Struct namegen::Markov

pub struct Markov { /* fields omitted */ }

Implementations

impl Markov

pub fn print(&self)

pub fn generate(&self, ws: &mut WorkingSet, rng: &mut impl Rng)

Generate a name. You need to provide your own WorkingSet and Rng, which is necessary to save on allocations. A dependent application should use the full name generator interface instead

pub fn learn(&mut self, sample_set: &SampleSet) -> Result<(), LearnError>

Learn learns samples from the sample set. The former state is copied and will be restored upon one of the samples failing to import.

pub fn learn_one(&mut self, sample: &Sample) -> Result<(), LearnError>

Learn rules from the sample. The generation is heavily optimized for speed, but learn is paying for that speed.

pub fn recalculate_weights(&mut self)

pub fn validate(&self) -> Result<(), ValidationError>

pub fn new() -> Markov

Create a new generator without any pre-defined tokens and constraints.

pub fn with_tokens<S: AsRef<str>>(tokens: &[S]) -> Markov

Create a new generator with pre-defined tokens and no constraints. The tokens allow you to define vowel pairs (e.g. ae, ay, ey), digraphs (e.g. th, nth, ng) so that they're treated as one token.

pub fn with_constraints<S: AsRef<str>>(
    tokens: &[S],
    lrs: bool,
    lrm: bool,
    lre: bool,
    rtf: bool
) -> Markov

Create a new generator with both pre-defined tokens and constraints. The constraints increases the faithfulness of the generator to the sample material, but at the cost of variety.

Trait Implementations

impl Clone for Markov

fn clone(&self) -> Markov

Returns a copy of the value.

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

Performs copy-assignment from source.