/// <summary> Computes entropy of distribution before splitting.</summary>
public double oldEnt(Distribution bags)
{
double returnValue = 0;
int j;
for (j = 0; j < bags.numClasses(); j++)
returnValue = returnValue + logFunc(bags.perClass(j));
return logFunc(bags.total()) - returnValue;
}
/// <summary> Computes entropy of distribution after splitting.</summary>
public double newEnt(Distribution bags)
{
double returnValue = 0;
int i, j;
for (i = 0; i < bags.numBags(); i++)
{
for (j = 0; j < bags.numClasses(); j++)
returnValue = returnValue + logFunc(bags.perClassPerBag(i, j));
returnValue = returnValue - logFunc(bags.perBag(i));
}
return - returnValue;
}
/// <summary> Computes entropy of test distribution with respect to training distribution.</summary>
public override double splitCritValue(Distribution train, Distribution test)
{
double result = 0;
int numClasses = 0;
int i, j;
// Find out relevant number of classes
for (j = 0; j < test.numClasses(); j++)
if (Utils.gr(train.perClass(j), 0) || Utils.gr(test.perClass(j), 0))
numClasses++;
// Compute entropy of test data with respect to training data
for (i = 0; i < test.numBags(); i++)
if (Utils.gr(test.perBag(i), 0))
{
for (j = 0; j < test.numClasses(); j++)
if (Utils.gr(test.perClassPerBag(i, j), 0))
result -= test.perClassPerBag(i, j) * System.Math.Log(train.perClassPerBag(i, j) + 1);
result += test.perBag(i) * System.Math.Log(train.perBag(i) + numClasses);
}
return result / log2;
}
/// <summary> Creates distribution with two bags by merging all bags apart of
/// the indicated one.
/// </summary>
public Distribution(Distribution toMerge, int index)
{
int i;
totaL = toMerge.totaL;
m_perClass = new double[toMerge.numClasses()];
Array.Copy(toMerge.m_perClass, 0, m_perClass, 0, toMerge.numClasses());
m_perClassPerBag = new double[2][];
for (int i2 = 0; i2 < 2; i2++)
{
m_perClassPerBag[i2] = new double[0];
}
m_perClassPerBag[0] = new double[toMerge.numClasses()];
Array.Copy(toMerge.m_perClassPerBag[index], 0, m_perClassPerBag[0], 0, toMerge.numClasses());
m_perClassPerBag[1] = new double[toMerge.numClasses()];
for (i = 0; i < toMerge.numClasses(); i++)
m_perClassPerBag[1][i] = toMerge.m_perClass[i] - m_perClassPerBag[0][i];
m_perBag = new double[2];
m_perBag[0] = toMerge.m_perBag[index];
m_perBag[1] = totaL - m_perBag[0];
}
/// <summary> Creates distribution with only one bag by merging all
/// bags of given distribution.
/// </summary>
public Distribution(Distribution toMerge)
{
totaL = toMerge.totaL;
m_perClass = new double[toMerge.numClasses()];
Array.Copy(toMerge.m_perClass, 0, m_perClass, 0, toMerge.numClasses());
m_perClassPerBag = new double[1][];
for (int i = 0; i < 1; i++)
{
m_perClassPerBag[i] = new double[0];
}
m_perClassPerBag[0] = new double[toMerge.numClasses()];
Array.Copy(toMerge.m_perClass, 0, m_perClassPerBag[0], 0, toMerge.numClasses());
m_perBag = new double[1];
m_perBag[0] = totaL;
}
