Markov Chains Part 3: Weighted Chains
Recently I remembered that I had all the pieces I need to make a weighted version of the unweighted markov chain I built in part 2 of this series - specifically the weighted random number generator I built shortly after the unweighted markov chain. With this in mind, I decided on a whim to put all the pieces of the puzzle together - and this post is the result!
Where to start... hrm. I know. To start with, I needed to perform a minor upgrade tot he WeightedRandom class I had. If you haven't read the original post about it, I'd recommend doing so now.
Finished reading that? Great! Lets talk about the changes I've made (they may show up there at the bottom, since I embedded it via GitHub Gist). Firstly, I needed a way to work out if the weighted random number generator was currently empty or not, leading me to add a Count property:
public int Count {
get {
return weights.Count;
}
}
With a count property in place, I also found I was going to need a way to dynamically swap out the weightings of the random number generator without creating a completely new instance - which would end up resetting the Random class instance it was working with, leading to a reduction in the quality in random numbers it uses under high load (see [this article]() for more information on that).
To that end, I ended up refactoring the constructor into a pair of methods: SetContents, and a companion method ClearContents. Since the weight calculations happen when the items are first added to the generator, and I'd need to completely recalculate them if another item is added, I wasn't able to emulate the API for another existing class in .NET, such as the List class, as I like to do.
Finally, I found later on I needed a way to initialise an empty weighted random generator, so I added a new empty constructor to facilitate that, along with an additional check in the Next() method that throws an InvalidOperationException if the generator is empty and you try to ask it to pick a random item.
Here's the updated WeightedRandomNumberGenerator:
(Can't see the above? Click here to view it on GitHub directly, or here for the raw code as plain text)
With the weighted random number generator updated to properly support the future weighted markov chain, let's get down to the markov chain itself. Firstly, let's create a skeleton that's based on the UnweightedMarkovChain class I wrote in the last post:
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);
/// <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();
/// <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);
}
}
As you can see, it is rather similar to the unweighted version. Fear not however, for the differences will become more apparent shortly. The only real difference so far is the extra private WeightedRandom<string> wrandom declaration at the top of the class. Let's change that though, by filling out the constructor:
ngrams = new Dictionary<string, double>();
foreach (string ngram in inNgrams)
{
if (ngrams.ContainsKey(ngram))
ngrams[ngram]++;
else
ngrams.Add(ngram, 1);
}
Here, we read in the raw n-grams and a dictionary that represents the number of times that a given n-gram has been discovered. It's got to be a double there as the type value of the dictionary, as apparently the C♯ compiler isn't clever enough to convert a Dictionary<string, int> to a Dictionary<string, double>. Hrm. Maybe they'll fix that in the future (or if not, does anyone know why not-)?
Anyway, let's move on to RandomNgram(). Here it is:
if (wrandom.Count == 0)
wrandom.SetContents(ngrams);
return wrandom.Next();
Quite simple, right? Basically, we populate the weighted random generator if it's currently empty, and then we simply ask it for a random item. We're on a roll, here! Let's keep going with Generate(). Here's the first part:
string result = RandomNgram();
string lastNgram = result;
while(result.Length < length)
{
// ......
}
Here, we declare an accumulator-like variable result to hold the word we're generating as we construct it, and another one to holdt he last n-gram we picked. We also create a while loop to make sure we keep adding to the word until we reach the desired length (we'll be adding a stop condition just in case we run into a brick wall later). Next, let's put some code inside that while loop. First up is the (re)population of the weighted random number generator:
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));
Ah, good ol' Linq to the rescue again! But wait, what's that ForEach() call there? I don't remember that being in core .NET! You'd be right of course, but through the power of [extension methods]() one can extend a class with an additional method that can then be used as if it were an integral part of that class, when in reality that isn't the case! Here's my definition for that ForEach() extension method I used above:
public static class LinqExtensions
{
public static void ForEach<T>(this IEnumerable<T> enumerable, Action<T> action)
{
foreach (T item in enumerable)
{
action(item);
}
}
}
Next, we need to add that stop condition we talked about earlier before I forget! Here it is:
// If there aren't any choices left, we can't exactly keep adding to the new string any more :-(
if(convNextNgrams.Count() == 0)
break;
Observant readers will notice that we haven't actually finished the (re)population process yet, so we should do that next. Once done, we can also obtain a random n-gram from the generator and process it:
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;
That completes my initial weighted markov chain implementation. Here's the class in full:
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;
}
}
}
You can find it on my private git server here, if you're interested in any future improvements I might have made to it since writing this post. Speaking of which, I've got a few in mind - mainly refactoring both this class and it's unweighted cousin to utilise lists of objects instead of strings. This way, I'll be able to apply it to anything I like - such as sentence generation, music improvisation, and more!
I'd also like to extend it such that I can specify the weights manually, giving me even more flexibility as to how I can put the engine to use.
(Found a cool use for a Markov Chain? Comment about it below!)
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