public void OrthoTest()
{
var w = RBFNet.Orthogonalize(new DenseVector(new double[] { 1, 1, 0 }), new List <Vec> {
new DenseVector(new double[] { 1, 0, 0 })
});
var w2 = RBFNet.Orthogonalize(new DenseVector(new double[] { 1, 1, 1 }), new List <Vec> {
new DenseVector(new double[] { 1, 0, 0 }),
w
});
}
public void RBFTrainingIsReproducible()
{
var data = NNTestUtils.GetData("2004-01-01", "2004-05-01");
var fitnesses = Lists.Repeat(5, i => {
var rbfNet = RBFNet.Train(data.Input, data.Output, 0.1, 1);
rbfNet.IsDegenerate.ShouldBeFalse();
return(Functions.ComputeFitness(new PredictorWithInputs(rbfNet, data.Input), data));
});
fitnesses.Distinct().Count().ShouldEqual(1);
}
public void RBFPredictsWell()
{
var data = NNTestUtils.GetData("2004-01-01", "2004-05-01");
Func <double, double> fitnessForTolerance = tolerance => {
var rbfNet = RBFNet.Train(data.Input, data.Output, tolerance, 1);
rbfNet.IsDegenerate.ShouldBeFalse();
var fitness = Functions.ComputeFitness(new PredictorWithInputs(rbfNet, data.Input), data);
Trace.WriteLine("RBF fitness: " + fitness);
return(fitness);
};
fitnessForTolerance(0.2).ShouldBeGreaterThan(0.9);
fitnessForTolerance(0.01).ShouldEqual(1.0);
}
