Struct lipsum::MarkovChain

pub struct MarkovChain<'a> { /* private fields */ }

Simple order two Markov chain implementation.

The Markov chain is a chain of order two, which means that it will use the previous two words (a bigram) when predicting the next word. This is normally enough to generate random text that looks somewhat plausible. The implementation is based on Generating arbitrary text with Markov chains in Rust.

Implementations

impl<'a> MarkovChain<'a>

pub fn new() -> MarkovChain<'a>

Create a new empty Markov chain.

Examples

use lipsum::MarkovChain;
use rand::SeedableRng;
use rand_chacha::ChaCha20Rng;

let mut chain = MarkovChain::new();
chain.learn("infra-red red orange yellow green blue indigo x-ray");

let mut rng = ChaCha20Rng::seed_from_u64(0);

// The chain jumps consistently like this:
assert_eq!(chain.generate_with_rng(&mut rng, 1), "Orange.");
assert_eq!(chain.generate_with_rng(&mut rng, 1), "Infra-red.");
assert_eq!(chain.generate_with_rng(&mut rng, 1), "Yellow.");

pub fn learn(&mut self, sentence: &'a str)

Add new text to the Markov chain. This can be called several times to build up the chain.

Examples

use lipsum::MarkovChain;

let mut chain = MarkovChain::new();
chain.learn("red green blue");
assert_eq!(chain.words(("red", "green")), Some(&vec!["blue"]));

chain.learn("red green yellow");
assert_eq!(chain.words(("red", "green")), Some(&vec!["blue", "yellow"]));

pub fn len(&self) -> usize

Returs the number of states in the Markov chain.

Examples

use lipsum::MarkovChain;

let mut chain = MarkovChain::new();
assert_eq!(chain.len(), 0);

chain.learn("red orange yellow green blue indigo");
assert_eq!(chain.len(), 4);

pub fn is_empty(&self) -> bool

Returns true if the Markov chain has no states.

Examples

use lipsum::MarkovChain;

let mut chain = MarkovChain::new();
assert!(chain.is_empty());

chain.learn("foo bar baz");
assert!(!chain.is_empty());

pub fn words(&self, state: Bigram<'a>) -> Option<&Vec<&str>>

Get the possible words following the given bigram, or None if the state is invalid.

Examples

use lipsum::MarkovChain;

let mut chain = MarkovChain::new();
chain.learn("red green blue");
assert_eq!(chain.words(("red", "green")), Some(&vec!["blue"]));
assert_eq!(chain.words(("foo", "bar")), None);

pub fn generate_with_rng<R: Rng>(&self, rng: R, n: usize) -> String

Generate a sentence with n words of lorem ipsum text. The sentence will start from a random point in the Markov chain generated using the specified random number generator, and a . will be added as necessary to form a full sentence.

See generate_with_rng_from if you want to control the starting point for the generated text and see iter_with_rng if you simply want a sequence of words.

Examples

Generating the sounds of a grandfather clock:

use lipsum::MarkovChain;
use rand_chacha::ChaCha20Rng;
use rand::SeedableRng;

let mut chain = MarkovChain::new();
chain.learn("Tick, Tock, Tick, Tock, Ding! Tick, Tock, Ding! Ding!");
println!("{}", chain.generate_with_rng(ChaCha20Rng::seed_from_u64(0), 15));

The output looks like this:

Ding! Tick, Tock, Tick, Tock, Ding! Ding! Tock, Ding! Tick, Tock, Tick, Tock, Tick, Tock.

pub fn generate(&self, n: usize) -> String

Generate a sentence with n words of lorem ipsum text. The sentence will start from a predetermined point in the Markov chain generated using the default random number generator and a . will be added as necessary to form a full sentence.

See generate_from if you want to control the starting point for the generated text and see iter if you simply want a sequence of words.

Examples

Generating the sounds of a grandfather clock:

use lipsum::MarkovChain;

let mut chain = MarkovChain::new();
chain.learn("Tick, Tock, Tick, Tock, Ding! Tick, Tock, Ding! Ding!");
println!("{}", chain.generate(15));

The output looks like this:

Ding! Tick, Tock, Tick, Tock, Ding! Ding! Tock, Ding! Tick, Tock, Tick, Tock, Tick, Tock.

pub fn generate_with_rng_from<R: Rng>( &self, rng: R, n: usize, from: Bigram<'a> ) -> String

Generate a sentence with n words of lorem ipsum text. The sentence will start from the given bigram and a . will be added as necessary to form a full sentence.

Use generate_with_rng if the starting point is not important. See iter_with_rng_from if you want a sequence of words that you can format yourself.

pub fn generate_from(&self, n: usize, from: Bigram<'a>) -> String

Generate a sentence with n words of lorem ipsum text. The sentence will start from the given bigram and a . will be added as necessary to form a full sentence.

Use generate if the starting point is not important. See iter_from if you want a sequence of words that you can format yourself.

pub fn iter_with_rng<R: Rng>(&self, rng: R) -> Words<'_, R>

Make a never-ending iterator over the words in the Markov chain. The iterator starts at a random point in the chain.

pub fn iter(&self) -> Words<'_, impl Rng>

Make a never-ending iterator over the words in the Markov chain. The iterator starts at a predetermined point in the chain.

pub fn iter_with_rng_from<R: Rng>( &self, rng: R, from: Bigram<'a> ) -> Words<'_, R>

Make a never-ending iterator over the words in the Markov chain. The iterator starts at the given bigram.

pub fn iter_from(&self, from: Bigram<'a>) -> Words<'_, impl Rng>

Make a never-ending iterator over the words in the Markov chain. The iterator starts at the given bigram.

Trait Implementations

impl<'a> Clone for MarkovChain<'a>

fn clone(&self) -> MarkovChain<'a>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<'a> Debug for MarkovChain<'a>

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

Formats the value using the given formatter.

impl<'a> Default for MarkovChain<'a>

fn default() -> MarkovChain<'a>

Returns the “default value” for a type.