public void TestLogLogisticWFeatures()
{
DataGen dg = new DataGen();
dg.GenLogLogisticWFeatures();
double shapeMax = 5.0;
double scaleMax = 500.0;
double[] arr = new double[] { 1.0, 150.0 };
Vector <double> init = Vector <double> .Build.DenseOfArray(arr);
LogLogistic modelLogLogistic = new LogLogistic(dg.organicRecoveryDurations,
dg.inorganicRecoverydurations);
modelLogLogistic.GradientDescent(init);
Console.WriteLine("LL without features is " +
modelLogLogistic.LogLikelihood(modelLogLogistic.Kappa, modelLogLogistic.Lambda) +
" with Kappa " + modelLogLogistic.Kappa + " and Lambda " + modelLogLogistic.Lambda);
double[,] warr = new double[2, dg.fCensored.ColumnCount];
warr[0, 0] = Sigmoid.InverseSigmoid(modelLogLogistic.Kappa, shapeMax);
warr[1, 0] = Sigmoid.InverseSigmoid(modelLogLogistic.Lambda, scaleMax);
Matrix <double> w = Matrix <double> .Build.DenseOfArray(warr);
LogLogistic modelLogLogisticFeatured = new LogLogistic(dg.organicRecoveryDurations,
dg.inorganicRecoverydurations,
dg.fSamples, dg.fCensored);
modelLogLogisticFeatured.ShapeUpperBound = shapeMax;
modelLogLogisticFeatured.ScaleUpperBound = scaleMax;
Matrix <double> logLogisticParameters = modelLogLogisticFeatured.GradientDescent(w, 2001);
Vector <double> frstSample = Vector <double> .Build.DenseOfArray(
new double[] { 1.0, 2.0, 3.0 });
Vector <double> scndSample = Vector <double> .Build.DenseOfArray(
new double[] { 1.0, 4.0, 2.0 });
Vector <double> res = logLogisticParameters.Multiply(frstSample);
var alpha_shape = res[0];
var shape = Sigmoid.Transform(alpha_shape, shapeMax);
var alpha_scale = res[1];
var scale = Sigmoid.Transform(alpha_scale, scaleMax);
res = logLogisticParameters.Multiply(scndSample);
alpha_shape = res[0];
shape = Sigmoid.Transform(alpha_shape, shapeMax);
alpha_scale = res[1];
scale = Sigmoid.Transform(alpha_scale, scaleMax);
Assert.IsTrue(Math.Abs(scale - 80.0) < 2.0);
}