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Write weighted markov chain! :D

master
Starbeamrainbowlabs 3 years ago
parent
commit
a08be7f61d
Signed by: sbrl GPG Key ID: 1BE5172E637709C2
5 changed files with 224 additions and 1 deletions
  1. +6
    -0
      MarkovGrams/MarkovGrams.csproj
  2. +30
    -1
      MarkovGrams/Program.cs
  3. +16
    -0
      MarkovGrams/Utilities/LinqExtensions.cs
  4. +87
    -0
      MarkovGrams/Utilities/WeightedRandom.cs
  5. +85
    -0
      MarkovGrams/WeightedMarkovChain.cs

+ 6
- 0
MarkovGrams/MarkovGrams.csproj View File

@ -36,6 +36,12 @@
<Compile Include="Properties\AssemblyInfo.cs" />
<Compile Include="NGrams.cs" />
<Compile Include="UnweightedMarkovChain.cs" />
<Compile Include="Utilities\WeightedRandom.cs" />
<Compile Include="WeightedMarkovChain.cs" />
<Compile Include="Utilities\LinqExtensions.cs" />
</ItemGroup>
<ItemGroup>
<Folder Include="Utilities\" />
</ItemGroup>
<Import Project="$(MSBuildBinPath)\Microsoft.CSharp.targets" />
</Project>

+ 30
- 1
MarkovGrams/Program.cs View File

@ -16,7 +16,9 @@ namespace MarkovGrams
Console.WriteLine();
Console.WriteLine("Available commands:");
Console.WriteLine(" markov:");
Console.WriteLine(" Generate new words using a markov chain.");
Console.WriteLine(" Generate new words using an unweighted markov chain.");
Console.WriteLine(" markov-w:");
Console.WriteLine(" Generate new words using a weighted markov chain.");
Console.WriteLine(" ngrams:");
Console.WriteLine(" Generate raw unique n-grams");
Console.WriteLine();
@ -57,6 +59,33 @@ namespace MarkovGrams
for(int i = 0; i < count; i++)
Console.WriteLine(chain.Generate(desiredStringLength));
break;
case "markov-w":
if (args.Length != 5)
{
Console.WriteLine("markov-w command usage:");
Console.WriteLine(" ./MarkovGrams.exe markov-w <wordlist.txt> <order> <length> <count>");
Console.WriteLine();
Console.WriteLine("<wordlist.txt> The path to the wordlist to read from.");
Console.WriteLine("<order> The order of the n-grams to use.");
Console.WriteLine("<length> The length of word to generate.");
Console.WriteLine("<count> The number of words to generate.");
Console.WriteLine();
return 1;
}
wordlistFilename = args[1];
order = int.Parse(args[2]);
int weightedDesiredStringLength = int.Parse(args[3]);
int weightedCount = int.Parse(args[4]);
words = File.ReadLines(wordlistFilename).SelectMany(word => word.Trim().Split(' '));
ngrams = NGrams.GenerateFlat(words, order);
WeightedMarkovChain weightedChain = new WeightedMarkovChain(ngrams);
for (int i = 0; i < weightedCount; i++)
Console.WriteLine(weightedChain.Generate(weightedDesiredStringLength));
break;
case "ngrams":
if(args.Length != 3)


+ 16
- 0
MarkovGrams/Utilities/LinqExtensions.cs View File

@ -0,0 +1,16 @@
using System;
using System.Collections.Generic;
namespace MarkovGrams.Utilities
{
public static class LinqExtensions
{
public static void ForEach<T>(this IEnumerable<T> enumerable, Action<T> action)
{
foreach (T item in enumerable)
{
action(item);
}
}
}
}

+ 87
- 0
MarkovGrams/Utilities/WeightedRandom.cs View File

@ -0,0 +1,87 @@
using System;
using System.Collections.Generic;
using System.Linq;
namespace SBRL.Algorithms
{
/// <summary>
/// Picks random items from a list, according to weights assigned to them.
/// </summary>
/// <remarks>
/// Higher weights mean that an item is picked more frequently than the other items.
/// </remarks>
/// <license>Mozilla Public License version 2.0</license>
/// <origin>https://gist.github.com/sbrl/9090a8c646b8d34b6e0170ddfd197d09</origin>
/// <author>Starbeamrainbowlabs (https://starbeamrainbowlabs.com/)</author>
/// <changelog>
/// v0.1 - 20th May 2017:
/// - Creation! :D
/// </changelog>
public class WeightedRandom<ItemType>
{
private Random rand = new Random();
protected Dictionary<ItemType, double> weights = new Dictionary<ItemType, double>();
public int Count {
get {
return weights.Count;
}
}
/// <summary>
/// Creates a new weighted random number generator.
/// </summary>
/// <param name="items">The dictionary of weights and their corresponding items.</param>
public WeightedRandom(IDictionary<ItemType, double> items)
{
SetContents(items);
}
/// <summary>
/// Createse a new empty weighted random number generator.
/// Remember to populate it before using!
/// </summary>
public WeightedRandom()
{
}
public void SetContents(IDictionary<ItemType, double> items)
{
if (items.Count == 0)
throw new ArgumentException("Error: The items dictionary provided is empty!");
double totalWeight = items.Values.Aggregate((double a, double b) => a + b);
foreach (KeyValuePair<ItemType, double> itemData in items) {
weights.Add(itemData.Key, itemData.Value / totalWeight);
}
}
public void ClearContents()
{
weights.Clear();
}
/// <summary>
/// Picks a new random item from the list provided at initialisation, based
/// on the weights assigned to them.
/// </summary>
/// <returns>A random item, picked according to the assigned weights.</returns>
public ItemType Next()
{
if (weights.Count == 0)
throw new InvalidOperationException("Error: No weights specified! Add some with SetContents() before generating random numbers.");
double target = rand.NextDouble();
double lower = 0;
double higher = 0;
foreach (KeyValuePair<ItemType, double> weightData in weights)
{
higher += weightData.Value;
if (target >= lower && target <= higher)
return weightData.Key;
lower += weightData.Value;
}
throw new Exception($"Error: Unable to find the weight that matches {target}");
}
}
}

+ 85
- 0
MarkovGrams/WeightedMarkovChain.cs View File

@ -0,0 +1,85 @@
using System;
using System.Collections.Generic;
using System.Linq;
using MarkovGrams.Utilities;
using SBRL.Algorithms;
namespace MarkovGrams
{
/// <summary>
/// An unweighted character-based markov chain.
/// </summary>
public class WeightedMarkovChain
{
private WeightedRandom<string> wrandom = new WeightedRandom<string>();
/// <summary>
/// The ngrams that this markov chain currently contains.
/// </summary>
Dictionary<string, double> ngrams;
/// <summary>
/// Creates a new character-based markov chain.
/// </summary>
/// <param name="inNgrams">The ngrams to populate the new markov chain with.</param>
public WeightedMarkovChain(IEnumerable<string> inNgrams)
{
ngrams = new Dictionary<string, double>();
foreach (string ngram in inNgrams)
{
if (ngrams.ContainsKey(ngram))
ngrams[ngram]++;
else
ngrams.Add(ngram, 1);
}
}
/// <summary>
/// Returns a random ngram that's currently loaded into this WeightedMarkovChain.
/// </summary>
/// <returns>A random ngram from this UnweightMarkovChain's cache of ngrams.</returns>
public string RandomNgram()
{
if (wrandom.Count == 0)
wrandom.SetContents(ngrams);
return wrandom.Next();
}
/// <summary>
/// Generates a new random string from the currently stored ngrams.
/// </summary>
/// <param name="length">
/// The length of ngram to generate.
/// Note that this is a target, not a fixed value - e.g. passing 2 when the n-gram order is 3 will
/// result in a string of length 3. Also, depending on the current ngrams this markov chain contains,
/// it may end up being cut short.
/// </param>
/// <returns>A new random string.</returns>
public string Generate(int length)
{
string result = RandomNgram();
string lastNgram = result;
while(result.Length < length)
{
wrandom.ClearContents();
// The substring that the next ngram in the chain needs to start with
string nextStartsWith = lastNgram.Substring(1);
// Get a list of possible n-grams we could choose from next
Dictionary<string, double> convNextNgrams = new Dictionary<string, double>();
ngrams.Where(gram_data => gram_data.Key.StartsWith(nextStartsWith))
.ForEach((KeyValuePair<string, double> ngramData) => convNextNgrams.Add(ngramData.Key, ngramData.Value));
// If there aren't any choices left, we can't exactly keep adding to the new string any more :-(
if(convNextNgrams.Count() == 0)
break;
wrandom.SetContents(convNextNgrams);
// Pick a random n-gram from the list
string nextNgram = wrandom.Next();
// Add the last character from the n-gram to the string we're building
result += nextNgram[nextNgram.Length - 1];
lastNgram = nextNgram;
}
wrandom.ClearContents();
return result;
}
}
}

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