// Constructor
public HierarchNGram(int nValue, int threshold)
{
// Keep the N value
NValue = nValue;
// Keep the threshold
this.threshold = threshold;
// Instantiate the array of N-Grams
predictors = new NGram <T> [nValue];
// Instantiate the individual N-Grams
for (int i = 0; i < nValue; i++)
{
predictors[i] = new NGram <T>(i + 1);
}
}
// Get the most likely action given a sequence of actions
// The actions array should be at least of size 1
public T GetMostLikely(T[] actions)
{
// Declare variable for best action and set it to its default value
T bestAction = default(T);
// Go through the various Ni-Grams
for (int i = 0; i < NValue; i++)
{
// Are there enough actions for the current Ni-Gram?
if (actions.Length >= NValue - i - 1)
{
// Get current Ni-Gram
NGram <T> p = predictors[NValue - i - 1];
// Create array of actions for the current Ni-Gram
T[] subactions = new T[NValue - i - 1];
// Copy the actions to the array of actions for the current
// Ni-Gram
Array.Copy(actions, actions.Length + i + 1 - NValue,
subactions, 0, NValue - i - 1);
// Get frequency of action sequence in the current Ni-Gram
int numActions = p.GetActionsFrequency(subactions);
// Is that frequency larger than the threshold?
if (numActions > threshold)
{
// Then use this action
bestAction = p.GetMostLikely(subactions);
break;
}
}
}
// Return the best action
return(bestAction);
}
