public MyEvaluation()
{
m_costMatrix = new weka.classifiers.CostMatrix(2);
m_costMatrix.setElement(0, 0, -1);
m_costMatrix.setElement(1, 1, -1);
m_costMatrix.setElement(0, 1, 1);
m_costMatrix.setElement(1, 0, 1);
}
public MyEvaluation()
{
m_costMatrix = new weka.classifiers.CostMatrix(2);
m_costMatrix.setElement(0, 0, -1);
m_costMatrix.setElement(1, 1, -1);
m_costMatrix.setElement(0, 1, 1);
m_costMatrix.setElement(1, 0, 1);
}
public CostMatrix(int numclasses, double[,] matrix)
{
Impl = new weka.classifiers.CostMatrix(numclasses);
for (int r = 0; r < matrix.GetLength(0); r++)
{
for (int c = 0; c < matrix.GetLength(1); c++)
{
Impl.setElement(r, c, matrix[r, c]);
}
}
}
/// <summary> Makes a copy of this ConfusionMatrix after applying the
/// supplied CostMatrix to the cells. The resulting ConfusionMatrix
/// can be used to get cost-weighted statistics.
///
/// </summary>
/// <param name="costs">the CostMatrix.
/// </param>
/// <returns> a ConfusionMatrix that has had costs applied.
/// </returns>
/// <exception cref="Exception">if the CostMatrix is not of the same size
/// as this ConfusionMatrix.
/// </exception>
public virtual ConfusionMatrix makeWeighted(CostMatrix costs)
{
if (costs.size() != size())
{
throw new System.Exception("Cost and confusion matrices must be the same size");
}
ConfusionMatrix weighted = new ConfusionMatrix(m_ClassNames);
for (int row = 0; row < size(); row++)
{
for (int col = 0; col < size(); col++)
{
weighted.setXmlElement(row, col, getXmlElement(row, col) * costs.getXmlElement(row, col));
}
}
return weighted;
}
public MyEvaluation(weka.classifiers.CostMatrix costMatrix)
{
m_costMatrix = costMatrix;
}
public MyEvaluation(weka.classifiers.CostMatrix costMatrix)
{
m_costMatrix = costMatrix;
}
