/// <summary> Constructor
///
/// </summary>
/// <param name="numCondSymbols">the number of conditioning 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 KDConditionalEstimator(int numCondSymbols, double precision)
{
m_Estimators = new KernelEstimator[numCondSymbols];
for (int i = 0; i < numCondSymbols; i++)
{
m_Estimators[i] = new KernelEstimator(precision);
}
}
/// <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 DKConditionalEstimator(int numSymbols, double precision)
{
m_Estimators = new KernelEstimator[numSymbols];
for (int i = 0; i < numSymbols; i++)
{
m_Estimators[i] = new KernelEstimator(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 KernelEstimator(m_Precision);
if (m_NumValues == 0)
{
return result;
}
double delta = 0, currentProb = 0;
double zLower, zUpper;
for (int i = 0; i < m_NumValues; i++)
{
delta = m_CondValues[i] - given;
zLower = (delta - (m_Precision / 2)) / m_StandardDev;
zUpper = (delta + (m_Precision / 2)) / m_StandardDev;
currentProb = (Statistics.normalProbability(zUpper) - Statistics.normalProbability(zLower));
result.addValue(m_Values[i], currentProb * m_Weights[i]);
}
return result;
}
