Struct MarkovModel 

pub struct MarkovModel {
    pub n: usize,
    pub transitions: HashMap<Vec<String>, Vec<(String, u32)>>,
    pub tagged_transitions: HashMap<String, HashMap<Vec<String>, Vec<(String, u32)>>>,
}

A trained Markov model storing n-gram probability tables.

Fields

n: usize

N-gram depth (e.g., 2 for bigrams, 3 for trigrams).

transitions: HashMap<Vec<String>, Vec<(String, u32)>>

Transition table: n-gram prefix → [(next_token, count)].

tagged_transitions: HashMap<String, HashMap<Vec<String>, Vec<(String, u32)>>>

Tag-specific transition tables.

Implementations

impl MarkovModel

pub fn generate( &self, rng: &mut StdRng, tag: Option<&str>, min_words: usize, max_words: usize, ) -> Result<String, MarkovError>

Generate text from this model.

Starts from a sentence-start state, walks the chain selecting next tokens by weighted probability, and stops at a sentence boundary within the word count range.

Trait Implementations

impl Clone for MarkovModel

fn clone(&self) -> MarkovModel

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for MarkovModel

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

Formats the value using the given formatter.

impl Default for MarkovModel

fn default() -> MarkovModel

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for MarkovModel

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for MarkovModel

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.