private NGramGraphMarkovChain(bool het)
{
this.graph = new Dictionary <NGram <T>, List <NonRecursiveNGramProbabilisticEdge <T> > >();
this.IsHeterogenous = het;
this.Distrubution = new NGramDistribution <T>(new NGram <T> [0]);
this.Grams = HomogenousNGrams <T> .DirectBuiltUnsafe(new NGram <T> [0], 1);
}
public HomogenousNGramMatrixMarkovChain(HomogenousNGrams <T> grams)
{
grams.NullCheck();
(grams as IHeterogenousNGrams <T> == null).AssertTrue();
var array = new HashSet <NGram <T> >(grams).ToArray();
var blockedGrams = NGramHomogenousMatrixMarkovChainHelper <T> .BuildBlockedNGrams(grams.ToArray());
this.distrubution = new NGramDistribution <T>(grams);
this.transitionMatrix = Matrix <float> .Build.Sparse(
array.Length,
array.Length,
new Func <int, int, float>(
(x, y) => NGramHomogenousMatrixMarkovChainHelper <T> .CalculateProbability(blockedGrams, distrubution, array, x, y)));
this.indices = new Dictionary <int, NGram <T> >();
for (int i = 0; i < array.Length; i++)
{
this.indices.Add(i, array[i]);
}
}
public static HeterogenousNGrams <T> BuildSingle(NGram <T> nGram)
{
return(new HeterogenousNGrams <T>(HomogenousNGrams <T> .BuildFromSingleNGrams(nGram).AsEnumerableObject().ToList(), nGram.N, nGram.N + 1));
}
public static HeterogenousNGrams <T> BuildNGrams(int p1, int p2, List <T> corp)
{
corp.NullCheck();
(p1 > 0).AssertTrue();
(p2 > p1).AssertTrue();
return(new HeterogenousNGrams <T>(Enumerable.Range(p1, p2 - p1).Select(x => HomogenousNGrams <T> .BuildNGrams(x, corp)).ToList(), p1, p2));
}
