public void TestLogLogLogisticFeaturedLL()
{
DataGen dg = new DataGen();
dg.GenTrivFeaturesData();
double shapeMax = 5.0;
double scaleMax = 900.0;
LogLogistic modelLogLogisticFeatured = new LogLogistic(dg.organicRecoveryDurations,
dg.inorganicRecoverydurations,
dg.fSamples, dg.fCensored);
modelLogLogisticFeatured.ShapeUpperBound = shapeMax;
modelLogLogisticFeatured.ScaleUpperBound = scaleMax;
double[,] warr = new double[2, 2] {
{ 1, 1 },
{ 1, 1 }
};
Matrix <double> w = Matrix <double> .Build.DenseOfArray(warr);
var loglik = modelLogLogisticFeatured.LogLikelihood(w, dg.fSamples, dg.fCensored);
System.Console.WriteLine("LogLikelihood is: " + loglik.ToString());
Assert.IsTrue(Math.Abs(loglik + 55.83229) < 1e-3);
}
public void TestLogLogisticFeaturedGrad()
{
DataGen dg = new DataGen();
dg.GenTrivFeaturesData();
double shapeMax = 5.0;
double scaleMax = 900.0;
LogLogistic modelLogLogisticFeatured = new LogLogistic(dg.organicRecoveryDurations,
dg.inorganicRecoverydurations,
dg.fSamples, dg.fCensored);
modelLogLogisticFeatured.ShapeUpperBound = shapeMax;
modelLogLogisticFeatured.ScaleUpperBound = scaleMax;
double[,] warr = new double[2, 2] {
{ 1, 1 },
{ 1, 1 }
};
Matrix <double> w = Matrix <double> .Build.DenseOfArray(warr);
var grd = modelLogLogisticFeatured.GradLL2(w, dg.fSamples, dg.fCensored);
var grd_numr = modelLogLogisticFeatured.NumericalGradLL(w);
System.Console.WriteLine("Gradient first component:" + grd[0, 0].ToString());
Assert.IsTrue(Math.Abs(grd[0, 0] - grd_numr[0, 0]) < 1e-3);
}
