Struct markov_strings::Markov [−][src]
pub struct Markov { /* fields omitted */ }
Expand description
The Markov chain generator
- Initialize it empty or from saved corpus
- Add data to complete the corpus
- Generate results
Implementations
Creates an empty Markov instance
use markov_strings::*;
let mut markov = Markov::new();
Creates a Markov instance from previously imported data
See Markov::export()
for more information.
Example: load your saved corpus from a flat file with the bincode
crate.
let file = File::open("dumped.db").unwrap();
let reader = BufReader::new(file);
let data = bincode::deserialize_from(reader).unwrap();
let mut markov = Markov::from_export(data);
Sets the “state size” of your Markov generator.
The result chain is made up of consecutive blocks of words, and each block is called a state. Each state is itself made up of one (1) or more words.
let data: Vec<InputData> = vec![];
let mut markov = Markov::new();
// We _must_ set the state_size before adding data...
assert!(markov.set_state_size(3).is_ok());
// ...or it will return an error
markov.add_to_corpus(data);
assert!(markov.set_state_size(4).is_err());
- A state size of
1
word will mostly output non-sense gibberish. - A state size of
2
words can produce interesting results, when correctly filtered. - A state size of
3
or more words will produce more intelligible results, but you’ll need a source material that will allow it while staying random enough.
! You CANNOT change the state_size once you’ve added data with Markov::add_to_corpus()
.
The internal data structure is reliant on the state size, and it cannot be changed without
rebuilding the whole corpus.
Default value 2
.
Adds data to your Markov instance’s corpus.
This is an expensive method that can take a few seconds, depending on the size of your input data. For example, adding 50.000 tweets while running on fairly decent computer takes more than 20 seconds.
To avoid rebuilding the corpus each time you want to generate a text,
you can use Markov::export()
and Markov::from_export()
You can call .add_to_corpus()
as many times as you need it.
Sets a filter to ensure that outputted results match your own criteria.
A good filter is essential to get interesting results out of Markov::generate()
.
The values you should check at minimum are the MarkovResult.score
and MarkovResult.refs
’ length.
The higher these values, the “better” the results. The actual thresholds are entierely dependant of your source material.
let mut markov = Markov::new();
// We're going to generate tweets, so...
markov
.set_filter(|r| {
// Minimum score and number of references
// to ensure good randomness
r.score > 50 && r.refs.len() > 10
// Max length of a tweet
&& r.text.len() <= 280
// No mentions
&& !r.text.contains("@")
// No urls
&& !r.text.contains("http")
});
Removes the filter, if any
let mut markov = Markov::new();
// Those two lines a functionally identical.
markov.set_filter(|r| true);
markov.unset_filter();
Sets the maximum number of times the generator will try to generate a result.
If Markov::generate
fails [max_tries] times to generate a sentence,
it returns an ErrorType.TriesExceeded
.
Default value: 100
Generates a random result from your corpus.
let mut markov = Markov::new();
let data: Vec<InputData> = vec![/* lots of data */];
markov.add_to_corpus(data);
let result = markov.generate().unwrap();
Gets an item from the original data.
Use this with the indices from MarkovResult.refs
let data: Vec<InputData> = vec![
InputData{ text: "foo bar lorem ipsum".to_string(), meta: Some("something".to_string()) },
];
let mut markov = Markov::new();
markov.add_to_corpus(data);
let result = markov.generate().unwrap();
// Since we only have 1 string in our corpus, we have 1 ref...
let mut expected: Vec<usize> = vec![];
expected.push(0);
assert_eq!(result.refs, expected);
let input_ref = *result.refs.get(0).unwrap();
assert_eq!(markov.get_input_ref(input_ref).unwrap().text, "foo bar lorem ipsum");
assert_eq!(markov.get_input_ref(input_ref).unwrap().meta, Some("something".to_string()));
Exports the corpus into a serializable structure.
The Markov::add_to_corpus()
method being expensive, you may want to build your corpus once,
then export it to a serializable file file for later use.
let data: Vec<InputData> = vec![];
let mut markov = Markov::new();
markov.add_to_corpus(data);
let export = markov.export();
let markov = Markov::from_export(export);
let result = markov.generate();
Trait Implementations
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more