Struct constriction::stream::model::LookupDecoderModel

pub struct LookupDecoderModel<Symbol, Probability, SymbolTable, LookupTable, const PRECISION: usize>
where
    Probability: BitArray,
{ /* private fields */ }

A tabularized DecoderModel that is optimized for fast decoding of i.i.d. symbols

You will usually want to use this type through one of the type aliases SmallContiguousLookupDecoderModel or SmallNonContiguousLookupDecoderModel. See these types for extended documentation and examples.

Implementations

impl<Symbol, Probability, const PRECISION: usize> LookupDecoderModel<Symbol, Probability, NonContiguousSymbolTable<Vec<(Probability, Symbol)>>, Box<[Probability]>, PRECISION>

where Probability: BitArray + Into<usize>, usize: AsPrimitive<Probability>, Symbol: Copy + Default,

pub fn from_symbols_and_floating_point_probabilities<F>( symbols: &[Symbol], probabilities: &[F] ) -> Result<Self, ()>

where F: FloatCore + Sum<F> + Into<f64>, Probability: Into<f64> + AsPrimitive<usize>, f64: AsPrimitive<Probability>, usize: AsPrimitive<Probability>,

Create a LookupDecoderModel over arbitrary symbols.

TODO: example

pub fn from_symbols_and_nonzero_fixed_point_probabilities<S, P>( symbols: S, probabilities: P, infer_last_probability: bool ) -> Result<Self, ()>

where S: IntoIterator<Item = Symbol>, P: IntoIterator, P::Item: Borrow<Probability>,

Create a LookupDecoderModel over arbitrary symbols.

TODO: example

pub fn from_iterable_entropy_model<'m, M>(model: &'m M) -> Self

where M: IterableEntropyModel<'m, PRECISION, Symbol = Symbol, Probability = Probability> + ?Sized,

TODO: test

impl<Symbol, Probability, const PRECISION: usize> LookupDecoderModel<Symbol, Probability, ContiguousSymbolTable<Vec<Probability>>, Box<[Probability]>, PRECISION>

where Probability: BitArray + Into<usize>, usize: AsPrimitive<Probability>, Symbol: Copy + Default,

pub fn from_floating_point_probabilities_contiguous<F>( probabilities: &[F] ) -> Result<Self, ()>

where F: FloatCore + Sum<F> + Into<f64>, Probability: Into<f64> + AsPrimitive<usize>, f64: AsPrimitive<Probability>, usize: AsPrimitive<Probability>,

Create a LookupDecoderModel over a contiguous range of symbols.

TODO: example

pub fn from_nonzero_fixed_point_probabilities_contiguous<I>( probabilities: I, infer_last_probability: bool ) -> Result<Self, ()>

where I: IntoIterator, I::Item: Borrow<Probability>,

Create a LookupDecoderModel over a contiguous range of symbols using fixed point arighmetic.

Example

See SmallContiguousLookupDecoderModel.

impl<Probability, Table, LookupTable, const PRECISION: usize> LookupDecoderModel<Probability, Probability, ContiguousSymbolTable<Table>, LookupTable, PRECISION>

where Probability: BitArray + Into<usize>, usize: AsPrimitive<Probability>, Table: AsRef<[Probability]>, LookupTable: AsRef<[Probability]>,

pub fn as_view( &self ) -> LookupDecoderModel<Probability, Probability, ContiguousSymbolTable<&[Probability]>, &[Probability], PRECISION>

Makes a very cheap shallow copy of the model that can be used much like a shared reference.

The returned LookupDecoderModel implements Copy, which is a requirement for some methods, such as Decode::decode_iid_symbols. These methods could also accept a shared reference to a NonContiguousCategoricalDecoderModel (since all references to entropy models are also entropy models, and all shared references implement Copy), but passing a view instead may be slightly more efficient because it avoids one level of dereferencing.

pub fn as_contiguous_categorical( &self ) -> ContiguousCategoricalEntropyModel<Probability, &[Probability], PRECISION>

TODO: documentation

pub fn into_contiguous_categorical( self ) -> ContiguousCategoricalEntropyModel<Probability, Table, PRECISION>

TODO: documentation

impl<Symbol, Probability, Table, LookupTable, const PRECISION: usize> LookupDecoderModel<Symbol, Probability, NonContiguousSymbolTable<Table>, LookupTable, PRECISION>

where Probability: BitArray + Into<usize>, usize: AsPrimitive<Probability>, Table: AsRef<[(Probability, Symbol)]>, LookupTable: AsRef<[Probability]>,

pub fn as_view( &self ) -> LookupDecoderModel<Symbol, Probability, NonContiguousSymbolTable<&[(Probability, Symbol)]>, &[Probability], PRECISION>

Makes a very cheap shallow copy of the model that can be used much like a shared reference.

The returned LookupDecoderModel implements Copy, which is a requirement for some methods, such as Decode::decode_iid_symbols. These methods could also accept a shared reference to a NonContiguousCategoricalDecoderModel (since all references to entropy models are also entropy models, and all shared references implement Copy), but passing a view instead may be slightly more efficient because it avoids one level of dereferencing.

Trait Implementations

impl<Symbol: Clone, Probability, SymbolTable: Clone, LookupTable: Clone, const PRECISION: usize> Clone for LookupDecoderModel<Symbol, Probability, SymbolTable, LookupTable, PRECISION>

where Probability: BitArray + Clone,

fn clone( &self ) -> LookupDecoderModel<Symbol, Probability, SymbolTable, LookupTable, PRECISION>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<Symbol: Debug, Probability, SymbolTable: Debug, LookupTable: Debug, const PRECISION: usize> Debug for LookupDecoderModel<Symbol, Probability, SymbolTable, LookupTable, PRECISION>

where Probability: BitArray + Debug,

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

Formats the value using the given formatter.

impl<Symbol, Probability, Table, LookupTable, const PRECISION: usize> DecoderModel<PRECISION> for LookupDecoderModel<Symbol, Probability, Table, LookupTable, PRECISION>

where Probability: BitArray + Into<usize>, Table: SymbolTable<Symbol, Probability>, LookupTable: AsRef<[Probability]>, Symbol: Clone,

fn quantile_function( &self, quantile: Probability ) -> (Symbol, Probability, Probability::NonZero)

Looks up the symbol for a given quantile.

impl<Symbol, Probability, Table, LookupTable, const PRECISION: usize> EntropyModel<PRECISION> for LookupDecoderModel<Symbol, Probability, Table, LookupTable, PRECISION>

where Probability: BitArray + Into<usize>,

type Symbol = Symbol

The type of data over which the entropy model is defined.

type Probability = Probability

The type used to represent probabilities, cumulatives, and quantiles.

impl<'m, Probability, Table, const PRECISION: usize> From<&'m ContiguousCategoricalEntropyModel<Probability, Table, PRECISION>> for LookupDecoderModel<Probability, Probability, ContiguousSymbolTable<Vec<Probability>>, Box<[Probability]>, PRECISION>

where Probability: BitArray + Into<usize>, usize: AsPrimitive<Probability>, Table: AsRef<[Probability]>,

fn from( model: &'m ContiguousCategoricalEntropyModel<Probability, Table, PRECISION> ) -> Self

Converts to this type from the input type.

impl<'m, Symbol, Probability, M, const PRECISION: usize> From<&'m M> for LookupDecoderModel<Symbol, Probability, NonContiguousSymbolTable<Vec<(Probability, Symbol)>>, Box<[Probability]>, PRECISION>

where Probability: BitArray + Into<usize>, Symbol: Copy + Default, usize: AsPrimitive<Probability>, M: IterableEntropyModel<'m, PRECISION, Symbol = Symbol, Probability = Probability> + ?Sized,

fn from(model: &'m M) -> Self

Converts to this type from the input type.

impl<'m, Probability, Table, LookupTable, const PRECISION: usize> IterableEntropyModel<'m, PRECISION> for LookupDecoderModel<Probability, Probability, ContiguousSymbolTable<Table>, LookupTable, PRECISION>

where Probability: BitArray + Into<usize>, usize: AsPrimitive<Probability>, Table: AsRef<[Probability]>, LookupTable: AsRef<[Probability]>,

type Iter = SymbolTableIter<Probability, Probability, ContiguousSymbolTable<&'m [Probability]>>

The type of the iterator returned by symbol_table.

fn symbol_table(&'m self) -> Self::Iter

Iterates over all symbols in the unique order that is consistent with the cumulative distribution.

fn floating_point_symbol_table<F>( &'m self ) -> FloatingPointSymbolTable<F, Self::Iter, PRECISION>

where F: From<Self::Probability>,

Similar to symbol_table, but yields both cumulatives and probabilities in floating point representation.

fn entropy_base2<F>(&'m self) -> F

where F: Float + Sum, Self::Probability: Into<F>,

Returns the entropy in units of bits (i.e., base 2).

impl<'m, Symbol, Probability, Table, LookupTable, const PRECISION: usize> IterableEntropyModel<'m, PRECISION> for LookupDecoderModel<Symbol, Probability, NonContiguousSymbolTable<Table>, LookupTable, PRECISION>

where Symbol: Clone + 'm, Probability: BitArray + Into<usize>, usize: AsPrimitive<Probability>, Table: AsRef<[(Probability, Symbol)]>, LookupTable: AsRef<[Probability]>,

type Iter = SymbolTableIter<Symbol, Probability, NonContiguousSymbolTable<&'m [(Probability, Symbol)]>>

The type of the iterator returned by symbol_table.

fn symbol_table(&'m self) -> Self::Iter

Iterates over all symbols in the unique order that is consistent with the cumulative distribution.

fn floating_point_symbol_table<F>( &'m self ) -> FloatingPointSymbolTable<F, Self::Iter, PRECISION>

where F: From<Self::Probability>,

Similar to symbol_table, but yields both cumulatives and probabilities in floating point representation.

fn entropy_base2<F>(&'m self) -> F

where F: Float + Sum, Self::Probability: Into<F>,

Returns the entropy in units of bits (i.e., base 2).

impl<Symbol: Copy, Probability, SymbolTable: Copy, LookupTable: Copy, const PRECISION: usize> Copy for LookupDecoderModel<Symbol, Probability, SymbolTable, LookupTable, PRECISION>

where Probability: BitArray + Copy,