Markov Model

// Sample text data for training the Markov model
const sampleText = `the tongue gets clogged with mud that can happen too only one remedy then pull it in and suck it swallow the mud or spit it out it’s one or the other and question is it nourishing and vistas last a moment with that
I fill my mouth with it that can happen too it’s another of my resources last a moment with that and question if swallowed would it nourish and opening up of vistas they are good moments
rosy in the mud the tongue lolls out again what are the hands at all this time one must always try and see what the hands are up to well the left as we have seen still clutches the sack and the right`;
display("Sample text loaded for Markov model training");

// Function to build a Markov chain from text
function buildMarkovChain(text, order = 2) {
  const words = text.toLowerCase().replace(/[^\w\s]/g, '').split(/\s+/);
  const chain = new Map();

  for (let i = 0; i <= words.length - order - 1; i++) {
    const state = words.slice(i, i + order).join(' ');
    const nextWord = words[i + order];

    if (!chain.has(state)) {
      chain.set(state, []);
    }
    chain.get(state).push(nextWord);
  }

  return chain;
}

// Build the Markov chain
const markovChain = buildMarkovChain(sampleText, 2);

display(`Markov chain built with ${markovChain.size} states`);

// Function to generate text using the Markov chain
function generateText(chain, length = 50, order = 2) {
  const states = Array.from(chain.keys());
  let currentState = states[Math.floor(Math.random() * states.length)];
  let result = currentState.split(' ');

  for (let i = 0; i < length - order; i++) {
    const possibleWords = chain.get(currentState);
    if (!possibleWords || possibleWords.length === 0) {
      // If we hit a dead end, pick a random state
      currentState = states[Math.floor(Math.random() * states.length)];
      result.push(...currentState.split(' '));
      continue;
    }

    const nextWord = possibleWords[Math.floor(Math.random() * possibleWords.length)];
    result.push(nextWord);

    // Update current state by shifting the window
    const stateWords = currentState.split(' ');
    stateWords.shift();
    stateWords.push(nextWord);
    currentState = stateWords.join(' ');
  }

  return result.join(' ');
}

// Generate sample text
const generatedText = generateText(markovChain, 30);
display(`Generated text: "${generatedText}"`);

// Interactive text generator
const lengthInput = view(Inputs.range([10, 100], {label: "Text length", value: 30, step: 1}));
const orderInput = view(Inputs.range([1, 4], {label: "Markov order", value: 2, step: 1}));
const generateButton = view(Inputs.button("Generate New Text"));

// Reactive text generation based on inputs
const currentChain = buildMarkovChain(sampleText, orderInput);
const newText = generateText(currentChain, lengthInput, orderInput);

// Trigger regeneration when button is clicked
generateButton;

display(htl.html`
  <div style="background: #f8f9fa; padding: 20px; border-radius: 8px; margin: 10px 0;">
    <h3>Generated Text (Order: ${orderInput}, Length: ${lengthInput} words)</h3>
    <p style="font-style: italic; line-height: 1.6;">"${newText}"</p>
  </div>
`);

// Visualize the Markov chain structure
const chainData = Array.from(markovChain.entries()).map(([state, nextWords]) => ({
  state,
  transitions: nextWords.length,
  uniqueTransitions: new Set(nextWords).size,
  nextWords: nextWords.join(', ')
}));

display(Inputs.table(chainData.slice(0, 10), {
  columns: ["state", "transitions", "uniqueTransitions"],
  header: {
    state: "State (2-gram)",
    transitions: "Total Transitions",
    uniqueTransitions: "Unique Next Words"
  }
}));