Struct CatCPD 

pub struct CatCPD { /* private fields */ }

A categorical CPD.

Implementations

impl CatCPD

pub fn new( states: States, conditioning_states: States, parameters: Array2<f64>, ) -> Self

Creates a new categorical conditional probability distribution.

Arguments

• states - The variable label and states.
• conditioning_states - The conditioning variables labels and states.
• parameters - The probabilities of the states.

Panics

• If the labels and conditioning labels are not disjoint.
• If the product of the shape of the of states does not match the number of columns.
• If the product of the shape of the of conditioning states does not match the number of rows.
• If the parameters do not sum to one by row, unless empty.

Returns

A new CatCPD instance.

pub const fn states(&self) -> &States

Returns the states of the conditioned variable.

Returns

The states of the conditioned variable.

pub const fn shape(&self) -> &Array1<usize>

Returns the shape of the conditioned variable.

Returns

The shape of the conditioned variable.

pub const fn multi_index(&self) -> &MI

Returns the ravel multi index of the conditioning variables.

Returns

The ravel multi index of the conditioning variables.

pub const fn conditioning_states(&self) -> &States

Returns the states of the conditioning variables.

Returns

The states of the conditioning variables.

pub const fn conditioning_shape(&self) -> &Array1<usize>

Returns the shape of the conditioning variables.

Returns

The shape of the conditioning variables.

pub const fn conditioning_multi_index(&self) -> &MI

Returns the ravel multi index of the conditioning variables.

Returns

The ravel multi index of the conditioning variables.

pub fn marginalize(&self, x: &Set<usize>, z: &Set<usize>) -> Self

Marginalizes the over the variables X and conditioning variables Z.

Arguments

• x - The variables to marginalize over.
• z - The conditioning variables to marginalize over.

Returns

A new instance with the marginalized variables.

pub fn with_optionals( state: States, conditioning_states: States, parameters: Array2<f64>, sample_statistics: Option<CatCPDS>, sample_log_likelihood: Option<f64>, ) -> Self

Creates a new categorical conditional probability distribution with optional fields.

Arguments

• states - The variables states.
• parameters - The probabilities of the states.
• statistics - The sufficient statistics used to fit the distribution, if any.

Panics

See new method for panics.

Returns

A new CatCPD instance.

pub fn from_phi(phi: CatPhi, x: &Set<usize>, z: &Set<usize>) -> Self

Converts a potential \phi(X \cup Z) to a CPD P(X | Z).

Arguments

• x - The set of variables.
• z - The set of conditioning variables.

Returns

The corresponding CPD.

pub fn into_phi(self) -> CatPhi

Converts a CPD P(X | Z) to a potential \phi(X \cup Z).

Arguments

• cpd - The CPD to convert.

Returns

The corresponding potential.

Trait Implementations

impl AbsDiffEq for CatCPD

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 CPD for CatCPD

type Support = ArrayBase<OwnedRepr<u8>, Dim<[usize; 1]>>

The type of the support.

type Parameters = ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>

The type of the parameters.

type Statistics = CatCPDS

The type of the sufficient statistics.

fn conditioning_labels(&self) -> &Labels

Returns the labels of the conditioned variables.

fn parameters(&self) -> &Self::Parameters

Returns the parameters.

fn parameters_size(&self) -> usize

Returns the parameters size.

fn sample_statistics(&self) -> Option<&Self::Statistics>

Returns the sufficient statistics, if any.

fn sample_log_likelihood(&self) -> Option<f64>

Returns the log-likelihood of the fitted dataset, if any.

fn pf(&self, x: &Self::Support, z: &Self::Support) -> f64

Returns the value of probability (mass or density) function for P(X = x | Z = z).

fn sample<R: Rng>(&self, rng: &mut R, z: &Self::Support) -> Self::Support

Samples from the conditional distribution P(X | Z = z).

impl CPDEstimator<CatCPD> for BE<'_, CatTable, ()>

fn fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD.

impl CPDEstimator<CatCPD> for BE<'_, CatTable, usize>

fn fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD.

impl CPDEstimator<CatCPD> for BE<'_, CatWtdTable, ()>

fn fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD.

impl CPDEstimator<CatCPD> for BE<'_, CatWtdTable, usize>

fn fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD.

impl CPDEstimator<CatCPD> for MLE<'_, CatTable>

fn fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD.

impl CPDEstimator<CatCPD> for MLE<'_, CatWtdTable>

fn fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD.

impl Clone for CatCPD

fn clone(&self) -> CatCPD

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CatCPD

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for CatCPD

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

where D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for CatCPD

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

Formats the value using the given formatter.

impl JsonIO for CatCPD

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 CatCPD

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 ParCPDEstimator<CatCPD> for BE<'_, CatTable, ()>

fn par_fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD in parallel.

impl ParCPDEstimator<CatCPD> for BE<'_, CatTable, usize>

fn par_fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD in parallel.

impl ParCPDEstimator<CatCPD> for BE<'_, CatWtdTable, ()>

fn par_fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD in parallel.

impl ParCPDEstimator<CatCPD> for BE<'_, CatWtdTable, usize>

fn par_fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD in parallel.

impl ParCPDEstimator<CatCPD> for MLE<'_, CatTable>

fn par_fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD in parallel.

impl ParCPDEstimator<CatCPD> for MLE<'_, CatWtdTable>

fn par_fit(&self, x: &Set<usize>, z: &Set<usize>) -> CatCPD

Fits the estimator to the dataset and returns a CPD in parallel.

impl PartialEq for CatCPD

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 CatCPD

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 CatCPD

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

where S: Serializer,

Serialize this value into the given Serde serializer.