public void AddRoomVisit(int room)
{
lastRoomsVisited.Add(room);
if (lastRoomsVisited.Count == nValue)
{
nGramPredictor.GetMostLikely(lastRoomsVisited);
}
roomsEntered++;
}
//computes the prediction
public T GetMostLikely(T[] actions)
{
T bestAction = default(T);
for (int i = 0; i < nValue; i++)
{
NGramPredictor <T> p = predictors[nValue - i - 1];
T[] subactions = new T[i + 1];
Array.Copy(actions, nValue - i - 1, subactions, 0, i + 1);
int numActions = p.GetActionsNum(ref actions);
if (numActions > threshold)
{
bestAction = p.GetMostLikely(actions);
}
}
return(bestAction);
}
public string GetMostLikely(string[] actions)
{
for (int i = 0; i < nValue - 1; i++)
{
NGramPredictor nGram = ngrams[nValue - i - 1];
//get substring
string[] subActions = new string[nValue - i - 1];
int startIndex = (actions.Length - 1) - (subActions.Length - 1);
for (int j = 0; j < subActions.Length; j++)
{
subActions[j] = actions[startIndex];
startIndex++;
}
string stringOfArray = string.Join("", subActions);
if (stringOfArray == "")
{
return(String.Empty);
}
if (subActions.Length == 1)
{
stringOfArray = subActions[0];
}
//check if there are enough entries
if (nGram.data.ContainsKey(stringOfArray))
{
KeyDataRecord data = (KeyDataRecord)nGram.data[stringOfArray];
if (data.total > threshold)
{
return(nGram.GetMostLikely(subActions));
}
}
}
return(string.Empty);
}