113 lines
4.0 KiB
C#
113 lines
4.0 KiB
C#
using System;
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using System.Collections.Generic;
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using System.Linq;
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using MarkovGrams.Utilities;
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using SBRL.Algorithms;
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namespace MarkovGrams
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{
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/// <summary>
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/// An unweighted character-based markov chain.
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/// </summary>
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public class WeightedMarkovChain
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{
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private WeightedRandom<string> wrandom = new WeightedRandom<string>();
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/// <summary>
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/// The ngrams that this markov chain currently contains.
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/// </summary>
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private Dictionary<string, double> ngrams;
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/// <summary>
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/// Whether to always start generating a new word from an n-gram that starts with
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/// an uppercase letter.
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/// </summary>
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public bool StartOnUppercase = false;
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/// <summary>
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/// The generation mode to use when running the Markov Chain.
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/// </summary>
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/// <remarks>
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/// The input n-grams must have been generated using the same mode specified here.
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/// </remarks>
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public GenerationMode Mode { get; private set; } = GenerationMode.CharacterLevel;
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/// <summary>
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/// Creates a new character-based markov chain.
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/// </summary>
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/// <param name="inNgrams">The ngrams to populate the new markov chain with.</param>
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public WeightedMarkovChain(Dictionary<string, double> inNgrams, GenerationMode inMode) {
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ngrams = inNgrams;
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Mode = inMode;
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}
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public WeightedMarkovChain(Dictionary<string, int> inNgrams, GenerationMode inMode) {
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ngrams = new Dictionary<string, double>();
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foreach (KeyValuePair<string, int> ngram in inNgrams)
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ngrams[ngram.Key] = ngram.Value;
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Mode = inMode;
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}
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/// <summary>
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/// Returns a random ngram that's currently loaded into this WeightedMarkovChain.
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/// </summary>
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/// <returns>A random ngram from this UnweightedMarkovChain's cache of ngrams.</returns>
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public string RandomNgram()
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{
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if (wrandom.Count == 0) {
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if (!StartOnUppercase)
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wrandom.SetContents(ngrams);
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else {
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Dictionary<string, double> filteredNGrams = new Dictionary<string, double>();
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foreach (KeyValuePair<string, double> pair in ngrams.Where((pair) => char.IsUpper(pair.Key[0])))
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filteredNGrams.Add(pair.Key, pair.Value);
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if (filteredNGrams.Count() == 0)
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throw new Exception($"Error: No valid starting ngrams were found (StartOnUppercase: {StartOnUppercase}).");
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wrandom.SetContents(filteredNGrams);
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}
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}
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return wrandom.Next();
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}
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/// <summary>
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/// Generates a new random string from the currently stored ngrams.
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/// </summary>
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/// <param name="length">
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/// The length of ngram to generate.
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/// Note that this is a target, not a fixed value - e.g. passing 2 when the n-gram order is 3 will
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/// result in a string of length 3. Also, depending on the current ngrams this markov chain contains,
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/// it may end up being cut short.
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/// </param>
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/// <returns>A new random string.</returns>
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public string Generate(int length)
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{
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string result = RandomNgram();
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string lastNgram = result;
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while(result.Length < length)
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{
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wrandom.ClearContents();
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// The substring that the next ngram in the chain needs to start with
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string nextStartsWith = Mode == GenerationMode.CharacterLevel ? lastNgram.Substring(1) : lastNgram.Split(' ')[0];
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// Get a list of possible n-grams we could choose from next
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Dictionary<string, double> convNextNgrams = new Dictionary<string, double>();
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ngrams.Where(gram_data => gram_data.Key.StartsWith(nextStartsWith))
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.ForEach((KeyValuePair<string, double> ngramData) => convNextNgrams.Add(ngramData.Key, ngramData.Value));
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// If there aren't any choices left, we can't exactly keep adding to the new string any more :-(
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if(convNextNgrams.Count() == 0)
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break;
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wrandom.SetContents(convNextNgrams);
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// Pick a random n-gram from the list
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string nextNgram = wrandom.Next();
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// Add the last character from the n-gram to the string we're building
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if (Mode == GenerationMode.CharacterLevel)
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result += nextNgram[nextNgram.Length - 1];
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else
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result += string.Join(" ", nextNgram.Split(' ').Skip(1));
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lastNgram = nextNgram;
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}
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wrandom.ClearContents();
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return result;
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}
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}
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}
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