private void BuildFromHomogenousNGrams(INGrams <T> grams, NGram <T>[] array, NGramDistribution <T> distrubution)
{
var blockedGrams = NGramHomogenousMatrixMarkovChainHelper <T> .BuildBlockedNGrams(grams.ToArray());
var transitionMatrix = Matrix <float> .Build.Sparse(
array.Length,
array.Length,
new Func <int, int, float>(
(x, y) => NGramHomogenousMatrixMarkovChainHelper <T> .CalculateProbability(blockedGrams, distrubution, array, x, y)));
int rowIndex = 0;
foreach (var row in transitionMatrix.EnumerateRows())
{
this.graph.Add(array[rowIndex], new List <NonRecursiveNGramProbabilisticEdge <T> >());
for (int i = 0; i < row.Count; i++)
{
if (row[i] != 0)
{
this.graph[array[rowIndex]].Add(new NonRecursiveNGramProbabilisticEdge <T>(row[i], array[i]));
}
}
rowIndex++;
}
}
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]);
}
}
