public void GeneralizedKullbackLeiblerDivergencePrimeTest()
{
var actual = new Matrix(4, 1);
actual.InRandomize();
var expected = new Matrix(4, 1);
expected.InRandomize();
var gradMatrix = actual.Duplicate();
for (var i = 0; i < actual.Rows; i++)
{
for (var j = 0; j < actual.Columns; j++)
{
gradMatrix[i, j] = (actual[i, j] - expected[i, j]) / actual[i, j];
}
}
var cost = new GeneralizedKullbackLeiblerDivergenceKernel();
var calculatedMatrix = cost.Backward(actual, expected);
Assert.IsTrue(gradMatrix == calculatedMatrix, "Generalized Kullback Leibler Divergence Derivative successful");
}
public void GeneralizedKullbackLeiblerDivergenceTest()
{
var error = 0.0;
var actual = new Matrix(4, 1);
actual.InRandomize();
var expected = new Matrix(4, 1);
expected.InRandomize();
for (var i = 0; i < actual.Rows; i++)
{
for (var j = 0; j < actual.Columns; j++)
{
error += expected[i, j] * Math.Log(expected[i, j] / actual[i, j]) - expected[i, j] + actual[i, j];
}
}
var cost = new GeneralizedKullbackLeiblerDivergenceKernel();
var calculatedError = cost.Forward(actual, expected);
Assert.IsTrue(Math.Abs(error - calculatedError) < 0.01f, "Generalized Kullback Leibler Divergence successful");
}
