/// <summary> Constructor
///
/// </summary>
/// <param name="numSymbols">the number of possible symbols (remember to include 0)
/// </param>
/// <param name="numCondSymbols">the number of conditioning symbols
/// </param>
/// <param name="laplace">if true, sub-estimators will use laplace
/// </param>
public DDConditionalEstimator(int numSymbols, int numCondSymbols, bool laplace)
{
m_Estimators = new DiscreteEstimator[numCondSymbols];
for (int i = 0; i < numCondSymbols; i++)
{
m_Estimators[i] = new DiscreteEstimator(numSymbols, laplace);
}
}
/// <summary> Get a probability estimator for a value
///
/// </summary>
/// <param name="data">the value to estimate the probability of
/// </param>
/// <param name="given">the new value that data is conditional upon
/// </param>
/// <returns> the estimator for the supplied value given the condition
/// </returns>
public virtual Estimator getEstimator(double given)
{
Estimator result = new DiscreteEstimator(m_Estimators.Length, false);
for (int i = 0; i < m_Estimators.Length; i++)
{
result.addValue(i, m_Weights.getProbability(i) * m_Estimators[i].getProbability(given));
}
return result;
}
/// <summary> Constructor
///
/// </summary>
/// <param name="numSymbols">the number of symbols
/// </param>
/// <param name="precision">the precision to which numeric values are given. For
/// example, if the precision is stated to be 0.1, the values in the
/// interval (0.25,0.35] are all treated as 0.3.
/// </param>
public DNConditionalEstimator(int numSymbols, double precision)
{
m_Estimators = new NormalEstimator[numSymbols];
for (int i = 0; i < numSymbols; i++)
{
m_Estimators[i] = new NormalEstimator(precision);
}
m_Weights = new DiscreteEstimator(numSymbols, true);
}
/// <summary> Get a probability estimator for a value
///
/// </summary>
/// <param name="data">the value to estimate the probability of
/// </param>
/// <param name="given">the new value that data is conditional upon
/// </param>
/// <returns> the estimator for the supplied value given the condition
/// </returns>
public virtual Estimator getEstimator(double given)
{
Estimator result = new DiscreteEstimator(m_Estimators.Length, false);
for (int i = 0; i < m_Estimators.Length; i++)
{
//System.out.println("Val " + i
// + " Weight:" + m_Weights.getProbability(i)
// +" EstProb(" + given + ")="
// + m_Estimators[i].getProbability(given));
result.addValue(i, m_Weights.getProbability(i) * m_Estimators[i].getProbability(given));
}
return result;
}
