public bool Equals(NGram <T> other)
{
if (this.hashCode != other.hashCode)
{
return(false);
}
if (other.N != this.N)
{
return(false);
}
for (int i = 0; i < other.N; i++)
{
if (null == other.Grams[i])
{
if (this.Grams[i] == null)
{
continue;
}
return(false);
}
if (!other.Grams[i].Equals(this.Grams[i]))
{
return(false);
}
}
return(true);
}
private NGram <T> GetNextState(NGram <T> curState, NextStateType searchType)
{
var edges = this.graph.Edges(curState);
if (null == edges || edges.Count == 0)
{
switch (searchType)
{
case NextStateType.PossibleHeterogenous:
var pos = this.graph.GetLowerConstruct(curState);
if (pos.HasValue)
{
curState = pos.Value;
}
else
{
return(NGram <T> .Empty);
}
break;
case NextStateType.Homogenous:
return(NGram <T> .Empty);
}
}
while (true)
{
foreach (var item in edges)
{
if (this.Random.NextDouble() < item.Probability)
{
return(item.Edge);
}
}
}
}
public NGramGraphMarkovChain <T> GetSubGraphFromNGram(NGram <T> gram, int depth)
{
this.ValidNode(gram).AssertTrue();
var set = this.RetrieveAppearedNGramsToSomeDepth(gram, depth);
Dictionary <NGram <T>, List <NonRecursiveNGramProbabilisticEdge <T> > > dictionary = new Dictionary <NGram <T>, List <NonRecursiveNGramProbabilisticEdge <T> > >();
foreach (var item in set)
{
dictionary.Add(item, new List <NonRecursiveNGramProbabilisticEdge <T> >());
foreach (var sub in this.graph[item])
{
if (set.Contains(sub.Edge))
{
dictionary[item].Add(sub);
}
else
{
dictionary[item].Add(sub);
if (!dictionary.ContainsKey(sub.Edge))
{
dictionary.Add(sub.Edge, new List <NonRecursiveNGramProbabilisticEdge <T> >());
}
}
}
}
return(new NGramGraphMarkovChain <T>(dictionary, this.IsHeterogenous, this.Distrubution.Filter(new Predicate <KeyValuePair <NGram <T>, int> >(x => dictionary.ContainsKey(x.Key)))));
}
public NonRecursiveNGramProbabilisticEdge(float prob, NGram <T> edge)
{
(prob <= 1F && prob >= 0).AssertTrue();
this.Probability = prob;
this.Edge = edge;
}
public int RetrieveCount(NGram <T> t)
{
if (this.distrubution.ContainsKey(t))
{
return(this.distrubution[t]);
}
return(0);
}
public NGramRandomGraphMarkovChainWalker(NGramGraphMarkovChain <T> graph, NGram <T> startingNode, Random rand)
{
graph.NullCheck();
graph.ValidNode(startingNode).AssertTrue();
rand.NullCheck();
this.graph = graph;
this.startingNode = startingNode;
this.Random = rand;
}
/*
* public bool Save(string filePath)
* {
* try
* {
* using (Stream stream = File.Open(filePath, FileMode.OpenOrCreate))
* {
* BinaryFormatter formatter = new BinaryFormatter();
* formatter.Serialize(stream, this);
* }
* }
* catch (Exception e)
* {
* return false;
* }
* return true;
* }
*
* public static NGramGraphMarkovChain<T> Load(string filePath)
* {
* using (Stream stream = File.Open(filePath, FileMode.Open))
* {
* BinaryFormatter formatter = new BinaryFormatter();
* var obj = (NGramGraphMarkovChain<T>)formatter.Deserialize(stream);
* return obj;
* }
* }
*/
private HashSet <NGram <T> > RetrieveAppearedNGramsToSomeDepth(NGram <T> gram, int depth, int curDepth = 0)
{
if (curDepth >= depth)
{
return(new HashSet <NGram <T> >());
}
HashSet <NGram <T> > set = new HashSet <NGram <T> >();
set.Add(gram);
foreach (var item in this.graph[gram])
{
foreach (var sub in RetrieveAppearedNGramsToSomeDepth(item.Edge, depth, ++curDepth))
{
set.Add(sub);
}
}
return(set);
}
public NGram <T>?GetLowerConstruct(NGram <T> curState)
{
if (curState.N > 1)
{
var lowerConstructData = curState
.AsEnumerable()
.Skip(1)
.ToArray();
NGram <T> lowerConstruct = new NGram <T>(lowerConstructData);
if (this.ValidNode(lowerConstruct))
{
return(lowerConstruct);
}
return(null);
}
return(null);
}
public NGram <T>[] WalkMarkovChain(int depth, NextStateType searchType = NextStateType.PossibleHeterogenous)
{
List <NGram <T> > path = new List <NGram <T> >(depth);
(depth > 0).AssertTrue();
NGram <T> curState = this.startingNode;
path.Add(curState);
for (int i = 1; i < depth; i++)
{
curState = this.GetNextState(curState, searchType);
if (curState == NGram <T> .Empty)
{
return(path.ToArray());
}
path.Add(curState);
}
return(path.ToArray());
}
public static HomogenousNGrams <T> BuildFromSingleNGrams(NGram <T> a)
{
return(new HomogenousNGrams <T>(a.AsEnumerableObject(), a.N));
}
public static HeterogenousNGrams <T> BuildSingle(NGram <T> nGram)
{
return(new HeterogenousNGrams <T>(HomogenousNGrams <T> .BuildFromSingleNGrams(nGram).AsEnumerableObject().ToList(), nGram.N, nGram.N + 1));
}
private static HashSet <NGram <T> > BuildSingleBlock(List <NGram <T>[]> grams, NGram <T> node, int min, int max)
{
HashSet <NGram <T> > blockedGram = new HashSet <NGram <T> >();
List <int> indices = new List <int>(20);
var first = grams[node.N - min];
var n = node.N;
for (int i = n; i < first.Length; i++)
{
if (first[i - n] == node)
{
indices.Add(i);
blockedGram.Add(first[i]);
}
}
for (int i = min; i < max; i++)
{
if (i == n)
{
continue;
}
var gram = grams[i - min];
foreach (var item in indices)
{
if (item < gram.Length)
{
blockedGram.Add(gram[item]);
}
}
}
return(blockedGram);
}
public IReadOnlyList <NonRecursiveNGramProbabilisticEdge <T> > Edges(NGram <T> gram)
{
this.ValidNode(gram).AssertTrue();
return(this.graph[gram].AsReadOnly());
}
public bool ValidNode(NGram <T> node)
{
return(this.graph.ContainsKey(node));
}
public double RetrieveProbability(NGram <T> t)
{
return((double)this.RetrieveCount(t) / (double)this.Size);
}