public override void buildClassifier(Instances instances)
{
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
instances = new Instances(instances);
instances.deleteWithMissingClass();
double sum = 0;
int cnt = 0;
foreach (Instance instance in instances)
{
if (!instance.classIsMissing())
{
sum += instance.value(idxAttribute);
cnt++;
}
}
mean = sum / cnt;
sum = 0;
foreach (Instance instance in instances)
{
if (!instance.classIsMissing())
{
sum += (instance.value(idxAttribute) - mean) * (instance.value(idxAttribute) - mean);
}
}
sigma2 = sum / cnt;
epsilon = SelectBestEpsilon(instances, mean, sigma2);
}
///
/// <summary> * Generates the classifier.
/// * </summary>
/// * <param name="instances"> set of instances serving as training data </param>
/// * <exception cref="Exception"> if the classifier has not been generated successfully </exception>
///
public override void buildClassifier(Instances instances)
{
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
instances = new Instances(instances);
instances.deleteWithMissingClass();
double sumOfWeights = 0;
WekaUtils.DebugAssert(instances.numClasses() == 3, "instance's numClasses should be 3.");
m_counts = new double[instances.numClasses()];
m_normalCounts = new double[instances.numClasses()];
for (int i = 0; i < m_counts.Length; i++)
{
m_counts[i] = 0;
m_normalCounts[i] = 0;
}
double c = m_tp / m_sl;
foreach (Instance instance in instances)
{
int v = (int)instance.classValue();
if (v == 2)
{
m_counts[v] += instance.weight() * c;
sumOfWeights += instance.weight() * c;
}
else
{
m_counts[v] += instance.weight();
sumOfWeights += instance.weight();
}
}
double start = 0;
for (int i = 0; i < m_counts.Length; ++i)
{
m_normalCounts[i] = (double)m_counts[i] / sumOfWeights + start;
start = m_normalCounts[i];
}
}
///
/// <summary> * Generates the classifier.
/// * </summary>
/// * <param name="instances"> set of instances serving as training data </param>
/// * <exception cref="Exception"> if the classifier has not been generated successfully </exception>
///
public override void buildClassifier(Instances instances)
{
// can classifier handle the data?
getCapabilities().testWithFail(instances);
// remove instances with missing class
var trainInstances = new Instances(instances);
trainInstances.deleteWithMissingClass();
WekaUtils.DebugAssert(instances.numClasses() == 3, "instance's numClasses should be 3.");
m_counts = new double[instances.numClasses()];
for (int i = 0; i < m_counts.Length; i++)
{
m_counts[i] = 0;
}
//double c = m_tp / m_sl;
foreach (Instance instance in instances)
{
int v = (int)instance.classValue();
m_counts[v] += 1;
sumOfWeights += 1;
}
}
