/// <summary> Adds one instance to the end of the set.
/// Shallow copies instance before it is added. Increases the
/// size of the dataset if it is not large enough. Does not
/// check if the instance is compatible with the dataset.
///
/// </summary>
/// <param name="instance">the instance to be added
/// </param>
public virtual void add(Instance instance)
{
Instance newInstance = (Instance) instance.copy();
newInstance.Dataset = this;
m_Instances.addElement(newInstance);
}
/// <summary> Evaluates the classifier on a single instance.
///
/// </summary>
/// <param name="classifier">machine learning classifier
/// </param>
/// <param name="instance">the test instance to be classified
/// </param>
/// <returns> the prediction made by the clasifier
/// </returns>
/// <throws> Exception if model could not be evaluated </throws>
/// <summary> successfully or the data contains string attributes
/// </summary>
public virtual double evaluateModelOnce(Classifier classifier, Instance instance)
{
Instance classMissing = (Instance) instance.copy();
double pred = 0;
classMissing.Dataset = instance.dataset();
classMissing.setClassMissing();
if (m_ClassIsNominal)
{
double[] dist = classifier.distributionForInstance(classMissing);
pred = Utils.maxIndex(dist);
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
if (dist[(int) pred] <= 0)
{
pred = Instance.missingValue();
}
updateStatsForClassifier(dist, instance);
}
else
{
pred = classifier.classifyInstance(classMissing);
updateStatsForPredictor(pred, instance);
}
return pred;
}
/// <summary> Calculates the class membership probabilities for the given test instance.
///
/// </summary>
/// <param name="instance">the instance to be classified
/// </param>
/// <returns> predicted class probability distribution
/// </returns>
/// <exception cref="Exception">if instance could not be classified
/// successfully
/// </exception>
public virtual double[] distributionForInstance(Instance instance)
{
instance = (Instance) instance.copy();
instance.Dataset = m_NumericClassData;
double[] pred = new double[m_NumClasses];
double[] Fs = new double[m_NumClasses];
for (int i = 0; i < m_NumGenerated; i++)
{
double predSum = 0;
for (int j = 0; j < m_NumClasses; j++)
{
pred[j] = m_Classifiers[j][i].classifyInstance(instance);
predSum += pred[j];
}
predSum /= m_NumClasses;
for (int j = 0; j < m_NumClasses; j++)
{
Fs[j] += (pred[j] - predSum) * (m_NumClasses - 1) / m_NumClasses;
}
}
return Calculateprobs(Fs);
}