Initial commit
This commit is contained in:
commit
9debe776f2
GPG Key ID:
FEECFF24EF0CE9E9
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/target
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# This file is automatically @generated by Cargo.
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# It is not intended for manual editing.
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version = 4
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@ -0,0 +1,8 @@
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[package]
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name = "chains-gen"
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version = "0.1.0"
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edition = "2024"
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[dependencies]
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indexmap = "2.7.0"
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rand = "0.8.5"
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@ -0,0 +1,10 @@
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# Chains-gen
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chains-gen is a simple little program to create a Markov chain and then generate some text using it.
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To use it, put your training data inside `data.txt` in a directory, and then run the program there.
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> [!NOTE]
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> You should try to keep each "entry" of data contained in one line, not bleeding out into multiple. The results could be weird if you don't..
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If everything is okay you should be able to hit enter and generate sentences.
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@ -0,0 +1,73 @@
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use std::time::Instant;
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use markov::{MarkovAllNodes, MarkovNode, MarkovToken};
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mod markov;
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fn main() {
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// chain creation
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let content = std::fs::read_to_string("./data.txt").unwrap();
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let lines = content.lines();
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let mut all_nodes = MarkovAllNodes::new();
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let root_node = MarkovNode::new(MarkovToken::Root);
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let end_node = MarkovNode::new(MarkovToken::End);
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let start = Instant::now();
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for line in lines {
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let mut nodes = line
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.split_whitespace()
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.filter(|s| s.chars().all(|c| c.is_ascii_alphanumeric()))
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.map(|t| all_nodes.node(t));
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let mut node = root_node.clone();
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while !node.value.is_end() {
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let next_node = nodes.next().unwrap_or_else(|| end_node.clone());
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// it will be an empty chain so skip
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if node.value.is_root() && next_node.value.is_end() {
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break;
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}
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node.conns.borrow_mut().connect(next_node.clone());
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node = next_node;
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}
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}
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println!("took {:?} to create chain", start.elapsed());
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// generation
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let mut rng = rand::thread_rng();
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loop {
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let mut picked_start_word = String::new();
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std::io::stdin().read_line(&mut picked_start_word).unwrap();
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let start = Instant::now();
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let mut result = String::new();
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let mut node = all_nodes
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.try_node(picked_start_word.trim())
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.unwrap_or_else(|| root_node.clone());
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loop {
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let next_node = node.conns.borrow().random_weighted(&mut rng);
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if let MarkovToken::Value(value) = &node.value {
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result += value;
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result.push(' ');
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}
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if next_node.value.is_end() {
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break;
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}
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node = next_node;
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}
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let elapsed = start.elapsed();
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println!("output: {result}");
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println!("gen took {elapsed:?}");
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}
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}
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@ -0,0 +1,92 @@
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use std::{cell::RefCell, collections::HashMap, hash::Hash, rc::Rc};
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use indexmap::IndexMap;
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use rand::{distributions::WeightedIndex, prelude::Distribution, rngs::ThreadRng};
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#[derive(PartialEq, Eq, Hash, Debug)]
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pub enum MarkovToken {
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Root,
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Value(String),
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End,
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}
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impl MarkovToken {
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pub fn is_root(&self) -> bool {
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matches!(self, Self::Root)
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}
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pub fn is_end(&self) -> bool {
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matches!(self, Self::End)
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}
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}
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pub struct MarkovNode {
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pub value: MarkovToken,
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pub conns: RefCell<MarkovConns>,
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}
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impl PartialEq for MarkovNode {
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fn eq(&self, other: &Self) -> bool {
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self.value.eq(&other.value)
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}
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}
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impl Eq for MarkovNode {}
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impl Hash for MarkovNode {
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fn hash<H: std::hash::Hasher>(&self, state: &mut H) {
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self.value.hash(state);
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}
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}
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impl MarkovNode {
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pub fn new(token: MarkovToken) -> Rc<Self> {
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Rc::new(Self {
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value: token,
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conns: RefCell::new(MarkovConns(IndexMap::new())),
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})
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}
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}
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pub struct MarkovConns(pub IndexMap<Rc<MarkovNode>, u32>);
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impl MarkovConns {
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pub fn connect(&mut self, word: Rc<MarkovNode>) {
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if let Some(count) = self.0.get_mut(&word) {
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*count += 1;
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} else {
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self.0.insert(word, 1);
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}
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}
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fn index(&self, i: usize) -> Rc<MarkovNode> {
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self.0.get_index(i).map(|(n, _)| n.clone()).unwrap()
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}
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pub fn random_weighted(&self, rng: &mut ThreadRng) -> Rc<MarkovNode> {
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let wi = WeightedIndex::new(self.0.iter().map(|(_, c)| c)).unwrap(); // SAFETY: there should always be at least an end token
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let i = wi.sample(rng);
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self.index(i)
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}
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}
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pub struct MarkovAllNodes(HashMap<String, Rc<MarkovNode>>);
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impl MarkovAllNodes {
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pub fn new() -> Self {
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Self(HashMap::new())
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}
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pub fn node(&mut self, word: &str) -> Rc<MarkovNode> {
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if let Some(node) = self.0.get(word) {
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node.clone()
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} else {
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let node = MarkovNode::new(MarkovToken::Value(word.to_string()));
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self.0.insert(word.to_string(), node.clone());
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node
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}
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}
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pub fn try_node(&self, word: &str) -> Option<Rc<MarkovNode>> {
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self.0.get(word).cloned()
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}
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}
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