedableRng> ParBNSampler<GaussBN> for ImportanceSampler<'_, R, GaussBN, GaussEv>

type Samples = GaussWtdTable

The samples type.

fn par_sample_n(&self, n: usize) -> Self::Samples

Sample from a Bayesian network in parallel.

impl PartialEq for GaussBN

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl RelativeEq for GaussBN

fn default_max_relative() -> Self::Epsilon

The default relative tolerance for testing values that are far-apart.

fn relative_eq( &self, other: &Self, epsilon: Self::Epsilon, max_relative: Self::Epsilon, ) -> bool

A test for equality that uses a relative comparison if the values are far apart.

fn relative_ne( &self, other: &Rhs, epsilon: Self::Epsilon, max_relative: Self::Epsilon, ) -> bool

The inverse of RelativeEq::relative_eq.

impl Serialize for GaussBN

fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>

where S: Serializer,

Serialize this value into the given Serde serializer.