Struct MixedBN 

pub struct MixedBN { /* private fields */ }

A mixed Bayesian network.

Trait Implementations

impl AbsDiffEq for MixedBN

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 MixedBN

type CPD = MixedCPD

The type of the CPD.

type Support = IndexMap<String, MixedSupport, BuildHasherDefault<FxHasher>>

The type of the support metadata (variable ranges / state sets).

type Evidence = MixedEv

The type of the evidence.

type Sample = MixedSample

The type of the sample.

type Samples = MixedTable

The type of the samples.

type IncSamples = MixedIncTable

The type of the incomplete samples.

type WtdSamples = MixedWtdTable

The type of the weighted samples.

fn support(&self) -> Cow<'_, Self::Support>

Returns the support metadata of the BN.

fn new<I>(graph: DiGraph, cpds: I) -> Result<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 a map labels-distributions.

fn parameters_size(&self) -> usize

Returns the parameters size.

fn select(&self, x: &Set<usize>) -> Result<Self>

where Self: Sized,

Restrict the model to the specified variables.

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, ) -> Result<Self>

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

Creates a new Bayesian network with optional fields.

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

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

fn cpace_estimate( &self, x: &Set<usize>, y: &Set<usize>, z: &Set<usize>, w: Option<&<MixedBN as BN>::Evidence>, ) -> Result<Option<<MixedBN as BN>::CPD>>

Estimate the conditional population average causal effect of X on Y given Z with optional evidence W = w as E(Y | do(X), Z, W = w).

fn csace_estimate<D>( &self, x: &Set<usize>, y: &Set<usize>, z: &Set<usize>, data: D, ) -> Result<Option<Vec<<MixedBN as BN>::CPD>>>

where D: Dataset<Evidence = <MixedBN as BN>::Evidence>,

Estimate the conditional sample average causal effect of X on Y given Z with evidence W = w from data D as E(Y | do(X), Z, W = w).

fn pace_estimate( &self, x: &Set<usize>, y: &Set<usize>, w: Option<&T::Evidence>, ) -> Result<Option<T::CPD>>

Estimate the population average causal effect of X on Y with optional evidence W = w as E(Y | do(X), W = w).

fn sace_estimate<D>( &self, x: &Set<usize>, y: &Set<usize>, data: D, ) -> Result<Option<Vec<T::CPD>>>

where D: Dataset<Evidence = T::Evidence>,

Estimate the sample average causal effect of X on Y with evidence W = w from data D as E(Y | do(X), W = w).

impl<R> BNInference<MixedBN> for ApproximateInference<'_, R, MixedBN, ()>

where R: Rng,

fn estimate( &self, x: &Set<usize>, z: &Set<usize>, w: Option<&<MixedBN as BN>::Evidence>, ) -> Result<<MixedBN as BN>::CPD>

Estimate the values of x conditioned on z and w.

impl<R, F> BNInference<MixedBN> for ApproximateInference<'_, R, MixedBN, F>

where R: Rng, F: Fn(&<MixedBN as BN>::WtdSamples, &Set<usize>, &Set<usize>) -> Result<<MixedBN as BN>::CPD>,

fn estimate( &self, x: &Set<usize>, z: &Set<usize>, w: Option<&<MixedBN as BN>::Evidence>, ) -> Result<<MixedBN as BN>::CPD>

Estimate the values of x conditioned on z and w.

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

type Sample = MixedSample

The sample type.

type Samples = MixedTable

The samples type.

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

Sample a single instance from a Bayesian network.

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

Sample from a Bayesian network.

impl Clone for MixedBN

fn clone(&self) -> MixedBN

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for MixedBN

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for MixedBN

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

where D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl HasLabels for MixedBN

fn labels(&self) -> &Labels

Returns the labels of the variables.

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

Return the variable index for a given label.

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

Return the label for a given variable index.

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

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

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

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

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

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

impl JsonIO for MixedBN

fn from_json_reader<R: Read>(reader: R) -> Result<Self>

Create an instance of the type from a JSON reader.

fn to_json_writer<W: Write>(&self, writer: W) -> Result<()>

Write the instance to a JSON writer.

fn from_json_string(json: &str) -> Result<Self>

Create an instance of the type from a JSON string.

fn to_json_string(&self) -> Result<String>

Convert the instance to a JSON string.

fn from_json_file(path: &str) -> Result<Self>

Create an instance of the type from a JSON file.

fn to_json_file(&self, path: &str) -> Result<()>

Write the instance to a JSON file.

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

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

fn par_cpace_estimate( &self, x: &Set<usize>, y: &Set<usize>, z: &Set<usize>, w: Option<&<MixedBN as BN>::Evidence>, ) -> Result<Option<<MixedBN as BN>::CPD>>

Estimate the conditional population average causal effect of X on Y given Z with optional evidence W = w as E(Y | do(X), Z, W = w) in parallel.

fn par_csace_estimate<D>( &self, x: &Set<usize>, y: &Set<usize>, z: &Set<usize>, data: D, ) -> Result<Option<Vec<<MixedBN as BN>::CPD>>>

where D: Dataset<Evidence = <MixedBN as BN>::Evidence>,

Estimate the conditional sample average causal effect of X on Y given Z with evidence W = w from data D as E(Y | do(X), Z, W = w) in parallel.

fn par_pace_estimate( &self, x: &Set<usize>, y: &Set<usize>, w: Option<&T::Evidence>, ) -> Result<Option<T::CPD>>

Estimate the population average causal effect of X on Y with optional evidence W = w as E(Y | do(X), W = w) in parallel.

fn par_sace_estimate<D>( &self, x: &Set<usize>, y: &Set<usize>, data: D, ) -> Result<Option<Vec<T::CPD>>>

where D: Dataset<Evidence = T::Evidence>,

Estimate the sample average causal effect of X on Y with evidence W = w from data D as E(Y | do(X), W = w) in parallel.

impl<R> ParBNInference<MixedBN> for ApproximateInference<'_, R, MixedBN, ()>

where R: Rng + SeedableRng,

fn par_estimate( &self, x: &Set<usize>, z: &Set<usize>, w: Option<&<MixedBN as BN>::Evidence>, ) -> Result<<MixedBN as BN>::CPD>

Estimate the values of x conditioned on z and w, in parallel.

impl<R, F> ParBNInference<MixedBN> for ApproximateInference<'_, R, MixedBN, F>

where R: Rng + SeedableRng, F: Fn(&<MixedBN as BN>::WtdSamples, &Set<usize>, &Set<usize>) -> Result<<MixedBN as BN>::CPD>,

fn par_estimate( &self, x: &Set<usize>, z: &Set<usize>, w: Option<&<MixedBN as BN>::Evidence>, ) -> Result<<MixedBN as BN>::CPD>

Estimate the values of x conditioned on z and w, in parallel.

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

type Samples = MixedTable

The samples type.

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

Sample from a Bayesian network in parallel.

impl PartialEq for MixedBN

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

Equality operator ==.

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

Inequality operator !=.

impl RelativeEq for MixedBN

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 MixedBN

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

where S: Serializer,

Serialize this value into the given Serde serializer.